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BIOMINERALIZATION AND BIOSORPTION INVOLVING BACTERIA: METAL PHOSPHATE PRECIPITATION AND MERCURY ADSORPTION EXPERIMENTS A Dissertation Submitted to the Graduate School of the University of Notre Dame in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Sarrah M. Dunham-Cheatham Jeremy B. Fein, Director Graduate Program in Civil and Environmental Engineering and Earth Sciences Notre Dame, Indiana August, 2012
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BIOMINERALIZATION AND BIOSORPTION INVOLVING BACTERIA:

METAL PHOSPHATE PRECIPITATION AND MERCURY ADSORPTION EXPERIMENTS

A Dissertation

Submitted to the Graduate School

of the University of Notre Dame

in Partial Fulfillment of the Requirements

for the Degree of

Doctor of Philosophy

by

Sarrah M. Dunham-Cheatham

Jeremy B. Fein, Director

Graduate Program in Civil and Environmental Engineering and Earth Sciences

Notre Dame, Indiana

August, 2012

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© Copyright 2012

Sarrah M. Dunham-Cheatham

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BIOMINERALIZATION AND BIOSORPTION INVOLVING BACTERIA:

METAL PHOSPHATE PRECIPITATION AND MERCURY ADSORPTION EXPERIMENTS

Abstract

by

Sarrah M. Dunham-Cheatham

The research conducted in these chapters focused on the transport and fate of a range

of metals in the presence of bacteria. In Chapter 2, I investigated the effects of bacteria on the

precipitation of metal phosphates and discovered 2 phenomena, passive cell wall mineralization

and the decreased size of precipitated minerals due to the presence of bacteria. In Chapters 3

and 4, I investigated the effects of 2 ligands (chloride in Chapter 3, fulvic acid in Chapter 4) on

the adsorption behavior on mercury to bacterial cells. I learned from these studies that the

presence of ligands can have a range of effects on the adsorption behavior of mercury to

bacterial cells.

In all of my investigations, I used thermodynamic models to calculate stability constants

for several metal-bacteria complexes formed in my experiments. These stability constants can

be used to better predict the behavior of metals in metal-bacteria-ligand systems, which is

potentially beneficial to several applications (e.g. developing effective remediation strategies).

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CONTENTS

Figures ...................................................................................................................................... iv

Tables ..................................................................................................................................... viii

Acknowledgments ......................................................................................................................ix

Chapter 1: Introduction............................................................................................................... 1

Chapter 2: The Effects of Non-Metabolizing Bacterial Cells on the Precipitation of Uranium, Lead and Calcium Phosphates ................................................................................................... 8

2.1 Abstract .................................................................................................................... 8 2.2 Introduction ............................................................................................................. 9 2.3 Methods ................................................................................................................. 12

2.3.1 General approach .................................................................................... 12 2.3.2 Experimental methods ............................................................................ 13 2.3.3 Analytical methods .................................................................................. 19 2.3.4 Thermodynamic modeling ....................................................................... 24

2.4 Results & Discussion ............................................................................................... 29 2.4.1 Uranium system ...................................................................................... 29 2.4.2 Lead system ............................................................................................ 51 2.4.3 Calcium system ....................................................................................... 55

2.5 Conclusions ............................................................................................................ 60 2.6 Acknowledgements ................................................................................................ 61

Chapter 3: The Effects of Chloride on the Adsorption of Mercury onto Three Bacterial Species . 62 3.1 Abstract .................................................................................................................. 62 3.2 Introduction ........................................................................................................... 63 3.3 Methods ................................................................................................................. 65

3.3.1 Experimental Methods ............................................................................ 65 3.3.2 Analytical Methods: Inductively-Coupled Plasma – Optical Emission

Spectroscopy (ICP-OES) ....................................................................... 68 3.3.3 Thermodynamic Modeling....................................................................... 68

3.4 Results & Discussion ............................................................................................... 70 3.4.1 Potentiometric Titrations ........................................................................ 70 3.4.2 Adsorption Experiments .......................................................................... 76 3.4.3 Thermodynamic Modeling....................................................................... 77

3.5 Conclusions ............................................................................................................ 87 3.6 Acknowledgements ................................................................................................ 88

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Chapter 4: The Effect of Natural Organic Matter on the Adsorption of Mercury to Bacterial Cells ................................................................................................................................ 89

4.1 Abstract .................................................................................................................. 89 4.2 Introduction ........................................................................................................... 90 4.3 Methods ................................................................................................................. 91

4.3.1 Experimental Methods ............................................................................ 91 4.3.2 Analytical Methods: Inductively Coupled Plasma – Optical Emission

Spectroscopy (ICP-OES) ....................................................................... 94 4.3.3 Thermodynamic Modeling....................................................................... 94

4.4 Results .................................................................................................................... 96 4.5 Discussion ............................................................................................................... 99 4.6 Conclusions .......................................................................................................... 104 4.7 Acknowledgements .............................................................................................. 105

Chapter 5: Conclusions............................................................................................................ 106

Bibliography ............................................................................................................................ 110

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FIGURES

Figure 1: XRD diffractogram from the isolated biotic precipitate used in the solubility experiment. The upper diffractogram is from the reference HUP mineral. .................... 18

Figure 2: Elemental map of biotic U11 sample. P is shown in red, U is shown in green. The scale bar is 500 nm. ............................................................................................................... 30

Figure 3: TEM bright field images for U system. (A) Abiotic U5 control; (B) Biotic U5 experiment; (C) Abiotic U11 control; (D) Biotic U11 experiment. All scale bars are 200 nm. The bacteria in (B) and (D) is B. subtilis. ............................................................................... 31

Figure 4: TEM bright field images for U system. (A) Biotic U10 experiment; (B) close up of area in the black box in image A to illustrate the texture of the biogenic U nanoparticulate precipitate; (C) Biotic U10 experiment; (D) close up of area located in black box in image C; (E) Biotic U10 experiment; (F) close up of area located in the black box in image F. The bacteria in all micrographs is B. subtilis. ........................................................................ 33

Figure 5: TEM bright field image of uranyl phosphate biomineralization in biotic (A) U5 and (B) U11 samples, showing texture and prevalence of minerals within the S. oneidensis cell walls. The scale bars represent (A) 200 nm, and (B) 100 nm.......................................... 34

Figure 6: XRD patterns from analysis of run products from U system experiments. ................... 36

Figure 7: k3-weighted EXAFS spectra of the biotic and abiotic samples plotted with the HUP standard. Except for Biotic U5, which exhibits an adsorption spectrum, all spectra have the small features around k = 10 Å-1, a signature feature of the autunite group. .......... 37

Figure 8: (A) Magnitude of U L3-edge EXAFS spectra after Fourier transformation for the abiotic samples overlaid by the HUP standard; (B) Magnitude of U L3-edge EXAFS spectra after Fourier transformation for the biotic samples overlaid by the HUP standard. Spectra shown were collected in transmission mode. ............................................................... 39

Figure 9: EXAFS data and fit in the magnitude of the Fourier transformed EXAFS spectrum. ...... 40

Figure 10: Changes in the aqueous concentrations of U and P in the U experiments. (A) B. subtilis; (B) S. oneidensis. All experiments were performed in triplicate (symbols represent the mean). Error bars represent one standard deviation (note that some error bars are smaller than the symbol). Each arrow connects the starting condition (arrow tail, asterisks) to the final U and P concentrations in the abiotic control or biotic experiments (arrow head, squares and circles). The numerals “1”, “2”, and “3” represent

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saturation state conditions discussed in detail in the text and are presented here for reference...................................................................................................................... 45

Figure 11: Aqueous chemistry results for the bacterial exudate experiment (shown as hollow triangles) compared to aqueous chemistry results for the U system (as shown in Figure 10A). Each arrow connects the starting condition (arrow tail, asterisks) to the final U and P concentrations in the abiotic control or biotic experiments (arrow head, squares and circles). The numerals “1”, “2”, and “3” represent saturation state conditions discussed in detail in the text and are presented here for reference. ............................................ 48

Figure 12: Measured U and P concentrations from the solubility experiments involving biogenic hydrogen uranyl phosphate (HUP) precipitates. Model P concentrations were fixed at the average experimental value, and the model U line is the calculated U concentration in equilibrium with macroscopic HUP, using the Ksp value reported by Gorman-Lewis et al. (2009). ..................................................................................................................... 50

Figure 13: TEM bright field images for Pb system: (A) Biotic Pb4 experiment (scale bar is 200 nm); (B) Biotic Pb8 (scale bar is 100 nm). ...................................................................... 51

Figure 14: XRD patterns for biotic samples from the Pb system. The lower pattern is a reference for Pb3(PO4)2. ................................................................................................................ 52

Figure 15: Changes in the aqueous concentrations of Pb and P in the Pb experiments with B. subtilis. All experiments were performed in duplicate. Error bars represent one standard deviation (note that some error bars are smaller than the symbol). Each arrow connects the starting condition (arrow tail, asterisks) to the final Pb and P concentrations in the abiotic control or biotic experiments (arrow head, squares and circles). The numerals “1” and “2” represent saturation state conditions discussed in detail in the text and are presented here for reference. ...................................................................................... 54

Figure 16: TEM bright field images for Ca system: (A) Abiotic Ca7 control; (B) Biotic Ca7 experiment; (C) Abiotic Ca11 control; (D) Biotic Ca11 experiment. All scale bars are 100 nm................................................................................................................................ 56

Figure 17: XRD data from run-products of Ca experiments. ....................................................... 57

Figure 18: Changes in the aqueous concentrations of Ca and P in the Ca experiments with B. subtilis. All experiments were performed in duplicate. Error bars represent one standard deviation (note that some error bars are smaller than the symbol). Each arrow connects the starting condition (arrow tail, asterisks) to the final Ca and P concentrations in the abiotic control or biotic experiments (arrow head, squares and circles). The numerals “1” and “2” represent saturation state conditions discussed in detail in the text and are presented here for reference. ...................................................................................... 59

Figure 19: Four replicate forward potentiometric titration of 100 gm L-1 G. sulfurreducens in 0.1 M NaClO4. .................................................................................................................... 73

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Figure 20: Best fit 4-site model results (smooth curve) for one representative potentiometric titration of G. sulfurreducens (data points). .................................................................. 74

Figure 21: Hg adsorption onto bacterial species normalized per gram of bacteria. The initial molality of Hg in the adsorption experiments is 7.41 x 10-5. .......................................... 77

Figure 22: Hg adsorption onto bacterial species, normalized per gram of bacteria, in the presence of chloride. The solid black curve represents the model fit for B. subtilis, the dashed black line represents the model fit for S. oneidensis, and the solid grey line represents the model fit for G. sulfurreducens. The initial molality of Hg in the adsorption experiments is 7.41 x 10-5 and the initial molality of Cl is 1.00 x 10-3. ........... 78

Figure 23: Aqueous Hg speciation in the (A) absence and (B) presence of chloride under the experimental Hg and chloride concentration conditions. Only species with calculated concentrations above 0.01 x 10-5 M are shown. ............................................................ 79

Figure 24: Comparison of model fits (curves) to B. subtilis experimental data (solid squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve); (5) and (6) (dotted black curve); and (5), (6), and (7) (solid black curve). ........................................ 83

Figure 25: Comparison of model fits (curves) to G. sulfurreducens experimental data (solid squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve); (5) and (6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); and (5), (6), (7), and (8) (solid black curve). Using only Reactions (5) through (7), as was used for the B. subtilis modeling, results in a model fit that poorly constrains the data at high pH, indicating that another reaction is necessary to account for the observed Hg adsorption. It is likely that Hg(OH)2

0 is involved in the high pH adsorption, as it is the dominant aqueous Hg species at high pH. Adding Hg(OH)2

0 onto R-A41- (Reaction (8)) yields a model

fit that fits the data well across the entire pH range. .................................................... 84

Figure 26: Comparison of model fits (curves) to S. oneidensis experimental data (solid squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve); (5) and (6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); (5), (6), (7), and (8) (solid grey curve); and (5), (6), (7), (8), and (9) (solid black curve). Using only Reactions (5) through (8), the model does not constrain the high pH data well, thus an additional surface species is necessary. It is likely that Hg(OH)2

0 is involved in the high pH adsorption because it is the dominant aqueous Hg species under the high pH conditions where we see a misfit between the data and the model predictions. Models invoking Hg(OH)2

0 adsorption onto R-A31- or onto R-A4

1- do not improve the model fit, as these reactions cause less HgCl(OH)0 to adsorb onto these sites due to site mass balance constraints. However, a model that involves Hg(OH)2

0 adsorption onto R-A21- (solid black

curve) yields an excellent fit to the data across the pH range studied. .......................... 85

Figure 27: Aqueous chemistry results for Hg isotherms in the absence and presence of FA at pH 4 (A, B, C), pH 6 (D, E, F), and pH 8 (G, H, I). Plots A, D, and G present the results for the FA-free controls, plots B, E, and H present the results for the 25 mg L-1 FA experiments, and plots C, F, and I present the results of the 50 mg L-1 FA experiments. B. subtilis is represented by the black-outlined, grey-filled squares, S. oneidensis is represented by

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the solid black diamonds, and G. sulfurreducens is represented by the hollow circles. The black line on each plot represents 100% Hg adsorption under each experimental condition. ..................................................................................................................... 98

Figure 28: Representative model fits for S. oneidensis at pH 6 under 0 mg L-1 FA (grey squares and grey curve) and 50 mg L-1 FA (solid black diamonds and black curve) conditions. The dotted line represents 100% Hg adsorption under each experimental condition......... 102

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TABLES

Table 1 Starting conditions for precipitation experiments (Uranium system) ............................. 20

Table 2 Starting conditions for precipitation experiments (Lead system) ................................... 21

Table 3 Starting conditions for precipitation experiments (Calcium system) .............................. 21

Table 4 System of equations used for saturation state and solubility calculations for uranium system .......................................................................................................................... 26

Table 5 System of equations used for saturation state calculations for lead system .................. 27

Table 6 System of equations used for saturation state calculations for calcium system ............. 28

Table 7 Fitting paths and corresponding parameters used for XAS analysis ............................... 41

Table 8 Parameters for major fitting paths used in the fitting of XAS data ................................. 42

Table 9 Hg reactions used to construct SCMs ............................................................................ 72

Table 10 Site concentrations and pKa values used for SCMs ...................................................... 75

Table 11 Calculated stability constants (log K) for Hg adsorption onto bacteria ......................... 86

Table 12 Hg reactions used in the speciation modeling ............................................................. 97

Table 13 Calculated log stability constant values for Reactions (12) – (15) ............................... 101

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ACKNOWLEDGMENTS

I would like to acknowledge and thank my advisor, Dr. Jeremy B. Fein, without whom

this work would not have been possible. I would also like to acknowledge all of my collaborators

for their helpful contributions to this work, and the reviewers for their useful feedback.

I would like to thank my mother and sister for supporting me throughout my education,

my father for inspiring me to continue my education, and my grandparents for encouraging me

to become the best person that I can be.

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CHAPTER 1:

INTRODUCTION

Metals are mobile in groundwater under a range of environmental conditions. Binding

of metals to aqueous ligands (Xu and Allard, 1991; Bäckström et al., 2003; Croué et al., 2003),

colloids (Beveridge and Murray, 1976; Fowle et al., 2000), and mineral surfaces (Bonnissel-

Gissinger et al., 1999; Bäckström et al., 2003) in groundwater systems affects metal mobility and

transport through a number of processes. These processes, such as biomineralization

(Beveridge, 1989; Schultze-Lam et al., 1996; Bazylinski and Moskowitz, 1997), metal transport

(Fein et al., 1999; Moura et al., 2007), and bioavailability (Niyogi and Wood, 2004; van Leeuwen

et al., 2005), can control metal speciation and behavior in the environment. The ability to

predict the fate of a metal under supersaturated conditions and in the presence of a range of

natural constituents, such as ligands, colloids (e.g. bacteria and clays), natural organic matter

(NOM), and aqueous complexes, is crucial to a wide range of applications (e.g. predicting

contaminant transport and implementing remediation strategies). In order to predict the

behavior of a metal, we must first understand how it reacts with each component of a natural

system. This research investigates the behavior of metals in the presence of bacterial cell walls

and a range of naturally-occurring metal-binding ligands.

Bacteria are present in a wide range of geologic systems and are ubiquitous in near-

surface environments (Madigan et al., 2009). These organisms can affect the fate of

contaminant metals by creating localized super-saturated conditions and precipitating metals

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through biomineralization processes (Lowenstam, 1981; Bazylinski and Moskowitz, 1997).

Bacteria can also affect the fate of metals through cell wall adsorption reactions (Beveridge and

Murray, 1976; Ledin et al., 1999). Through these reactions, aqueous metal cations, or charged

metal complexes, bind to negatively-charged deprotonated functional groups within the

bacterial cell wall matrix, tying the mobility of the metal to that of the bacterial cell to which it is

attached. For example, Pang et al. (2005) demonstrate that Cd is up to 20 times more mobile

when it is adsorbed to bacterial cells than when the metal is a free cation, indicating that the

mobility of a metal ion that is adsorbed to a bacteria cell is linked to the mobility of the bacteria.

If bacterial cells are immobile, however, metal binding onto bacterial cells can result in

decreased metal mobility.

Bacteria can also affect metal transport through a range of biomineralization processes.

These processes, such as biologically-induced and biologically-controlled mineralization, result in

the precipitation of metals from solution either from direct contact with bacteria cells or their

exudates (Beveridge 1989; Ghiorse and Ehrlich 1992; Southam and Beveridge 1992; Mandernak

et al., 1995; McLean et al., 1996; Warren et al., 2001; Perez-Gonzalez et al., 2010). Biologically-

induced precipitation occurs when metal cations react with bacterial metabolic products causing

supersaturation and precipitation of a solid phase, whereas biologically-controlled precipitation

is the result of an organism expending energy to exert a direct control on the precipitation of a

metal cation for a specific purpose. For instance, Rivadeneyra et al. (2006) demonstrate that the

addition of magnesium and calcium to a carbonate-, phosphate-, and bacteria-bearing system

results in the precipitation of carbonate phases through biologically-induced mineralization

processes that are not observed in bacteria-free controls. The researchers attribute the

mineralization to the fact that the metabolism of the bacteria creates changes in pH, ionic

strength and ionic make-up of the local medium, which in turn creates favorable conditions for

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magnesium and calcium adsorption to the bacterial cell wall. The adsorbed metal ions then

attract carbonate anions, which result from the metabolism of organic nutrients, beginning the

precipitation of calcium and magnesium carbonate phases on the bacterial cell wall.

Virtually all research investigating biomineralization has involved metabolizing bacteria.

However, bacteria exist under oligotrophic conditions in a wide range of natural systems (Billen

et al., 1990; Noe et al., 2001). A number of studies (e.g. Ferris et al., 1987; Lowenstam & Weiner,

1989; Châtellier et al., 2001; Ben Chekroun et al., 2004; Beazley et al., 2007; Dupraz et al., 2009)

have proposed that the functional groups on the cell walls of bacteria can act as nucleation sites

for the non-metabolic precipitation of minerals, leading to a third type of biomineralization

which I refer to as passive biomineralization. Despite these claims in the literature, the evidence

in support of passive biomineralization is equivocal. Studies have shown associations between

bacterial cells and mineral precipitates (e.g. Konhauser et al., 1993), but a spatial association

itself does not prove that the cell wall caused the mineral precipitation; the association could be

a result of electrostatic interactions between previously precipitated minerals and the cells.

Despite the growing number of claims, no study to date has unequivocally demonstrated that

the process of passive binding of metal cations to cell wall ligands affects mineral precipitation

or that cell wall nucleation of precipitates can occur. Chapter 2 presents research that

unequivocally demonstrates the ability of cell walls to passively nucleate the precipitation of

minerals within the cell wall matrix under some saturation state conditions and for some

elements.

Metal transport in groundwater systems can also be affected by the adsorption of

aqueous metal cations onto charged surfaces (e.g., bacterial cell walls) and by the formation of

aqueous complexes. The adsorption of a wide range of metals onto bacterial cells has been

studied (e.g. Beveridge and Murray, 1976, 1980; Beveridge, 1989; Mullen et al., 1989; Fein et al.,

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1997, 2002; Borrok et al., 2004, 2007; Wu et al., 2006). The cell wall of a bacterium contains

proton-active functional groups, such as carboxyl, phosphoryl, hydroxyl, amino, and sulfhydryl

groups (Beveridge and Murray, 1976; Degens and Ittekkot, 1982; Guiné et al., 2006; Madigan et

al., 2009; Mishra et al., 2009, 2010). When deprotonated, these functional groups have the

ability to adsorb cations (e.g. metals, aqueous complexes) from solution (Beveridge and Murray,

1976; Ledin et al., 1996; Fortin and Beveridge, 1997; Warren and Ferris, 1998; Ohnuki et al.,

2005; Borrok et al., 2007). It has been shown that adsorption of metals to bacterial surfaces is

rapid (Fowle and Fein, 2000; Yee et al., 2000), dependent on solution pH (Fein, 2006), and

reversible (Fowle and Fein, 2000). In addition to affecting metal mobility, metal adsorption likely

represents the first step in bioavailability of metals to bacteria. According to the Biotic Ligand

Model, the bioavailability of toxic metals, such as Hg, is a result of the adsorption of the metal to

a biological surface of the living organism (Di Toro et al., 2001; Santore et al., 2001; Paquin et al.,

2002; Niyogi and Wood, 2004; van Leeuwen et al., 2005). Thus, it is important to construct

quantitative models of Hg adsorption onto bacteria that are capable of accounting for Hg

partitioning under a range of conditions of geologic and environmental interest. Mercury is of

particular interest because it might exhibit different aqueous complexation behavior and/or

form different types of bonds than other previously studied metals. For instance, because it is a

B-type metal, Hg has a high affinity to bond with sulfur ligands (Reddy and Aiken, 2000;

Ravichandran et al., 2004). Because bacterial cell walls contain sulfhydryl functional groups

(Mishra et al., 2009, 2010) and natural organic matter contains sulfur compounds (Haitzer et al.,

2003; Hertkorn et al., 2008), the affinity of Hg for sulfur compounds may have a significant

effect on the behavior of Hg adsorption behavior in the presence of bacteria and natural organic

matter.

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Quantitative models have been developed and extensively used to quantify metal

adsorption onto bacterial surfaces. Many researchers utilize empirical models to quantify the

extent of metal adsorption to a surface. Dissociation constants, Kd values, used in empirical

models are affected by any change to a system parameter, including pH, ionic strength, and fluid

composition, and thus the results cannot be applied to conditions other than those studied

directly in the laboratory (Bethke and Brady, 2000; Koretsky, 2000). Other researchers have

used surface complexation models to calculate equilibrium constants for metal binding to

bacterial surfaces (Plette et al., 1995; Fein et al., 1997; Cox et al., 1999; Fowle et al., 2000; Fein

et al., 2001; Yee and Fein, 2001). This type of modeling approach can account for effects of

changing pH, solution composition, and solute:sorbent ratios because the approach explicitly

accounts for the reactions that occur on bacterial surfaces and within the aqueous phase;

however, applying a surface complexation model can be difficult due to the necessity of

obtaining or calculating a stability constant for each of the metal-bacteria surface complexes

that occur in the system of interest. Currently, theoretical models of metal bioavailability involve

simplistic and unrealistic representations of metal binding onto organisms (e.g., the Biotic

Ligand Model) (Di Toro et al., 2001; Santore et al., 2001; Paquin et al., 2002; Niyogi and Wood,

2004; van Leeuwen et al., 2005). Improvements in these models requires a more sophisticated

and accurate understanding of the binding of metals of environmental interest, especially in

complex geologic systems that may contain competing ligands, such as NOM or colloids (Ledin

et al., 1999; Daughney et al., 2002; Moura et al., 2007). Chapter 3 presents research that

investigates the effects of chloride on the adsorption of mercury onto a range of bacterial

species and provides thermodynamic equilibrium constants for mercury adsorption onto the

bacterial cell wall functional groups as calculated by surface complexation models. Chapter 4

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presents research that examines the effects of fulvic acid on the adsorption behavior of mercury

to a range of bacterial species.

Despite the growing body of research aimed at determining the effect of bacteria on the

environmental fate of metals in groundwater systems, a number of key questions remain

unanswered. The research in this dissertation answers some of these questions. In Chapter 2, I

investigate the effects of non-metabolizing bacteria on the precipitation of metal phosphates.

The results show that non-metabolizing bacteria can passively precipitate uranyl phosphate

nanoparticles within the cell wall matrix from over-saturated conditions, but do not lead to

passive precipitation of lead phosphates or calcium phosphates. Additionally, non-metabolizing

bacteria control the size of the precipitate formed in both the uranyl and calcium systems,

precipitating smaller particles in the biotic samples relative to the abiotic controls. In Chapter 3,

I probe the effects of three species of bacteria and chloride on the adsorption behavior of

mercury. The results show that each bacterial species has an extremely high binding affinity for

mercury in both the absence and presence of chloride, more so than has been observed for

other metals. More importantly, the adsorption behavior of mercury to bacterial cells in both

the absence and presence of chloride does not exhibit typical cation adsorption behavior as a

function of pH, and I construct a surface complexation model that accounts for this unique

behavior. Chapter 4 presents my study of the effects of fulvic acid on the adsorption behavior of

mercury in the presence of a range of bacterial species. The experiments show that the

presence of fulvic acid results in high aqueous mercury concentrations relative to FA-free

controls. These findings suggest that fulvic acid competes with bacterial surfaces for mercury

ions and results in higher concentrations of available mercury relative to FA-free systems.

Surface complexation models were constructed to calculate Hg-bacteria binding equilibrium

constants for results from both Chapters 3 and 4; these binding constants can be used in future

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studies to predict the behavior of Hg under environmental conditions in the presence of

bacteria. In general, the results of my dissertation research expand our understanding of the

effects of bacteria on the environmental fate and speciation of some key metals in groundwater

systems, and the results can be used to not only model the mobility of those metals but also to

guide remediation strategies aimed at removing those metals from contaminated systems.

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CHAPTER 2:

THE EFFECTS OF NON-METABOLIZING BACTERIAL CELLS ON THE

PRECIPITATION OF URANIUM, LEAD AND CALCIUM PHOSPHATES

2.1 Abstract

In this study, I tested the potential for passive cell wall biomineralization by determining

the effects of non-metabolizing bacteria on the precipitation of uranyl, lead, and calcium

phosphates from a range of over-saturated conditions. Experiments were performed using

Gram-positive Bacillus subtilis and Gram-negative Shewanella oneidensis MR-1. After

equilibration, the aqueous phases were sampled and the remaining metal and P concentrations

were analyzed using inductively coupled plasma-optical emission spectroscopy (ICP-OES); the

solid phases were collected and analyzed using X-ray diffractometry (XRD), transmission

electron microscopy (TEM), and X-ray absorption spectroscopy (XAS).

At the lower degrees of over-saturation studied, bacterial cells exerted no discernible

effect on the mode of precipitation of the metal phosphates, with homogeneous precipitation

occurring exclusively. However, at higher saturation states in the U system, I observed

heterogeneous mineralization and extensive nucleation of hydrogen uranyl phosphate (HUP)

mineralization throughout the fabric of the bacterial cell walls. This mineral nucleation effect

was observed in both B. subtilis and S. oneidensis cells. In both cases, the biogenic mineral

precipitates formed under the higher saturation state conditions were significantly smaller than

those that formed in the abiotic controls.

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The cell wall nucleation effects that occurred in some of the U systems were not

observed under any of the saturation state conditions studied in the Pb or Ca systems. The

presence of B. subtilis significantly decreased the extent of precipitation in the U system, but

had little effect in the Pb and Ca systems. At least part of this effect is due to higher solubility of

the nanoscale HUP precipitate relative to macroscopic HUP. This study documents several

effects of non-metabolizing bacterial cells on the nature and extent of metal phosphate

precipitation. Each of these effects likely contributes to higher metal mobilities in geologic

media, but the effects are not universal, and occur only with some elements and only under a

subset of the conditions studied.

2.2 Introduction

Mineral precipitation reactions affect the mobility and distribution of mass in a wide

range of geochemical systems. Bacteria are ubiquitous in near-surface environments, and can

control precipitation reactions in these systems through a number of biomineralization

mechanisms. Two general classifications of biomineralization reactions have been described

(Lowenstam, 1981; Bazylinski and Moskowitz, 1997): biologically-induced mineralization (BIM)

and biologically-controlled mineralization (BCM), both of which are driven by bacterial

metabolic processes. In BIM, precipitation is not directly controlled by the organism, but occurs

in response to interactions between elements in bulk solution and metabolic exudates from the

organism. For example, sulfate-reducing bacteria produce sulfide, which can react with aqueous

Zn when released from the cell to precipitate extracellular sphalerite (ZnS) (Labrenz et al., 2000).

In BCM, organisms expend energy to exert a direct control on precipitation, and the biominerals

are used for a specific function and are typically located within a cell. For example,

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magnetotactic bacteria promote the internal formation of magnetite crystals for use as a

navigational aide (Lefevre et al., 2009; Yu-Zhang et al., 2009).

There has been considerable speculation that a third type of biomineralization reaction,

non-metabolic passive cell wall nucleation of minerals, occurs and that this process, integrated

over time for the bacterial biomass in soils and surface water systems, represents a significant

vector for transformation of aqueous ions to clay minerals and other inorganic and organic

phases (e.g., Urrutia and Beveridge, 1994; Schultze-Lam et al., 1996). Both field (Ferris et al.,

1987; Konhauser et al., 1993; Bonny and Jones, 2003; Fortin and Langley, 2005; Demergasso et

al., 2007) and laboratory (Macaskie et al., 2000; Warren et al., 2001; Rivadeneyra et al., 2006)

studies have examined mineral formation in super-saturated systems and have found a close

spatial association between bacterial cells and a range of extracellular precipitated mineral

phases. Despite the increasing number of studies to claim the importance of passive cell wall

biomineralization (Lowenstam and Weiner, 1989; Châtellier et al., 2001; Ben Chekroun et al.,

2004; Beazley et al., 2007; Dupraz et al., 2009), the nature of the evidence to date is equivocal. A

range of studies have documented associations between bacterial cells and mineral precipitates

(Konhauser, 1997; Arp et al., 1998; Douglas and Beveridge, 1998; Konhauser, 1998; Warren et

al., 2001; Perez-Gonzalez et al., 2010), but a spatial association in and of itself does not prove a

role of the cell wall in the precipitation reaction. Spatial associations between cells and

precipitates that form away from the cells can be promoted through electrostatic attraction

between cells and precipitates (Ams et al., 2004). Although passive binding of aqueous cations

to anionic sites located within bacterial cell walls can affect the speciation and distribution of

metals in bacteria-bearing systems (Beveridge and Murray, 1976; Fein et al., 1997; Kulczycki et

al., 2002; Deo et al., 2010; Li and Wong, 2010), no study has demonstrated that this process

affects mineral precipitation or that cell wall nucleation of precipitates can occur.

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In addition to possible cell wall influences on precipitation, bacteria may influence

mineral precipitation by exuding a range of organic molecules. For example, organic molecules

exuded by biofilms widely affect the precipitation of calcite, influencing not only the growth

kinetics, but the morphology as well (Mann et al., 1990: Archibald et al., 1996; McGrath, 2001;

Meldrum and Hyde, 2001; Braissant et al., 2003; Hammes et al., 2003; Tong et al., 2004; Bosak

and Newman, 2005; Dupraz et al., 2009), likely through incorporation effects (Lowenstam and

Weiner, 1989). Studies have also shown that various organic molecules widely affect the

structure and morphology of a range of minerals, including numerous iron oxides (Châtellier et

al., 2001; Châtellier et al., 2004; Larese-Casanova et al., 2010; Perez-Gonzalez et al., 2010),

uranyl phosphate (Macaskie et al., 2000), and silica (Williams, 1984).

In this study, I probed the role of non-metabolizing bacteria in the formation of metal

phosphate minerals from over-saturated solutions. I selected U, Pb, and Ca in order to

investigate metals that exhibit a broad range of binding affinities with phosphorus. In general,

authigenic precipitation of minerals from saturated solutions in bacteria-rich settings is an

important geochemical process in a number of natural and engineered geological systems, so it

is crucial to understand bacterial effects on the precipitation reactions in order to model mass

transport in these systems. For example, the exposure of Fe(II)-bearing anaerobic groundwaters

to oxidizing bacteria-bearing conditions leads to Fe(III)-oxide precipitation and coating of

mineral grains which is ubiquitous in subsurface environments (Schwertmann et al., 1985;

Sullivan and Koppi, 1998). Phosphate systems are of particular interest due to the importance of

P cycles and the low solubilities of many metal-phosphate phases. Reduction of Fe(III)-oxides by

iron-reducing bacteria releases Fe(II) to solution and can lead to the precipitation of vivianite

(Fe3(PO4)2·8H2O), which is a major sink for Fe and for heavy metals in fresh water sedimentary

systems (Taylor and Boult, 2007); anthropogenic contamination of groundwater and soil

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systems can lead to precipitation (or co-precipitation) of heavy metals as oxides and phosphate

phases in these systems (e.g., Kirpichtchikova et al., 2006; Manceau et al., 2007; Terzano et al.,

2007); and remediation strategies such as phosphate amendments rely on precipitation

reactions in bacteria-bearing systems to reduce concentrations of dissolved metals in systems,

such as those contaminated with dissolved U (e.g., Beazley et al., 2007; Martinez et al., 2007;

Wellman et al., 2007; Ndiba et al., 2008) or by acid mine drainage (e.g., Schultze-Lam et al.,

1996). The common denominator between all of these systems is the precipitation of phosphate

and other mineral phases in environments that can be rich in non-metabolizing bacterial cells

and/or bacterial exudates. Though most natural systems may not attain the degrees of

supersaturation investigated in this study, some may, including mid-ocean ridge hydrothermal

systems (Dekov et al., 2010), and groundwater mixing zones where ferrous iron oxidizes and

precipitates as ferric oxide coatings (James and Ferris, 2004).

The objective of this study was to determine if, and under what conditions, the presence

of non-metabolizing bacteria or bacterial exudates can influence precipitation reactions. My

experimental results can be used, therefore, to determine if the mobilities of the precipitating

elements are likely to be markedly different than they would be if the precipitation occurred

without bacteria present.

2.3 Methods

2.3.1 General approach

I measured the nature and extent of metal phosphate precipitation as a function of

aqueous saturation state in systems that contained suspensions of non-metabolizing cells of

either Bacillus subtilis (ATCC 23875) or Shewanella oneidensis MR-1 (ATCC BAA-1096),

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comparing the results to those of abiotic controls. In the experiments, I created a range of over-

saturated solutions by adding various concentrations of P in the form of Na2HPO4 to solutions

containing dissolved U, Pb, or Ca in 0.1 M NaClO4 in which washed, non-metabolizing bacterial

cells were suspended. I sampled the aqueous phase and analyzed for total remaining metal and

P in solution using ICP-OES. In addition, I characterized the solid phase of each system using

TEM, XRD, and XAS.

2.3.2 Experimental methods

2.3.2.1 Bacterial preparation

Bacillus subtilis and S. oneidensis cells were grown aerobically in 5 mL of trypticase soy

broth medium with 5% yeast extract for 24 hours at 32 oC. The cells were then transferred to 1 L

of trypticase soy broth medium with 5% yeast extract and incubated at 32 oC for another 24

hours. The cells were then collected via centrifugation at 8100g for 5 min. The resulting pellet

was washed five times with 0.1 M NaClO4 (following a procedure described in more detail by

Borrok et al., 2007), and pelleted after each wash using the centrifugation method described

above. After five washes, the pellet was centrifuged for 1 hour at 8100g to remove all excess

liquid and to obtain a wet biomass value.

2.3.2.2 Kinetics experiments

Kinetics experiments were performed to determine the time required for the metal and

P concentrations in the experiments to reach steady state. Precipitation experiments were

prepared according to the method described below. Aqueous samples were extracted from

each precipitation kinetics experiment at 0.25, 0.5, 1, 2, 4, 6, 18, 24, and 48 hours. The samples

were filtered through 0.2 μm PTFE syringe filters, acidified using trace metal grade 15.8 N HNO3

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at a sample:acid ratio of 5 mL:8 μL, and refrigerated pending ICP-OES analysis. Results (not

shown) indicated that no change in metal or P concentration occurred after 2 hours in the

abiotic controls and the B. subtilis experiments, and after 3 hours in the S. oneidensis

experiments; all subsequent abiotic controls and B. subtilis experiments were allowed to react

for 2 hours, and subsequent S. oneidensis experiments were allowed to react for 3 hours.

2.3.2.3 Batch precipitation experiments

To prepare the experiments, aqueous metal, P, and suspended bacteria parent solutions

were mixed in different proportions to achieve the desired final concentrations. A 10-3.08 M U

parent solution was prepared in a Teflon bottle by dissolving UO2(NO3)2 in 0.1 M NaClO4; a 10-2.30

M Ca parent solution was prepared in a Teflon bottle by dissolving Ca(ClO4)2(H2O)4 in 0.1 M

NaClO4; and a 10-3.02 M Pb parent solution was prepared in a Teflon bottle by diluting a

commercially-supplied 1000 ppm aqueous Pb standard (in which the Pb is dissolved in 2% HNO3)

using 0.1 M NaClO4; a 10-2.19 M P parent solution was prepared in a Teflon bottle by dissolving

Na2HPO4 in 0.1 M NaClO4. A 6.25 gm (wet mass) L-1 bacterial parent solution was prepared by

suspending a known mass of washed, non-metabolizing bacterial cells in 0.1 M NaClO4.

Each experimental system was prepared by adding a weighed mass of bacterial parent

suspension, followed by a weighed mass of the U, Ca, or Pb parent solution, to 0.1 M NaClO4 in

Teflon tubes to achieve the desired concentrations. The final parent solution to be added was

the P one. In the U experiments, the initial U concentration was 10-4.20 M and the initial P

concentrations ranged from 10-5.50 to 10-3.50 M. In the Pb experiments, the initial Pb

concentration was 10-4.20 M and the initial P concentrations ranged from 10-5.50 to 10-3.50 M. The

initial Ca concentration in all Ca experiments was 10-3.00 M and the initial P concentrations

ranged from 10-5.00 to 10-2.00 M. The bacterial concentration for all biotic experiments ranged

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from 0.31 gm wet biomass L-1 to 2.50 gm wet biomass L-1 (the bacterial concentration for all

results presented hereafter was 0.62 gm wet biomass L-1, unless otherwise noted), and the

abiotic controls were conducted with identical metal and P concentrations to those used in the

biotic experiments, but with no bacteria present. Cells were assumed to be non-metabolizing

due to the lack of nutrients and electron donors in the suspensions; however, no direct

confirmation of their metabolic state was performed. Inactivated cells could not be used as

controls due to likely changes to cell wall chemistry and/or structure that accompany any

passivation procedure.

After the P parent solution was added to each metal-bearing bacterial solution, the pH

of each experiment was adjusted immediately to the desired pH using 0.2 M HNO3 and/or 0.2 M

NaOH. The final pH values of the U, Pb and Ca systems were 4.50±0.10, 6.00±0.10, and

8.00±0.20, respectively. The pH of each experimental system was adjusted manually every 15

minutes throughout each experiment to maintain the desired pH, except for the last thirty

minutes during which the experiments were undisturbed. In general, the pH drifted slightly

toward circum-neutral values, but only minor adjustments, if any, were necessary after the first

hour of each experiment. The suspensions were constantly agitated on an end-over-end rotator

at 40 rpm for the duration of the experiment. After the prescribed equilibration time, all

suspensions were centrifuged at 8100g for 5 minutes. The supernatant was filtered through 0.2

μm PTFE syringe filters, acidified using trace metal grade 15.8 N HNO3 at a sample:acid ratio of 5

mL:8 μL, and refrigerated pending ICP-OES analyses. The solid phase was maintained at 4 oC

pending XRD, TEM, and XAS analysis. All U and Pb experiments were conducted under

atmospheric conditions, and all Ca experiments were conducted in a N2/H2 atmosphere in order

to exclude atmospheric CO2 and to prevent possible calcium carbonate precipitation. All

experiments were performed in triplicate by conducting three independent experiments.

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2.3.2.4 Precipitation experiments using bacterial exudate solution

A solution containing bacterial exudate molecules with no cells present was prepared in

the following manner: B. subtilis cells were added to 0.1 M NaClO4 to reach a concentration of

0.62 gm wet biomass L-1. The pH of the suspension was adjusted to 4.50 ± 0.10 using small

amounts 0.2 M HCl and/or 0.2 M NaOH. The pH was monitored every 15 minutes and

adjustments were made for two hours. The suspension was then centrifuged at 8100g for 10

minutes to remove all bacteria from solution. An aliquot of the supernatant was immediately

collected, filtered through a 0.2 μm PTFE syringe filter, and acidified using 15.8 N HNO3 at a

sample:acid ratio of 5 mL:8 μL. This sample was analyzed with ICP-OES to determine the starting

concentration of P in the exudate solution and with a total organic carbon (TOC) analyzer to

determine the concentration of dissolved carbon in the solution. The resulting concentrations

were 10- 5.41 ± 0.74 M P and 10-2.71 ± 0.17 ppm C. The remainder of the supernatant was then used in

place of the 0.1 M NaClO4 in an abiotic control precipitation experiment for the U system only.

At the completion of the experiment, samples were collected and analyzed as described above.

2.3.2.5 Biogenic mineral isolation

As describe below, the U experiments were the only ones to yield cell wall-nucleated

biomineralization under some of the conditions studied. In order to measure the solubility of

these precipitates in separate experiments, I isolated the particles from their cell wall

framework using a procedure similar to the one described by Ulrich et al. (2008). Biotic U

precipitation experiments were prepared according to the above method using B. subtilis cells.

After the prescribed equilibration time, the biomass was centrifuged for 5 minutes at 8100g, and

the supernatant was decanted. The bacteria/mineral pellet was re-suspended in a 20% bleach

solution, diluted with 18 MΩ ultrapure water, and placed on a rotating table at 32 oC overnight.

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The suspension was centrifuged for 10 minutes at 8100g and decanted. The pellet was then

rinsed three times with 18 MΩ ultrapure water, until the pH of the wash supernatant was

circum-neutral, centrifuging for 10 minutes at 8100g and decanting between each rinse. The

pellet was suspended in 10 mL of 18 MΩ ultrapure water, transferred into a 60 mL separatory

funnel, and 50 mL of hexane was added to separate the organic debris from the minerals. The

funnel was capped and shaken vigorously for 3 minutes, then left undisturbed overnight. The

water portion was collected, centrifuged for 10 minutes at 8100g, and the supernatant was

decanted. The pellet was rinsed once with 18 MΩ ultrapure water, then centrifuged for 10

minutes at 8100g and decanted. The bleach/hexane process was repeated until no bacterial

remnants were present in the collected sample as determined by optical microscopy. Once the

biogenic minerals were isolated, the pellet was washed a final time with 18 MΩ ultrapure water,

centrifuged for 10 minutes at 8100g, the supernatant was decanted, and the particles were

allowed to air dry. XRD analysis of the biogenic minerals suggested that the minerals were

unaffected by the bleach/hexane treatment, and that they had the same crystal structure as the

precipitates that formed in the parallel abiotic controls (Figure 1). Scanning electron microscopy

(SEM) analysis showed that the minerals were needle-like with a length ranging from 10 to 30

nm.

2.3.2.6 Solubility experiments

Separate solubility experiments were performed using the isolated and washed biogenic

HUP particles. A known mass of the dry mineral powder was transferred to a Teflon tube and 18

MΩ ultrapure water was added to reach a concentration of 3 gm L-1. Small aliquots of 0.2 M

HNO3 or 0.2 M NaOH were added to adjust the pH of the solution to 4.20 ± 0.10. The pH of the

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Figure 1: XRD diffractogram from the isolated biotic precipitate used in the solubility experiment. The upper diffractogram is from the reference

HUP mineral.

solution was adjusted every hour in the first 24 hours until the pH value remained within the

desired range. A 2 mL sample was extracted after 24 hours, and every 48 hours after that for a

total of 23 days. After extraction, samples were filtered immediately through 0.2 μm PTFE

syringe filters, gravimetrically diluted with 18 MΩ ultrapure water, acidified using trace metal

grade 15.8 N HNO3 at a sample:acid ratio of 5 mL:8 μL, and refrigerated pending ICP-OES

analysis of dissolved U and P concentrations.

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2.3.3 Analytical methods

2.3.3.1 TEM

Using TEM, I examined the solid phase run products from both abiotic and biotic

samples, and from a high and low saturation state for each metal system studied. For the U

system, the P concentration conditions studied with TEM were 10-4.49 (sample U5), 10-3.89 (U8),

10-3.65 (U10), and 10-3.49 M (U11) (Table 1); for the Pb system, the P concentration conditions

studied were 10-4.49 (Pb4), 10-3.79 (Pb6), 10-3.65 (Pb7), and 10-3.49 M (Pb8) (Table 2) ; for the Ca

system, the P concentration conditions studied were 10-3.09 (Ca4), 10-2.49 (Ca7), and 10-2.01 M

(Ca11) (Table 3). At the completion of each precipitation experiment, the pellet was suspended

in a 2% gluteraldehyde fixative solution. The suspension was rotated end-over-end for 1 hour,

then centrifuged and decanted. The pellet was rinsed three times with 18 MΩ ultrapure water.

The suspension was suspended in a 0.2% OsO4 fixative solution and rotated end-over-end for 1

hour, then centrifuged and decanted. The pellet was rinsed three times with 18 MΩ ultrapure

water. The pellet was subjected to a series of ethanol solutions, starting at 50% ethanol and

ending with 100% ethanol, to remove all water from the pellet. The dehydrated pellet was

suspended in a series of Spurs resin solutions, starting with a 1:1 mixture of resin and 100%

ethanol and ending with 100% resin, enabling infiltration of the bacteria by the resin. The

infiltrated pellet was placed in the tip of a 1 mL BEEM capsule, and the capsules were filled with

100% resin and placed in a 70 oC oven for 24 hours. The sample blocks were removed from the

capsules, sectioned by ultramicrotomy to a 110 nm thickness, and mounted onto 200 mesh

copper grids. Only the grids for the Pb and Ca systems were stained with uranyl acetate and lead

citrate; the U system grids were not stained. TEM images were collected using a Hitachi H-600

TEM operated at 75 kV acceleration voltage, as well as a JEOL 2100F TEM operated at 200 kV

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using various modes: bright field (BF), dark field (DF), and scanning TEM (STEM). Chemical maps

were determined by an electron dispersive X-ray (EDX) detector using the K line for P and the M

line for U using the JEOL 2100F TEM.

TABLE 1

STARTING CONDITIONS FOR PRECIPITATION EXPERIMENTS (URANIUM SYSTEM)

ID Initial [U] (log M)

Initial [P] (log M)

Saturation Index

(log (Q/K)) XRD

TEM & XAS

U1 -4.20 -5.49 0.74 U2 -4.20 -5.09 1.13 U3 -4.20 -4.79 1.41 U4 -4.20 -4.62 1.58 U5 -4.20 -4.49 1.69

U6 -4.20 -4.19 1.94 U7 -4.20 -4.01 2.07 U8 -4.20 -3.89 2.14

U9 -4.20 -3.79 2.20 U10 -4.20 -3.65 2.27

U11 -4.20 -3.49 2.32

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TABLE 2

STARTING CONDITIONS FOR PRECIPITATION EXPERIMENTS (LEAD SYSTEM)

ID Initial [Pb]

(log M) Initial [P] (log M)

Saturation Index

(log (Q/K)) XRD TEM

Pb1 -4.20 -5.79 4.29 Pb2 -4.20 -5.19 4.91 Pb3 -4.20 -4.71 5.77 Pb4 -4.20 -4.49 6.20

Pb5 -4.20 -4.01 7.15 Pb6 -4.20 -3.79 7.60

Pb7 -4.20 -3.65 7.89

Pb8 -4.20 -3.49 8.19

TABLE 3

STARTING CONDITIONS FOR PRECIPITATION EXPERIMENTS (CALCIUM SYSTEM)

ID

Initial [Ca] (log M)

Initial [P] (log M)

Saturation Index

(log (Q/K)) XRD TEM

Ca1 -3.00 -4.49 2.31 Ca2 -3.00 -3.79 5.25 Ca3 -3.00 -3.49 5.26 Ca4 -3.00 -3.09 6.36

Ca5 -3.00 -2.79 7.12 Ca6 -3.00 -2.62 7.51 Ca7 -3.00 -2.49 7.75

Ca8 -3.00 -2.31 8.04 Ca9 -3.00 -2.19 8.20

Ca10 -3.00 -2.09 8.29 Ca11 -3.00 -2.01 8.34

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2.3.3.2 XRD

Some of the solids from the abiotic control experiments and from the biotic

experiments were selected for detailed characterization by XRD. These solids were ground into a

fine powder using acetone and an alumina mortar and pestle. The slurry was transferred onto a

zero-background silica XRD slide and allowed to air dry. The slide was then measured at room

temperature using a Scintag X-1 Powder XRD with a copper radiation source. Data were

collected every half-degree from 5 to 60 degrees.

2.3.3.3 Synchrotron experiments

The solid run products from four biotic experiments and from the corresponding four

abiotic controls in the U system were prepared for XAS analysis to characterize the crystallinity

and structure of the precipitates. The concentrations of P in these four experiments were 10-4.49

(sample U5), 10-3.89 (sample U8), 10-3.65 (sample U10), and 10–3.49 (sample U11) M (Table 1).

Resulting bacteria/mineral pellets were immediately packaged on ice for overnight shipment. X-

ray absorption near edge structure (XANES) and extended X-ray absorption fine structure

(EXAFS) at the U L3-edge (17166) were collected at room temperature for all pellets. A silicon (1

1 1) crystal monochromator was used to select a single energy beam. A Rh-coated harmonic

rejection mirror was used to further eliminate the high harmonic component in the beam. The

incident ionization chamber was filled with 100% N2 gas, and the transmission and reference

ionization chambers were filled with 50% N2 gas and 50% Ar gas, respectively. All of the spectra

were collected in transmission mode as the fluorescence spectra suffered self-absorption

problems due to the high concentration of uranyl phosphate mineral in the samples (Bunker,

2010).

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Abiotic control samples were precipitated and air dried before processing. Samples

were ground into fine powder using a corundum mortar and pestle, then mixed with graphite

powder to reach relative homogeneity before being loaded into Plexiglas holders and sealed

with Kapton film. At the energy of the U L3-edge, the extra coverage of Kapton film did not

affect the measurements. Biotic samples, present as a paste, were prepared for measurement

by loading the paste into slotted Plexiglas holders, which were then covered with Kapton film.

Prepared biotic samples were refrigerated until data collection. All measurements were

conducted within 72 hours of sample preparation.

For every sample, 10 XANES spectra were initially collected, each lasting less than a

minute, in order to monitor for possible radiation damage to the sample. Due to the

heterogeneity of the samples, EXAFS spectra were collected after the XANES measurements at

10 different spots, with two measurements at each spot. No radiation damage was observed in

the spectra within the 1 minute data acquisition period.

The data were processed using the UWXAFS package (Stern et al., 1995). The program

Athena (Ravel and Newville, 2005) was used to remove the background using the AUTOBK

algorithm (Newville et al., 1993) and to convert the data from k space into R space via Fourier

transformation. The cutoff of background-Rbkg was set to 1.1 for all measurements. The

program Artemis (Ravel and Newville, 2005) was used to fit the experimental EXAFS spectra.

Well defined mineral structures were input into Atom (Ravel et al., 2001) and used to generate

theoretical EXAFS paths in FEFF6 (Zabinsky et al., 1995). Shell-by-shell fitting was obtained using

the program FEFFIT (Newville, 2001), and the statistical factors reduced-χ2 and R-factor were

used as criteria to optimize the fitting.

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2.3.3.4 ICP-OES

ICP-OES element standards with the same ionic strength matrix as the experimental

samples were prepared gravimetrically by diluting commercially-supplied 1,000 ppm aqueous

Ca, Pb, U, and P standards with 0.1 M NaClO4. The concentrations of the U and Pb standards

ranged from 10-6.70 to 10-4.10 M. The concentrations of the Ca standards ranged from 10-4.90 to 10-

3.00 M, and the concentrations of the P standards ranged from 10-5.80 to 10-2.60 M. The standards

were acidified following the same procedure as was applied to the samples. The standards and

samples were analyzed with a Perkin Elmer 2000DV ICP-OES within 5 days of collection. U was

analyzed at 424.167 nm, Pb was analyzed at 220.356 nm, Ca was analyzed at 227.546 nm, and P

was analyzed at 214.914 nm. The set of standards was analyzed before, in between, and after

the samples were analyzed to check for machine drift. Analytical uncertainty, as determined by

repeat analyses of the standards, was ±2.75%.

2.3.3.5 TOC

TOC standards were prepared by gravimetrically diluting commercially-supplied 1,000

ppm C aqueous standard using the same ionic strength buffer solution as the experimental

samples. The standards were then acidified with 6M HCl and immediately sealed with parafilm.

The standards and samples were analyzed with a Shimadzu TOC – V/TNM within 24 hours of

collection.

2.3.4 Thermodynamic modeling

2.3.4.1 Saturation state calculations

To determine initial saturation state values for each of the experimental systems,

activity quotients (Q) were calculated using a Newton-Raphson iteration technique to solve the

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non-linear system of mass balance and mass action equations listed in Tables 4, 5, and 6. The

starting molarities of each metal and P were used as mass balance constraints, and the resulting

Q was calculated according to the following dissolution reactions for hydrogen uranyl

phosphate, lead phosphate, and hydroxylapatite:

(UO2)(HPO4)·3H2O (s) 3 H2O + UO22+ + HPO4

2-

Pb3(PO4)2 (s) 3 Pb2+ + 2 PO43-

Ca5(PO4)3OH (s) 5 Ca2+ + 3 PO43- + OH-

so that the Q value for each reaction corresponds to the following terms, respectively:

QU = aH2O3 • aUO2 • aHPO4

QPb = aPb3 • aPO4

2

QCa = aCa5 • aPO4

3 • aOH

Activity coefficients were calculated using an extended Debye-Hückel equation with A,

B, and å values of 0.5101, 0.3285, and 5.22, respectively (Helgeson et al., 1981). Saturation state

values were then calculated by comparing the resulting Q values to the equilibrium constants, K,

for the respective mineral, according to Equation 7:

Saturation Index = log (Q / K)

In the calculations, I assume water activities of unity, and the equilibrium constant

values that were used for Reactions 1-3 were 10-13.17, 10-43.53, and 10-53.28, respectively (Zhu et al.,

2009; Martell and Smith, 2001; Gorman-Lewis et al., 2009).

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TABLE 4

SYSTEM OF EQUATIONS USED FOR SATURATION STATE AND SOLUBILITY CALCULATIONS FOR

URANIUM SYSTEM

Reaction Log K

(UO2)(HPO4)·3H2O (s) → 3 H2O + UO22+ + HPO4

2- -13.17 (Ksp) a UO2

2+ + PO43- → UO2PO4

- 13.23 b UO2

2+ + HPO42- → UO2HPO4

0 7.24 b UO2

2+ + H3PO40 → UO2H2PO4

+ + H+ 1.12 b UO2

2+ + H3PO40 → UO2H3PO4

2+ 0.76 b UO2

2+ + 2 H3PO40 → UO2(H2PO4)2

0 + 2 H+ 0.64 b UO2

2+ + 2 H3PO40 → UO2(H2PO4)(H3PO4)+ + 2 H+ 1.65 b

UO22+ + H2O → UO2OH+ + H+ -5.25 b

UO22+ + 2 H2O → UO2(OH)2

0 + 2 H+ -12.15 b UO2

2+ + 3 H2O → UO2(OH)3- + 3 H+ -20.25 b

2 UO22+ + H2O → (UO2)2OH3

+ + H+ -2.70 b 2 UO2

2+ + 2 H2O → (UO2)2(OH)22+ + 2 H+ -5.62 b

3 UO22+ + 5 H2O → (UO2)3(OH)5

+ + 5 H+ -15.55 b UO2

2+ + CO32- → UO2CO3

0 9.94 b UO2

2+ + 2 CO32- → UO2(CO3)2

2- 16.61 b 2 UO2

2+ + CO32- + 2 H2O → (UO2)2(CO3)(OH)3

- + 3 H+ -0.86 b HPO4

2- → H+ + PO43- -12.35 b

H+ + HPO42- → H2PO4

- 7.21 b H+ + H2PO4

- → H3PO40 2.14 b

H2O → OH- + H+ -14.00 c H2CO3

0 → HCO3- + H+ -6.35 c

H2CO30 → CO3

2- + 2 H+ -16.68 c mH2CO3 = 10-4.97

(a) Gorman-Lewis et al., 2009.

(b) Guillaumont et al., 2003.

(c) Martell and Smith, 2001.

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TABLE 5

SYSTEM OF EQUATIONS USED FOR SATURATION STATE CALCULATIONS FOR LEAD SYSTEM

Reaction Log K

Pb3(PO4)2 (s) = 3 Pb2+ + 2 PO43- -43.53 (Ksp)

Pb2+ + OH- = PbOH+ 6.3 Pb2+ + 2 OH- = PbOH2

0 10.9 Pb2+ + 3 OH- = PbOH3

- 13.9 2 Pb2+ + OH- = Pb2OH3+ 7.6

3 Pb2+ + 4 OH- = Pb3OH42+ 32.1

4 Pb2+ + 4 OH- = Pb4OH44+ 35.1

6 Pb2+ + 8 OH- = Pb6OH84+ 68.4

Pb2+ + HPO42- = PbHPO4

0 3.1 Pb2+ + H2PO4

- = PbH2PO4+ 1.5

Pb2+ + 2 CO32- = Pb(CO3)2

2- 9.05 HPO4

2- = H+ + PO43- -12.35

H+ + HPO42- = H2PO4

- 7.21 H+ + H2PO4

- = H3PO40 2.14

H2O = OH- + H+ -14.00 H2CO3

0 = HCO3- + H+ -6.35

H2CO30 = CO3

2- + 2 H+ -16.68

mH2CO3 = 10-4.97

(a) Martell and Smith, 2001.

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TABLE 6

SYSTEM OF EQUATIONS USED FOR SATURATION STATE CALCULATIONS FOR CALCIUM SYSTEM

Reaction Log K

Ca5(PO4)(OH)3 (s) = 5 Ca2+ + 3 PO43- + OH- -53.28 (Ksp)a

Ca2+ + OH- = CaOH+ 1.3b Ca2+ + PO4

3- = CaPO4- 6.46 b

Ca2+ + HPO42- = CaHPO4

0 2.74 b Ca2+ + H2PO4- = CaH2PO4

+ 1.4 b HPO4

2- = H+ + PO43- -12.35 b

H+ + HPO42- = H2PO4

- 7.21 b H+ + H2PO4

- = H3PO40 2.14 b

H2O = OH- + H+ -14.00 b

(a) Zhu et al., 2009.

(b) Martell and Smith, 2001.

2.3.4.2 HUP solubility calculation

The solubility of the isolated biogenic HUP particles was calculated using a similar

Newton-Raphson program to the one used to calculate saturation states to solve the non-linear

set of mass action and mass balance equations corresponding to the reactions listed in Table 1.

The total dissolved P concentration for the calculation was fixed at the average P concentration

from the biogenic HUP solubility experiments. The model was used to calculate the expected U

concentration based on the solubility product for macroscopic HUP reported by Gorman-Lewis

et al. (2009).

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2.4 Results & Discussion

2.4.1 Uranium system

2.4.1.1 TEM

Element maps (a representative example of which is shown in Figure 2) of U and P

distributions in the biotic B. subtilis samples indicate that while P is distributed throughout the

cells, U is concentrated on the cell walls. These results suggest that the cells in these

experiments did not actively incorporate U into the cytoplasm through metabolic processes, and

that the U distribution in the biotic experiments is controlled by adsorption and/or precipitation

reactions on or within the bacterial cell walls.

The TEM images of the samples taken from the lower saturation state conditions

investigated (samples U5 and U8) suggest that precipitation of uranyl phosphates was

homogeneous, occurring exclusively in solution, and that the cell walls did not appear to

influence the mineralization reaction (Figs. 3a,b). The figures show some contact between the

precipitate and the bacterial cells in these samples, but the images do not offer evidence that

the cells were involved in the precipitation, and it is likely that the cell-mineral association is

coincidental only. Figure 3a and 2b also show no significant difference in the size of the mineral

precipitate between the abiotic control and the biotic experiment, which is consistent with a

lack of influence of the bacterial cells on the precipitation reaction at the lower saturation state

conditions investigated.

TEM evidence, however, indicates that under the higher saturation state conditions

investigated (sample U11), uranyl phosphate precipitation was heterogeneous, with nano-scale

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Figure 2: Elemental map of biotic U11 sample. P is shown in red, U is shown in green. The scale bar is 500 nm.

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Figure 3: TEM bright field images for U system. (A) Abiotic U5 control; (B) Biotic U5 experiment; (C) Abiotic U11 control; (D) Biotic U11 experiment. All scale bars are

200 nm. The bacteria in (B) and (D) is B. subtilis.

crystals appearing to nucleate within the three-dimensional macromolecules that comprise the

bacterial cell walls (Figure 3c,d). Under these conditions, there is a distinct difference in

precipitate size between the abiotic control and the biotic experiment. The abiotic control

(Figure 3c) exhibits plate-like precipitates with edge lengths ranging from approximately 50 to

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150 nm and thicknesses of approximately 10 nm. The lath-like precipitates observed in the

abiotic controls represent cross-sections of the plate-like precipitates that are oriented

perpendicular to the plane of the page. Close examination of the cell wall-controlled

precipitation (Figure 4) demonstrates that precipitation was uniformly distributed around each

cell and that the crystals are all plate-like in morphology with edge lengths ranging from

approximately 10 to 30 nm and a thickness of approximately 1 to 5 nm, with nucleation

occurring throughout the cell wall matrix and with crystals growing both into and out of the cell

itself. The same cell wall nucleation phenomenon was observed in samples from the parallel

systems that contained S. oneidensis MR-1 (Figure 5); however, with the Gram-negative species,

the nucleation appears to be restricted between the outer and plasma membranes, and the

particles are oriented parallel to the cell membranes. This can be compared to the randomly

oriented crystals that formed within the cell wall matrices of the Gram-positive B. subtilis

species.

These images provide unequivocal evidence that bacterial cell walls can nucleate

mineral formation. The particles visible within the bacterial cell walls depicted in Figures 3d, 4,

and 5 clearly nucleated in place, most likely nucleated on one or more types of cell wall

functional groups. Surface controlled precipitation is thought to stem from adsorption onto

surface binding sites (e.g., Farley et al., 1985; Warren and Ferris, 1998), and in the experiments

in which cell wall nucleation was evident, precipitation likely begins with uranyl adsorption onto

a cell wall binding site. The adsorbed uranyl forms a positively charged site, and in this way

phosphate adsorption can alternate with uranyl adsorption at this site to form a bacterial cell

wall precipitate.

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Figure 4: TEM bright field images for U system. (A) Biotic U10 experiment; (B) close up of area in the black box in image A to illustrate the texture of the biogenic U nanoparticulate precipitate; (C) Biotic U10

experiment; (D) close up of area located in black box in image C; (E) Biotic U10 experiment; (F) close up of area located in the black box in

image F. The bacteria in all micrographs is B. subtilis.

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Figure 5: TEM bright field image of uranyl phosphate biomineralization in biotic (A) U5 and (B) U11 samples, showing texture and prevalence of minerals within the S. oneidensis cell walls. The scale bars represent (A)

200 nm, and (B) 100 nm.

2.4.1.2 SAED and XRD

Selected area electron diffraction (SAED) patterns of the abiotic run products tested

indicated that the precipitated solids exhibit a high degree of crystallinity. SAED results for the

biotic samples exhibit a diffuse ring pattern, with some evidence of weak and ephemeral

diffraction patterns. This is evidence that the nanoparticles are crystalline, but because of their

small size they rapidly become amorphous under the electron beam. Solid run products from

abiotic controls and biotic experiments with starting P concentrations of 10-4.49, 10-3.89, 10-3.65,

and 10-3.49 M, the same samples (U5, U8, U10, and U11) that were analyzed with TEM, were

characterized using XRD to determine the crystallinity and identity of the precipitates. Each of

the samples exhibits a number of peaks in common with the diffractogram for a reference

sample of hydrogen uranyl phosphate (UO2HPO4•4H2O), or HUP, as well as some different peaks

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(Figure 6). Each of the sample diffractograms exhibit peak shoulders at 2θ equal to 24.25 and

25.75 that correspond to characteristic peak angles in the reference pattern. Similarly, the

reference pattern and all of the samples exhibit a peak at 2θ equal to 51.75. Additionally, all of

the biotic experiments exhibit a peak at 2θ equal to 27.25, which corresponds with the peak at

the same angle in the reference pattern. However, the peaks exhibited at 2θ equal to 22.7 are

only present in the biotic U8 and U10 experiment diffractograms, and not exhibited in the

reference pattern. These peaks are likely a result of minor, unidentified mineral phases only

present in the biotic samples, or they may result from the HUP in the sample containing a

different number of water molecules than the HUP XRD standard. Additionally, the peak at 2θ

equal to 24.7 in the bacteria-only sample is present in diffractograms for each abiotic and biotic

sample, but is not present in the diffractogram for the mineral reference sample. This peak likely

results from a salt precipitate from the experimental solutions. Although there are variations in

peak intensities in the diffractograms between the precipitates from the abiotic controls and the

biotic experiments, and between precipitates from experiments with varying P concentrations,

the peak positions and intensities in each diffractogram are consistent with the HUP reference

pattern.

2.4.1.3 XAS

XANES spectra (Figure 7) indicate a U(VI) valence state for all of the samples, with no

reduction of U to U(IV) observed. The edge position of the U(IV) spectrum is shifted

approximately 4 eV towards lower energy relative to the U(VI) spectrum (Kelly et al., 2002), and

this shift was not observed in any of the samples. The shoulder structure approximately 15 eV

above the edge due to the multiple-scattering of the two axial oxygen atoms of the uranyl ion

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Figure 6: XRD patterns from analysis of run products from U system experiments.

(Hennig et al., 2001) is a characteristic feature of the U(VI) valence state (Boyanov et al,, 2007),

and is present in the spectra of all of the samples. Both lines of evidence indicate that the vast

majority of the uranium in the biotic and abiotic samples remained as U(VI) during the

experiments, with no measureable reduction to U(IV).

EXAFS spectra at the U L3-edge show that at low saturation state conditions (biotic

sample U5), uranyl ions are present in the biotic sample dominantly as adsorbed species, bound

to carboxyl and phosphoryl groups on the bacterial cell walls. The signal strength of the

phosphorous peak (located at 3.0 Å) is weak compared to the HUP reference spectrum (Figure

8), and in general, the biotic U5 sample exhibits a markedly different spectrum than does the

HUP standard. The second oxygen peak is more distinguishable from the other samples, and the

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Figure 7: k3-weighted EXAFS spectra of the biotic and abiotic samples plotted with the HUP standard. Except for Biotic U5, which exhibits an adsorption spectrum, all spectra have the small features around k = 10

Å-1, a signature feature of the autunite group.

peak at approximately 3.0 Å is damped. At 2.2 Å, the biotic U5 spectrum does not dip as much as

the HUP mineral spectrum, which corresponds to the contribution of a carbon atom. The fitting

suggests a binding environment of two axial oxygen atoms at 1.75 Å, and two split equatorial

oxygen shells: one at 2.19 Å with approximately 2.2 oxygen atoms, and the other at 2.34 Å with

approximately 5.3 oxygen atoms. This split of the equatorial oxygen shells results from the

uranyl ion binding to a phosphate group so that the symmetry of equatorial oxygen is

perturbed. The average number of bound C atoms at 2.90 Å from the U atom is 1.1, and the

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average number of bound P atoms at a distance of 3.54 Å is 0.78. These results suggest that the

uranyl ion in biotic sample U5 is bound to both carboxyl and phosphoryl sites, a result that is

consistent with the findings of Kelly et al., (2002) who examined the adsorption of uranyl onto B.

subtilis cells. The model fit of this EXAFS spectrum is shown in Figure 9.

Although adsorbed U is the only form of U detected by XAS in the biotic U5 sample, with

increasing saturation state conditions, the EXAFS spectra indicate that U is present

predominantly as solid phase HUP. Figure 8 compares the EXAFS spectra from the abiotic and

biotic samples with that of the HUP standard. All the abiotic samples and most of the biotic

samples (except biotic U5) match the HUP mineral spectrum, exhibiting an axial oxygen peak at

1.4 Å, an equatorial oxygen peak at 1.8 Å, and a peak at 3.0 Å. (corresponding to phosphorus

atoms). Slight differences exist between the spectra from the abiotic and the biotic samples, but

these are likely due to experimental artifacts from the sample preparation procedure.

Heterogeneous samples are well known to exhibit amplitude reduction, known as “thickness

effects”, in transmission measurements, and can also introduce background variations in the

spectra. Because only small amounts of the abiotic precipitates were available for the

experiment, the dried precipitates were ground and mixed with graphite powder before being

mounted for measurement to obtain relatively homogenous samples. The EXAFS spectra were

taken from different spots of the sample, and the spots which exhibited obvious anomalous

background were abandoned. Despite these efforts to eliminate the artifacts from

heterogeneity, spectra from some samples still exhibited background anomalies. In addition to

the background artifacts, the possibility of amorphous phases existing together within the

mineral crystal cannot be ignored. In the amorphous phase, the disorder of the local structure

around uranium would reduce the amplitudes of the oxygen peaks. The biotic samples, on the

other hand, were more homogenous as a result of the biomass matrix. The differences in biotic

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Figure 8: (A) Magnitude of U L3-edge EXAFS spectra after Fourier transformation for the abiotic samples overlaid by the HUP standard; (B) Magnitude of U L3-edge EXAFS spectra after Fourier transformation for the biotic samples overlaid by the HUP standard. Spectra shown were

collected in transmission mode.

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Figure 9: EXAFS data and fit in the magnitude of the Fourier transformed EXAFS spectrum.

samples were relatively small, except for the biotic U5 sample, which indicates U ions adsorbed

to the bacterial cell wall rather than nanoparticle formation. Fluorescence measurements (data

not shown here) of the samples in Figure 8 are consistent with transmission measurements,

which corroborates the validity of the measurements.

The k3-weighted EXAFS spectra (Figure 7) show the suppressed oscillations around

k~10, which is a characteristic signature for HUP/autunite/chernikovite group minerals (Fuller et

al., 2003). This feature is present in every sample (except biotic U5), which supports the

conclusion that the dominating phase in the abiotic and biotic samples is the HUP mineral

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phase. With the exception of the biotic U5 sample, all of the spectra could be fit to the HUP

structure (Morosin, 1978) with 2 axial oxygen atoms at 1.78 Å, approximately 4 equatorial

oxygen atoms at 2.3 Å, and approximately 4 phosphorus atoms at 3.6 Å. The fitting to each

spectrum is shown in Figure 9 (details of the fitting paths and parameters are available in Tables

7 and 8). Fittings show consistent distances between the axial and equatorial oxygen and

uranium as well as the phosphorus and uranium atoms compared to the known HUP structure.

The shell coordination numbers are also consistent, within uncertainty, with the HUP structure.

Multiple scattering paths from the axial oxygen atoms and from the equatorial oxygen-

phosphorous atoms were also included to improve the quality of the fit.

TABLE 7

FITTING PATHS AND CORRESPONDING PARAMETERS USED FOR XAS ANALYSIS

Path R(Å) Ncoor σ2 (x10-3 Å2) ΔE0(eV)

U Oax ΔR1 2 σ21 ΔE01

U Oeq1 ΔR2 N1 σ22 ΔE02

U Oeq2 b ΔR3 N2 σ22 ΔE02

U P ΔR4 N3 σ23 ΔE03

U Oax1 U Oax1 ΔR1 X 2 2 σ21

X 2 ΔE01

U Oax2 U Oax1 ΔR1 X 2 2 σ21

X 2 ΔE01

U Oax2 Oax1 ΔR1 X 2 2 σ21

X 2 ΔE01

U P Oeq ΔR4 N3 X 2 σ23 ΔE03

U Oeq P Oeq ΔR4 N3 σ23 ΔE03

U Oeq1 Oax a ΔR2 N1 X 4 σ2

2 ΔE04

U C b ΔR5 N4 σ23 ΔE03

U C Oeq b ΔR5 N1 X 4 σ23 ΔE03

(a) The coordination number of the axial oxygen in uranyl ion is 2, this number is set during the fitting.

(b) Additional path used for uranyl phosphate mineral fitting.

(c) Additional path used for uranyl adsorption spectra fitting of biotic U5 sample.

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TABLE 8

PARAMETERS FOR MAJOR FITTING PATHS USED IN THE FITTING OF XAS DATA

Sample Path R(Å) Nd,eq σ2 (x10-3 Å2)

Biotic U5 U Oax 1.75 ±0.02 2 3.2 ± 1.2 U Oeq1 2.28 ± 0.04 3.69 ±1.72 5.7 ±3.0

U Oeq1 2.13 ±0.05 2.53 ±1.42 5.7 ±3.0

U P 3.58 ±0.02 1.48 ±1.03 3.2 ±3.3

U C 2.54 ±0.03 2.56 ±0.91 3.2 ±3.3

Biotic U8 U Oax 1.78 ±0.01 2 2.4 ±0.4

U Oeq1 2.28 ±0.01 5.37 ±0.77 6.1 ±1.2

U P 3.57 ±0.03 5.33 ±2.63 6.1 ±3.3

Biotic U10 U Oax 1.78 ±0.01 2 2.1 ±0.4

U Oeq1 2.27 ±0.01 4.24 ±0.58 4.8 ±1.2

U P 3.58 ±0.02 4.84 ±2.22 6.0 ±3.1

Biotic U11 U Oax 1.78 ±0.01 2 2.1 ±0.4

U Oeq1 2.27 ±0.01 4.67 ±0.56 5.7 ±1.1

U P 3.54 ±0.03 4.56 ±1.73 9.5 ±3.2

Abiotic U5 U Oax 1.80 ±0.01 2 4.7 ±0.6

U Oeq1 2.27 ±0.01 4.48 ±0.78 5.0 ±1.3

U P 3.57 ±0.03 6.34 ±2.8 8.1 ±3.4

Abiotic U8 U Oax 1.78 ±0.01 2 1.6 ±0.3

U Oeq1 2.27 ±0.01 4.23 ±0.45 5.4 ±1.0

U P 3.56 ±0.02 4.67 ±2.05 7.3 ±3.2

Abiotic U10 U Oax 1.79 ±0.01 2 1.7 ±0.4

U Oeq1 2.27 ±0.01 4.02 ±0.58 4.1 ±1.1

U P 3.57 ±0.03 2.69 ±1.70 2.7 ±3.5

Abiotic U11 U Oax 1.77 ±0.01 2 2.2 ±0.4

U Oeq1 2.27 ±0.01 5.44 ±0.81 7.0 ±1.3

U P 3.59 ±0.02 2.85 ±1.40 2.0 ±2.6

The XAS results indicate that bacteria do not affect the mineral that precipitates during

the experiments, and that HUP is the only significant solid phase to form in both the abiotic

controls and the biotic experiments. Fittings of the EXAFS spectra (Figure 9) to the theoretical

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model indicate that the structure of the precipitate in all of the abiotic controls, as well as in all

biotic experiments, is consistent with the mineral structure of HUP. Furthermore, and perhaps

most importantly, the XAS results strongly suggest that, as predicted by surface precipitation

theory, uranyl adsorption onto cell wall functional groups represents the first step in cell wall

nucleation of uranyl phosphate minerals. Under the lower saturation state conditions studied,

even though uranyl phosphate precipitation occurred in the system, uranium is present in the

sample dominantly as adsorbed uranyl species. With increasing saturation state conditions, the

adsorbed uranyl signal becomes overwhelmed with the uranyl phosphate precipitate, and under

the highest saturation states studied, the precipitation becomes clearly nucleated within the cell

wall.

2.4.1.4 ICP-OES

In the discussion of the aqueous chemistry results, I referred to example saturation

state conditions that correspond to the numbers in Figures 10a and 10b. Both the starting and

final concentrations for those example experiments are shown with corresponding number

labels and arrows. Saturation state condition 1 represents the lowest saturation state studied;

increasing saturation state condition numbers indicate increasing saturation state conditions.

For saturation state conditions 2 and 3 (Figure 10a), the abiotic controls removed significantly

more U from solution than the B. subtilis biotic experiments performed at 0.62 gm wet biomass

L-1. At saturation state condition 1, the biotic experiments removed slightly more U from

solution than the abiotic controls. This slight increase in removed U is likely in part a result of U

adsorption onto the biomass in the experiment, a result consistent with the XAS findings for

these low saturation state conditions. Additionally, the biotic experiments show an increase in

final P concentrations relative to the experimental starting conditions at saturation state

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condition 1. This increase is likely due to P exuded from the bacteria during the experiment, and

some of the enhanced U removal relative to the abiotic controls may be due to enhanced HUP

precipitation from this additional P in the system. At saturation state conditions 2 and 3, the

amount of P exuded represents a lower percentage of the total P in the experimental systems,

and no significant increase in P is observed in those systems. Under all saturation states

investigated, the abiotic controls removed more P from solution than did the biotic experiments

relative to the starting conditions.

As the bacterial concentration was varied from 0.31 to 2.50 gm (wet biomass) L-1, the

amount of U removed from solution did not exhibit a consistent trend as a function of bacterial

concentration (Figure 10a). At all of the bacterial concentrations studied, the abiotic controls

removed more U from solution at saturation state conditions 2 and 3 than did the biotic

experiments. With increasing bacterial concentration, the final aqueous P concentration in the

biotic experiments increased as well, likely due to bacterial exudates which contain P. However,

the relative increase in P concentration decreased as the saturation state increased to condition

3.

Shewanella oneidensis biotic experiments removed slightly more U from solution at low

saturation states (condition 1) than did the abiotic controls, but the two types of experiments

removed approximately equal concentrations of U from solution under higher saturation state

conditions (Figure 10b, condition 2). The abiotic controls removed up to one log unit more P

from solution at low saturation states than did the biotic experiments. Similar to the B. subtilis

biotic experiments, the lowest saturation state S. oneidensis biotic experiments exhibited

elevated final P concentrations, relative to both the starting conditions and the abiotic controls.

This elevated P concentration is likely due to P that is exuded from the bacteria. The bacterially-

exuded P in the S. oneidensis system is more readily available for U removal than the P exuded

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Figure 10: Changes in the aqueous concentrations of U and P in the U experiments. (A) B. subtilis; (B) S. oneidensis. All experiments were performed in triplicate (symbols represent the mean). Error bars

represent one standard deviation (note that some error bars are smaller than the symbol). Each arrow connects the starting condition (arrow

tail, asterisks) to the final U and P concentrations in the abiotic control or biotic experiments (arrow head, squares and circles). The numerals

“1”, “2”, and “3” represent saturation state conditions discussed in detail in the text and are presented here for reference.

1 1

2

2

3

1

2

2

2

1

1 B

A

1

2

3

3

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by B. subtilis, as evidenced by the greater removal of U from solution at the lowest saturation

state condition in the S. oneidensis system relative to the B. subtilis system (Figs. 10b and 10a,

respectively). At high saturation states, there was no significant difference in final U and P

concentrations between the abiotic controls and the biotic experiments in the S. oneidensis

system.

The higher aqueous U concentrations in the biotic experiments relative to the abiotic

controls are not likely caused by nucleation kinetics effects. If the presence of the bacteria

accelerated the nucleation kinetics, a result consistent with the presence of the smaller crystals

in the biotic experiments relative to the abiotic controls, then one would expect lower

concentrations of U to remain in solution as faster precipitation kinetics usually cause more

complete precipitation reactions (Kasama and Murikami, 2001; Fritz and Noguera, 2009).

Similarly, cell wall adsorption of U should cause enhanced removal of U from solution relative to

the abiotic control experiments (Fowle et al., 2000; Gorman-Lewis et al., 2005; Knox et al. 2008).

However, the opposite occurs in most of the experiments, with higher aqueous U

concentrations in the B. subtilis biotic experiments. The concentration of bacteria in the system

does not significantly affect the extent of U and P removal within a range of 0.31 to 2.50 gm wet

biomass L1 (Figure 10a), also suggesting that U adsorption onto the bacteria does not control U

concentrations in the higher saturation state experiments. This behavior is not a result of

increased saturation state conditions in biotic experiments, since higher saturation states would

result in less U remaining in solution in the biotic experiments compared to the abiotic controls

(Ohnuki et al., 2005).

Elevated U concentrations can be caused by inhibition of precipitation by aqueous U

complexation with organic exudates. To test whether aqueous U-organic complexes affected the

extent of precipitation and were the cause for the observed elevated aqueous U concentrations

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in the biotic experiments, I used an organic exudate solution to perform a cell-free control

experiment. Figure 11 shows that at low saturation states (condition 1), the exudate solution

contained an elevated P concentration relative to both the starting conditions and the abiotic

control, confirming that bacteria exude P into solution. This effect is less apparent as the

experimental P concentration increases. At the lowest saturation states investigated, there was

no significant removal of U by the exudate solution, which is consistent with the XAS results

which show that at low saturation states, U is dominantly removed by adsorption to cell walls.

This also suggests that the exuded P is present as an organo-phosphate and is unavailable for

precipitation with U. If the exudates contained orthophosphate, we would expect to observe

enhanced U removal in the exudates solutions relative to the abiotic controls. As the saturation

state increases to conditions 2 and 3, the exudate solution removes more U from solution than

the biotic experiments, but removes less U from solution than the abiotic controls. These results

suggest that U-organic aqueous complexes form under the experimental conditions, accounting

for at least a portion of the increased final U concentration in the biotic experiments. However,

because the exudate solution experiments result in more U removal than do the biotic

experiments, it is evident that these aqueous complexes only account for a portion of the

elevated U concentrations in the biotic experiments, and that another process also contributes

to the observed elevated U concentrations in the biotic experiments.

2.4.1.5 Solubility

Complexation of U with organic exudates explains at least part of the enhanced U

concentrations observed in the biotic experiments; however, at higher initial P concentrations,

complexation does not explain the discrepancy between the abiotic controls and the biotic

experiments. It is under these conditions that I observed cell wall mineralization and smaller

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Figure 11: Aqueous chemistry results for the bacterial exudate experiment (shown as hollow triangles) compared to aqueous chemistry results for the U system (as shown in Figure 10A). Each arrow connects

the starting condition (arrow tail, asterisks) to the final U and P concentrations in the abiotic control or biotic experiments (arrow head,

squares and circles). The numerals “1”, “2”, and “3” represent saturation state conditions discussed in detail in the text and are

presented here for reference.

particle sizes. These particles appear to be plate-like in morphology, with edge dimensions of

much less than 30 nm in all dimensions. It is possible that the solubility of these nanoparticles is

higher than the solubility of the much larger abiotic precipitates, and the solubility experiments

were designed to test this hypothesis.

-8

-7

-6

-5

-4

-7 -6 -5 -4

Fin

al

[U]

(log M

)

Final [P] (log M)

Starting Conditions

Abiotic Control

0.62 g wet biomass / L

Bacterial Exudate Experiment

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Figure 12 depicts the experimental measurements of the solubility of the isolated

biogenic precipitates (isolated from biotic U10). The measured U and P concentrations attained

steady-state log molalities of total U and P in solution were -4.34 ± 0.07 and -3.13 ± 0.08,

respectively, with no consistent change in concentration after 2 days. The solubility product of

HUP, determined by Gorman-Lewis et al. (2009) using 300 μm crystals, was used to calculate an

expected solubility of macroscopic HUP crystals for comparison. For these calculations, I account

for aqueous U and P speciation using the reactions and equilibrium constants shown in Table 1.

At the measured equilibrium P concentration of the biogenic HUP solubility experiment,

macroscopic HUP would be in equilibrium with a solution with a U log molality of -5.86 (-

0.10/+0.08). The biogenic HUP exhibited a U concentration approximately 1.5 orders of

magnitude higher than the concentration calculated for macroscopic HUP, suggesting that the

particle size of these nanoscale-sized particles can exert a large influence on their solubilities.

The results of the solubility measurements suggest that in addition to the effect of the aqueous

U-exudate complexation, the size of the biogenic nanoprecipitates that form under high

saturation state conditions likely contributes to the enhanced U concentrations that I observed

in the biotic experiments.

2.4.1.6 Effects of bacteria on uranyl phosphate precipitation

My results present evidence for passive cell wall biomineralization, a type of

biomineralization in which the high binding affinity of cell walls for aqueous metal cations

creates nucleation sites for mineral precipitation reactions in saturated systems. Although it is

not clear from the data which cell wall functional groups are involved and what the exact

precipitation mechanism is, the data demonstrate unequivocally that the presence of bacteria in

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Figure 12: Measured U and P concentrations from the solubility experiments involving biogenic hydrogen uranyl phosphate (HUP)

precipitates. Model P concentrations were fixed at the average experimental value, and the model U line is the calculated U

concentration in equilibrium with macroscopic HUP, using the Ksp value reported by Gorman-Lewis et al. (2009).

some precipitating systems can alter the extent and morphology of the precipitation reaction,

and is likely to affect the fate and mobility of the precipitating elements.

Passive cell wall biomineralization and the formation of nanoprecipitates of uranyl

phosphate could significantly affect the mobility of U compared to the mobility exhibited if the

precipitation occurred without bacteria present. Nanoprecipitates of uranyl phosphate may be

released from the cell walls in which they formed after cell death, and due to their small size,

the particles may be highly mobile in a geologic matrix. In addition, as the data suggest,

nanoprecipitates can exhibit markedly higher solubilities than macro-scale crystals, and organic

bacterial exudates can form aqueous complexes with dissolved uranium. Both of these

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processes affect the mobility of uranium in the aqueous phase, increasing the equilibrium

concentration of U in solution at a given P concentration.

2.4.2 Lead system

2.4.2.1 TEM

Figure 13 shows TEM micrographs of biotic samples under high and low saturation

states (biotic Pb4 and Pb8). All of the electron dense (dark) particles in the bulk solution in the

figure represent the mineral precipitate. The mineral precipitates in these images exhibit the

same morphology and are similar in size (note that the scale bars are different in each

micrograph). It is also evident that although the precipitate and the bacteria are in contact at

some points, the contact appears to be coincidental only and no strong spatial correlation exists.

I concluded from this visual evidence that passive cell wall mineralization does not occur in the

Pb system under any of the saturation state conditions investigated.

Figure 13: TEM bright field images for Pb system: (A) Biotic Pb4 experiment (scale bar is 200 nm); (B) Biotic Pb8 (scale bar is 100 nm).

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2.4.2.2 XRD

The solid run products from biotic experiments Pb4, Pb6, and Pb8 were analyzed by XRD

(Figure 14). The diffractograms for these samples exhibit the same peaks, suggesting that the

precipitate in each biotic experiment was the same mineral, a result that is consistent with the

TEM results above. Therefore, the precipitate in the Pb system is unaffected by varying

saturation states within the range investigated in this study. Additionally, the diffractograms of

the biotic experiments are all consistent with the reference pattern (ICDD 00-002-0750) for lead

phosphate (Pb3(PO4)2). XRD analyses were not performed on the abiotic controls due to the

difficulty of harvesting a large enough mass of precipitate at the low Pb concentrations

investigated.

Figure 14: XRD patterns for biotic samples from the Pb system. The lower pattern is a reference for Pb3(PO4)2.

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2.4.2.3 ICP-OES

Under saturation state condition 1, the abiotic controls removed half a log unit less Pb

from solution than did the biotic experiments (Figure 15). Under this condition, the biotic

experiments exhibited an increase in the final concentration of P relative to the abiotic controls

and the starting condition. This increase in P in the biotic experiments, which is not seen in the

abiotic controls, is likely a result of P exuded from the bacteria during the experiment.

Therefore, if the exuded P is at least in part present as orthophosphate, the enhanced Pb

removal from solution in the biotic case could be due to enhanced Pb3(PO4)2 precipitation due to

the elevated saturation state that results from the exuded P. Alternatively, the enhanced

removal in the biotic experiments could be due to Pb adsorption onto the biomass in the biotic

experiments. At saturation state condition 2, the extents of Pb removal by the abiotic controls

and the biotic experiments were not significantly different, nor did the P concentration change

during the course of either the biotic or abiotic experiments.

2.4.2.4 Effect of bacteria on lead phosphate precipitation

The Pb system results demonstrate that the presence of bacteria does not strongly

affect the extent or nature of Pb-phosphate precipitation under the conditions studied. Under

low saturation state conditions, I observed enhanced removal of Pb from solution in the biotic

systems relative to the abiotic controls, and this effect could be due either to the P that is

exuded by the bacteria or to biomass adsorption of Pb. The bacteria do not affect the

mineralogy nor the morphology of the precipitates in the Pb system, and consistent with these

observations, the TEM images showed little or no association between the bacteria and the

precipitate.

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Figure 15: Changes in the aqueous concentrations of Pb and P in the Pb experiments with B. subtilis. All experiments were performed in

duplicate. Error bars represent one standard deviation (note that some error bars are smaller than the symbol). Each arrow connects the

starting condition (arrow tail, asterisks) to the final Pb and P concentrations in the abiotic control or biotic experiments (arrow head,

squares and circles). The numerals “1” and “2” represent saturation state conditions discussed in detail in the text and are presented here

for reference.

-6.5

-6

-5.5

-5

-4.5

-4

-5.5 -5 -4.5 -4 -3.5

[Pb

] fi

nal

(log M

)

[P] final (log M)

Starting Conditions

Abiotic Control

0.62 g wet biomass / L

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2.4.3 Calcium system

2.4.3.1 TEM

Under low saturation state conditions (Figs. 16a,b), the precipitates in both the abiotic

controls (abiotic Ca7) and the biotic experiments (biotic Ca7) exhibit plate-like morphologies

with average dimensions of approximately 50 nm x 50 nm x 10 nm. Under higher saturation

state conditions (Figs. 16c,d) the precipitate in the abiotic control (abiotic Ca11) exhibits the

same characteristics as the abiotic control precipitate at low saturation states. However, the

biotic experiment at high saturation states (biotic Ca11) produces smaller precipitates, with

average dimensions of approximately 20 x 20 x < 10 nm. In Figures 16b and 16d, there appears

to be a spatial association between the mineral precipitate and the cell wall; however, it is

uncertain whether this association is coincidental or a result of the cell wall involvement in the

precipitation process. Therefore, although there is no evidence that passive cell wall nucleation

occurs in this system under the investigated conditions, the presence of the bacteria affects the

size of the mineral precipitate under high saturation state conditions.

2.4.3.2 XRD

Biotic Ca4 and abiotic and biotic Ca7 and Ca11 samples were characterized with XRD

(Figure 17). The abiotic controls each exhibit distinct peaks (e.g. at 2θ equal to 16.3, 26.1, 31.7,

and 32.6), but the peaks in the biotic experiment diffractograms are less distinct, with significant

peak broadening becoming more apparent with increasing saturation state. For example, in the

diffractogram for biotic Ca11, the peaks at 2θ of 26.1, 31.7, and 32.6 appear to be one broad

peak instead of the three distinct peaks seen in abiotic Ca11. The peak broadening effect that is

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Figure 16: TEM bright field images for Ca system: (A) Abiotic Ca7 control; (B) Biotic Ca7 experiment; (C) Abiotic Ca11 control; (D) Biotic

Ca11 experiment. All scale bars are 100 nm.

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Figure 17: XRD data from run-products of Ca experiments.

evident at the high saturation states in the biotic experiments likely results from the formation

of smaller precipitates under these conditions, as observed in the TEM images (Figure 16d).

Furthermore, the diffractograms from all of the Ca experiments are consistent with the

reference diffractograms (ICDD 01-071-5049) for hydroxylapatite (HA, Ca10(PO4)6(OH)2),

suggesting that HA is the dominant precipitate to form under all of the experimental conditions.

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2.4.3.3 ICP-OES

Under virtually all of the saturation state conditions studied, the bacteria do not affect

the extent of Ca removal during precipitation relative to the abiotic controls (Figure 18). The

bacteria do, however, release P into solution, resulting in higher final P concentrations in

solution relative to both the abiotic controls and the starting conditions. With increasing

experimental P concentration, the input of P from the bacteria becomes less important relative

to the starting P concentration. At the highest saturation states studied (condition 2), the biotic

samples exhibit Ca concentrations that are approximately 0.25 log molality units higher than

those of the abiotic controls. As I observed in the U experiments, the elevated aqueous Ca

concentrations remaining in solution in the biotic experiments are likely a result of aqueous Ca

complexes with organic exudates. These complexes render the Ca unavailable for mineral

precipitation, and as a result the remaining aqueous Ca concentrations in the biotic experiments

are elevated relative to the abiotic controls.

2.4.3.4 Effect of bacteria on calcium phosphate precipitation

The results of the Ca experiments indicate that the presence of bacteria does not affect

the extent of Ca precipitation from solution, except at the highest saturation state conditions

investigated, where binding with bacterial exudates may affect the extent of Ca removal.

Bacterial cells do not affect the mineralogy of the precipitates in the Ca system. However, the

presence of bacteria results in a more fibrous morphology of the precipitates compared to that

seen in the abiotic controls, and results in a decrease in the size of the precipitate under high

saturation state conditions, as indicated by the TEM results. The size effect of the bacteria in the

Ca experiments is likely due to the presence of organic bacterial exudates in solution and the

interaction of these molecules with the precipitating HA particles. Lebron and Suarez (1996)

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Figure 18: Changes in the aqueous concentrations of Ca and P in the Ca experiments with B. subtilis. All experiments were performed in duplicate. Error bars represent

one standard deviation (note that some error bars are smaller than the symbol). Each arrow connects the starting condition (arrow tail, asterisks) to the final Ca and P

concentrations in the abiotic control or biotic experiments (arrow head, squares and circles). The numerals “1” and “2” represent saturation state conditions discussed in

detail in the text and are presented here for reference.

reported a similar effect on the size of calcite precipitates in the presence of varying

concentrations of dissolved organic carbon (DOC). With increased concentrations of DOC,

Lebron and Suarez (1996) observed a decrease in calcite particle sizes from >100 μm at a DOC

concentration of 0.02 mM to <2 μm at a DOC concentration of 0.15 mM. Consistent with this

observation, the biotic experiment diffractograms exhibited a general peak broadening effect,

-4.5

-4

-3.5

-3

-5 -4 -3 -2

[Ca]

fin

al

(log M

)

[P] final (log M)

Starting Conditions

Abiotic Control

0.62 g wet biomass / L

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60

which became more pronounced with increasing saturation states. Studies have reported that

particle size and peak width in XRD diffractograms are inversely correlated, such that smaller

particles produce wider peaks in the diffractogram relative to the same mineral with a larger

particle size (Weibel et al., 2005; Sanchez-Bajo et al., 2006). The observed gradual peak

broadening effect in the biotic experiments with increasing saturation state suggests that the

precipitate size and/or crystallinity decrease as the saturation state increases.

2.5 Conclusions

In this study, I investigated the effects of non-metabolizing bacteria on the precipitation

of metal phosphates under a range of saturation states. The results demonstrate several distinct

bacterial effects. At high saturation states in the U system, I observed passive cell wall

nucleation of uranyl phosphate minerals within the cell wall framework of both B. subtilis and S.

oneidensis cells. These nucleated particles, although of the same mineralogy and morphology as

forms under abiotic conditions, were dramatically smaller than the abiotic precipitates.

Furthermore, the extent of U removal in the biotic systems was significantly reduced relative to

the abiotic controls, in part due to the elevated solubility of the smaller nucleated particles, and

in part due to the presence of bacterial exudate molecules that formed aqueous complexes with

U and prevented the same degree of uranyl phosphate precipitation as occurred in the abiotic

experiments. I did not observe the same passive cell wall mineralization phenomenon in the Ca

or Pb systems. However, the presence of bacteria did decrease the size of the precipitates in the

Ca system at high saturation state. The experimental results strongly suggest that the bacterial

effects that I observed are likely to be element and/or saturation state specific. It is likely that

highly stable metal-phosphoryl binding, such as exists in the U system, is required to trigger

metal-phosphate cell wall mineralization.

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The observations provide the first comprehensive evidence for the passive cell wall

biomineralization of metal phosphates, in which the high binding affinity of cell walls for

aqueous metal cations creates nucleation sites for mineral precipitation reactions in saturated

systems. These nucleation sites likely promote heterogeneous nucleation of metal phosphates

on or in the cell wall through surface complexation reactions, as seen by Fowle and Fein (2001).

The passive cell wall biomineralization mechanism does not change the mineral that

precipitates. It does, however, exert a strong control on the size of the precipitate that forms

during the experiments.

2.6 Acknowledgements

Funding for this research was provided in part by a U.S. Department of Energy, Office of

Science and Technology and International (OST&I) grant under the Source Term Thrust program,

and in part by a U.S. Department of Energy, Environmental Remediation Science Program grant.

The experiments and analyses were performed at the Center for Environmental Science &

Technology, University of Notre Dame. The XAS measurements were obtained at the MRCAT-10-

ID Beamline at the Advanced Photon Source (APS), Argonne National Laboratory. TEM images

were obtained at the Integrated Imaging Facility at the University of Notre Dame and at the

Institut de Minéralogie et de Physique des Milieux Condensés, Paris, France. I would like to

thank Andrew Quicksall for providing suggestions and feedback throughout the project. Three

journal reviews were extremely helpful, and significantly improved the presentation of this

work.

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CHAPTER 3:

THE EFFECTS OF CHLORIDE ON THE ADSORPTION

OF MERCURY ONTO THREE BACTERIAL SPECIES

3.1 Abstract

Bulk adsorption experiments were conducted in order to investigate the ability of three

bacterial species to adsorb Hg in the absence and presence of chloride from pH 2 to 10.

Adsorption experiments were performed using non-metabolizing cells of Bacillus subtilis,

Shewanella oneidensis MR-1, and Geobacter sulfurreducens suspended in a 0.1 M NaClO4

electrolyte to buffer ionic strength. After equilibration, the aqueous phases were sampled and

analyzed using inductively coupled plasma-optical emission spectroscopy (ICP-OES) for

remaining Hg concentrations.

In both chloride-free and chloride-bearing systems, the three bacterial species studied

exhibited similar adsorption behaviors. Chloride causes a dramatic shift in the adsorption

behavior of each of the bacterial species. In the absence of chloride, each of the species exhibits

maximum adsorption between pH 4 and 6, with decreasing but still significant adsorption with

increasing pH from 6 to approximately 10. The extent of Hg adsorption in the chloride-free

systems is extensive under all of the experimental conditions, and the concentration of

adsorbed Hg exceeds the concentration of any individual binding site type on the cell envelope,

indicating that binding onto multiple types of sites occurs even at the lowest pH conditions

studied. Because binding onto an individual site type does not occur exclusively under any of the

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experimental conditions, individual stability constants for Hg-bacterial surface complexes cannot

be determined in the Cl-free system. In the presence of chloride, all of the bacterial species

exhibit minimal Hg adsorption below pH 4, increasing adsorption between pH 4 and 8, and

slightly decreasing extents of adsorption with increasing pH above 8. The low extent of

adsorption at low pH suggests that HgCl20, which dominates aqueous Hg speciation below pH

5.5, adsorbs only weakly. The increase in Hg adsorption above pH 4 is likely due to adsorption of

HgCl(OH)0, and is limited by site availability and transformation to Hg(OH)20 as pH increases. I

use the adsorption data to determine stability constants of the HgCl(OH)- and Hg(OH)2-bacterial

cell envelope complexes, and the values enable estimations to be made for Hg adsorption

behavior in bacteria-bearing geologic systems.

3.2 Introduction

Bacteria are present in soils and groundwater systems (Madigan et al., 2009), and

adsorption onto bacterial cell envelope functional groups can affect the speciation, distribution

and transport of heavy metals (Beveridge and Murray, 1976; Fortin et al., 1997; Ledin et al.,

1999; Small et al., 1999; Daughney et al., 2002). Although the adsorption behaviors of a wide

range of bacteria have been studied for a wide range of metals (e.g., Beveridge and Murray,

1976, 1980; Beveridge, 1989; Mullen et al., 1989; Fein et al., 1997, 2002; Borrok et al, 2004,

2007; Wu et al., 2006), Hg has received less attention. Recent studies have found that proton-

active sulfhydryl functional groups exist on the surface of bacterial cell envelopes (Guine et al.,

2006; Mishra et al., 2009; 2010). Many studies have demonstrated that Hg has a high binding

affinity for sulfur compounds (Fuhr and Rabenstein, 1973; Blum and Bartha, 1980; Compeau and

Bartha, 1987; Winfrey and Rudd, 1990; Benoit et al., 1999), and thus the adsorption of Hg to

bacteria may be dominated by this type of binding. Due to the high affinity for this bond to

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form, bacteria have the potential to drastically affect the distribution, transport and fate of Hg in

soil and groundwater systems.

Several studies have investigated the extent to which bacteria adsorb Hg (Chang and

Hong, 1994; Ledin et al., 1997; Green-Ruiz, 2006; Mo and Lian, 2011), and all have observed that

Hg is extensively removed from solution in the presence of bacteria under a range of

experimental conditions, with Hg adsorption typically more extensive than that of other heavy

metals (Hassen et al., 1998). The extent of metal adsorption to bacteria can be quantified using

surface complexation models (SCMs). SCMs have been applied to a range of metal-bacteria

systems (e.g., Plette et al., 1996; Fein et al., 1997; Daughney and Fein, 1998; Cox et al., 1999;

Fowle and Fein, 2000; Borrok et al., 2004), though only one study has used this approach to

model Hg adsorption onto bacteria (Daughney et al., 2002). Daughney et al. (2002) measured Hg

adsorption onto Bacillus subtilis, a Gram-positive bacterial species, as a function of bacteria-to-

Hg ratio, pH, chloride concentration, bacterial growth phase and reaction time, and used the

data to constrain stability constants for both chloride-free and chloride-bearing Hg-bacterial

surface complexes. It is crucial to test the accuracy of these stability constants, and also to

determine if other bacterial species exhibit similar Hg adsorption behavior. Adsorption

represents the first, and rate controlling, step in the bioavailability of some metals to bacteria

(Borrok et al., 2004; Sheng et al., 2011), so determining accurate and precise stability constant

values for Hg-bacterial surface complexes may be crucial for quantitative modeling of processes

such as bacterial Hg-methylation and Hg toxicity.

In this study, I test the findings of Daughney et al. (2002) by measuring Hg adsorption

onto B. subtilis, and I expand on the Daughney et al. (2002) study by measuring Hg adsorption

behavior onto two other representative bacterial species. Bacterial adsorption experiments

were conducted as a function of pH and chloride concentration using intact washed non-

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metabolizing bacterial cells. In addition to the experiments involving the Gram positive species

Bacillus subtilis, I conducted parallel experiments involving a common Gram negative bacterial

species (Shewanella oneidensis MR-1) and a Gram negative species that is capable of Hg

methylation (Geobacter sulfurreducens) in order to investigate if cell envelope type affects Hg

adsorption and/or if methylating species exhibit unique Hg binding properties. I used the

experimental results to construct surface complexation models that enable the calculation of Hg

speciation and distribution in a wide range of natural and engineered bacteria-bearing systems.

3.3 Methods

3.3.1 Experimental Methods

3.3.1.1 Bacterial Growth & Washing Procedure

Bacillus subtilis (ATCC 23875) and Shewanella oneidensis MR-1 (ATCC BAA-1096) cells

were cultured and prepared aerobically following the procedures outlined in Borrok et al.

(2007). Briefly, cells were maintained on agar plates consisting of trypticase soy agar with 0.5%

yeast extract added. Cells for all experiments were grown by first inoculating a test-tube

containing 3 mL of trypticase soy broth with 0.5% yeast extract, and incubating it for 24 h at 32

C. The 3 ml bacterial suspension was then transferred to a 1 L volume of trypticase soy broth

with 0.5% yeast extract for another 24 h on an incubator shaker table at 32 C. Cells were

pelleted by centrifugation at 8100g for 5 min, and rinsed 5 times with 0.1 M NaClO4.

Geobacter sulfurreducens (ATCC 51573) cells were cultured and prepared using a

different procedure than described above. Cells were maintained in 50 mL of anaerobic

freshwater basal media (ATCC 51573) at 32 oC (Lovely and Phillips, 1988). Cells for all

experiments were grown by first inoculating an anaerobic serum bottle containing 50 mL of

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freshwater basal media, and incubating it for 5 days at 32 oC. Cells were pelleted by

centrifugation at 8100g for 5 minutes, and rinsed 5 times with 0.1 M NaClO4 stripped of

dissolved oxygen by bubbling a 85%/5%/10% N2/H2/CO2 gas mixture through it for 30 minutes.

After washing, the three types of bacteria used in this study were then pelleted by

centrifugation at 8100g for 60 minutes to remove excess water to determine the wet mass so

that suspensions of known bacterial concentration could be created. All bacterial concentrations

in this study are given in terms of gm wet biomass L-1.

3.3.1.2 Bacterial Potentiometric Titrations

Surface complexation modeling requires determination of bacterial cell envelope site

concentrations and acidity constants. These parameters have been determined previously for B.

subtilis (Fein et al., 2005) and S. oneidensis MR-1 (Mishra et al., 2010), but they have not been

determined for G. sulfurreducens. To obtain these values, four replicate potentiometric

titrations of G. sulfurreducens cells (100 gm L-1) were conducted in 0.1 M NaClO4 under a N2

atmosphere with an automated burette assembly. The biomass suspension was prepared using

washed biomass and 0.1 M NaClO4 that was purged with N2 gas for 30 minutes prior to the

preparation of the suspension. The suspension pH was measured using a glass electrode filled

with 4 M KCl that was standardized using commercially supplied pH standards. The titrations

were performed by measuring the pH after each addition of aliquots of commercially supplied

volumetric standard of 1.030 M NaOH or 1.048 M HCl to the suspension. Acid or base additions

were made only after a maximum drift of 0.01 mV/s was attained.

The biomass suspension was titrated first with HCl to achieve a pH of ~2.0. The

suspension was then titrated with NaOH to a pH of ~10.0. Titrations of the electrolyte solution

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only were performed before and after each biomass titration to verify mechanical accuracy and

reproducibility.

3.3.1.3 Batch Adsorption Experiments

Aqueous Hg(II), chloride, and suspended bacteria parent solutions were prepared using

circum-neutral 0.1 M NaClO4 electrolyte solution (pH adjusted to 7.0 ± 0.5 using 0.2 M HNO3

and/or 0.2 M NaOH), and either 1,000 ppm Hg(II) or Cl- volumetric aqueous standards, or

washed bacterial cells (as described above). The parent solutions were mixed together in the

following order: an aliquot of chloride parent solution was added to a bacterial suspension, and

then an aliquot of Hg(II) parent solution was added and the mixture was diluted with 0.1 M

NaClO4 to achieve a suspension with a log molality of Hg of -4.13, a log molality of Cl- of -3.00,

and either 0.2 gm wet biomass L-1 (for the B. subtilis and S. oneidensis experiments) or 0.1 gm

wet biomass L-1 (for the G. sulfurreducens experiments). Chloride-free experiments were also

conducted and prepared in an identical fashion, but excluding the chloride addition. Eight mL

aliquots of the suspension were added to 20 Teflon reaction vessels and the pH of each aliquot

was immediately adjusted to cover the pH range from 2 to 10, using 0.2 M HNO3 and/or NaOH,

and the vessels were placed on an end-over-end rotator for the duration of the experiment (2 h

for B. subtilis and G. sulfurreducens, and 3 h for S. oneidensis). Kinetics experiments (data not

shown) were conducted to determine the duration required for each system to attain steady-

state conditions. The pH of each experiment was monitored and adjusted if necessary using 0.2

M HNO3 and/or NaOH every 15 minutes throughout the duration of the experiment except

during the last 30 minutes, during which the suspensions were undisturbed. At the completion

of each experiment, the final pH of each solution was measured and the contents were filtered

through a 0.2μm PTFE syringe filter to remove the bacteria. The aqueous phase was collected

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and acidified using 15.8 N HNO3 at a sample:acid ratio of 5 mL:8 μL and refrigerated pending

aqueous Hg analysis. All experiments were performed under atmospheric, room temperature

conditions. Three replicate experiments were conducted for each experimental condition.

3.3.2 Analytical Methods: Inductively-Coupled Plasma – Optical Emission Spectroscopy (ICP-OES)

Ionic strength matrix-matched ICP-OES standards were prepared gravimetrically by

diluting a commercially-supplied 1,000 ppm Hg(II) aqueous standard with 0.1 M NaClO4, and

each standard was acidified using 8 μL of 15.8 N HNO3 per 5 mL sample. The log molality of the

Hg standards ranged from -6.30 to -4.05. The standards and samples were analyzed with a

Perkin Elmer 2000DV ICP-OES at a wavelength of 253.652 nm within 2 days of collection. The set

of standards was analyzed before and after all of the samples were analyzed, as well as after

every 15 samples, to check for machine drift. Analytical uncertainty, as determined by repeat

analyses of the standards, was ± 5.6%.

3.3.3 Thermodynamic Modeling

I used a non-electrostatic surface complexation approach to model proton and Hg

adsorption onto bacterial cell envelope functional groups (Fein et al., 1997; 2005). That is, I

modeled the acidity of surface functional groups via deprotonation reactions:

(1) R-AiH R-Ai- + H+

where R represents the cell envelope macromolecule to which each type of functional group,

Ai, is attached. The distribution of protonated and deprotonated functional group sites can be

quantified via mass action equations, such as:

(2) ][

][

o

i

Hi

iHAR

aARK

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69

where Ki represents an acidity constant, a represents the activity of the subscripted species, and

the brackets represent the activities of surface sites in moles L-1 of solution.

In applying this approach to modeling the surface acidity of bacteria, I implicitly

assumed that the deprotonation of each type of functional group, Ai, can be represented as a

single deprotonation of an organic acid. Because all of the experiments were conducted at the

same ionic strength, I ignored potential ionic strength effects on the surface electric field,

applying a non-electrostatic model to account for the titration and Hg adsorption data.

Potentiometric titration experiments are essentially studies of proton adsorption and

desorption, yet because the solvent contains the same element as is reacting with the surface of

interest, it is impossible to apply a traditional mass balance approach. Instead, one must define

a zero proton condition for the bacterial cell envelope, and account for changes in proton

concentrations relative to that condition (e.g., Westall et al., 1995; Fein et al., 2005). After the

approach by Fein et al. (2005), I chose fully protonated cell envelope sites to represent the zero

proton condition, and I used FITEQL (Westall, 1982) to solve for the initial state of protonation in

each titration (Westall et al., 1995).

As is discussed below, the extent of Hg adsorption that I observed in the chloride-free

systems was too extensive to be able to isolate or model the extent of adsorption onto

individual cell envelope sites. For the chloride systems, I model the observed adsorption as

interactions between aqueous Hg species and deprotonated bacterial cell envelope sites:

(3) Hg species+x + R-Ai- (R-Ai)(Hg species)x-1

where ‘Hg species+x’ represents the specific aqueous Hg species tested in each model, ‘(R-Ai)(Hg

species)x-1 represents the Hg-bacterial cell envelope complex, and x represents the charge of the

aqueous Hg species. The mass action equation for Reaction 3 is:

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70

(4) ][)(

]))([( )1(

i

x

x

iads

ARspeciesHga

speciesHgARK

where Kads is the thermodynamic equilibrium constant for Reaction 3, a represents the activity

of the species in parentheses, and brackets represent concentrations in mol L-1. Acid/base

potentiometric titration data provide constraints on the number of site types, their Ki values and

their site concentrations; Hg adsorption measurements conducted as a function of pH constrain

the number of sites involved in Hg binding, the pH range of influence, and the stability constants

for the important Hg-bacterial cell envelope complexes. I used the program FITEQL 2.0 (Westall,

1982) for the equilibrium thermodynamic modeling of the adsorption data, using the aqueous

speciation equilibria and equilibrium constants given in Table 9, and using the program’s activity

coefficient calculations via the Davies equation.

3.4 Results & Discussion

3.4.1 Potentiometric Titrations

Potentiometric titrations of G. sulfurreducens biomass were performed in order to calculate site

concentrations and pKa values for discrete proton-active cell envelope functional groups. G.

sulfurreducens exhibits significant proton buffering behavior over the entire pH range studied.

Each of the four replicate G. sulfurreducens sets of titration data is depicted in Figure 19. G.

sulfurreducens exhibits a similar total buffering capacity ((C(a) – C(b) – [H+] + [OH-]) / mb) to that

measured for other bacterial species. For example, between pH 3 and 9, G. sulfurreducens has a

buffering capacity of 3.5 ± 0.6 x 10-4 mol/g (reported error represents 1σ uncertainty), compared

to a value of 3.0 x 10-4 mol/g for Bacillus subtilis (Fein et al., 2005), 3.1 x 10-4 for Shewanella

oneidensis (Mishra et al., 2010), and 1.27 x 10-4 and 2.23 x 10-4 mol/g for Acidiphilium

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acidophilum and Bacillus pseudofirmus, respectively (Kenney and Fein, 2011). Borrok et al.

(2005) observed that a wide range of bacterial species exhibit similar buffering behaviors, and

the titration data demonstrate that G. sulfurreducens exhibits that same buffering behavior.

The potentiometric titration data were used to quantify the site concentrations and acidity

constants for G. sulfurreducens. One-, 2-, 3-, 4-, and 5-site models were tested in order to

determine the number of proton-active surface site types on G. sulfurreducens cell envelopes

needed to account for the observed buffering behavior. The addition of each additional site

significantly lowered the V(Y) (variance) value from an average of 185.6 for the 1-site models of

the 4 titrations to an average of 0.26 for the 4-site models (an ideal V(Y) value is 1). A 5-site

model failed to converge for each set of titration data, indicating insufficient experimental data

to constrain parameters for 5 site types. Figure 20 shows a representative titration for G.

sulfurreducens with the corresponding best fit 4-site model. The model yields an excellent fit to

the observed buffering behavior across the pH range of the study. G. sulfurreducens has similar

site concentrations and pKa values to B. subtilis and S. oneidensis (Table 10), though G.

sulfurreducens has the lowest concentration of total surface sites of the three species. The

presented site concentrations and pKa values in Table 10 represent averages of the 4 individual

forward titration model results, and are used as a basis for the Hg adsorption modeling.

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TABLE 9

HG REACTIONS USED TO CONSTRUCT SCMS

Reaction Log K

H2O – H+ = OH- -14.00 b H2CO3

0 – H+ = HCO3- -6.355 a

H2CO30 – 2H+ = CO3

2- -16.67 a H2CO3

0 – H2O = CO20 2.770 b

Na+ + H2CO30 – 2H+ = NaCO3

- -15.41 b Na+ + H2CO3

0 – H+ = NaHCO30 -6.60 b

Na+ + H2O – H+ = NaOH0 -14.2 b Hg2+ + H2O - H+ = HgOH+ -3.40 a

Hg2+ + 2H2O - 2H+ = Hg(OH)20 -5.98 a

Hg2+ + 3H2O - 3H+ = Hg(OH)3- -21.1 a

2Hg2+ + H2O - H+ = Hg2(OH)3+ -3.30 b 3Hg2+ + 3H2O - 3H+ = Hg3(OH)3

3+ -6.40 b Hg2+ + H2CO3

0 – 2H+ = HgCO30 -3.91 a

Hg2+ + H2CO30 – H+ = HgHCO3

+ 0.42 a Hg2+ + H2CO3

0 + H2O – 3H+ = Hg(OH)CO3- -11.355 a

Hg2+ + Cl- = HgCl+ 7.31 a Hg2+ + 2Cl- = HgCl2

0 14.00 a Hg2+ + 3Cl- = HgCl3

- 14.925 a Hg2+ + 4Cl- = HgCl4

2- 15.535 a Hg2+ + Cl- + H2O – H+ = HgCl(OH)0 4.27 a

(a) Powell et al., 2005.

(b) Martell and Smith, 2001.

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Figure 19: Four replicate forward potentiometric titration of 100 gm L-1 G. sulfurreducens in 0.1 M NaClO4.

-1.0E-04

-5.0E-05

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

2 4 6 8 10

[c(a

) -

c(b

)] /

gm

ba

cter

ia

pH

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Figure 20: Best fit 4-site model results (smooth curve) for one representative potentiometric titration of G. sulfurreducens (data

points).

-1.0E-04

-5.0E-05

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

2 3 4 5 6 7 8 9 10

(Ca

- C

b)

/ m

b

pH

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TABLE 10

SITE CONCENTRATIONS AND PKA VALUES USED FOR SCMS

Bacteria Site 1 Site 2 Site 3 Site 4

Site Concentrations (mol sites / gm bacteria) [sites]total

B. subtilisa 8.1 ± 1.6 x 10-5 1.1 ± 0.36 x 10-4 4.4 ± 1.3 x 10-5 7.4 ± 2.1 x 10-5 3.1 x 10-4 S. oneidensisb 8.9 ± 2.6 x 10-5 1.3 ± 0.20 x 10-4 5.9 ± 3.3 x 10-5 1.1 ± 0.60 x 10-4 3.9 x 10-4

G. sulfurreducens 8.4 ± 0.66 x 10-5 9.1 ± 0.41 x 10-5 4.1 ± 0.24 x 10-5 3.4 ± 0.63 x 10-5 2.5 x 10-4

pKa

B. subtilisa -3.3 ± 0.2 -4.8 ± 0.1 -6.8 ± 0.3 -9.1 ± 0.2 S. oneidensisb -3.3 ± 0.2 -4.8 ± 0.2 -6.7 ± 0.4 -9.4 ± 0.5

G. sulfurreducens -3.4 ± 0.1 -4.8 ± 0.1 -6.5 ± 0.2 -8.8 ± 0.3

Reported uncertainties are 1σ errors.

(a) Fein et al., 2005.

(b) Mishra et al., 2010

75

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76

3.4.2 Adsorption Experiments

In the absence of chloride, the adsorption behavior as a function of pH is more complex

than has been observed for other metal cations (e.g., Fein et al., 1997; Fein et al., 2001), with

the extent of adsorption increasing from pH 2 to 4, and in general decreasing from 4 to 9 (Figure

21). The extent of Hg adsorption that I observed is notable. In the chloride-free experiments, B.

subtilis, S. oneidensis, and G. sulfurreducens adsorbed a maximum of approximately 2.0 x 10-4,

3.0 x 10-4, and 3.5 x 10-4 mol Hg per gm (wet mass) of bacteria, respectively. In similar Hg

adsorption experiments involving B. subtilis but conducted under much lower Hg loading

conditions, Daughney et al. (2002) measured a maximum of only approximately 5.0 x 10-6 mol

Hg per gm (wet mass). Clearly, these bacteria exhibit a much higher capacity for Hg than was

probed by the Daughney et al. (2002) experiments.

In the presence of chloride, the pH dependence of Hg adsorption that I observed

changes dramatically to that typically observed with metal cations, even though the dominant

aqueous Hg species are neutral or negatively charged. There is only a small extent of adsorption

below pH 4, with adsorption increasing slightly from pH 2 to 4; the extent of adsorption

increases more markedly with increasing pH between approximately pH 4 and 8, and the extent

of adsorption decreases slightly with increasing pH above pH 8. The addition of chloride to the

experimental system significantly decreases the extent of Hg adsorption onto the bacteria under

low pH conditions relative to the chloride-free system, and does not markedly affect the extent

of Hg adsorption above pH 6 (Figure 22).

In general, the bacterial species tested exhibit broadly similar bulk adsorption behaviors

in the absence and presence of chloride, although there are some differences. In the chloride-

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Figure 21: Hg adsorption onto bacterial species normalized per gram of bacteria. The initial molality of Hg in the adsorption experiments is 7.41 x 10-5.

free system, although experimental uncertainties are relatively high, in the mid-pH range tested,

G. sulfurreducens removes more Hg from solution than S. oneidensis which removes more than

B. subtilis; the differences between species are less under lower and higher pH conditions. In the

chloride systems, B. subtilis and G. sulfurreducens remove nearly identical amounts of Hg from

solution, but above pH 4, S. oneidensis removes more Hg than the other two species.

3.4.3 Thermodynamic Modeling

The effects of pH and chloride on the adsorption of Hg onto the bacteria studied here

likely reflect both changes to the speciation of the cell envelope functional groups and changes

in the aqueous Hg speciation that accompany the pH and chloride concentration changes. In

order to determine the dominant adsorption reactions, it is crucial to define the speciation of Hg

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

3.0E-04

3.5E-04

4.0E-04

0 2 4 6 8 10 12

[Hg]

ad

sorb

ed (

mo

l) /

gm

bac

teri

a

pH

B. subtilis

S. oneidensis

G. sulfurreducens

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78

Figure 22: Hg adsorption onto bacterial species, normalized per gram of bacteria, in the presence of chloride. The solid black curve represents the model fit for B. subtilis, the dashed black line represents the model fit for S. oneidensis, and the solid grey line

represents the model fit for G. sulfurreducens. The initial molality of Hg in the adsorption experiments is 7.41 x 10-5 and the initial molality of Cl is 1.00 x 10-3.

in solution. Aqueous Hg speciation diagrams, calculated for the experimental conditions using

the aqueous complexation reactions and stability constants listed in Table 9, are depicted in

Figure 23.

In the chloride-free system, the extent of Hg adsorption that I observed is greater under

all pH conditions than any of the individual binding site concentrations, meaning that under all

conditions multiple site types must be responsible for the observed adsorption. For example,

approximately 1.0 x 10-4 mol of Hg are adsorbed per gram of B. subtilis at pH 2 (Figure 21), which

represents a total concentration of adsorbed Hg of 2.1 x 10-5 M. However, the total

concentration of Site 1, which deprotonates at the lowest pH of the 4 potential binding site

0.0E+00

5.0E-05

1.0E-04

1.5E-04

2.0E-04

2.5E-04

1 2 3 4 5 6 7 8 9 10 11

[Hg]

ad

sorb

ed (

mo

l) /

gm

bac

teri

a

pH

B. subtilis

S. oneidensis

G. sulfurreducens

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79

Figure 23: Aqueous Hg speciation in the (A) absence and (B) presence of chloride under the experimental Hg and chloride concentration

conditions. Only species with calculated concentrations above 0.01 x 10-

5 M are shown.

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80

types, is only 1.6 x 10-5 M. At pH 4.5, B. subtilis exhibits a maximum extent of Hg adsorption with

4.3 x 10-5 M Hg adsorbed. The total concentration of all four binding site concentrations is 6.2 x

10-5 M, again suggesting that more than one type of site is involved in Hg binding. It is unusual to

observe such a high degree of site saturation by an adsorbing metal, and this behavior suggests

that Hg-bacterial site stability constants are quite high. However, because individual Hg-site

binding could not be isolated under any of the experimental conditions, individual Hg-site

stability constants could not be determined in the chloride-free system.

In the chloride system, I observed a stronger pH dependence for the Hg adsorption, and

under low pH conditions the extent of adsorption is less than the concentration of any individual

binding site type. Because Hg-chloride species dominate in the chloride experiments, and

because chloride-free bacterial species are unlikely to be important under those conditions, the

lack of stability constants for the chloride-free system does not hinder the ability to model the

chloride system. The dramatic effect of chloride on the adsorption behavior is paralleled by the

drastic change in aqueous Hg speciation with the addition of chloride (Figure 23b). Under the

conditions of my experiments and below pH 6, HgCl20 is the dominant Hg species; however,

under those pH conditions little to no Hg adsorption is observed, suggesting that HgCl20 does not

adsorb to bacterial functional groups strongly. Adsorption in the chloride systems increases with

increasing pH above pH 4, similar to the behavior of HgCl(OH)0. Although Daughney et al. (2002)

modeled Hg adsorption onto B. subtilis as HgCl20 and HgCl(OH)0 binding onto a protonated

bacterial site in order to account for the pH-independent adsorption that they observed under

low pH conditions, I tested a range of models involving deprotonated sites only due to the

absence of adsorption in the experiments under the low pH conditions at which protonated

sites dominate the cell wall speciation. My general approach was to model the adsorption

behavior using the minimum number of adsorbed Hg species required. Because more binding

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81

site types become deprotonated with increasing pH, I modeled the low pH adsorption data first

and determined whether additional adsorbed Hg species were required in order to account for

the higher pH data. For each bacteria, Hg adsorption increased slightly from pH 2 to 4, under

conditions where HgCl20 dominates the aqueous Hg speciation and R-A1

1- increases in

concentration due to the deprotonation of this site over this pH range. Therefore, I modeled the

pH 2-4 data for each bacterial species with the following reaction:

(5) HgCl20 + R-A1

1- R-A1-HgCl21-

In order to determine if an additional complex is required to account for the observed Hg

adsorption, I used the calculated value for the equilibrium constant for Reaction (5) to predict

the adsorption behavior under higher pH conditions, assuming that only the R-A1-HgCl21-

complex controls the Hg adsorption behavior. For example, Figure 24 depicts the fit of Reaction

(5) to the data from the B. subtilis experiments. The predicted Hg adsorption behavior using

Reaction (5) fits the experimental data well from pH 2 to 4 (the pH range used initially to

constrain the K value for this reaction), but dramatically under predicts the extent of adsorption

that I observed at higher pH values. This under-prediction represents strong evidence for the

presence of an additional adsorbed Hg species or multiple species above pH 4. Above

approximately pH 4, HgCl(OH)0 increases in concentration markedly (Figure 23b), mirroring the

dramatic increase in Hg adsorption that I observed above pH 4. For this reason, I added the

following reaction to the model:

(6) HgCl(OH)0 + R-A21- R-A2-HgCl(OH)1-

and used the pH 2-6 data from the B. subtilis experiments to simultaneously solve for K values

for Reactions (5) and (6). Again, I used these calculated K values to predict the adsorption

behavior above pH 6 (Figure 24), and find that as expected Reactions (5) and (6) provide an

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82

excellent fit to the data up to pH 6, but that the concentrations of sites R-A1 and R-A2 limit the

predicted extent of adsorption which plateaus significantly below the observed extent of Hg

adsorption, and then drops to even lower concentrations with increasing pH as the

concentration of HgCl(OH)0 in solution decreases and the concentration of Hg(OH)20 increases

above pH 7. Following the same modeling approach, I determined that the observed Hg

adsorption data from the B. subtilis experiments require an additional Hg-bacterial surface

complex to account for the pH dependence across the pH range studied, represented by the

following adsorption reaction:

(7) HgCl(OH)0 + R-A31- R-A3-HgCl(OH)1-

I solved for K values for Reactions (5) – (7) simultaneously with the entire dataset from the B.

subtilis experiments, and the resulting model yields an excellent fit to the data across the pH

range studied (Figure 24). Models that involve adsorption of Hg(OH)20 onto any of the binding

sites yielded significantly worse fits to the data. Similar modeling exercises were applied to the

G. sulfurreducens and the S. oneidensis datasets (Figures 25 and 26), and the calculated K values

from each of the three datasets are listed in Table 11.

For the G. sulfurreducens model, the results are similar to the B. subtilis model, with

HgCl20 adsorbing to R-A1

1- and HgCl(OH)0 adsorbing to R-A21- and R-A3

1-. However, the G.

sulfurreducens data also require an additional high pH Hg-bacterial species, and the data are

best-fit with the inclusion of the following reaction:

(8) Hg(OH)20 + R-A4

1- R-A4-Hg(OH)21-

Similarly, the S. oneidensis model results are comparable to the G. sulfurreducens model,

requiring Reactions (5) – (8) to constrain the data. In addition, a fifth Hg species, presented in

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Figure 24: Comparison of model fits (curves) to B. subtilis experimental data (solid squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve); (5) and (6) (dotted black curve); and (5), (6), and (7) (solid black curve).

0.0E+00

5.0E-06

1.0E-05

1.5E-05

2.0E-05

2.5E-05

3.0E-05

3.5E-05

1 2 3 4 5 6 7 8 9 10 11

[Hg]

ad

sorb

ed

(M

)

pH

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84

Figure 25: Comparison of model fits (curves) to G. sulfurreducens experimental data (solid squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve); (5) and (6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); and

(5), (6), (7), and (8) (solid black curve). Using only Reactions (5) through (7), as was used for the B. subtilis modeling, results in a model fit that poorly constrains the data at high

pH, indicating that another reaction is necessary to account for the observed Hg adsorption. It is likely that Hg(OH)2

0 is involved in the high pH adsorption, as it is the dominant aqueous Hg species at high pH. Adding Hg(OH)2

0 onto R-A41- (Reaction (8))

yields a model fit that fits the data well across the entire pH range.

0.0E+00

5.0E-06

1.0E-05

1.5E-05

2.0E-05

2.5E-05

1 2 3 4 5 6 7 8 9 10 11

[Hg]

ad

sorb

ed (

M)

pH

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85

Figure 26: Comparison of model fits (curves) to S. oneidensis experimental data (solid squares) for the adsorption of Hg according to Reaction(s): (5) only (dashed grey curve);

(5) and (6) (dotted black curve); (5), (6), and (7) (long dashed grey curve); (5), (6), (7), and (8) (solid grey curve); and (5), (6), (7), (8), and (9) (solid black curve). Using only

Reactions (5) through (8), the model does not constrain the high pH data well, thus an additional surface species is necessary. It is likely that Hg(OH)2

0 is involved in the high pH adsorption because it is the dominant aqueous Hg species under the high pH conditions

where we see a misfit between the data and the model predictions. Models invoking Hg(OH)2

0 adsorption onto R-A31- or onto R-A4

1- do not improve the model fit, as these reactions cause less HgCl(OH)0 to adsorb onto these sites due to site mass balance

constraints. However, a model that involves Hg(OH)20 adsorption onto R-A2

1- (solid black curve) yields an excellent fit to the data across the pH range studied.

0.0E+00

1.0E-05

2.0E-05

3.0E-05

4.0E-05

5.0E-05

1 2 3 4 5 6 7 8 9 10 11

[Hg]

ad

sorb

ed (

M)

pH

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86

TABLE 11

CALCULATED STABILITY CONSTANTS (LOG K) FOR HG ADSORPTION ONTO BACTERIA

Bacteria Rxn (5) Rxn (6) Rxn (7) Rxn (8) Rxn (9)

B. subtilis 3.9 ± 0.2 4.3 ± 0.4 5.1 ± 0.3 - - G. sulfurreducens 3.9 ± 0.4 6.4 ± 0.4 3.6 ± 0.2 8.4 ± 0.5 -

S. oneidensis 3.7 ± 0.2 5.4 ± 0.3 8.9 ± 0.5 7.6 ± 0.4 4.2 ± 0.2

Reported uncertainties were determined by varying each average K value individually to create an adsorption envelope that encompasses 95% of the data within the pH range of influence.

the following reaction, was required to fully constrain the data (see explanations in captions for

Figures 25 and 26):

(9) Hg(OH)20 + R-A2

1- R-A2-Hg(OH)21-

The thermodynamic modeling results suggest there is some variance between stability

constants for each bacterial species. The stability constants for Reaction (5) and (8) do not vary

between the bacterial species studied within experimental uncertainty, however the stability

constants for Reactions (6) and (7) exhibit significant variation between the species. Some of this

variation certainly reflects real differences between the bacterial species. However, the

differences between the K values for G. sulfurreducens and B. subtilis, which exhibit similar

extents of adsorption and similar site concentrations and pKa values, may reflect the fairly large

experimental uncertainty associated with the G. sulfurreducens data.

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Though my study is similar to that of Daughney et al. (2002), both my observed Hg

adsorption behaviors and the models that I used to account for that adsorption differ from

those of the Daughney et al. (2002) study in some ways. Daughney et al. (2002) observed

significant and relatively pH-independent adsorption below pH 4-5 and above pH 7-8, and

modeled that behavior by invoking HgCl20 and HgCl(OH)0 onto protonated sites. I used the

reactions and calculated stability constants from Daughney et al. (2002) to predict the extent of

adsorption under the experimental conditions. The resulting predicted extents of adsorption

(not shown) are inconsistent with the measurements, yielding pH-independent adsorption

across the pH range of my study at a level that indicates complete saturation of bacterial binding

sites under all pH conditions. This result is likely due to an inconsistency between the Daughney

et al. (2002) K values and their reported reaction stoichiometries, and for this reason my

reported model will likely yield more accurate predictions of Hg binding behavior in a wide

range of geologic and engineered systems.

3.5 Conclusions

In this study, I documented extensive adsorption onto three different bacterial

envelopes in both chloride-free and chloride-bearing systems. The experimental results

demonstrate that Hg adsorption to bacterial species is dependent upon pH, chloride

concentration, and bacterial surface site speciation. In the absence of a competitive ligand, such

as chloride, Hg adsorption to bacterial cells does not exhibit typical metal cation adsorption

behavior. Additionally, the extent of Hg adsorption onto surface sites in the absence of a ligand

is extensive, with the concentration of adsorbed Hg exceeding the concentration of any

individual site type under all of the pH conditions tested. In the presence of chloride, the

behavior of Hg adsorption changes dramatically, with increasing adsorption as pH increases

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88

likely due to the relatively weak interaction of the aqueous HgCl20 complex with bacterial

binding sites. Thermodynamic modeling results suggest that the adsorption of HgCl(OH)0 and

Hg(OH)2 onto bacterial surface sites are the dominant adsorption reactions under most

conditions studied, with log stability constant values ranging from 4.2 to 8.9. These results can

be used to help better understand the thermodynamics of Hg-Cl-bacterial interactions under

natural geologic conditions, such as in chloride-rich seawater and bacteria-laden groundwater,

and the results suggest that bacteria are likely to compete effectively with a range of other

ligands present in geologic environments to control Hg distribution and speciation.

3.6 Acknowledgements

The experiments and analyses were performed at the Center for Environmental Science

& Technology, University of Notre Dame. I would like to thank Jennifer Szymanowski and Brian

Farrell for assistance with data collection and processing.

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CHAPTER 4:

THE EFFECT OF NATURAL ORGANIC MATTER ON THE

ADSORPTION OF MERCURY TO BACTERIAL CELLS

4.1 Abstract

I investigated the ability of non-metabolizing Bacillus subtilis, Shewanella oneidensis

MR-1, and Geobacter sulfurreducens bacterial species to adsorb mercury in the absence and

presence of Suwanee River fulvic acid. Bulk adsorption experiments were conducted at three pH

conditions, and the results indicate that the presence of FA decreases the extent of Hg

adsorption to biomass under all of the pH conditions studied. I used the experimental results to

calculate apparent binding constants for Hg onto both the bacteria and the FA. The calculations

yield similar binding constants for Hg onto each of the bacterial species studied. The calculations

also indicate similar binding constants for Hg-bacteria and Hg-FA complexes, and the values of

these binding constants suggest a high degree of covalent bonding in each type of complex,

likely due to the presence of significant concentrations of sulfhydryl functional groups on each.

My results suggest that although FA can compete with bacterial binding sites for aqueous Hg,

because of the relatively similar binding constants for the types of sorbents, the competition is

not dominated by either bacteria or FA unless the concentration of one type of site greatly

exceeds that of the other.

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4.2 Introduction

Heavy metals, such as Hg, adsorb to proton-active functional groups on bacterial cell

envelopes (e.g., Beveridge and Murray, 1976; Fortin and Beveridge, 1997; Daughney et al., 2002;

Fein, 2006; Kenney and Fein, 2011), affecting the speciation and distribution of these metals in

geologic systems. Recent studies (e.g., Guiné et al., 2006; Mishra et al. 2009; 2010) have shown

that at least some bacterial cell envelopes contain proton-active sulfhydryl functional groups.

Because Hg binds readily and strongly to sulfur compounds (Compeau and Bartha, 1987;

Winfrey and Rudd, 1990; Benoit et al., 1999), bacterial adsorption of Hg may dramatically affect

the distribution, transport and fate of Hg in geologic systems.

Natural organic matter (NOM) is present in nearly every near-surface geologic system,

and complexation reactions between metals and NOM can dramatically change the behavior of

the metals in the environment (McDowell, 2003; Ravichandran, 2004). NOM molecules contain

a range of functional group types, including carboxyl, phenol, amino, and sulfhydryl groups, that

have the potential to create highly stable complexes with metal ions across the pH range

(Ephraim, 1992; Ravichandran et al., 1999; Drexel et al., 2002; Haitzer et al., 2002; Croué et al.,

2003; Ravichandran, 2004). Hg binds strongly to the sulfhydryl groups present within the NOM

structure (Dong et al., 2011; Muresan et al., 2011). The relative thermodynamic stabilities of Hg-

NOM and Hg-bacteria complexes are not well known. Depending on these relative stabilities,

the formation of metal-NOM complexes may decrease adsorption of Hg to bacteria cell

envelopes due to a competitive ligand effect, or under certain conditions may increase

adsorption of Hg to bacteria due to ternary complexation with NOM. For example, investigating

Pb, Cu, and Ni separately, Borrok et al. (2007) found that ternary metal-FA-bacteria complexes

form, and that the importance of the complexes is strongly affected by pH. Conversely,

Wightman and Fein (2001) found that the presence of NOM decreases the amount of Cd

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adsorbed to bacteria under mid- and high-pH conditions, and that the presence of Cd does not

affect the adsorption of NOM to bacteria, suggesting that ternary complexes do not occur. No

studies have been conducted to date to determine the effects of NOM on Hg binding to

bacteria. However, because Hg forms strong complexes both with cell envelopes (Daughney et

al., 2002; Dunham-Cheatham et al., 2012) and NOM (Loux et al., 1998; Ravichandran, 2004;

Skyllberg et al., 2006), it is likely that significant changes to Hg adsorption behavior occur in the

presence of NOM.

In this study, I used bulk adsorption experiments, conducted as a function of pH and FA

concentration, using intact non-metabolizing bacterial cells to study Hg binding onto three

different bacterial species and to compare the ability of bacteria to adsorb mercury in the

presence and absence of a fulvic acid (FA). I used the experimental results to calculate apparent

stability constants for Hg-bacteria and Hg-FA complexes, allowing for quantitative modeling of

the competitive binding that can occur between bacteria and FA in more complex settings. This

study examined both Gram-positive and Gram-negative bacterial species in order to determine

if cell envelope structure affects the binding reactions, and one species was a Hg methylator,

which I examined in order to determine if the extent or nature of Hg binding onto that species

differed from that exhibited by the non-methylators.

4.3 Methods

4.3.1 Experimental Methods

4.3.1.1 Bacterial Growth and Washing Procedure

Bacillus subtilis (ATCC 23875), a Gram-positive aerobic soil species, and Shewanella

oneidensis MR-1 (ATCC BAA-1096), a Gram-negative facultative anaerobic species, cells were

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cultured and prepared following the procedures outlined in Borrok et al. (2007). Briefly, cells

were maintained on agar plates consisting of trypticase soy agar with 0.5% yeast extract added.

Cells for all experiments were grown by first inoculating a test-tube containing 3 mL of

trypticase soy broth with 0.5% yeast extract, and incubating it for 24 h at 32 C. The 3 ml

bacterial suspension was then transferred to 1 L of trypticase soy broth with 0.5% yeast extract

for another 24 h on an incubator shaker table at 32 C. Cells were pelleted by centrifugation at

8100g for 5 min, and rinsed 5 times with 0.1 M NaClO4.

Geobacter sulfurreducens (ATCC 51573), a Gram-negative species capable of Hg

methylation, cells were cultured and prepared using a different procedure than detailed above.

Cells were maintained in 50 mL of anaerobic freshwater basal media at 32 oC (Lovely and

Phillips, 1988). Cells for all experiments were grown by first inoculating an anaerobic serum

bottle containing 50 mL of freshwater basal media, and incubating it for 5 days at 32 oC. Cells

were pelleted by centrifugation at 8100g for 5 minutes, and rinsed 5 times with 0.1 M NaClO4

stripped of dissolved oxygen by bubbling a 85%/5%/10% N2/H2/CO2 gas mixture through it for 30

minutes. After washing, each of the three types of bacteria was then pelleted by centrifugation

at 8100g for 60 minutes to remove excess water in order to determine the wet mass so that

suspensions of known bacterial concentration could be created. All bacterial concentrations in

this study are given in terms of gm wet biomass L-1. Bacterial cells were harvested during

stationary phase, and all experiments were performed under non-metabolizing, electron donor-

free conditions.

4.3.1.2 Batch Adsorption Experiments

To prepare experiments, aqueous Hg, NOM, and suspended bacteria stock solutions

were mixed in different proportions to achieve the desired final concentrations for each

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experiment. The experiments were conducted in sets with constant pH (at pH 4.0 ± 0.1, 6.0 ±

0.1, or 8.0 ± 0.3) and constant bacterial concentration (0.2 gm bacteria L-1 in all cases) at three

different FA concentrations (0, 25, or 50 mg L-1), with Hg log molalities ranging from -6.30 to -

5.00 (0.1 to 2.0 mg L-1).

FA stock solutions were prepared in Teflon bottles by dissolving dried, powdered

International Humic Substances Society Suwannee River FA Standard I in a 0.1 M NaClO4 buffer

solution to achieve the desired final FA concentration for each experiment. A known mass of

wet biomass was then suspended in the FA stock solution, and the pH of the FA-bacteria parent

solution was immediately adjusted to the experimental pH using 0.2 M HNO3 and/or NaOH. To

prepare experimental solutions, aliquots of the FA-bacteria parent solution were added

gravimetrically to Teflon reaction vessels, followed by a small aliquot of commercially-supplied

1,000 mg L-1 Hg aqueous standard to achieve the desired final Hg concentration. The pH of each

suspension was again adjusted immediately to the experimental pH. The vessels were placed on

an end-over-end rotator to agitate the suspensions for the duration of the experiment (2 h for B.

subtilis and G. sulfurreducens and 3 h for S. oneidensis, as determined by initial kinetics

experiments (results not shown)). The pH of the suspensions was monitored and adjusted every

15 minutes throughout the duration of the experiment, except during the last 30 minutes, when

the suspensions were undisturbed. At the completion of each experiment, the pH of the

suspensions was measured and the experimental suspensions were centrifuged at 8100g for 5

minutes. The aqueous phase was collected for Hg analysis by inductively-coupled plasma optical

emission spectroscopy (ICP-OES). Duplicate experiments were performed for each experimental

condition.

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4.3.2 Analytical Methods: Inductively Coupled Plasma – Optical Emission Spectroscopy (ICP-OES)

ICP-OES standards were prepared gravimetrically by diluting a commercially-supplied

1,000 mg L-1 Hg aqueous standard with pH-adjusted 0, 25, or 50 mg L-1 FA stock solution made in

0.1 M NaClO4 so that the pH, ionic strength, and FA concentration of the standards closely

matched that of the samples. I found significant interference when standards and samples were

not closely matched in this way. The log molality of the Hg standards ranged from -6.60 to -5.00.

The standards and samples were all stored in Teflon containers and analyzed with a Perkin

Elmer 2000DV ICP-OES at wavelength 253.652 nm within 1 day of collection. The set of

standards was analyzed before and after all of the samples were analyzed, as well as after every

15 samples, to check for machine drift. Analytical uncertainty, as determined by repeat analyses

of the standards, was ± 2.8% for the 0 mg L-1 FA samples, ± 7.7% for the 25 mg L-1 FA samples,

and ± 9.5% for the 50 mg L-1 FA samples. Neither standards nor samples were acidified prior to

analysis. Fulvic acid concentration strongly affected system performance and signal strength,

likely due to spectral interferences caused by the FA molecule. For each pH and FA

concentration condition studied, I conducted biomass-free control experiments to determine

the extent of Hg loss due to adsorption onto the experimental apparatus as well as any

interferences caused by the presence of FA during the ICP-OES analysis.

4.3.3 Thermodynamic Modeling

Surface-complexation models were constructed to model Hg binding with bacterial cell

envelope functional groups and with those on the FA molecules, and to quantify the

competition between the two. Observed adsorption reactions between aqueous Hg species and

deprotonated bacterial cell envelope sites and/or FA binding sites were modeled according to

the following generic reaction:

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(10) Hg speciesx+ + R-Ai- (R-Ai)(Hg species)(x-1)+

where ‘Hg speciesx+’ represents the specific aqueous Hg species considered, ‘R-Ai-’ represents

the deprotonated cell or FA binding site, ‘(R-Ai)(Hg species)’ represents the Hg-bacterial cell

envelope or Hg-FA complex, and the ‘x’ represents the charge of the aqueous Hg species.

Stability constants for each of the Hg-bacterial cell envelope and Hg-FA complexes are expressed

as the corresponding mass action equation for Reaction (10):

(11) ][)(

]))([( )1(

i

x

x

iads

ARspeciesHga

speciesHgARK

where Kads is the thermodynamic equilibrium constant for Reaction (10), the square brackets

represent concentrations in mol L-1, and a represents the activity of the species in parentheses.

I used FITEQL 2.0 (Westall, 1982) for the equilibrium thermodynamic modeling of the

adsorption data, using the aqueous speciation equilibria and equilibrium constants given in

Table 12, and using the Davies equation within FITEQL to calculate activity coefficients. Because

all of my experiments were conducted at the same ionic strength, I applied a non-electrostatic

model to account for the Hg adsorption data. Bacterial site concentrations and acidity constants

used in the calculations for B. subtilis, for S. oneidensis, and for G. sulfurreducens are from Fein

et al. (2005), Mishra et al. (2010), and Dunham-Cheatham et al. (2012), respectively. The

objective of the modeling exercise was not to construct precise site-specific mechanistic binding

models, but rather to provide a quantitative means of estimating the competitive binding of

bacteria and FA under a range of relative concentration conditions. Toward this end, because

specific binding constants for Hg with each site type on the FA molecule are not known, I

modeled Hg binding with the FA as a single complexation reaction between Hg2+ and the

deprotonated form of a generic FA site. I assumed that this generic binding site exhibits an

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acidity constant equal to the average of the acidity constants of all of the FA sites, with a site

concentration equal to the total concentration of all FA sites, using the average values from

Borrok and Fein (2004) as a model of the FA site speciation. The calculated acidity constant and

site concentration for this generic site are listed in Table 12.

4.4 Results

Consistent with previous studies of Hg adsorption onto bacteria (Daughney et al., 2002;

Dunham-Cheatham et al., 2012), I observed extensive adsorption of Hg onto the bacterial

species studied in the absence of FA, with the extent of adsorption relatively independent of pH

between pH 4 and 8 (Figure 27, top plots). For example, approximately 77% of the Hg in a 2 mg

L-1 Hg solution adsorbs at pH 4 onto 0.2 gm L-1 S. oneidensis, while approximately 75% adsorbs at

pH 8. The presence of FA decreases the amount of Hg adsorbing to cell envelopes of each of the

bacterial species and at each of the pH conditions studied (Figure 27, middle and bottom plots).

With 50 mg L-1 FA, the extent of adsorption at pH 4 decreases to 65%, and at pH 8 to 50%. My

experimental results also indicate that the three bacterial species studied here exhibit similar

extents of Hg adsorption under each experimental condition, consistent with the observations

from a number of previous studies (e.g. Cox et al., 1999; Yee and Fein, 2001; Borrok et al., 2005;

Johnson et al., 2007). The data suggest that as the concentration of FA increases, so does the

amount of Hg remaining in solution. These results indicate that FA competes with the bacterial

cells for the adsorption of Hg, and that the adsorption of Hg to FA results in a competitive ligand

effect. As a result, less Hg is available for adsorption to proton-active functional groups on the

bacterial cell envelope, and less Hg is removed from solution. These results are not surprising, as

FA molecules contain sulfhydryl groups within their structure and sulfhydryl groups bind

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TABLE 12

HG REACTIONS USED IN THE SPECIATION MODELING

Reaction Log K

H2O – H+ = OH- -14.00 b H2CO3

0 – H+ = HCO3- -6.355 a

H2CO30 – 2H+ = CO3

2- -16.67 a H2CO3

0 – H2O = CO20 2.770 b

Na+ + H2CO30 – 2H+ = NaCO3

- -15.41 b Na+ + H2CO3

0 – H+ = NaHCO30 -6.60 b

Na+ + H2O – H+ = NaOH0 -14.2 b Hg2+ + H2O - H+ = HgOH+ -3.40 a

Hg2+ + 2H2O - 2H+ = Hg(OH)20 -5.98 a

Hg2+ + 3H2O - 3H+ = Hg(OH)3- -21.1 a

2Hg2+ + H2O - H+ = Hg2(OH)3+ -3.30 b 3Hg2+ + 3H2O - 3H+ = Hg3(OH)3

3+ -6.40 b Hg2+ + H2CO3

0 – 2H+ = HgCO30 -3.91 a

Hg2+ + H2CO30 – H+ = HgHCO3

+ 0.42 a Hg2+ + H2CO3

0 + H2O – 3H+ = Hg(OH)CO3- -11.355 a

B1- + H+ = B1-H0

Bacillus subtilis 3.30 c

Shewanella oneidensis 3.30 d Geobacter sulfurreducens 3.36 e

B2- + H+ = B2-H0

Bacillus subtilis 4.80 c Shewanella oneidensis 4.80 d

Geobacter sulfurreducens 4.81 e B3

- + H+ = B3-H0 Bacillus subtilis 6.80 c

Shewanella oneidensis 6.70 d Geobacter sulfurreducens 6.49 e

FA- + H+ = FA-H0 5.85 f

(a) Powell et al., 2005.

(b) Martell and Smith, 2001.

(c) Fein et al., 2005.

(d) Mishra et al., 2010.

(e) Dunham-Cheatham et al., 2012.

(f) Calculated as the average of all reported pKa values in Table 2 from Borrok and Fein (2004). Assumed total site concentration is the sum of the average site concentrations for the individual FA sites: 5.50 x 10-3 moles of sites per gram of humic substance.

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Figure 27: Aqueous chemistry results for Hg isotherms in the absence and presence of FA at pH 4 (A, B, C), pH 6 (D, E, F), and pH 8 (G, H, I). Plots A, D, and G present the results for the FA-free controls, plots B, E, and H present the results for the 25 mg L-1 FA experiments, and plots C, F,

and I present the results of the 50 mg L-1 FA experiments. B. subtilis is represented by the black-outlined, grey-filled squares, S. oneidensis is represented by the solid black diamonds, and G.

sulfurreducens is represented by the hollow circles. The black line on each plot represents 100% Hg adsorption under each experimental condition.

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strongly with Hg (Xia et al., 1999; Hesterberg et al., 2001; Drexel et al., 2002; Haitzer et al., 2002;

2003), leading to effective competition with bacterial cell envelopes which also contain proton-

active sulfhydryl functional groups (Guiné et al., 2006; Mishra et al., 2009; 2010). In the

experimental systems, FA binding sites outnumber those present on the bacteria. For example,

50 mg L-1 FA corresponds to approximately 2.8 x 10-4 moles of sites L-1 (Borrok and Fein, 2004),

while 0.2 gm L-1 B. subtilis biomass contains 4.7 x 10-5 total moles of sites L-1. At pH 8, 50 mg L-1

FA does diminish the extent of Hg adsorption, but only from approximately 70% (with no FA

present) to 60%. It appears that given equal site concentrations, bacterial binding of Hg would

dominate the competition with FA.

4.5 Discussion

The experimental results presented here suggest that bacterial cell envelope functional

groups and FA functional groups exhibit reasonably similar binding affinities for Hg under the

experimental conditions. Hg binding onto the bacterial cell envelopes is extensive, and although

Hg binds strongly with FA, especially with the sulfhydryl groups present within FA (Xia et al.,

1999; Hesterberg et al., 2001; Drexel et al., 2002; Haitzer et al., 2002; 2003), the presence of

even up to 50 ppm FA with only 0.2 gm (wet mass) L-1 of bacteria does not cause the speciation

of Hg to be dominated by the FA. The results strongly suggest that there is a fairly equal

competition between the bacterial and FA binding sites for the available Hg.

In order to quantify the competitive binding, I used a semi-empirical surface

complexation approach. First, I used the FA-free adsorption data at pH 4, 6, and 8 to solve for

equilibrium constants for the following Hg2+ adsorption reactions, respectively:

(12) R-A1- + Hg2+ R-A1-Hg+

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(13) R-A2- + Hg2+ R-A2-Hg+

(14) R-A3- + Hg2+ R-A3-Hg+

where R-A1, R-A2, and R-A3 represent the bacterial functional groups with the three lowest pKa

values, respectively. At pH 4, the R-A1 sites are the dominant deprotonated sites available for

Hg2+ binding for each bacterial species; at pH 6, both R-A1 and R-A2 sites are deprotonated; and

at pH 8, R-A1, R-A2, and R-A3 sites likely contribute to the binding of Hg2+. Therefore, I used the

pH 4 data to constrain the stability constant value for Reaction (12) alone, then fixed that value

and used the pH 6 data to solve for the stability constant value for Reaction (13) with a model

that involved Reactions (12) and (13) simultaneously. I then used the values that I calculated for

the stability constants for Reactions (12) and (13) and the pH 8 data to solve for the best-fitting

value for Reaction (14) with a model that involved Reactions (12) - (14) simultaneously. This

modeling approach assumes that Hg2+ binding at a given pH occurs dominantly onto sites with

pKa values lower than the pH of the experiments; that is, dominantly onto deprotonated sites.

However, the resulting stability constant values, which are tabulated in Table 13, yield excellent

fits to the FA-free Hg adsorption data as a function of pH and Hg loading (e.g., Figure 28). The

calculated stability constants for each reaction for each bacterial species studied here are similar

to each other. The log stability constant values for Reaction (3) range from 7.3 for B. subtilis to

7.8 for G. sulfurreducens; those for Reaction (13) range from 11.2 for S. oneidensis to 11.6 for

both B. subtilis and G. sulfurreducens; and those for Reaction (14) range from 15.6 for S.

oneidensis to 16.5 for G. sulfurreducens. The fact that the stability constant values increase by

four-to-five orders of magnitude from one site to the next likely is due to the simplified nature

of the adsorption model. I assumed that Hg2+ is the adsorbing aqueous Hg species under all pH

conditions. However, Hg(OH)2 is the dominant aqueous Hg species under the experimental

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TABLE 13

CALCULATED LOG STABILITY CONSTANT VALUES FOR REACTIONS (12) – (15)

[FA]

(mg L-1) pH Bacteria Reaction

(12)a Reaction

(13)b Reaction

(14)c Reaction

(15)d 25 mg L-1

Reaction (15)d

50 mg L-1

0 B. subtilis 7.3 ± 0.1 11.6 ± 0.2 16.4 ± 0.1

S. oneidensis 7.6 ± 0.2 11.2 ± 0.1 15.6 ± 0.1 G. sulfurreducens 7.8 ± 0.2 11.6 ± 0.1 16.5 ± 0.1

25, 50

4 B. subtilis

13.4 ± 0.2 13.4 ± 0.1 S. oneidensis 13.8 ± 0.2 13.6 ± 0.3

G. sulfurreducens 13.8 ± 0.1 13.6 ± 0.1

6 B. subtilis 14.3 ± 0.1 14.0 ± 0.1

S. oneidensis 14.4 ± 0.2 14.2 ± 0.3 G. sulfurreducens 14.4 ± 0.2 14.2 ± 0.1

8 B. subtilis 14.9 ± 0.2 14.4 ± 0.2

S. oneidensis 14.9 ± 0.2 15.0 ± 0.4 G. sulfurreducens 14.6 ± 0.3 14.6 ± 0.2

Average value: 14.3 ± 0.2 14.1 ± 0.2

(a) R-A1- + Hg2+ R-A1-Hg+

(b) R-A2- + Hg2+ R-A2-Hg+

(c) R-A3- + Hg2+ R-A3-Hg+

(d) FA- + Hg2+ FA-Hg+. Both columns present the calculated log stability constant values for the adsorption of Hg to deprotonated FA, as expressed in Reaction (15). The left column presents the values for the 25 mg L-1 FA conditions, and the right column presents the values for the 50 mg L-1 FA conditions.

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Figure 28: Representative model fits for S. oneidensis at pH 6 under 0 mg L-1 FA (grey squares and grey curve) and 50 mg L-1

FA (solid black diamonds and black curve) conditions. The dotted line represents 100% Hg adsorption under each experimental

condition.

conditions, and the concentration of Hg2+ is small and becomes smaller with increasing pH over

the pH range of my experiments. Therefore, because the extent of adsorption is relatively pH

independent, the stability constants that describe adsorption of Hg2+ onto bacterial binding sites

must become larger with each site considered.

Site-specific Hg binding constants have not been determined for Suwanee River FA, so I

could not compare the measured effects of the presence of FA with those I would predict from

speciation calculations. However, I used the measured extents of Hg adsorption in the presence

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of FA to calculate empirical generic site Hg binding constants for the FA. That is, I modeled the

Hg binding onto the FA with the following single site reaction:

(15) FA- + Hg2+ FA-Hg+

where FA- represents the generic deprotonated site on the FA molecule. I modeled this site as a

hybrid of the 4 sites used by Borrok and Fein (2004) to account for FA protonation behavior,

with the pKa value of the hybrid FA site equal to the average of the pKa values used by Borrok

and Fein (2004) and the site concentration equal to the average of the total of the 4 sites for all

9 FAs modeled by Borrok and Fein (2004). Clearly, modeling Hg2+ adsorption onto this hybrid

generic FA binding site is a simplification of the complex binding environment of Hg on the FA

molecule, but it allows us to quantify the competition between the FA and the bacterial cell

envelope, and to calculate quantitative estimates of the effects of each binding environment in

more complex settings.

The calculated stability constants, tabulated in Table 13, yield an excellent fit to the

observed effects of the presence of FA on Hg adsorption onto the bacteria studied here (e.g.,

Figure 28). The stability constants calculated for the three bacterial species are similar to each

other and do not vary systematically between bacterial species. Additionally, the 25 mg L-1 FA

data yield calculated Hg-FA stability constant values that are not significantly different from

those calculated using the 50 mg L-1 FA data. The calculated stability constant values do change

systematically with pH, with values increasing with increasing pH. This trend is likely a result of

the oversimplification of the Hg-FA binding model; it is probable that the FA molecule contains

multiple functional group types that deprotonate sequentially with increasing pH, not just the

one site type that I assumed in the models. However, the calculated log stability constant values

are not strongly dependent upon pH, with the largest spread being from 13.4 to 14.9 for the pH

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4 to 8, 25 mg L-1 FA data for B. subtilis. Thus, the values in Table 13 can be used to yield

reasonable estimates of the competition between bacteria and FA in the pH and FA:bacteria

concentration ratio conditions studied here.

The calculated K values can be used to illustrate the direct competition between

bacteria and FA for available aqueous Hg2+. For example, the competition reaction between

bacterial site A2 and the FA binding site can be expressed as:

(16) R-A2-Hg+ + FA- FA-Hg+ + R-A2-

where the log equilibrium constant for Reaction (16) can be calculated as the log K value for

Reaction (15) minus the log K value for Reaction (13), or values of 2.4 for B. subtilis, 3.0 for S.

oneidensis, and 2.6 for G. sulfurreducens under pH 6 conditions with 50 mg L-1 , 0.2 gm L-1

bacteria. These calculated equilibrium constant values for Reaction (16) can be used to quantify

the distribution of Hg between bacterial and fulvic acid binding sites for conditions with

different relative concentrations of each site type, and the large positive values suggest that on

a mass normalized basis, bacterial binding of Hg is greater than that exhibited by fulvic acid.

Although both bacteria and fulvic acids contain sulfhydryl binding sites that are especially

effective at binding Hg, the results suggest that these sites may exhibit a higher density on

bacteria than they do on fulvic acid.

4.6 Conclusions

The results from this study show that the presence of FA decreases the extent of Hg

adsorption onto three different bacterial species through competitive binding of the Hg. I used

the experimental results to calibrate a quantitative semi-empirical model of the binding of Hg to

bacteria and FA, and the stability constants that I calculated can be used to estimate the

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distribution and speciation of Hg in bacteria- and FA-bearing geologic systems. Because

accessibility of Hg to bacteria for metabolic processes such as methylation may be controlled by

adsorption, the stability constants calculated in this study may also be useful in estimating the

bioavailability of Hg in soil and groundwater systems that contain significant concentrations of

fulvic acid.

4.7 Acknowledgements

The experiments and analyses were performed at the Center for Environmental Science

& Technology, University of Notre Dame.

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CHAPTER 5:

CONCLUSIONS

Geochemists are faced with the challenge of quantifying the mobility and bioavailability

of contaminants in the subsurface. Due to the innate complexity of subsurface environments,

using simplified models to predict the mobility of these contaminants is impractical. Therefore,

it is beneficial to understand how each component of natural systems may affect the

contaminant of interest. Each project included in this research aimed to fill in holes in our

understanding of contaminant migration in subsurface environments by providing more

accurate and flexible geochemical models through the incorporation of parameters based on

experimental measurements.

In Chapter 2, I investigated the potential for passive cell wall biomineralization in the

presence of metals (e.g. uranium, lead and calcium), phosphate and non-metabolizing bacterial

cells. Prior to this study, research was presented that suggested the potential for passive cell

wall biomineralization, but the results from the research was equivocal. The previous research

could not prove that the association between the bacterial cells and the precipitates was not

merely a result of electrostatic interactions drawing the particle and the cell together, or a result

of metabolic processes of the bacteria. To determine if passive cell wall biomineralization can

occur, I used non-metabolizing bacterial cells to minimize the potential for interactions between

the metal and metabolic exudates, and conducted precipitation experiments under a range of

saturation state conditions. The results demonstrate that passive cell wall biomineralization

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occurs under specific environmental conditions, and that the presence of bacteria may have a

significant effect on the size of metal phosphate precipitates. This effect results in a relatively

higher aqueous metal concentration compared to an abiotic system, the result of which is an

increased availability of the heavy metal.

In Chapter 3, I explored the adsorption behavior of Hg to a variety of bacterial cell types

in both the absence and presence of a competitive ligand, Cl. I conducted batch adsorption

experiments, measuring Hg adsorption onto 3 bacterial cell species in both the presence and

absence of Cl. The results show that Hg extensively binds to bacterial cell envelopes in both the

absence and presence of Cl, but does not exhibit typical metal cation adsorption behavior. The

stability constants calculated using the experimental data will yield more accurate predictions of

Hg binding behavior than the previously reported stability constants, as discussed in Chapter 3.

The results from Chapter 3 showed extensive and strong Hg binding to bacterial cells

despite the addition of a competitive ligand, which raises the question whether natural organic

matter (NOM) can compete with bacterial cell envelopes for the adsorption of Hg, since they are

both composed of proton-active functional groups that readily bind metals. In Chapter 4, I

conducted batch adsorption experiments adsorbing Hg onto bacterial cell walls in the absence

and presence of fulvic acid (FA). Fulvic acid-metal interactions have been widely studied;

however, no study to date has investigated the interactions between bacterial cells, Hg and FA,

and used the data to calculate stability constants for the Hg-bacteria and Hg-FA reaction.

Previous studies have focused on one ligand only and did not consider a 3-component system.

My results show that the extent of Hg binding to bacterial cell envelopes is decreased in the

presence of FA, and that FA does not form ternary complexes with bacteria and Hg, but instead

behaves as a competitive ligand for Hg binding. The results from this study can be used to

predict the distribution and speciation of Hg in FA- and bacteria-bearing systems. The

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quantification of thermodynamic stabilities of the important mercury species in Chapters 3 and

4 are crucial to understanding the transport of the contaminant and in creating bioavailability

models to predict the fate of the metal.

The discoveries from each of the 3 studies lead to additional questions. For example, an

extension of the study in Chapter 2 would be to examine whether passive cell wall

biomineralization leads to the formation of other mineral types than phosphate minerals. The

phosphate minerals observed in my study likely occurred because of the phosphate groups

within the cell envelope; however, do other groups influence mineralization as well? Is the

mineralization specific to the group or can any of the groups present on the cell envelope

nucleate mineralization? Additionally, future studies could probe whether passive cell wall

mineralization occurs for a range of metals at the same saturation state or whether the

phenomenon is saturation state independent, and investigate a range of metals to determine if

passive cell wall biomineralization increases the aqueous metal concentrations relative to the

abiotic controls for all metals. To expand upon the studies in Chapters 3 and 4, one could

investigate the potential for reversibility of Hg adsorption to bacterial cell envelopes, and

determine if a competitive ligand has the potential to remove bound Hg from cell envelopes to

form aqueous complexes. Because Hg binds so readily and strongly with the sulfhydryl sites on

cell envelopes, it is possible that Hg will not exhibit reversible adsorption typical of most metals.

Additionally, research could be conducted to determine what types of sites, in addition to

sulfhydryl, dominate Hg binding to organics and if the type of binding site affects Hg binding

behavior, speciation and distribution throughout the experimental system.

Various approaches can be taken in order to answer these questions. Conducting

precipitation experiments similar to those outlined in Chapter 2 using a wider range of metals

and saturation state conditions would determine if passive cell wall biomineralization is

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saturation state- or metal-specific. Once these questions are answered, a more in-depth

investigation of the function groups involved in the phenomenon could be conducted, using

analytical methods such as X-ray absorption spectroscopy to probe the binding environment of

the precipitates with the cells, to determine whether functional groups other than phosphates

participate in passive cell wall biomineralization. To answer the question regarding the

reversibility of Hg binding to cell envelopes, one could determine the difference in aqueous Hg

concentrations in a system with bacterially-bound Hg before and after the addition of a ligand

with a high binding affinity for Hg (e.g. sulfide or a reduced-sulfur containing complex, such as

NOM). If the aqueous Hg concentration increases after the addition of the ligand, it is likely that

the binding of Hg to bacterial cell envelopes is reversible. Using a variety of ligands with a range

of binding affinities for Hg would determine how easily reversible Hg binding to bacterial cell

envelopes is, the result of which could have major implications for the mobility of the metal in

geologic systems.

One challenge of understanding metal mobility in the environment is the complexity of

each geologic system. In order to predict the speciation, distribution, and mobility of each

element within a system, we must first know how each element reacts with every other element

present and be able to determine whether the element will adsorb to a surface to become

immobile, form a mobile aqueous complex, or precipitate from solution. If the element is

precipitated from solution, it may still be mobile in the solid phase or it may return to the

aqueous phase upon dissolution of the precipitate. In an effort to further our knowledge, the

studies contained within this dissertation were aimed to provide data and thermodynamic

models that will help us better predict the behavior of metals in the environment.

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