UC CEIN Research Integration
Property/Activity Relationships
Theme 1:
ENM Physical/Chemical
Characteristics
Theme 2:
HTS and Predictive Toxicology
Environmental Modeling
Theme 3:
Environmental Fate & Transport; Life Cycle Modeling
Theme 6:
Exposure Modeling; QSARs
Ecosystems Impacts
Theme 4:
Terrestrial Impacts (Food supply)
Theme 5:
Estuarine Impacts (Benthic and
Pelagic Organisms)
Societal Outputs Theme 7:
Stakeholder Engagement and Translational Activities
Theme 8: Educational Programs and Workforce
Development
Major Goals for Renewal • To develop hazard ranking and structure-activity relationships (SARs) that relate the
physicochemical properties of compositional and combinatorial ENM libraries to toxicological responses in cells, bacteria and multi-cellular organisms, with a goal to develop predictive toxicological paradigms to understand the environmental impact of nanotechnology;
• To estimate environmentally relevant exposure concentrations of high-volume and potentially high-impact ENMs (primary nanoparticles as well as commercial nano-enabled products) using life cycle assessment (LCA) and fate and transport modeling to obtain quantitative information about the uptake, bioaccumulation, and hazard of nanoparticles in terrestrial and estuarine ecosystems;
• To determine the potential of ENMs, selected through high throughput screening (HTS), SAR analysis, LCA and multimedia modeling, to impact ecosystem services in model ecosystems. These include terrestrial mesocosms with food crop plans and bacterial populations that control nutrient cycles, and estuarine mesocosms comprised of a representative natural food web;
• To use UC CEIN knowledge and environmental impact assessment tools to educate the next generation as well as to inform and engage academic, government, industrial and societal stakeholders involved in risk perception, regulatory decision-making, policy development, risk management and safe implementation of nanotechnology.
Approach
Incorporate commercial (e.g., semiconductors, catalysts, Ag, silica, CNT) and newly evolving (e.g., graphene, multifunctional) ENMs into our libraries
Use of ENM libraries and sources to develop additional safer by design strategies
More HTS on organisms, including the use of genomics to establish new predictive paradigms
Lifecycle analysis premised on commercial and manufactured nanomaterials
Develop sophisticated and predictive SAR models based on libraries, HTS and machine learning tools for environmental prediction making
3
Approach
More integrated modeling of ecosystems impact premised on critical services and ecological processes that can be used for environmental decision analysis
Improved handling of large data sets through nano informatics and decision-making tools to build missing knowledge domains in collaborative projects
Streamlined center outreach efforts to engage industry, regulators, the public and experts in nano EHS roundtable interactions
Continued development of educational tools and building of a diverse and multidisciplinary nano EHS workforce that prepare us for implementation of a sustainable technology
4
Meng et al. ACS Nano. 2009
Nel et al. Accounts Chem Res, 2012
http://www.nap.edu/catalog.php?record_id=11970
http://www.epa.gov/ncct/toxcast
“Toxicity Testing in the 21st Century: A Vision
and a Strategy”
US National Academy of
Science (2007)
• Wide coverage of toxicants
• Robust scientific platform for
screening
• Predictive tests utilizing
toxicity mechanisms
• High throughput discovery
• Connectivity to in vivo
Current: One material at a time descriptive animal testing
Proposed: Rapid mechanism-based predictive testing
100’s/year 1000’s/year 10,000’s/day 100,000’s/day
High Throughput Bacterial, Cellular, Yeast, Embryo or Molecular Screening
Immediate Relevance
Predictive Environmental Toxicology in CEIN
Prioritize in vivo testing
at increasing trophic levels
Exposure/Life cycle Approach Google Images
Nano-Ecotoxicology Concerns
CO2 N2
• Bioavailability, bioaccumulation, & biomagnification
• Ecosystem “services”, including: – food production
– nutrient cycling
• Predictive capacity – mechanistic understanding
– modeling
population
Assays (growth;
mechanisms)
Mesocosms (interactions)
ecosystem
Microcosms (predator/prey;
biodiversity)
community
Modeling (dynamic energy budget)
Ecological Nanotoxicology: scales & approaches Holden, Nisbet, Lenihan, Miller, Cherr, Schimel, Gardea-Torresdey, 2013, ACR
Dynamic Energy Budget (DEB) Modeling
Populations
Communities
CONCEPT
Changes in
ENERGY
Generation
Transduction
Investment
Individual
Organisms
Bio-Effects
Ecosystems
Bacterial Membrane
Cytoplasm
NADH cyt
cyt
cyt O2 + H+
H2O
e-
e-
e-
e-
H+
H+
H+
H+
ADP
ATP
NP
•Electron Transport Chain (ETC) •Lyon & Alvarez. ES&T (2008)
•Membrane Potential (MP) Lyon & Alvarez. ES&T (2008)
NP
ROS H2O2
OH• O2
-
•Membrane Integrity (MI) Priester et al. ES&T (2009), Su et al. Biomaterials (2009)
•Reactive Oxygen Species (ROS) Nel et al. Science (2006) Priester et al. ES&T (2009)
4. Assays: stress/damage
Allison Horst
Bacterial Nanotoxicology Approaches 1. Bacterial strain selection
3. NM Dispersion
Horst, et al. J.Nanopart. Res. 2012
Horst et al. Small. 2013
2. Oligotrophic media
Direct Interference
Indirect Interference
HCS: Assessment of Fluorescence & Colorimetric Assay Interferences.
Horst et al. Small. 2012
Bacteria: Modeling Population Growth and Effects Mechanisms By
Dynamic Energy Budget (DEB)
Klanjscek et al. 2012, PLoS ONE
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 20 40 60 80 100
Lo
g (
RO
S w
ith
QD
/RO
S w
ith
dis
so
lved
Cd
)
Total Cd (mg/L)
Data
Best model
Other model 1
Other model 2
Klanjscek et al. 2013, Ecotoxicology
Modeling Cd(II) effects on Population Growth Modeling CdSe QD-specific effects
Population Scale
hydroponic crop plants
planktonic bacteria
Community Scale soil microbial communities
Ecosystem Scale agricultural plants in soil
plant-microbe root symbioses soil microbial communities
Priester et al. 2012. PNAS.
Hernandez-Viezcas et al.
2013. ACS Nano. in press
ENMs in Agriculture • Working on
determining bioavailability of ENMs to different plants
• Understand different ENM application methods and releases
NP stability and mobility in suspension is strong function of aqueous chemistry
Nanoparticle Freshwater Groundwater Seawater
TiO2
CeO2
ZnO
CuO
Ag – citrate
Ag – PVP
Pt
Pd
Fe(0) - coated
Most Me & MOx NPs are stable in freshwater due to NOM
Lower stability in groundwater due to high Ca2+
Most NPs are unstable in seawater due to high ionic strength
Longer polymeric coating (e.g. PVP) increase stability
Deposition of unstable solutions occurs in min to hr
Stability is a strong function of surface charge, which is a function of [NOM], pH, and ionic strength
Keller et al. ES&T. 2010; Thio et al. ES&T. 2011; Thio et al. J HazMat. 2011
Suspensions are
considered stable
when the particle
concentration
remains constant
CeO2
18
NSF: DBI-0830117
Flow of ENMs through global economy
Arturo Keller, Sangwon Suh, Sheetal Gavankar University of California Santa Barbara
• Starting with an ENM market survey, the mass flow of ENMs through the global economy was estimated
• 65-90% of ENMs will end up in landfills
• ~85% ENMs that pass through a WWTP will end up in biosolids, which may be applied to soils
• Fraction of ENMs going to water bodies and atmosphere are small, and dominated by TiO2, SiO2, Fe oxides and ZnO
(all flows in metric tons/yr, 2010 estimates from Future Markets, Inc.)
Keller, Suh et al., 2013
Theme 5: Marine and Freshwater Ecosystems Impact – Hunter Lenihan
Marine Pelagic
Freshwater Stream
Marine Benthic 2° Production
Phytoplankton 1° Production
Zooplankton
Higher
consumers
(Seafood)
Bio-accumulate
ENMs
Research Emphasis on:
Ecosystem services: Food webs,
Biodiversity
Ecological Processes: Production,
Trophic-transfer
Sunlight
Marine Pelagic
Daphnia
20
ZnO mg L-1 (ppm)
RF
ZnO mg L-1 (ppm)
Hypothesis: ZnO disrupts membrane function, produces ROS leading to
cell death, which leads to reduced population growth
Reactive oxygen species (ROS)
production
Membrane permeability (Cell death ) Mitochondrial membrane potential
ZnO mg L-1 (ppm)
Dynamic Energy Budget
(DEB) modeling of NEC
NEC = 223 ± 56 ppb
Rela
tive f
luo
resc
en
ce (
RF
)
Isochrysis galbana
Miller et al. ES&T. 2012
21
Kahru and Dubourguier, 2009 (Toxicology)
Algae
Crustaceans
Fish
Bacterial
Dose-dependent effects on diatom growth – ZnO
Med
ium
L(E
)C5
0 (
pp
m)
0.01
0.10
1
10
100
1000
10000
100000
Use of Nanoecotoxicity Meta analysis Data to plan Marine Studies
NEC = 223 ug L-1
Dose-dependent effects on diatom growth – TiO2
UC CEIN Research Integration
Property/Activity Relationships
Theme 1:
ENM Physical/Chemical
Characteristics
Theme 2:
HTS and Predictive Toxicology
Environmental Modeling
Theme 3:
Environmental Fate & Transport; Life Cycle Modeling
Theme 6:
Exposure Modeling; QSARs
Ecosystems Impacts
Theme 4:
Terrestrial Impacts (Food supply)
Theme 5:
Estuarine Impacts (Benthic and
Pelagic Organisms)
Societal Outputs Theme 7:
Stakeholder Engagement and Translational Activities
Theme 8: Educational Programs and Workforce
Development
Copper Products
• Cu NPs, 40nm
• Cu Bulk, <60 µm
• CuO NPs <50nm
• CuO Bulk, <5µm
• Cu(OH)2 DuPont kocide 2005 (Fungicide/Bactericide)
• Cu(OH)2 DuPont kocide 3000 (Minimal dose recommended: 84 mg/m2)
• CuCl2
Exposure Characterization
• Batch studies
• Aggregation
• Dissolution
• Changes in pH, IS, NOM
• Effect of sediments
• Cycles of salinity and flow
• LCA
• Exposure modeling
HTS Characterization
• Individual cells
• Zebra fish embryos
HCS Characterization
• Herring/Killifish
• Clams
• Oysters
• Annelid worms
Estuarine mesocosm studies
• Selection of Cu ENMs
to evaluate
(applications)
• Selection of
environmental
conditions
• Selection of organisms
and endpoints
• Selection of exposure
concentrations
Hypotheses to be tested with regards to
• Actual exposure • Hazards
Hypotheses
Predicted Initial [ENM] in SF Bay
Estimates of ENM concentrations at point of release indicate ng/L to ug/L levels to be expected
Alicia and Prof. Sharon Walker
Samples:
#1: Diluted Colon Effluent (directly from colon)
#2: Diluted Greywater Fluid (from synthetic greywater stock)
Directly from septic tank effluent:
#3: Baseline Septic Tank (no nanoparticles added)
#4: Septic Tank (week 1, nano Cu)
#5: Septic Tank (week 2, nano Cu)
#6: Septic Tank (post week 1, nano Cu)
#7: Septic Tank (post week 2, nano Cu(OH)2 CuPro)
#8: Septic Tank (post week 3, nano Cu)
Aims:
• Toxicity evaluations using zebrafish embryos HTS
• Quantify the Cu contents from these effluents
• Identify Cu speciation
• Correlate the properties of Cu-formulations and toxicological outcomes with the
aim to establish SARs
Artificial Colon and Septic Tank Effluent
-20.0%
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
0 2 4 6 8 10
nano Cu Micro Cu nano CuO
Micro CuO CuPro Kocide
% h
atc
hin
g
% h
atc
hin
g
Cu Concentrations (mg/L) Nominal Particle
Concentrations (mg/L)
-20.0%
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
0 2 4 6 8 10
nano Cu Micro Cu nano CuO
Micro CuO CuPro Kocide
Previous Converted dose
ZHE1
Zn2+
% hatching of zebrafish
embryos at 72 hpf
Effects of CuO NM on Herring Development
0
20
40
60
80
100
0 0.005 0.01 0.05 0.5 5 25 50 100 200
% a
bn
orm
al
ppm Cu in CuO NM
CuO NM: Percent dead and abnormal (day 11pf)
Hatched Abnormal
Hatched Dead
UnhatchedAbnormal
0
20
40
60
80
100
0 0.005 0.01 0.05 0.5 5 25 50 100 200
% a
bn
orm
al
ppm Cu in CuSO4
CuSO4: Percent dead and abnormal (day 11pf)
Hatched Abnormal
Hatched Dead
UnhatchedAbnormal
Images: E. Fairbairn E.A. Fairbairn & G.N. Cherr
water
sediment
ENMs
Salinity
Estuarine MendNano Model
Estuarine Mesocosm f r e s h w a t e r i n f l o w + C u N P s ; C u N P - c o n t a m i n a t e d p h y t o p l a n k t o n ; C u N P c o n t a m i n a t e
p a r c u l a t e o r g a n i c m aterial r C u - N P b o a t - b o o m p a i n t
Copper Project-Terrestrial Plants
Alfalfa and Lettuce Hydroponic Study
Alfalfa Lettuce
Jie Hong, PhD Student Jorge Gardea-Torresdey, Department of Chemistry Environmental Science & Engineering PhD Program
University of Texas at El Paso
Theme 4
UC CEIN Research Integration
Property/Activity Relationships
Theme 1:
ENM Physical/Chemical
Characteristics
Theme 2:
HTS and Predictive Toxicology
Environmental Modeling
Theme 3:
Environmental Fate & Transport; Life Cycle Modeling
Theme 6:
Exposure Modeling; QSARs
Ecosystems Impacts
Theme 4:
Terrestrial Impacts (Food supply)
Theme 5:
Estuarine Impacts (Benthic and
Pelagic Organisms)
Societal Outputs Theme 7:
Stakeholder Engagement and Translational Activities
Theme 8: Educational Programs and Workforce
Development
Tools to Develop Predictive Toxicology: Composition and Property-based Nanomaterial Libraries
TiO2, ZnO, CuO, NiO, Cr2O3 etc
Transition MOx
RE Oxides
Amorphous Fumed
Crystalline Mesoporous
Silica
SWCNT
MWCNT
fCNT
Carbon Nanotubes
Compositions
Godwin et al, EST. 2009 Thomas et al. ACS Nano. 2011 Nel et al. Small. 2012
Crystal Structure
ENMs -
- - -
Size
Shape AR
Metals
Surface chemistry
Eg
CB
VB
Eg
CB
VB
Eg CB
VB
Band Gap
Dissolution chemistry
-
+ - +
+
+ + +
Surface Functionalization
CO
OH
COO
H
HOO
C
COOH
COO
H
HO
OC
N
NH 2
CN
Ts
MOx
Silic
a
Surface Charge
Nel et al. Nature Materials. 2008 Xia et al. ACS Nano. 2008 George et al. ACS Nano. 2011
33
Cu, Ag, Pt, Co
Metals
CeO2, GdO3, La2O, SbO3 etc
DynaPro
Plate Reader
Materials of interest OECD-WPMN (2008)
SWCNTs MWCNTs Ag nanoparticles Fe nanoparticles Ti dioxide Al oxide Ce oxide Zn oxide Si dioxide Nanoclays Dendrimers Au nanoparticles Fullerenes (C60)
No Agent
Time (h)
BSA
2 mg/mL
2% FBS
100 nm
500 nm
1000 nm
5% FBS
1% FBS
0 1 2 3 4 5 6 8 10 12 14 16 18 20 22 24 H 2 O
BEGM DMEM
LB TSB
SD YPD H 2 O
BEGM DMEM
LB TSB
SD YPD H 2 O
BEGM DMEM
LB TSB
SD YPD H 2 O
BEGM DMEM
LB TSB
SD YPD H 2 O
BEGM DMEM
LB TSB
SD YPD
TEM
DLS/ ZetaSizer
MALLS
SEC
Groupings: Properties/SARS Toxicological mechanisms Usage/exposure
Appropriate Physicochemical Characterization
etc
Properties
Intrinsic
Material as acquired or synthesized
Extrinsic
Altered properties in
biological medium
Tox SAR
Properties proximately
associated with injury
High Throughput DLS
Tools to Develop Predictive Toxicology
Silica Types: SAR - strained silanol rings and surface OH -
COOH-MWCNTs
sw-NH2-MWCNTs
NH2-MWCNTs
PEG-MWCNTs
PEI-MWCNTs
Raw
COOH
R1 R3
R5 R7 R6
R0
{111
}
{111
}
Tools: Examples of Libraries
CeO2 shape and AR library: SAR – Long aspect ratio and Ce valency
R1 R3
R5 R7 R6
R0
{111
}
{111
}
Surface Functionalized MWCNT Library
Mitochondrial damage
ROS generation
Stress response
Cellular apoptosis
Reporter genes for
sublethal effects
Cell growth
Assessment of Inflammation
RBC lysis
Tools: Cellular High Throughput Screening
George et al. ACS Nano. 2010
George et al. ACS Nano. 2011
Nel et al. ACR. 2012
Zebrafish HTS platform automated imaging of
developmental abnormalities and transgenic responses
Hatching Start feeding
NP
s
NP
s
Embryonic development Larval effects
0 4 24 48 72 120
Image acquisition @ 24 hr intervals
HTS Brightfield
(Developmental, morphological abnormalities)
Ctrl
CuO
ZnO
NiO
Co3O4
Ag
CuO
HTS Screening
Transgenic Fish)
neg
pos He
at
sh
oc
k p
rote
in 7
0 Robotic pick-and-plate system
Xia et al. ACS Nano. 2011
Lin et al. ACS Nano. 2011
George et al. ACS Nano. 2011
Newport Green
Dissolution, shedding
toxic Ions, e.g., ZnO, CuO
Metal Metal ions
Nucleus
Cationic toxicity
e.g., cationic polystyrene,
PEI-MSNP
+ +
+ +
+
+ +
+ +
+
+ +
+ +
+
+
+
+ +
+
+
+ +
+ +
+
+
mitochondria
lysosome +
+
+
Inflammasome
activation
e.g., CNT, CeO2 rods
Nucleus
Inflammasome
IL-1β
IL-1β
pro-IL-1β
NALP3
O2· – O2
e–
h+
Redox activity and ROS
e.g., TiO2, CuO, CoO
A B C
D
Photoactivation
e.g., TiO2
Conduction Band
Valence Band
-
+
ΔEg
hν
N
P
O O
O O P
O O
O O
P
O O
O O
P
O O
O O
N N
N
Si O
O Si
O
O Si
O
O
Si O
Membrane Lysis
e.g., SiO2 nanoparticle,
Ag-plates
Silica
Cell membrane
O O
F E
Tools: Mechanistic Toxicological Pathways in Cells
for Predictive Toxicological Modeling
Nel et al. Nature Material, 2009
Xia et al, ACS Nano, 2008
Xia et al. ACS Nano. 2011
George et al. ACS Nano. 2010
George et al. ACS Nano. 2011
George et al JACS 2011
Lin et al. ACS Nano. 2011
Xia et al ACS Nano. 2009
Zhang et al ACS Nano 2011
Wang et al. ACS Nano. 2010
Wang et al ACS Nano. 2011
ZHE1 Hatching Enzyme
Abiotic ZHE 1 Assay for MOx Dissolution Predicts Zebrafish
Embryo Hatching Interference
Lin et al. ACS Nano. 2011
Lin et al. Small. 2012
Polyhistidine tagged
ZHE1 Plasmid BL21 E. coli
strain
pET3c-ZHE1
IPTG induction
MOx ions
Flu
oro
gen
ic S
ub
str
ate
Abiotic Enzyme Assay
Automated Zebrafish HTS
En
zym
atic a
ctivity
* *
* *
1-4
0
0.2
0.4
0.6
0.8
1
1.2
5 24
En
zym
atic a
ctivity
* *
* *
1-4
0
0.2
0.4
0.6
0.8
1
1.2
5 24
Bacterial HTS: Cationic PS MNMs Destabilize Membranes and Inhibit through ROS
(toxicogenomics)
Ivask et al. ES&T. 2012.
CEIN Approaches, Models and Nanoinformatics Tools
Developed for ENMs Environmental Impact Analysis
Exposure Likelihood
Environmental Hazard Ranking
EHR-Nano
Environmental Impact Evaluation
- ENMs F&T prop. - Geographical & meteorological info. - Emissions
Multimedia Analysis M
en
d-N
an
o
ENM Concentrations
& Mass Distribution
ENMs biota uptake parameters HTS/LTS Analysis Tools
Inter-Plate Normalization
Inter-Plate Normalization
Normalized Activity
Smallest Value*
X
X
X
1st Quartile
3rd Quartile
Median
Largest Value*
Outliers
Outlier
Knowledge Extraction:
Toxicity Metrics & QSARs
Data
Studies HTS/LTS
UC CEIN Research Integration
Property/Activity Relationships
Theme 1:
ENM Physical/Chemical
Characteristics
Theme 2:
HTS and Predictive Toxicology
Environmental Modeling
Theme 3:
Environmental Fate & Transport; Life Cycle Modeling
Theme 6:
Exposure Modeling; QSARs
Ecosystems Impacts
Theme 4:
Terrestrial Impacts (Food supply)
Theme 5:
Estuarine Impacts (Benthic and
Pelagic Organisms)
Societal Outputs Theme 7:
Stakeholder Engagement and Translational Activities
Theme 8: Educational Programs and Workforce
Development
Standard rodent
Toxicological tests
10-100/year
Biochemical and cell-based
in vitro assays
> 10,000/day
Alternative
Animal models
100-10,000/year
Human experience
1-3 studies/year
Predict
Knowledge
Computational toxicology Critical toxicology pathways
Immediate human relevance High Throughput
Francis Collins et al. Science. 2008
HTS Genome
Proteome Epigenome
Transcriptome
Disease Pathology Phenome
Fig. 2
A
Systems Toxicology at EPA and Tox-21
Disorder Categories being Probed
by Toxcast Assays
In vitro/in vivo datasets Data integration Signatures Computational Modeling
Meng et al. ACS Nano. 2009
Nel et al. Accounts Chem Res, 2012
Nanomaterial Predictive Toxicology
(proportional weighted discovery)
In Vivo Adverse Outcomes
• mechanism of injury
• toxicological pathway
ENM Libraries
of different
composition
and accentuated
Physchem
Properties
Validation
(102 observations
days-months)
(102 – 105 observations/day
by HCS and HTS
approaches
Cellular or Bio-molecular
Endpoints
Mechanistic
Toxicological
pathway
ATS widely accepted to prioritize ENM hazard assessment but not yet ready for quantitative risk assessment or regulation
Hazard ranking and grouping of ENMs could assist regulatory and occupational decision making
ATS and predictive toxicological paradigms can be used to establish hazard categories and material grouping as a 1st tier of testing, which is used to prioritize more costly and elaborate animal studies
Any framework that considers ATS for regulatory purposes needs to be transparent, participatory and engage a broad stakeholder community
A predictive toxicological approach for CNT is potentially helpful for hazard ranking, prioritizing animal experiments, and grouping of materials
The development of hazard ranking, material grouping and SARs can become an integral part of new product development
It is important to consider dose-response extrapolation and exposure scenarios that link mechanistic and predictive toxicological assessment to risk assessment
Provisional Consensus about ATS use for nano EHS
“IMPLEMENTATION OF ALTERNATIVE TESTING METHODS.—To promote the
development and timely incorporation of new testing methods that are not
laboratory animal-based…..”:
‘‘(A) ….develop a strategic plan to promote the development and
implementation of alternative test methods and testing strategies to generate
information used for any safety-standard determination made that reduce,
refine, or replace the use of laboratory animals, including toxicity pathway-
based risk assessment, in vitro studies, systems biology, computational
toxicology, bioinformatics, and high-throughput screening”
‘‘(B) beginning on the date …and every 5 years thereafter, submit to Congress
a report that describes the progress ……”
‘‘(C) fund and carry out research, development, performance assessment, and
translational studies to accelerate the development of test methods and testing
strategies that reduce, refine, or replace the use of laboratory animals in any
safety-standard”
IN THE SENATE OF THE UNITED STATES: a bipartisan bill to modernize title I of the Toxic Substances Control 14 Act (15 U.S.C. 2601 et seq.) –
May 24 2013
Toxicity explained by Dissolution and Conduction Energy
(statistical testing of scientific hypothesis)
Dissolution in BEGM < 13.05
Al2O3, CeO2,
Gd2O3, HfO2,
In2O3, La2O3,
NiO, Sb2O3,
SiO2, SnO2,
TiO2, Yb2O3,
Y2O3, ZrO2
Fe2O3
Fe3O4
WO3
CoO
Co3O4
Cr2O3
Mn2O3
Ni2O3
ZnO
CuO
Ec < -4.80
Ec < -4.22
Metal dissolution in BEGM < 13.05 Al2O3
SiO2
Y2O3
La2O3
Gd2O3
HfO2
Yb2O3
ZrO2
In2O3 NiO
Sb2O3 CeO2
SnO2
TiO2
Ni2O3
Cr2O3
Mn2O3
CoO
Co3O4
CuO
ZnO
Fe2O3 Fe3O4
WO3
-4
-3
-2
-5
Ec (
eV
)
20 10 0 30 40
Metal Dissolution (%)
Low/no Toxic Highly Toxic
George e al. ACS Nano. 2010
Xia et al. ACS Nano. 2011
Zhang et al. ACS Nano. 2012
Regression Tree
Epithelial
mesenchymal
fibroblast
cellular axis
(progressive,
chronic)
Cathepsin B
Lysosome
-sw-NH2
-NH2
-PEI
+f-
TGF-β1 , PDGF
Lysosome injury Intact lysosome
Lysosome
PF108-CNTs BSA-CNTs
In vitro hazard
ranking of extensive
batches of CNT
materials to prioritize
animal testing and in
vivo hazard ranking
Predictive Toxicological Profiling
NALP3 inflammasome activation
in macrophages (subacute)
IL-1β
-PEG --f- -COOH
BSA-coated
Multiple CNTs libraries
SG65
Arc
Hipco
-NH2
-sw-NH2
-PEI
-PEG (-)
(+++)
Raw MWCNTs
Purified MWCNTs
COOH-MWCNT
f-
CheapTube®
acid treat
carboxylate
Functionalized
(++)
(+/-)
MWCNT
SWCNT Raw-SWCNT
Purified-
SWCNT
Density column purification after BSA and
polymer coating of SWCNT and MWCNTs
Several
suppliers
Pleuronic-coated lung fibrosis
Predictive Toxicology Approaches allows Large Numbers of
Materials to be grouped in Hazard Band Categories
CeO2 Gd2O3
La2O Sb2O3
Yb2O3 Y2O3
etc George e al. ACS Nano. 2010
Xia et al. ACS Nano. 2011
Zhang et al. ACS Nano. 2012
Nel et al. ACR. 2012
lung injury
SWCNT & MWCNT
Libraries (>5 batches)
Lysome injury
Harmful SARs
Ostwald Ripening
{111
}
{111
}
LAR Metal oxides (2) Rare Earth Oxides (>10)
NLRP3
CoO
Co3O4
Cr2O3
Mn2O3
Ni2O3
etc
Al2O3, HfO2
In2O3, NiO
SnO2, TiO2
ZrO2 etc Strained
siloxane rings H-bonded silanols
High and Low Temp Silicas
(>5 Si types)
+
Oxidative
stress
Inflamma-
tion
Transition MOx’s (>30)
NLRP3
CB
VB
Cellular SAD mg/cm2
In Vivo Dose in Mouse
Extrapolated Human SAD (mg/cm2)
Lung Alveolar SAD mg/cm2
Total Alveolar area = 0.05 m2
brain
lung skin
Total Alveolar area = 102 m2
NIOSH REL for CNTs
= 1 mg/m3
Worker alveolar exposure levels Chronic: 8hr/day x 40wks/yr x 45yr Acute: 24 hr
Dose-response Extrapolations
?
Intracellular dose determination by ISSD modeling
•1st tier – In vitro – Predictive assays to study specific mechanisms of injury
– Rank potency of test materials vs well-defined positive and
negative controls from libraries – Develop quantitative SAR analysis for in silico predictions
•2nd tier – short term in vivo
– Test selected materials within a category/mechanism/SAR – Focused/limited animal studies
– Validate mechanism and potency within a group
– In vivo hazard ranking (pathophysiology of disease outcome)
•3rd tier – short-term or 90 day inhalation studies
– Test the most potent materials within a tier 2 category/group
– Dose-response extrapolation using benchmark materials
to allow risk assessment
– Establish OEL’s
– Use for read-across regulatory decision making
Tiered Approach Using Predictive Toxicological
Modeling for Hazard Ranking and Risk Translation
UC CEIN Research Integration
Property/Activity Relationships
Theme 1:
ENM Physical/Chemical
Characteristics
Theme 2:
HTS and Predictive Toxicology
Environmental Modeling
Theme 3:
Environmental Fate & Transport; Life Cycle Modeling
Theme 6:
Exposure Modeling; QSARs
Ecosystems Impacts
Theme 4:
Terrestrial Impacts (Food supply)
Theme 5:
Estuarine Impacts (Benthic and
Pelagic Organisms)
Societal Outputs Theme 7:
Stakeholder Engagement and Translational Activities
Theme 8: Educational Programs and Workforce
Development
UC CEIN Publications
Results found: 177
Sum of the Times Cited : 3214
Average Citations per Item : 18.16
h-index : 27
Publications by Year
Citations by Year
Source: Web of Knowledge
Theme 1 (plus 2 and 6) Publications
Results found: 54
Sum of the Times Cited : 2016
Average Citations per Item : 37.33
h-index : 20
Publications by Year
Citations by Year
Source: Web of Knowledge Includes Cross-Theme Publications
Theme 2 (plus 1 and 6) Publications
Results found: 45
Sum of the Times Cited : 1910
Average Citations per Item : 42.44
h-index : 19
Publications by Year
Citations by Year
Source: Web of Knowledge Includes Cross-Theme Publications
Theme 6 Publications (independent of 1 and 2)
Results found: 18
Sum of the Times Cited: 193
Average Citations per Item : 10.72
h-index : 8
Publications by Year
Citations by Year
Source: Web of Knowledge Includes Cross-Theme Publications
Theme 3 Publications
Results found: 45
Sum of the Times Cited : 1388
Average Citations per Item : 30.84
h-index : 12
Publications by Year
Citations by Year
Source: Web of Knowledge Includes Cross-Theme Publications
Theme 4 Publications
Results found: 70
Sum of the Times Cited : 722
Average Citations per Item : 10.31
h-index : 14
Publications by Year
Citations by Year
Source: Web of Knowledge Includes Cross-Theme Publications
Theme 5 Publications
Results found: 24
Sum of the Times Cited : 345
Average Citations per Item : 14.38
h-index : 9
Publications by Year
Citations by Year
Source: Web of Knowledge Includes Cross-Theme Publications
NSF: DBI-0830117
NSF: SES-0938099
The Hierarchy of EHS Practices in the US Nanotechnology Workplace C.D. Engeman1,2,3, L. Baumgartner3,4, B.M. Carr3,4, A.M. Fish3,4, J.D. Meyerhofer3,4, T.A. Satterfield2,3,5,
P.A. Holden3,4, B.H. Harthorn*2,3,6 1 Sociology Dpt, UCSB; 2 Center for Nanotechnology in Society, UCSB; 3 UC CEIN; 4 Bren School of Environmental Science & Management, UCSB; 5
Institute for Resources, the Environment, & Sustainability, University of British Columbia; 6 Feminist Studies, Anthropology, & Sociology Dpt.’s, UCSB
*-corresponding author
Journal of Occupational and Environmental Hygiene
(forthcoming, 2013)
Go
vern
men
t re
com
men
ded
pra
ctic
es
Hierarchical approach to exposure controls:
Bey
on
d g
ove
rnm
ent
reco
mm
end
atio
ns
Cleaning practices
Waste management:
Product stewardship: • Advertise /disclose that products contain NMs • Providing nano-specific guidance to customers
regarding product safe use and/or disposal
• Disposing NMs as hazardous waste • Using separate disposal containers for NMs • Having a nano-specific waste handling program • Listing NMs separately on waste manifests
Recommend: Wet wiping, absorbent materials Avoid: Sweeping, use of household vacuum or
compressed air
Monitoring the workplace for nanoparticles
1. (Elimination or substitution of material) 2. Engineering controls 3. Administrative controls 4. Personal protective equipment (PPE) + Respiratory protection
Key findings:
● Practices span current government-recommended
hierarchical approach to MNM exposure controls
● Practices tailored to current MNM hazard and exposure
knowledge reported less frequently than general chemical
hygiene practices
● Product stewardship and waste management practices –
whose influences manifest much farther down the product
life cycle – reported less frequently
● Smaller companies more frequently identified
impediments to implementing nano-protective practices
Analysis based on responses of 45 US-based company participants in
a 2009-2010 international survey of private companies that use or
produce manufactured nanomaterials (MNMs).
NSF: DBI-0830117
Informal Science Education: Oil Spill Clean Up Activity
Christine Truong, Catherine Nameth, Hilary Godwin University of California Los Angeles
CEIN ’ s Education group developed an
environmental nanotechnology activity called
“Oil Spill Clean Up Simulation”. This activity
is designed for audiences ages 8 and older
and can be used as either a small group
activity or a 5-10 minute demonstration.
During the activity, participants learn how
nanotechnology can help clean up a
simulated oil spill (made with corn oil and
water) using “nano sand”. Each grain of
nano sand is coated with a 1-nanometer
thick layer of silicon compound (silicon
dioxide + trimethylhydroxysilane). This nano
coating gives the sand hydrophobic
properties and allows it to bind with other
hydrophobic substances, such as oil. After
the nano sand binds with the oil, the oil-
soaked sand falls to the bottom of the cup,
leaving cleaner water behind.
This simulation activity demonstrates how
nanotechnology can be applied to help the
environment. A demonstration video can be
viewed on YouTube:
http://youtu.be/ckQDg3WHPXw