UNIVERSIDAD DE INVESTIGACIÓN DE

TECNOLOGÍA EXPERIMENTAL YACHAY

Escuela de Ciencias Químicas e Ingeniería

TÍTULO: “A dual experimental-theoretical approach to the

spectroscopic properties of lignin originating from rose

stems”

Trabajo de integración curricular presentado como requisito para

la obtención del título de Químico(a)

Autor:

Delgado Andrade Joselyn Nohemi

Tutor: PhD. Floralba López González

Cotutor: PhD. Thibault Terencio

Urcuquí, Noviembre 2020

Dedication

I dedicate this thesis to my parents because without them this achievement would not

have been possible.

Acknowledgments

Firstly, I would like to express my sincere gratitude to my advisor Flor Alba López PhD.

and my co-advisor Thibault Terencio PhD. for the continuous support of my bachelor

project, for his patience, and immense knowledge. His guidance helped me in all the time

of research and writing of this thesis.

Besides my advisor and co-advisor, I would like to thank the rest of the professors of my

career, especially to Juan Pablo Saucedo, Solmar Varela, Sandra Hidalgo, and Kamil

Makoski Professors of my school for their help and support during my academic college

training.

Also, I want to thanks to my parents by their support and unconditional love. My mother

taught me to see academic failure as opportunities to do my best. Thanks to her I kept an

optimistic attitude during my university life. My father taught me to follow the path of

responsibility and effort, and I thank him for that.

I would like to thank to my friends that I made at this college. Thanks friends for moments

shared.

Finally, I place on record, my sense of gratitude to one and all, who directly or indirectly,

have let their hand in this work.

Joselyn Nohemi Delgado Andrade

Resumen

En este trabajo se realizó la comparación entre los espectros calculados para modelos de

estructuras de lignina con el espectro obtenido experimentalmente para la lignina extraída

de una fuente natural. Para ello, se desarrollaron dos etapas, una mediante un

procedimiento experimental, y otra mediante herramientas computacionales de Química

Cuántica. La etapa experimental condujo a la extracción de lignina de los tallos de rosas

mediante el uso de solventes ácidos apropiados, seguida de un conveniente proceso de

purificación, para su posterior caracterización por espectroscopia IR y UV-Vis. Para la

etapa computacional, se utilizaron métodos DFT y semiempíricos, como B3LYP y PM3,

para optimizar las estructuras del modelo y luego se calcularon los espectros IR y UV-

Vis. De la comparación realizada se evidencia que los tres monómeros alcohólicos de la

lignina contribuyen a las propiedades espectroscópicas de la molécula resultante, y su

proporción en la estructura final para condicionar la distribución e intensidades de las

señales espectrales observadas. El método computacional que mejor concuerda con la

información experimental corresponde al método B3LYP, aunque se deben considerar

limitaciones o desventajas, como el tamaño de la estructura del modelo a optimizar.

Palabras clave: lignina, biomasa, química computacional, teoría funcional de la

densidad, métodos semiempíricos.

Abstract

In this work, the comparison between calculated spectra for models of lignin structure

with the experimental obtained spectrum for the lignin extracted from a natural source

was carried out. For this, two stages were developed, one through an experimental

procedure, and another using computational tools of Quantum Chemistry. The

experimental stage led to extraction of lignin from rose stems by the use of appropriate

acid solvents, followed by a convenient purification process, for its further

characterization by IR and UV-Vis spectroscopy. For the computational stage, DFT and

semiempirical methods, such as B3LYP and PM3, were used to optimize the model

structures and then IR and UV-Vis spectra were calculated. From the comparison carried

out it is evidenced that the three alcohol monomers of lignin contribute to spectroscopic

properties of the resulting molecule, and their proportion in the final structure to condition

the distribution and intensities of spectral signals observed. The computational method

that best agreed with the experimental information corresponds to the B3LYP method,

although limitations or disadvantages should be considered, such as the size of model

structure to be optimized.

Keywords: lignin, biomass, computational chemistry, density functional theory, semi

empirical methods

Contents

1 Introduction ............................................................................................................... 1

2 Theoretical Frame ..................................................................................................... 3

2.1 Biomass ................................................................................................................... 3

2.2 Lignocellulosic Biomass ......................................................................................... 3

2.3 Lignin ...................................................................................................................... 4

2.3.1 Structure of lignin............................................................................................. 4

2.3.2 Some properties of lignin ................................................................................. 7

2.3.3 Applications of lignin ....................................................................................... 8

2.4 Computational Methods for studies of lignin ....................................................... 11

2.4.1 Semi-empirical methods ................................................................................. 12

2.4.2 Density Functional Theory methods .............................................................. 12

3 Problem statement ................................................................................................... 14

4 Objectives ................................................................................................................ 15

4.1 General Objective ................................................................................................. 15

4.2 Specific Objectives ............................................................................................... 15

5 Methodology ........................................................................................................... 16

5.1 Experimental procedure ........................................................................................ 16

5.1.1 Reagents ......................................................................................................... 16

5.1.2 Equipment ...................................................................................................... 16

5.1.3 Method for obtaining lignin ........................................................................... 16

5.1.4 Hydrothermal carbonization of lignin ............................................................ 20

5.1.5 Characterization of lignin ............................................................................... 21

5.2 Computational Chemical for spectroscopic analysis of lignin ............................. 22

5.2.1 Selection and Pre-Optimization of lignin molecule ....................................... 22

5.2.2 Optimization of the molecule of lignin .......................................................... 23

5.2.3 Spectroscopy properties Calculations ............................................................ 23

5.2.4 Visualization of Molecules............................................................................. 24

6 Results and Discussions .......................................................................................... 25

6.1 From Extraction-Isolation of lignin ...................................................................... 25

6.1.1 FTIR Spectroscopy ......................................................................................... 25

6.1.2 UV-VIS Spectroscopy .................................................................................... 27

6.2 From hydrothermal carbonization of lignin. ......................................................... 29

6.2.1 Characterization ............................................................................................. 29

6.3 From Computational Chemical Calculations of lignin ......................................... 32

6.3.1 Spectra IR created from combination lineal ................................................... 33

6.3.2 Comparison between B3LYP IR spectra of basic unit of lignin and PM3 IR

spectra of macromolecular lignin ............................................................................ 36

6.3.3 UV-Vis Spectroscopy by different computational methods .......................... 39

6.3.4 Comparison between experimental and theoretical FTIR and UV-VIS spectra

for lignin .................................................................................................................. 44

7 Conclusions ............................................................................................................. 47

8 Recommendations ................................................................................................... 48

9 References ............................................................................................................... 49

List of Figures

Figure 1. A schematic representation of lignin molecule . ............................................... 5

Figure 2.The three monolignol precursors and their corresponding fragments in the

macromolecules. ............................................................................................................... 6

Figure 3. Soxhlet Equipment used in the lignin extraction process. .............................. 17

Figure 4.Filtering system used in the lignin extraction process ..................................... 18

Figure 5. Rotary evaporator system used in the lignin extraction process. .................... 18

Figure 6. . Representation of lignin extraction and isolation processes. ........................ 18

Figure 7. Dialysis setup used in the lignin extraction process........................................ 19

Figure 8. Representation of dialysis process to lignin purification. ............................... 19

Figure 9.Hydrothermal reactor lined with polytetrafuoroethylene used in the lignin

hydrothermal treatment................................................................................................... 20

Figure 10. Representation of lignin hydrothermal carbonization process. ..................... 20

Figure 11. Cary 630 FTIR spectrometer used in characterization of lignin samples. .... 21

Figure 12. UV/Vis/NIR PerkinElmer Lambda 1050 spectrometer used in characterization

of lignin samples ............................................................................................................. 22

Figure 13.Final lignin samples extracted from rose stems. ............................................ 25

Figure 14.IR spectrum obtained for lignin extracted from rose stems. .......................... 26

Figure 15.Spectrum obtained by UV-VIS spectroscopy for lignin of rose stems. ......... 28

Figure 16.Suspensions of carbonized lignin resulting from the hydrothermal treatment at

250°C-3h, 100°C-5h and 100°C-3h (from left to right). ................................................ 29

Figure 17.FTIR spectra of the resulting samples from carbonization of lignin at different

temperatures along with spectrum for lignin experimentally extracted (Pure lignin). ... 30

Figure 18. UV-VIS spectra of lignin samples before (Pure lignin) and after hydrothermal

treatment ......................................................................................................................... 31

Figure 19. The three monomers alcohol precursors of lignin (left to right) p-coumaryl,

coniferyl, and sinapyl alcohol optimized by B3LYP. .................................................... 33

Figure 20.Basic unit of lignin calculated by PM3; the elements are oxygen(red),

carbon(gray) and hydrogen(white). ................................................................................ 33

Figure 21. Organosolv lignin structure found by SMILES and optimized by PM3 method.

........................................................................................................................................ 33

Figure 22. IR spectra for the basic unit of lignin and for the combination of alcohol

monomers (construction) calculated by the PM3 method under conditions of (a) without

solvent and (b) with solvent........................................................................................... 34

Figure 23. IR spectra for the basic unit of lignin and for the combination of alcohol

monomers (construction) calculated by the B3LYP method under conditions of (a)

without solvent and (b) with solvent. ............................................................................. 35

Figure 24. Experimental IR spectrum for extracted lignin (Pure lignin) compared with the

spectrum calculated for the basic unit under conditions of solvent present by B3LYP

(Basic unit -solvent (B3LYP)). ....................................................................................... 36

Figure 25 . Experimental IR spectrum for extracted lignin (Pure lignin) compared with

the spectrum calculated for the basic unit without solvent by B3LYP (Basic unit

(B3LYP)). ....................................................................................................................... 37

Figure 26. Experimental IR spectrum for extracted lignin (Pure lignin) compared with the

spectrum calculated for the lignin macromolecule by PM3 (Lignin (PM3)) for the IR

region (a) 4000-2000 cm-1 and (b) 2000-350 cm-1. ........................................................ 38

Figure 27. UV-Vis spectra calculated for alcohol monomers and the basic unit of lignin

by the PM3 method. ....................................................................................................... 40

Figure 28. UV-vis spectra calculated for alcohol monomers and the basic unit of lignin

by the B3LYP method. ................................................................................................... 41

Figure 29. UV-Vis spectra calculated for alcohol monomers and the basic unit of lignin

in presence of solvent by the B3LYP method. ............................................................... 42

Figure 30. Theoretical UV-visible spectra of lignin using PM3 method. ...................... 43

Figure 31. Deconvolution of experimental UV-vis spectra ............................................ 44

List of tables

Table 1. Linear combination used for construction of the basic unit spectra ................. 34

Table 2. UV-Vis spectra signals resulting from experimental measurement and from the

structures of monomeric alcohols and the basic unit of the lignin model obtained using

the PM3 method. ............................................................................................................ 40

Table 3. UV-Vis spectra signals resulting from experimental measurement and from the

structures of monomeric alcohols and the basic unit of the lignin model (with and without

solvent) obtained using the B3LYP method. .................................................................. 43

Table A 1.Physical and chemical properties of reagents used in the extraction and analysis

of lignin. ......................................................................................................................... 58

Table A 2.Details of Materials used in the extraction and analysis of lignin. ................ 58

Table A 3.Details of equipment used in the extraction and analysis of lignin. .............. 58

Table A 4.Details of equipment used in the extraction and analysis of lignin. .............. 59

Table A 5.IR Signals of constructed spectra through a combination of monomer alcohols

........................................................................................................................................ 59

Table A 6.IR signals of basic unit spectra by different computational methods ............ 60

Table A 7. IR signals of macromolecular lignin obtained experimentally and by PM3

method ............................................................................................................................ 61

1

1 Introduction

Lignin is a complex three-dimensional, cross-linked1, and aromatic polymer present in

the secondary cell walls of vascular plants2. Lignin has several functions in the cell wall,

such as modifying permeability and temperature stability, but its main function is to

function as a structural material that improves the strength and rigidity of plants3. It is the

most abundant natural aromatic polymer and it approximately represents 30% of all

organic carbon in the biosphere4. Also, it provides a great number of advantages such as

capacity antioxidant and antimicrobial, availability in huge amounts as a byproduct of

industrial waste, and being biodegradable5, which are used for applications such as bio-

oils, adhesives, binders, resins and lignin based biomaterials6. Lignin is also able to

absorb UV-radiation and has fire-retardant properties. Furthermore, lignin has several

potentially beneficial physicochemical properties that make it a promising component for

the renewable energy industry and other lignin-based products7.

Lignin is composed of three phenylpropane alcohol monomers, namely p-coumaryl,

coniferyl, and sinapyl alcohols, which are randomly joined by different types of carbon–

carbon/and carbon–oxygen bonds to form a three-dimensional aromatic macromolecular

polymer8,5. Other constituents are also observed in lesser amounts, especially in grassy

and herbaceous species9, but this work only focuses on the three most prevalent alcohol

monomers. The heterogeneity, complexity, and amount of monolignols, as well as the

differences between the linkages present in the macromolecular structure of lignin,

depends on the lignocellulose source and the extraction methodology8. The lignin studied

in this work was extracted of rose stems by acidic extraction methods, which were

obtained in Tabacundo.

Despite considerable investigation, the complex and irregular structure of lignin is not

completely understood. Analysis of lignin with different spectroscopy techniques and

model studies on dehydrogenative polymerization of coniferyl alcohol has resulted in

different models of lignin structure. However, each technique gives limited information

and is not able to provide a general model of the macromolecular lignin structure10. The

lack of structural integrity makes it difficult to determine its correct macromolecular

structure11. In this context, the computational approaches are a great choice for study and

characterization of the lignin. Ab initio, density functional theory (DFT), semi empirical

and molecular mechanics methods serve as the main tools of computational chemistry.

2

DFT calculations have been used to investigate the structure, electronic properties, and

reactivity of monolignols, dehydrodilignols, and other lignin-like compounds. Also, semi

empirical methods have been used for calculating molecular structures and their

properties which extremely efficiency but low accuracy9. In this work DFT and

semiempirical methods were used by calculate spectroscopic proprieties of lignin

molecules.

Given the capabilities of computational tools in addressing structures of the size and

complexity of lignin models, this work address the use of computational methods to aid

in interpreting experimental results of the characterization of pure and carbonized lignin

by UV Spectroscopy and Fourier-Transform Infrared Spectroscopy.

3

2 Theoretical Frame

2.1 Biomass

The term Biomass is defined as any organic matter in our ecological system that is

available as a renewable resource, including dedicated energy crops and trees, agricultural

food , feed crop residues, aquatic plants, wood and wood residues, animal wastes, and

other waste materials12.

Nature provides us an estimated amount of 170 billion metric tons of plant biomass

annually but only 3% of this is used worldwide in annual production of the “non-food”

sector in fuels, bio-based chemicals or biopolymers13. Numerous biopolymers have been

studied regarding their appositeness for the elaboration of drug releasing biomaterials

and/or tissue engineering scaffolds14. These biomaterials are ecofriendly, renewable and

biocompatibles15.

2.2 Lignocellulosic Biomass

Lignocellulosic biomass is the most economical and a ubiquitous renewable resource in

the world. Apart from being a promising alternative energy or biofuel source, this biomass

is also acknowledged for its potential use to produce chemicals and biomaterials,

replacing traditional synthetic routes and consequently reducing energy consumption and

pollution 5,16,17. Its main constituents are cellulose, hemicellulose and lignin18. Together,

they form the scaffold of the plant cell wall12.The exact composition of the lignocellulosic

constituents depends upon their origin and species. Generally, lignocellulosic materials

from hardwood stems contain 40–55% cellulose, 24–40% hemicellulose, and 18–25%

lignin while softwood-stem-based materials contain 45–50% cellulose, 25–35%

hemicellulose, and 25–35% lignin5.

Among all the components of lignocellulosic biomass, cellulose is the most predominant,

in fact, it is the most abundant biopolymer in nature. Cellulose is a linear polysaccharide

polymer of glucose disaccharide with β-1,4-glucosidic linkages with a high degree of

hydrogen bonding that ensures its structural integrity. On the other hand, lignin is a

complex aromatic hetero polymer that is present in the middle lamella between wood

cells and the secondary cell wall. Lignin plays an important role in the structural

4

properties of plants, providing strength to the cell walls, controlling fluid flow and

protecting the plant against biochemical attacks19,20. Additionally, lignin prevents the

penetration of water into the cell wall due to its hydrophobic nature5. Hemicellulose is a

branched hetero polysaccharide constituted of both pentose and hexose units21 that

interact with cellulose chains, and when found as a crosslinking material between

cellulose and lignin, it imparts enhanced structural stability to the cell wall.

The lignocellulose structure is the basis of the semi-rigid fibers present in all multi-

cellular plants. The structural difference between a corn stalk, a tree, a flower stem, and

a piece of waste paper is due to the different amounts of hemicellulose, cellulose and

lignin present as well as the shape and length of the fibers formed by the intertwined

lignocellulosic chains12.

2.3 Lignin

The term lignin is derivative from the latin word lignum, meaning wood. It was initially

separated from wood, described in a scientific manner by the French scientist Payen in

1838, and later specified its modern name in 1857 by Schulze3.

Lignin is the second most abundant natural polymer on earth after cellulose 22,17 and also

a renewable resource obtained from Earth biomass, comprising of 20-35% of

lignocellulosic materials18. In general, lignin abundance decreases in the following order

of origin: softwoods > hardwoods >grasses23.

Depending on the source from which it is extracted, lignin presents a variable structure

with different properties. The mass of lignin varies in the range 1,000–20,000 g/mol. The

determination of the degree of polymerization of lignin is a very complicated task because

the extraction process would invariably fragment it. Lignin also behaves as a

thermoplastic material and exhibits a glass transition temperature that is difficult to

determine in case of isolated lignin5.

2.3.1 Structure of lignin

Lignin consists primarily of complex phenolic heteropolymers24, with a three-

dimensional highly cross-linked structure presenting different functional groups:

5

aliphatic and phenolic hydroxyls, carboxylic, carbonyl and methoxyl groups3, as shown

in the Figure 1.

Figure 1. A schematic representation of lignin molecule 1.

Three types of substituted phenols are constituents of lignin, namely coniferyl, sinapyl,

and p-coumaryl alcohols, collectively called monolignols. These differ in structure

depending on the plant type from which it is extracted16as shown in Figure 2. These

monolignols present into a lignin macromolecule are named as guaiacyl (G), syringyl (S)

and p-hydroxyphenyl (H)- units, respectively. The three monolignols share the same p-

coumaryl alcohol core, but they have different degrees of methoxylation at the 3 or 3-5

position of phenolic rings9,1. In softwood, the lignin is formed almost exclusively by

coniferyl alcohol (G-units) and low amounts of coumaryl alcohol (H-units). In

hardwoods, coniferyl alcohol and sinapyl alcohol (S-units) are the main building blocks

while in monocotyledonous tissue (grasses), three alcohols are lignin precursors25.

6

Figure 2.The three monolignol precursors and their corresponding fragments in the macromolecules14.

In nature, these monomer units oxidize to phenoxy radicals by peroxidase, and then

random polymerization can occur at many different sites of the phenylpropane monomers.

As a result, lignin adopts complex three-dimensional structures with various types of

functionality. This biosynthesis is known as dehydration polymerization26. These

building blocks are connected via many types of linkages. These building blocks are

connected via many different bonding patterns. The main bonding patterns are : β–O–4′

ether, α–O–4′ ether, 4–O–5′ diphenyl ether, β–5 phenylcoumaran, β–β′ resinol, 5–5′

biphenyl and β–1′ diphenyl methane linkages8. These different bonding patterns between

the units can be observed in the structure drawn in Figure 1.

The factors that determine the chemical structure of lignin are the following: botanical

origin, environmental conditions of growth and conditions of lignin extraction3,27. The

delignification techniques, or lignin extraction, consist of the cleavage of covalent

linkages of the natural lignin in order to solubilize polymers fragments. Different

experimental conditions can be used during delignification processes. These conditions

can be divided into chemical environment (such as the use of acid or alkali environment,

the use of organic solvent or aqueous solutions) and other physical parameters such as

time, temperature, among others. These conditions could affect the molar mass,

functionality, cross linking, density and bioactivity of lignin27.

7

2.3.2 Some properties of lignin

Antioxidant Capacity

Due to its polyphenolic structure, lignin has a very important antioxidant activity 14,27.

The scavenging action of their phenolic structures on oxygen containing reactive free

radicals provides most antioxidant effects of lignin14. This property of lignin has a lot of

applications in fields such as industrial, medical and agricultural fields. For example, in

the food industry, lignin plays the role of avoiding to remove color of food, taste and

active vitamins concentration and molecules stabilization that are involved in these

features. Another example is when lignin can also be used as a stabilizer in plastics or has

positive effects on mice DNA protection against oxidation damage due to lignin’s

scavenging effects14. Lignin presents advantageous thermal resistance and biological

activity compared to lower molecular weight molecules. Consequently, the use of lignin

is preferred in those cases where the antioxidant activity of the single-molecule is

insufficient3.

Antimicrobial Activity

The ability of lignin to inhibit the microbial growth is based on the phenolic fragments,

the nature of further functional groups and specific side chain constitution. The presence

of a double bond in α, β positions of the side chain and a methyl group in the γ position

increases the potency against microorganisms of phenolic fragments14.

One clear example of lignin antimicrobial activity is its effectiveness against E. coli and

S. aureus of polyethylene films, for which lignin has been used as an additive. In fact, a

similar activity to other bactericides, such as bronopol or chlorohexidine, has been found,

without altering the mechanical properties of the films28.

Antiviral activity

Lignin can also have an antiviral activity due to the polymerized structure of phenyl

propenoids from the lignin carbohydrate complexes, while the immune potentiating

effect is due to the higher order structure of lignin and polysaccharides8.

8

2.3.3 Applications of lignin

High amounts of lignin, approximately 55 ×106 tons per year, are generated as a by-

product of pulp and paper industry 14. On the other hand, lignin is also obtained as a

residue from lignocellulosic biomass-based ethanol production11. Nevertheless, only 5%

of residual lignin is used as chemical reagents, polymers and fuel. The main reason for

this very low percentage is due to the complex and variable lignin structure that changes

depending on the source and isolation procedure as discussed before. Also, the high

polydispersity and immiscibility of lignin with host polymer matrices, make it

inconvenient to be used7.

However, overcoming these shortcomings would transform lignin into a very abundant

ecological resource and usable in many different applications such as the ones mentioned

below in this part.

2.3.3.1 Biofuels

The lignin-based fuels include char, syngas, hydrogen and aromatic hydrocarbons. The

obtainment of biofuels from biomass begins with pyrolysis producing a large number of

chemical substances that can be used as substitutes for conventional fuels, followed by

catalytic upgrading of the resulting materials. During this process, oil and char can be

obtained with fast and slow pyrolysis respectively. In fact, char can be combusted to

produce heat, which is currently the main use of isolated lignin. Besides, the gasification

process can transform lignin into gases that can be used in fuel cells, gas turbines or as

synthesis gas. Specifically, the gas mixture obtained from lignin gasification consists of

hydrogen and carbon monoxide, which can be used subsequently for the synthesis of

methanol and Fischer–Tropsch fuels. The syngas can be then combusted in gas turbines

to generate heat and power7,29.

2.3.3.2 Chemical/Polymers

Lignin can be utilized as a phenol substitute in the production of phenol formaldehyde

resins which are exploited as binders, water-barrier coatings for cardboard substrates or

added into wood adhesives. This substitution is environmentally friendly and less

expensive than the more conventionally used phenol. Other application includes the

elaboration of lignin-based polyurethane foams, which show their mechanical properties,

9

thermal stability, curing rates, and flame resistance improved. Furthermore, due to its

high carbon content and being a low cost precursor, lignin can be used as a starting

material for carbon fibers production7,30. Another example, amphiphilic lignosulfonates

conjugated with poly(ethylene glycol) diglycidyl ether had promising cement dispersant

properties, with a mechanical strength that maintain stability even through winter7.

2.3.3.3 Lignin biomedical applications

Due to its renewable, biodegradable and biocompatible character, low cytotoxicity,

antioxidant activity and antimicrobial capacity, currently lignin is being investigated for

advanced applications for therapeutic purposes as drug delivery systems and scaffolds

for tissue engineering14. In addition, lignin constitutes a good choice for new targeting

therapies due to the presence of aromatic and aliphatic hydroxyl groups, which allows

chemical modifications or ligand attachments15. In the same time, the circular

bioeconomy concept has increased the interest of the scientific community toward lignin

exploiting8.

2.3.3.3.1 Lignin-based materials as delivery systems

Numerous research has been focused on the design of biobased nanocarriers, wherein

active bio agents can be encapsulated or physically attached. Drug delivery systems with

better targeting mechanisms could improve the efficiency of therapeutics by increasing

bioavailability of a drug locally rather than systemically8. In this context, lignin has

caused much interest in the development of nanoparticles for drug encapsulation and

release.

Low-sulfonated lignin nanoparticles that were prepared based on a solvent-exchange

precipitation method with an acid-precipitation technology do not show any cytotoxicity

for yeast and microalgae, and thus, they were considered promising vehicles for drug

delivery31. Besides, pH-responsive lignin based nanocapsules were prepared via an

interfacial miniemulsion crosslinking reaction, showing a potential for the encapsulation

and controlled release of drugs, dyes, other hydrophobic compounds as essential oils and

antioxidants32. Moreover, Lignin-based nanoparticles used for encapsulating lipophilic

molecules were also reported. These nanoparticles show a better bioavailability of orally

10

administered lipophilic molecules, such as curcumin, due to a novel nanoparticle

formulation using phase separation stabilized by citric acid crosslinking15.

Alkali lignin (AL) nanoparticles have been fabricated via a simple self-assembly method

without chemical modification, which could be used as a carrier for bioactive molecule

resveratrol (RSV) and Fe3O4 magnetic nanoparticles. In cytological and animal tests, the

magnetic RSV loaded lignin nanoparticles showed an improved RSV stability and drug

accumulation, and better reduction of tumors, as well as lower adverse effects in

comparison to free drugs, indicating its potential use in anticancer therapy33.

Considering the intrinsic biological properties, the presence of hydrophilic functional

groups in the chemical structure that support hydrogel preparation along with the swelling

capacity of lignin-based hydrogels, several articles about drug delivery systems have been

reported every year34. Lignin-based hydrogels were reported as capable to be loaded with

hydrophobic compounds and to sustain their release for up to 4 days. These hydrogels

were elaborated through an esterification reaction among lignin with GAN (the acid form

of an methylvinylether and maleic anhydride copolymer), a polyacid, and poly

(ethyleneglycol)35.

2.3.3.3.2 Lignin-based scaffolds for tissue engineering

Tissue engineering is a promising tool in the regenerative medicine field for repairing,

replacing or enhancing the function of a specific tissue or organ through the usage of

biocompatible and biodegradable polymers. Use of lignin in tissue engineering provides

an assembly of greener and natural based scaffolds with good tissue compatibility and

less toxic effects. There are various recent studies about the potential use of lignin in the

development of scaffolds for possible tissue engineering applications, including

hydrogels, aerogels and nanofibers7,36. One of them is the elaboration of agarose-lignin

hydrogel composites which exhibit enhanced mechanical properties compared to pure

agarose gels34. Other scientific research reported biodegradable xanthan/lignin hydrogels

obtained by crosslinking with epichlorhydrine which presented antimicrobial and

antioxidative properties, as well as an increased thermal stability, hydrophilicity and

biocompatibility due to the presence of lignin37. Also, alginate-lignin aerogels using CO2

induced gelation followed by solvent exchange and supercritical drying presented high

11

equilibrium water uptake, and interesting textural and morphological properties suitable

for tissue engineering applications38.

2.3.3.3.3 Lignin-based nanomaterials for nanocomposites

In recent years, many investigations were focused on the lignin-based nanocomposites

synthesis due mainly because lignin contains phenyl and hydroxyphenyl groups that

intensify many catalytic properties, which is particularly convenient for the development

of chemically modified composites6. Indeed, lignin nanoparticles act as reinforcing agents

in polymer matrices resulting in a better biocompatibility, mechanical and thermal

properties compared to the original polymers7. For instance, the synthesis of the lignin-

based carbon/CePO4 nanocomposites was made using a hydrothermal method at 200 °C

for 24 h. The experimental results indicate that the nanocomposite obtained has

photoluminescence properties39. Another instance is the development of Bio-Poly

(trimethylene terephthalate) (Bio-PTT) hybrid nanocomposites, which were prepared

using melt extrusion followed by microinjection molding technique. The experimental

results indicated improvements in the mechanical properties of the nanocomposites, such

as tensile strength and tensile modulus, as well as in the thermal properties and in

biodegradation characteristics40.

2.4 Computational Methods for studies of lignin

Theoretical and Computational Chemistry based studies play an important role in

characterizing the structural and energetic properties of the biomass constituents such as

lignin 41,9.

The application of quantum mechanically based electronic structure calculations on lignin

dates back to 1966 and the work of Lindberg and co-workers42. In the intervening years,

methods like Infrared (IR) and Raman Spectroscopy have improved technologically, and

consequently vibrational spectra have become increasingly complex. In this context,

theoretical chemistry simulations have become indispensable for their interpretation43.

Different research has used reported using computational tools for studying lignin. One

of them is the use of Molecular Dynamics simulations to explain the results obtained by

Neutron Scattering experiments, allowing to show that lignin aggregates are characterized

by a surface-invariant under change of scale from ∼1–1000 Å44. Another study made was

12

about modeling interactions of cellulose, hemicellulose, and lignin using electronic

structure methods. Methods such as semiempirical, Hartree–Fock, and Density

Functional Theory using different basis sets, have been used to evaluate the structure and

formation mechanisms that can occur in lignin, exploiting the ability of these methods to

predict energetically favorable H-bond and other intermolecular interactions for phenolic

structures45. Additionally, other results show that theoretical calculations can be used to

simulate model structures of wood components46.

2.4.1 Semi-empirical methods

Semi-empirical methods are a version modified of Hartree–Fock or Density Functional

Theory by applying systematic approximations and empirical parameters introduced in

order to improve computational performance, not accuracy but faster than ab initio

calculations47,48. The most prevalent semi-empirical methods are those based on

approximations of the Hartree–Fock (HF) theory, leading to methods such as AM1, PM3,

MNDO/d, and OMx48.

Within semi empirical methods, the family NDDO methods, such as MNDO, AM1 and

PM3, are parametrized to work properly with organic molecules49 containing elements of

the main group of the periodic table50. Also, these semi empirical methods have lower

computational cost, that is the execution time per time step during simulation is lower,

which is suitable to model larger biomass structures originating from wood such as

lignin46. The semi-empirical method used in this work was PM3 (Parametric Method 3),

which is a reparametrization of AM1, which only uses two Gaussian functions to correct

the core–core repulsion45.

2.4.2 Density Functional Theory methods

Density Functional Theory (DFT) methods have the capability to calculate the energy of

a molecule based on its electronic density. These methods provide chemically accurate

results for a variety of systems, including small biomolecules with less than 500 atoms.

Nevertheless, the exact potential forms of electron exchange and correlation in this

method remain unknown51. This problem gave rise to different functionals with different

exchange-correlation terms. Among them, B3LYP is by far the most popular density

functional in chemistry 52. It is a hybrid of exact Hartree-Fock exchange with local and

semilocal exchange and correlation terms on the basis of the adiabatic 53. Many research

13

using DFT calculation-based studies on the chemistry of lignin have been made. One of

those discuss the combination of X-ray induced Photoelectron Spectroscopy (XPS)

measurements of the valence band structure of cellulose and lignin and theoretical

reconstruction of the spectra based on Density Functional Theory (DFT) calculations54.

Another instance is the experimental and quantum chemical study of infrared spectra of

biosynthetic lignin structures that were carried out within the density functional theory

with the B3LYP functional in the 631+G(d,p) basis55.

14

3 Problem statement

Harnessing of natural products is a topic of great interest to the scientific community due

the peculiar behavior of the different compounds obtained from them. However, the

degree of complexity of the chemical structures of these bio-compounds is a limiting

feature for their implementation in different applications, mainly because their properties

are closely related to the behavior of its structure in a given environment. For poly-

phenolic compounds, as is the case of lignin, the bonding patterns and H-bonds

interactions govern the linkage between the primary units of formation and hence the

structure of the resulting macromolecule. Since the vast majority of the properties depend

on the structure, being able to determine the structure of lignin would be an asset in

determining its properties. With the help of computational chemistry methods, we will

determine the best approximation for the natural structure of poly-phenols in lignin to be

compared with experimental results. Although have been reported some structural

analysis by computational calculations, they have been focused in the independent

alcohol monomers and not in the macromolecular structure of lignin, so this is a

pioneering work in this topic. Also, the structural modification after thermal treatment of

lignin is evaluated.

15

4 Objectives

4.1 General Objective

Study the spectroscopic properties of lignin obtained from rose stems through a dual

experimental and theoretical approach.

4.2 Specific Objectives

Extraction of lignin from rose stem by an acidic extraction method along the

characterization of obtained lignin by IR and UV spectroscopy.

Hydrothermal carbonization treatment of obtained lignin to obtain carbonaceous

agglomerates.

Construction of lignin molecules from their alcohol monomers units.

Design of computational code for implementation of calculation of spectroscopic

properties of the lignin molecule.

Comparison of spectroscopic properties between experimental obtained lignin

and the simulated spectroscopic spectra.

16

5 Methodology

Developed methodology for this work included two main stages: an experimental and a

computational one. The first stage involved the extraction of lignin from rose stems using

acidic conditions and its respective characterization through spectroscopic techniques,

such as IR and UV spectroscopy. Also, the obtained lignin is treated by a hydrothermal

carbonization, which is then characterized. The computational stage, the second one,

consisted in the implementation of calculations of spectra, using DFT and semi-empirical

methods, of different structures of lignin resulting from the combination of alcohol

monomers.

5.1 Experimental procedure

5.1.1 Reagents

The reagents used were: Formic acid (99%), glacial acetic acid, hydrogen peroxide (35%),

ethanol (96%), and distilled water. The physical and chemical properties of used reagents

can be found in the Table A1 of Annexes section.

5.1.2 Equipment

The required equipment for extraction and hydrothermal treatment consisted of coffee-

grinder, Soxhlet equipment, hot mantle, hot-stir plate, vacuum filtration system, rotary

evaporator, electrical oven, hydrothermal reactor, and centrifuge. For characterization of

obtained materials were used FT-IR and UV-Vis spectrometers. The details of materials

and equipment are placed in Table A2 and Table A3 of the Annexes section, respectively

5.1.3 Method for obtaining lignin

5.1.3.1 Formic acid/acetic acid treatment of rose stems for lignin extraction

The extraction process of lignin from rose stems began by drying the stems in an electrical

oven. Then, the dried stems are pulverized in a coffee mill and placed inside Soxhlet

equipment, shown in Figure 3. The 12 g of stems pulverized are previously placed in a

coffee filter. After, 150 mL of a mixture of 85% organic acid, which was formic

acid/acetic acid with 70:30 by volume, was added to the extraction flask and allowed to

boil on a heating mantle among 100 °C and 200 °C for 3 h approximately16.

17

Figure 3. Soxhlet Equipment used in the lignin extraction process.

5.1.3.2 Bleaching treatment of residual cellulose for delignification

Lignin extracted inside the extraction flask and the extraction thimble was placed in a

beaker. Remaining solid in the coffee filter was further delignified by treating it with a

mixture of 8 mL of hydrogen peroxide, 7 mL of water, and 85 mL of a mixture of formic

acid and acetic acid 70:30 by volume. For this step was used a hot plate stirrer at 80 °C

and 200 rpm during 2 hours under the extractor hood. Finally, the resulting material was

filtered using a vacuum filtration system as shown in Figure 4, and the filtrate was added

to the beaker containing previously obtained liquid (liquid of the extraction flask and

extraction thimble)16.

5.1.3.3 Isolation of lignin

The liquid solution of lignin contained in the beaker was vapored to 100 °C in a heating

plate until that liquid volume was reduced to 40 mL. Once this volume was reached,

distilled water 5 times more than volume of concentrated lignin was added to the beaker,

and subsequently was filtered in a Buchner funnel using a two-micron filter paper. This

step is needed to ensure that lignin, dissolved in formic acid/acetic acid mixture,

precipitates. The filtrate was placed in a rotary evaporator, shown in Figure 5, and the low

amount of solid collected in 2-microns filter paper was placed in an electrical oven to 30

°C for 12 hours, obtaining a solid with particle size larger than 2 microns. At the resulting

free-solvent lignin obtained from the process of rotavaporation was gradually added

sodium carbonate until reaching a neutral pH. The lignin obtained was stored in

Eppendorf tubes and placed in an electrical oven to 40 °C until lignin had a gummy

texture. Finally, lignin was stored until its use is needed16.

18

Figure 4.Filtering system used in the lignin

extraction process

Figure 5. Rotary evaporator system used in the lignin

extraction process.

The lignin extraction and isolation processes are represented in the Figure 6.

Figure 6. . Representation of lignin extraction and isolation processes.

19

5.1.3.4 Room Temp / Dialysis

Two steps were carried out in order to obtain lignin particles through dialysis process.

The first step consisted in dissolving lignin into distilled water with a 1:2 ratio. Then, the

resulting suspension was stirred about 600 rpm and at room temperature (~25 °C). The

second step consisted of the gradual addition of distilled water (4 times the volume used

in the suspension formation) to the lignin-water suspension at an addition rate of 2

mL/min. Due to the hydrophobic character of lignin agglomerates; the formation of pure

lignin hollow nanoparticles is expected22. The liquid suspension containing lignin hollow

nanoparticles was stirred to reduce volume water. After 4 h, the suspension liquid was

introduced into a dialysis bag(shown in Figure 7) from 12,000 to 14,000 Daltons,

equivalent to a pore size of ~ 2 nm 56, which was then immersed in distilled water for

removing impurities. The content of the dialysis bag was centrifuged at 4000 rpm during

5 minutes.

Figure 7. Dialysis setup used in the lignin extraction process.

In Figure 8, the representation of dialysis process to lignin purification is shown below.

Figure 8. Representation of dialysis process to lignin purification.

20

5.1.4 Hydrothermal carbonization of lignin

In this process, the isolated lignin was placed in a beaker and mixed with distilled water

for a concentration of 0.04 g/mL. This mixture was stirred at 220 rpm during 30 minutes.

Then, the sample was transferred to a hydrothermal reactor lined with

polytetrafuoroethylene, as shown in Figure 9, which was placed in an electrical oven at

250 °C for 3 hours. The hydrothermal reactor was cooled to room temperature in the oven

and then its content was poured into a beaker. After, 10 mL of ethanol at 96% is added to

the mixture treated. Finally, the mixture was centrifuged at 2000 rpm during 10 minutes57.

The process described above was repeated for two different conditions of temperature.

Figure 9.Hydrothermal reactor lined with polytetrafuoroethylene used in the lignin hydrothermal treatment.

The process of lignin hydrothermal carbonization is shown in Figure 10.

Figure 10. Representation of lignin hydrothermal carbonization process.

21

5.1.5 Characterization of lignin

The lignin samples, those obtained from dialysis process and hydrothermal carbonization

treatment, were characterized by FT-IR and UV-Vis spectroscopy.

5.1.5.1 FT-IR Spectroscopy Analysis

Infrared spectra of different lignin samples were obtained using the Agilent Cary 630

FTIR equipment, shown in Figure 11, and using the MicroLab software. The first step for

this characterization is preparation of samples, which consists of pouring a small quantity

of each one of samples of lignin in vials of 3 mL, and then heated at 40 °C inside of an

electric oven until the water is removed. Once the water had completely removed, the

samples were analyzed to the FTIR. After a background was recorded for each one of

analysis, the sample was placed in a sample holder, which was previously cleaned with

acetone, and then it was pressed with a sampling press of diamond. The MicroLab

software provides the spectra of samples. The wavenumber range for each one of analysis

was between 4000 to 350 cm-1 with a total number of scans of 64 and a 4 cm-1

wavenumber resolution.

Figure 11. Cary 630 FTIR spectrometer used in characterization of lignin samples.

5.1.5.2 UV-Vis Spectroscopy Analysis

The different lignin samples were characterized with UV/VIS/NIR PerkinElmer Lambda

1050, shown in Figure 12, and their analysis was carried out with PerkinElmer WinLab

software. The solid samples analyzed by UV-Vis Spectroscopy were previously placed

in an electrical oven at 40 °C in order to remove water. The wavelength range used was

200-800 nm.

22

Figure 12. UV/Vis/NIR PerkinElmer Lambda 1050 spectrometer used in characterization of lignin samples

5.2 Computational Chemical for spectroscopic analysis of lignin

The steps carried out in this computational stage are described below.

5.2.1 Selection and Pre-Optimization of lignin molecule

The first step for carrying out the chemical calculations is the selection and pre

optimization of molecules to be evaluated, which was performed in the Avogadro

program, which is a 3D molecule builder and viewer that is easy to use and install. The

pre-optimization process is described in the following.

The structure of lignin molecule was constructed through a canonical SMILES of lignin

organosolv representation (Table A4), subsequently a random conformational search was

performed using Genetic Algorithm. In general, this algorithm generates a large

population of random conformations where the more fit members are selected, and the

procedure is repeated to identify more convenient low-energy structures54. The low-

energy conformation of the lignin molecule was chosen for a pre-optimization process in

the option Optimize Geometry of Extensions in the Avogadro program.

For pre-optimization of each one of the phenolic units of lignin (coniferyl, p-coumaryl,

and sinapyl alcohol monomers), and for basic unit, which was constructed through the

three alcohol monomers of lignin in a stoichiometric proportion 1:1:1, the previously

described steps were carried out.

The construction of the basic unit of lignin was made in order to compare the

spectroscopic properties of this model with those exhibited by the lignin model obtained

from SMILES, as well as with the experimentally obtained lignin. Although the

stoichiometry of native lignin is not well reflected in the constructed model or that

23

obtained from SMILES, the sufficiently small size was a convenient choice for

computational calculation performance, also of that structure included all building units.

5.2.2 Optimization of the molecule of lignin

The total optimization of the lignin model obtained from SMILES was carried out using

the Semi empirical method PM3 through Orca Extensions of Avogadro program. Orca is

a program package that has all modern electronic structure methods, such as Semi-

empirical methods, Density Function Theory (DFT), many-body perturbation and

coupled cluster theories, and multireference methods58. For the optimization process, the

basis set chosen was def2-SVP, which has the polarization functions on all atoms, and it

is a small basis set that allows evaluation of large molecules, such as lignin, in a personal

computer with a short period of time. Due to the high number of constituent atoms for

this lignin model, that is 201 atoms, the input configuration for this optimization process

was carried out without the solvent presence, although this condition emulates the

experimental spectroscopic analysis of solid samples of lignin.

In the case of optimization of each one of monomers alcohols and the constructed basic

unit of lignin, PM3 and B3LYP methods were used. The basis set def2-SVP was used for

PM3. On the other hand, the B3LYP functional required the use of Time-dependent DFT

(DDFT) as an approach to computing excited states. In addition, J auxiliary basis

set(def2/J) and the RIJCOSX approximation were used in order to accelerate the Self-

Consistent Field (SCF) procedure. The optimization was carried out with and without

presence of solvent.

5.2.3 Spectroscopy properties Calculations

The calculation of IR and UV-vis spectra of the lignin model obtained from SMILES was

made with the PM3 method, using as initial configuration the output of the molecule

totally optimized by Orca. On the other hand, the IR and UV-Vis spectra of coniferyl,

sinapyl, coumaryl alcohols and the constructed basic unit of lignin were calculated using

B3LYP method in addition to PM3 method. This was possible because of the smaller

number of constituent atoms of these species: 25, 29, 21 and 69, respectively, instead of

the hundreds of atoms in the lignin model obtained from SMILES, which allowed an

optimal performance of the heaviest B3LYP method.

24

Commonly quantum chemical calculations have been done in vacuum, however the most

chemical reactions take place in solution. In this context, the optimization of the lignin

model and generation of UV-Vis and FTIR spectra with solvent used an implicit solvent

model named Conductor-like Polarizable Continuum model (CPCM). In the CPCM , the

interaction with the surrounding dielectric environment is represented by point charges

on an encompassing surface. One of the most important elements in the implicit solvent

method is this molecular cavity, which is generated by a slightly modified version of the

GEPOL Algorithm, either as a solvent excluding surface (SES) or a solvent accessible

surface (SAS)59.

For other representations of the basic unit, another model of lignin was generated through

a combination of monomer alcohols, whose optimal proportion between them was 1:1:1,

in order to emulate the basic unit constructed by Avogadro, which proportion of alcohol

monomers also was 1:1:1. The model constructed was obtained by a linear combination

of the calculated IR spectra of each one monomer alcohols: coniferyl, sinapyl and

coumaryl, multiplied by a same factor. The linear combination that provides the spectrum

most similar to that of the lignin basic unit spectrum was chosen. This was made in order

to observe the shifts among signals of the basic unit drawn in Avogadro and the spectra

constructed through the combination lineal of alcohol monomers. The assignment of the

signals shifted was made by Gabedit program.

5.2.4 Visualization of Molecules

The Mercury software was used to visualize the 3D structures of lignin, its basic unit and

of three alcohol precursors. On the other hand, the free graphical user interface Gabedit

was used in order to identify what part of the molecule corresponds to the vibrations

calculated in IR and UV-Vis spectra, allowing the analysis of spectroscopic results

obtained by computational methods.

25

6 Results and Discussions

6.1 From Extraction-Isolation of lignin

The lignin extracted from rose stems is a thick liquid of yellowish brown color as is shown

in Figure 13, with a sweet smell similar to honey. The unsaturated groups, such as

conjugated carbonyl groups, aromatic rings, and carbon-carbon double bonds, absorb

visible light, being the main light absorbers that provide a brownish to black color to

lignin60.

Figure 13.Final lignin samples extracted from rose stems.

6.1.1 FTIR Spectroscopy

Fourier Transform Infrared (FTIR) was used to determine the main functional groups

present on the lignin extracted from rose stems. The analyzed region was mid-infrared

(4000–200 cm-1) because in previous research had been shown to provide reliable

information on the chemical properties of lignin in wood61. The comparative analysis

between the experimentally obtained spectrum, shown in Figure 14, with the information

given in the appropriate literature with the respect the main peaks observed is discussed

in the following.

26

Figure 14.IR spectrum obtained for lignin extracted from rose stems.

In this figure, showing transmittance as a function of wavenumber, it can be observed that

the region of 3800–2750 cm-1 has some bands due to the presence of alcoholic and

phenolic hydroxyl groups as well as of methyl and methylene groups in lignin61. The

strong broad peak at 3250 cm-1 occurring in the experimental spectrum of lignin in Figure

11 is caused by the stretching vibrations of alcoholic and phenolic hydroxyl groups62. The

bands between 3000–2750 cm-1 correspond from C-H stretching in aromatic methoxyl

groups and in methyl and methylene groups of the side chain61.

Around 1726 cm-1 occur a weak peak, which is barely perceptible in the spectrum of pure

lignin, and can be attributed to non-conjugated carbonyl groups63. The peak at 1572 cm-1

is associated with Aryls or conjugated aryl groups of lignin64, whereas that weak band at

1460 cm-1 corresponds to stretching vibrations in aromatic groups65.

The spectral region below 1400 cm-1 is more difficult to analyze, since the interpretation

of most bands becomes complex due to contribution from various vibration modes66.

Despite that, this spectral region represents specific vibrations to the monolignol

27

precursors, therefore, the occurrence of bands in this region evidences the presence of

lignin, and thus its detailed analysis allows a better lignin structural characterisation67.

The band at 1375 cm-1 is due to phenolic OH and aliphatic C–H in methyl groups66.

According to previous studies of lignin model compounds, the band at 1340 cm-1

corresponds to phenolic hydroxils68. Bands in the range of 1000–1250 cm-1 correspond

to chemical bond C-O, specifically, the band at 1250 cm-1 could be attributed to G ring

breathing with C-O stretching, although in the literature this band is reported exactly at

1265 cm-169. The small shift of this signal towards a lower wavenumber could be due that

lignin has a variant structure depending on the sources from which it is extracted. This

may be better understood considering that the force constant associated to a chemical

bond changes with the electronic and steric effects of a population of different chemical

species, and therefore the vibrational frequencies could be shifted from their normal

values70.

The weak band at 1144 cm-1 corresponds to deformation vibrations of C-H bonds in the

guaiacyl ring68. As a small amount of carbohydrate can is associated with lignin61, the

aromatic C–H deformation at 1032 cm-1 takes shape as a complex vibration associated

with the C–O, C–C stretching and C–OH bending in polysaccharides66. The peaks present

in the region 900-700 cm-1 are caused by deformation vibrations of C-H bonds associated

with aromatic rings68, whereas peaks present among 400-700 cm-1 correspond to aliphatic

C-C71.

The results analyzed above indicate that the oxygenated groups present in the pure

(extracted) lignin are carbonyl, phenolic hydroxyl group and aliphatic hydroxyl group.

According to the literature, the lignin sample extracted from rose stems is mainly

composed of guaiacyl (G) and syringyl (S) units (GS-type), which are suggested to be the

main components of hardwood lignin65.

6.1.2 UV-VIS Spectroscopy

The aromatic structure of lignin has a wide variety of UV absorbing functional groups

such as phenolic, ketone and other chromophores72,73, and depending on the source from

which it is extracted, the UV spectroscopic patterns of lignin could change. In the

28

particular case of lignin of rose stems studied in this work, the UV-Vis spectrum

experimentally obtained is shown in Figure 15.

Some research about UV-Vis spectroscopy of lignin has been carried out during decades,

indicating that the absorbance decreases continuously without any clear features for

values above 300 nm, while a sharp increase is observed at lower wavelength values.

Also, a solvent-dependence as well as an extraction process dependence are indicated74.

These observations can be appreciated in the spectrum of lignin in Figure 15.

Figure 15.Spectrum obtained by UV-VIS spectroscopy for lignin of rose stems.

The spectrum of lignin from rose stems exhibit two evident maxima of absorption at

around 270 nm and 240 nm, which are perhaps due to presence of free and etherified

hydroxyl groups, and bound p-coumaril acid61. The absorption intensity is heavily

influenced by the presence of unsaturated substituents on α-carbon of a side chain, such

as double bond, carbonyl group among others75. The occurring bands in the near UV

region (300-400 nm) tend to have the shape of broad peaks or shoulders, which are

attributed to presence of unsaturated groups such as carbonyl groups and/or double bonds

conjugated with an aromatic ring75. In the experimentally obtained lignin spectrum, a soft

shoulder occurs at 360 nm, that is attributed to bound phenolic acids76. Although a

bathochromic shift is observed with respect to the reported value for this band,

29

corresponding to 320 nm77 according to the literature. Presumably, the different chemical

structure of the lignin extracted in this work related with that for which the wavelength

value is reported, as well as the extraction method implemented, are responsible for this

slight shift.

6.2 From hydrothermal carbonization of lignin.

The final product of lignin hydrothermal carbonization changes according to the

conditions with which the carbonization process was carried out. The carbonized lignin

at 250°C during 3 hours shows a dark brown color and a strong burning smell, whereas

carbonized lignin at 100°C during the same time, shows a light orange color and the

burning smell is almost absent.

In the case of carbonized lignin at 100 °C during 5 hours shows a dark orange brown

color. The figure 16 shows the color changes in lignin under the different hydrothermal

carbonization conditions.

Figure 16.Suspensions of carbonized lignin resulting from the hydrothermal treatment at 250°C-3h, 100°C-5h and

100°C-3h (from left to right).

6.2.1 Characterization

6.2.1.1 FTIR Spectroscopy of lignin carbonized by hydrothermal process.

To analyze the effect of reaction temperature and time on the structure of lignin, FTIR

spectra of samples of lignin that were subjected to different hydrothermal carbonization

treatment, that is 100 °C and 250 °C for 3 hours, were recorded. The resulting spectra are

shown in Figure 17 for comparison with the untreated lignin sample, called pure lignin.

30

Figure 17.FTIR spectra of the resulting samples from carbonization of lignin at different temperatures along with

spectrum for lignin experimentally extracted (Pure lignin).

As can be seen in FTIR shown in Figure 17, in contrast to the lignin experimentally

extracted, denoted in the figure as Pure lignin, the carbonized lignin samples show a lower

intensity of the band related to the vibration of OH around 3250 cm1. This is caused by a

consequent loss of oxygenated alcoholic and phenolic groups during the process of

carbonization, that in fact occurs in all biomass. It follows that these functional groups

are thermally unstable65. On the other hand, the intensity of bands assigned to the

stretching vibrations of methyl and methylene groups around 3000–2750 cm-1 decrease

when the temperature is 100 °C but this increases at 250 °C.

The spectra of the carbonized lignin also suggest an increase in the aromaticity proportion

in samples as the treatment temperature increases. That is, the bands corresponding to

Aryls (1572 cm-1), phenolic hydroxyls (1340 cm-1 and at 1375 cm-1), and stretching

vibrations in aromatic groups (1460 cm-1) increase their intensity as the temperature

arises. Also, the C-H out-of-plane deformation vibrations, often occurring at the range

900-700 cm-1, increase their intensity (and narrowness) during carbonization at 250 °C.

31

Particularly, the band in 764 cm-1 increases considerably due to that it is associated with

the above mentioned vibrations65.

The peaks corresponding to vibrations associated to carbonyl groups (1726 cm-1 ,1250

cm-1 and 1032 cm-1) are no longer discernible in spectra of carbonized lignin, which

means that C−O linkage is broken under these hydrothermal conditions78. The occurrence

of weak bands at 250 °C (1061 cm-1, 1013 cm-1, 923 cm-1, 662 cm-1 and 614 cm-1)

indicates the presence of a considerable amount of incompletely carbonized material still

present in the carbon matrix79.

The signals present in the range 400-700 cm-1 corresponding to aliphatic C-C71 decrease

its intensity as temperature increases. The reduction of these signals is in favor of a

reduction in aliphaticity65. In spite of differences evidenced between the spectra for

carbonized lignin samples; at both temperatures evaluated, and for untreated lignin, the

general features in all of them suggest the remanence of lignin macromolecules that were

no destroyed during the hydrothermal treatment, which can be understood because of the

high thermal stability of lignin.

6.2.1.2 UV-VIS Spectroscopy of lignin carbonized by hydrothermal process.

Figure 18. UV-VIS spectra of lignin samples before (Pure lignin) and after hydrothermal treatment

32

With respect to the UV-Vis absorption of lignin samples after the hydrothermal treatment,

in Figure 18 is shown the resulting spectra for them for comparison with that

corresponding to untreated lignin. As mentioned before, the spectrum for untreated lignin

shows two maximum absorption peaks, one at 270 nm and another at 240 nm, which are

shifted to shorter wavelengths for the case of spectra of both carbonized lignin samples.

The maximum absorption peak occurs at 217 nm for the two carbonized lignin samples,

with the additional occurrence of a shoulder band around 250 nm. Also, the two spectra

present signals in 320 nm and 383 nm.

For the higher treatment temperature sample, the maximum absorption peak at 217 nm,

which is caused by hydroxyl groups and bound p-coumaril, presented a lower intensity

that observed for the lowest treatment temperature sample, suggesting a loss of hydroxyl

groups during the carbonization65, which is concordant with results of IR results.

The qualitative features of spectra for different hydrothermal treatment samples are

similar between them, and the few changes observed allow us to deduce that there were

no important changes in the structure of the carbonized lignin, presumably because the

highest temperature tested was not high enough to produce significant changes in the

structure of the lignin.

6.3 From Computational Chemical Calculations of lignin

Using the Avogadro program, through the option Conformer Search, only one

conformation for each one of the monomers alcohols precursors of lignin was found, from

which the basic unit of lignin was subsequently constructed. The representation of the

monomers and the basic unit structures, after being optimized by the B3LYP method, can

be observed in Figure 19 and Figure 20, respectively. Additionally, a structure for an

organosolv lignin macromolecule containing 210 atoms was found, as represented in

Figure 21. However, due to its high number of atoms, the optimization was realized only

through the semi-empirical method PM3.

In what follows, a description of the calculations of spectroscopic properties is presented

along with the differences exhibited between the structures evaluated, as well as a

comparison between the calculated spectra and the experimentally obtained spectrum for

lignin.

33

Figure 19. The three monomers alcohol precursors of lignin (left to right) p-coumaryl, coniferyl, and sinapyl alcohol

optimized by B3LYP.

Figure 20.Basic unit of lignin calculated by PM3; the elements are oxygen(red), carbon(gray) and hydrogen(white).

Figure 21. Organosolv lignin structure found by SMILES and optimized by PM3 method.

6.3.1 Spectra IR created from combination lineal

The lignin structure called basic unit was constructed from the three monomers precursor:

coniferyl, coumaryl and sinapyl alcohols, with a stoichiometric proportion of (1:1:1)

between them, and once optimized, its IR spectrum was obtained. The central species was

chosen to be coumaryl, following what was suggested by a previous report54, and the

other two units were placed one on each side of it.

34

On the other hand, a combined spectrum was generated from a linear combination of the

spectra obtained for each one of the three alcohol monomers precursors, in order to

represent a possible spectrum resulting from the combination of the three monomeric

alcohols. The objective of this comparison is to highlight the differences appearing when

the units are bonded together in contrast when encountered separately.

All spectra were obtained from the optimized structures using both B3LYP and PM3

methods, under the conditions with and without solvent. The spectra obtained by PM3

and B3LYP methods are shown in Figures 22 and 23, respectively. Also, in the table A5

in the annexes section can be find the main vibrations of each one of construction

spectrums. As expected, the results for the combined spectrum, referred to as the resulting

construction spectrum, shows a great similarity to the spectrum of the basic unit,

regardless of the computational method used, with a few differences associated with the

links between monomer units.

Table 1. Linear combination used for construction of the basic unit spectra

B3LYP Method PM3 Method O.33(Tsinapyl)+0.33(Tcoumaryl)+0.33(Tconiferyl) 2(Tsinapyl)+2(Tcoumaryl)+2(Tconiferyl)

Both in the spectrum of the basic unit obtained by the B3LYP method and that resulting

from the PM3 method, the bands exhibit a slight shift with respect to those that appear in

the combined spectrum, referred to as the construction spectrum. Through the Gabedit

program the analysis of different vibration modes associated with the bands appearing in

IR spectra was possible. The mentioned shifts can be explained by the bonds that alcohol

monomers form with each other when the basic unit is generated, which is not considered

in the construction spectrum.

Figure 22. IR spectra for the basic unit of lignin and for the combination of alcohol monomers (construction) calculated

by the PM3 method under conditions of (a) without solvent and (b) with solvent.

35

In the case of spectra for the structures obtained from the PM3 method, only a notorious

shift for the peak at 747 cm-1 (basic unit) to 804 cm-1 is observed in Figure 22, being more

notable in the presence of solvent. This shift can be explained by considering that the

peak at 747 cm-1 corresponds to the vibration of the bond that links coniferyl with

coumaryl units. The resulting construction spectrum from structures obtained by PM3

with solvent shows more peaks shifted in respect to the spectrum of the basic unit, which

may be due to the possibility of hydrogen bond formation, modifying the vibration

frequency of the O-H bonds in the range 4000- 4700 cm-1, which were visualized by

Gabedit. These specific spectroscopic values do not have concordance with the

experimental spectrum and neither appear in basic unit spectra, indicating that PM3 is a

method with only limited accuracy.

Figure 23. IR spectra for the basic unit of lignin and for the combination of alcohol monomers (construction)

calculated by the B3LYP method under conditions of (a) without solvent and (b) with solvent.

As can be seen in Figure 23, the spectra calculated for the structures obtained by the

B3LYP method (with and without solvent) show a higher number of visible peaks, which

is more consistent with the complex structure of lignin. This fact allows to suggest that

the B3LYP method is more convenient in the description of molecules than the semi-

empirical PM3 method. The shift of equivalent peaks in both the spectrum of the basic

unit and combined spectrum is again observed, which is attributable to the bond vibrations

associated with the link between alcohol monomers.

36

6.3.2 Comparison between B3LYP IR spectra of basic unit of lignin and PM3 IR spectra of

macromolecular lignin

Among all the IR spectra of the model structures tested, the one obtained for the basic

unit under non-solvent conditions using the B3LYP method was the one that presented

the greatest similarity with the IR spectrum experimentally obtained for the extracted

lignin. 10 of 17 signals that appear in the calculated spectrum for this model (basic unit

without solvent by B3LYP) match with those that appear in the experimental spectrum,

while for the case in which the presence of the solvent is considered in the model, only 5

of the 16 signals match. The detailed information about these signals is shown in the

Table A6 of Annexes section. The better correspondence with the model obtained under

conditions of without solvent may be attributed to the fact that the experimental spectrum

was recorded for a solid sample of lignin which can be different from a solvated medium.

Figure 24. Experimental IR spectrum for extracted lignin (Pure lignin) compared with the spectrum calculated for

the basic unit under conditions of solvent present by B3LYP (Basic unit -solvent (B3LYP)).

As mentioned before, in the spectrum of the basic unit optimized considering the presence

of solvent by B3LYP, shown in Figure 24, were generated 16 signals, but only 5 of them

had wavenumbers similar (or corresponding) to those observed in the experimental

spectrum of lignin. These signals were 1139 cm-1, 1268 cm-1, 1357 cm-1, 1728 cm-1 and

37

3234 cm-1. Using the Gabedit program, the assignment of each signal to its respective

vibration modes is possible.

In this way, the peak at 1139 cm-1 corresponds to aromatic C-H out of plane bending and

Aryl–O of aryl–O–CH3 of sinapyl alcohol. The 1268 cm-1 signal is due to aromatic C-H

out of plane bending of conjugated system of coumaryl-coniferyl, aliphatic C-H of

coumaryl. The 1357 cm-1 correspond to aromatic C-O stretching, ─HC═CH- out-of-plane

deformation and aliphatic O–CH3 deformation of sinapyl. The 1728 cm-11 signal is due to

aliphatic C-H out of plane in bending and aliphatic –OC=CH- out of plane in bending of

sinapyl. And finally, the signal of 3234 cm-1 corresponds to aromatic C-H stretching of

sinapyl.

Figure 25 . Experimental IR spectrum for extracted lignin (Pure lignin) compared with the spectrum calculated for

the basic unit without solvent by B3LYP (Basic unit (B3LYP)).

In the spectrum obtained for the basic unit generated by B3LYP without considering the

presence of solvent, which is shown in Figure 25, 17 signals were generated, but only 10

of them did match with those observed in the experimental spectrum. In the order of

decreasing wavenumber, the first signal matching with experimental spectrum is found at

3209 cm-1, which is attributed to aromatic C-H stretching in coniferyl. The signal at 1743

cm-1 is caused by aliphatic C-H out of plane in bending aliphatic –OC=CH- out of plane

38

in bending of sinapyl. The 1533 cm-1 is due to aromatic C-H stretching and aliphatic O–

CH3 deformation of sinapyl. The aromatic C-H stretching, aliphatic O–CH3 deformation

and Aryl–O of aryl–OH of coniferyl and aliphatic C-H from HO-CH3, and aromatic C-H

out of plane in bending of conjugated system are responsible of 1367 cm-1 signal. The

signal at 1254 cm-1 corresponds to ─HC═CH- out-of-plane deformation and aliphatic C-

H of sinapyl. The aromatic C-H out-of-plane deformation from coumaryl, aliphatic –

OC=CH- out of plane in bending and aliphatic C-H out of plane in bending of sinapyl

caused the 1148 cm-1 signal. The 1036 cm-1 signal is due to aliphatic –OC=CH- out of

plane in bending of sinapyl, while the one that appears at 841 cm-1 is caused by aromatic

C-H out of plane in bending of coumaryl. The signal at 769 cm-1 is aromatic C-H out-of-

plane bending and ─HC═CH- out-of-plane deformation of sinapyl, aromatic C-H out of

plane in bending of coumaryl. Finally, the signal at 505 cm-1 is caused by ─HC═CH- out-

of-plane deformation and aliphatic –OC=CH- out of plane in the bending of sinapyl.

Figure 26. Experimental IR spectrum for extracted lignin (Pure lignin) compared with the spectrum calculated for the

lignin macromolecule by PM3 (Lignin (PM3)) for the IR region (a) 4000-2000 cm-1 and (b) 2000-350 cm-1.

For the case in which the model structure used to calculate the IR spectrum corresponds

to the lignin macromolecule, whose configuration was obtained from SMILES code and

optimized by the PM3 method, the resulting spectrum showed a high number of signals,

but only 5 peaks did match with peaks appearing in the experimental IR spectrum, as can

be appreciated in Figure 26. The peaks are shown in the table A 7 in the annexes section.

The wavenumber values for these peaks were 2910 cm-1, 1467 cm-1, 1379 cm-1, 1138 cm-

1 and 1030 cm-1. The aliphatic C-H stretching from R3-CH (guaiacyl) corresponds to 2910

cm-1. The signal at 1467 cm-1 is caused by aliphatic C─H stretching, aryl ring stretching,

aliphatic O of HO-Aryl and aliphatic C-H out-of-plane bending of guaiacyl. The signal at

39

1379 cm-1 corresponds to aromatic C-H out-of-plane bending and aliphatic C-H out-of-

plane bending of O-CH3 of syringyl. The signal at 1338 cm-1 is due to deformation

vibrations of C-H bonds in guaiacyl ring, aliphatic O-CH3 deformation, aliphatic C-H

deformation of OH-CH2-R and aliphatic C-H deformation of OH-CH-R2 of syringyl.

Finally, the peak occurring at 1030 cm-1 is caused by aliphatic C-H of -CHOH-CHOH-

and aliphatic C-H of CH3.

According to the results of different IR spectra obtained for the basic unit by B3LYP

method and for macromolecular lignin (SMILES) by PM3 method, the former model is

associated to a higher number of peaks similar to those occurring in the experimental IR

spectrum for extracted lignin. Thus, the IR spectra of a small representative system of the

lignin molecule (basic unit) generated by B3LYP is more consistent with the experimental

result than that obtained for the lignin macromolecule generated by the PM3 method, in

spite of the higher number of atoms involved in the structure of this last case. The balance

between accuracy of calculations and system size is an ever present dilemma of

theoretical calculations, and in that case it weighs towards accuracy: a rather small system

using B3LYP is quite an accurate representation of the real lignin. Logically, the best

compromise would be to use B3LYP for calculating the macromolecular system. Indeed,

we tried it but it suffered from a very slow convergence.

6.3.3 UV-Vis Spectroscopy by different computational methods

6.3.3.1 UV-Vis Spectroscopy of lignin molecules by PM3 method

From theoretical UV-Vis spectra of each one of alcohol monomers shown in Figure 27,

it is possible to infer that sinapyl and coniferyl units have common signals with the

experimental spectrum of lignin, indicating that these precursor units are the main

components of lignin analyzed. In Table 2 are indicated the signals corresponding to the

three monomeric alcohols and to the basic unit calculated by the PM3 method.

40

Figure 27. UV-Vis spectra calculated for alcohol monomers and the basic unit of lignin by the PM3 method.

Table 2. UV-Vis spectra signals resulting from experimental measurement and from the structures of monomeric

alcohols and the basic unit of the lignin model obtained using the PM3 method.

Wavelength (nm)

From experimental spectrum

From calculated spectra by PM3 method

Basic unit Coniferyl Sinapyl Coumaryl

205 244 215 211 216

234 253 233 227 254

266 266 268 240 316

342 297 338 269

332 343

360

It was not possible to generate UV-Vis spectrum of the lignin macromolecule with solvent

presence obtained by PM3 method because no transitions appeared in the 200-800 nm

range. This is possibly due to the fact that PM3 is a simplified semi-empirical method and

the effect of solvent on poorly described orbitals could lead to unacceptable errors.

The occurrence of signals in the region 200 - 800 nm, as is indicated in Figure 27 and

Tabla 2, suggests that lignin is a good UV absorber, which is due to its polyphenolic

structure. Although the position and intensity of absorption maxima depends on the

extraction source, nature and type of lignin, as well as the structural modification that it

undergoes by physical or chemical treatment, the position of the occurrence of main

41

signals are confined in a narrow range. The wavelength of the signals associated with

each of one of evaluated structures are , at higher energy, very similar to each other, being

the those occurring at lower values than 260 nm associated with π→π* electronic

transitions in the aromatic ring, those appearing at wavelength values between 260 - 280

may be assigned to the free and etherified hydroxyl groups, whereas those signals at

higher wavelengths (> 300 nm) to π→π* and n→ π* transitions due the presence of

aliphatic Cα=Cβ and Cα= O units (conjugated with aromatic rings), respectively61.

6.3.3.2 UV-Vis Spectroscopy of alcohol monomers by B3LYP method

According to spectra for alcohol monomers calculated by B3LYP method, which are

shown in Figure 28, both coniferyl and sinapyl units present a signal appearing at ~270

nm that is present in the experimental UV-Vis spectrum for extracted lignin, which is also

observed for the results obtained by PM3 method. The wavelength values corresponding

to the signals of calculated spectra by B3LYP are shown in Table 3. It is difficult to affirm

the presence of one unit rather than another. However, this result is relatively accurate,

usually UV-Vis spectra at TD-DFT levels suffer from errors, and errors of 30-40 nm are

not surprising (depending on the functional used). Moreover, the transitions are typically

blue-shifted as it is the case presented here.

Figure 28. UV-vis spectra calculated for alcohol monomers and the basic unit of lignin by the B3LYP method.

42

In the case of UV-Vis spectra obtained for alcohol monomers calculated by B3LYP when

the solvent presence is considered, which are shown in Figure 29 and whose wavelength

values are indicated Table 3, the signals match between calculated spectra for the different

model structures evaluated and the experimental spectrum is poorer than for the case of

the spectra obtained in the absence of solvent. However, one interesting aspect of the

comparison with and without solvent lies in the very small changes observed for UV-Vis

transitions, only small shifts of the signals are appreciated. While an implicit solvation

model has influence in that case on the IR spectrum, it has very small influence on the

UV-Vis. This could suggest that for determining the units constituting a lignin

macromolecule, UV-Vis could be a more reliable technique to quantify the proportion of

each unit.

Figure 29. UV-Vis spectra calculated for alcohol monomers and the basic unit of lignin in presence of solvent by the

B3LYP method.

The use of solvent for generation of spectra did not provide signals that emulate the

experimental spectra in a more satisfactory manner than without solvent. This failure

could also origin in the different phases considered experimentally and theoretically: the

lignin sample was solid (without solvent) when it was measured in the UV-vis

spectrometer through diffuse reflectance methods.

43

Table 3. UV-Vis spectra signals resulting from experimental measurement and from the structures of monomeric

alcohols and the basic unit of the lignin model (with and without solvent) obtained using the B3LYP method.

Wavelength (nm)

From experimental spectrum

From calculated spectra by B3LYP method

Basic unit Coniferyl Sinapyl Coumaryl

205 212 210 209 205

234 228 228 227 212

266 271 256 278 223

342 303 278 256

From calculated spectra by B3LYP- solvent method

Basic unit Coniferyl Sinapyl Coumaryl

205 216 213 205 205

234 229 227 216 211

266 254 257 227 230

342 276 284 286 259

303

6.3.3.3 UV-Vis Spectroscopy of the lignin macromolecule by PM3 method

Figure 30 shows the UV-Vis spectrum of the lignin macromolecule obtained by SMILES

and optimized by PM3 method. From this spectrum two peaks. one at 284 nm and the

second at 337 nm, are appreciated. Compared to the spectra calculated by the B3LYP

method, discussed above, the spectrum obtained for the lignin macromolecule using the

PM3 method is less consistent with the spectrum obtained experimentally for the

extracted lignin. Although the number of atoms of the macromolecule model used is

higher than that for the model structures used by the B3LYP method, the resulting

spectrum suggests the semi-empirical method used has a lower level of accuracy.

Figure 30. Theoretical UV-visible spectra of lignin using PM3 method.

44

6.3.4 Comparison between experimental and theoretical FTIR and UV-VIS spectra for

lignin

Figure 31. Deconvolution of experimental UV-vis spectra

From deconvolution of the experimental UV-Vis spectrum of lignin, shown in Figure 31,

it was possible to identify an acceptable distribution of peaks, which resulted in four bands

located at 205 nm, 234 nm, 266 nm and 342 nm. By comparison of these wavelength

values from experimental UV-Vis spectrum with calculated spectra for different

structures by both B3LYP and PM3 methods, one can infer that the best model - method

combination evaluated in this project corresponds to the basic unit (without solvent)

optimized by B3LYP, although it is necessary to mention that the fact that this model

consists of few atoms, which could be considered a disadvantage. The correspondence of

all signals appearing in the calculated and experimental spectra is not completely

accurate, one can appreciate a great similarity with few differences. This can be observed

from the date given in the table A8 in the annexes section. The UV-Vis spectrum of the

lignin macromolecule calculated by PM3, shown Figure 30, presents only two peaks, and

both are shifted with respect to the experimental UV-Vis spectrum.

Although the hybrid functional B3LYP appears to provides the greatest contribution in

computational applications that have been published, it has many limitations in a various

applications, such the following: (1) excitations using time dependent theory (TD-DFT)

and (2) charge transfer (CT) excitations80. This fact could explain the lack of accuracy of

results obtained in UV-Vis spectra calculated by B3LYP. The calculations by this method

were made using Time-dependent DFT (DDFT) as an approach to computing excited

45

states. While giving a general idea of the range of transitions, it is difficult to predict with

certainty each band. Another functional, like CAM-B3LYP could give better results

regarding TD-DFT simulated UV-Vis spectra.

On the other hand, with respect to the resulting structure after hydrothermal treatment of

extracted lignin, the information given by experimental IR spectra suggested the

occurrence of some C-O bonds breaking, increasing the aromaticity proportion in

samples. However, the resulting material remains structural features of lignin, even for

the highest temperature tested. This fact is evidenced using Gabedit program, and also is

supported by a previous reported work, which states that an increase in the carbonization

temperature increased the ratio of aromatic carbons to aliphatic carbons81.

The theoretical bands exhibited by the lignin model obtained from the semi-empirical

method (PM3) are very different to experimental bands. The theoretical UV-vis and IR

spectra do not fit with the experimental spectrum. The main causes of the inconsistencies

are the low level of accuracy of the semi-empirical method used and that calculation was

made on the basis of an organosolv lignin model obtained from an available database of

3D chemical structure of molecules, which not necessarily corresponds to the chemical

structure of the lignin extracted from rose stems.

Although thermochemical predictions of PM3 method have an accuracy slightly

better than other semi-empirical methods, such as AM1, and is ideal for rapid estimation

of properties 47, in general semi-empirical methods have limited accuracy compared with

ab initio and DFT methods.82. A large number of common errors that limit relevancy of

these methods belonging to the NDDO family are mentioned in the literature. For

example (1) flawed energetics for molecules with multiple rings, (2) underestimation of

the stabilization energy of delocalized systems, and (3) inaccurate description of

intermolecular interactions, especially hydrogen bonding83.

The semi-empirical methods are often the favorites in computational chemistry in spite

of their limitations. This happens because they enable study of molecules consisting of

thousands of atoms, while that ab initio calculations are feasible on molecules consisting

of less than 50–70 atoms47. The organosolv lignin model analyzed in this work, obtained

from SMILES, has 201 atoms, as above mentioned.

46

Besides the low accuracy of the semi-empirical method, the fact that structure of lignin

change depending on the source from was extracted, its exact elucidation is difficult to

achieve. This is another important factor that would allow us to explain the discrepancies

among experimental and theoretical spectra. In spite of these difficulties accompanying

the determination of the exact structure of natural lignin, the construction of theoretical

models and fragments of the lignin molecule were obtained by SMILES of organosolv

lignin to perform this project.

47

7 Conclusions

In general, determinate the exact structure of a macromolecule is not an easy task and

much less even if the structure depends of various factors such as extraction methodology

and vegetal source as is the case of the lignin. With helping of the generation of spectra

of basic unit and alcohol monomers of lignin by computational method was possible

obtain clues of how is the lignin structure.

Although there was a slight decrement of intensity and resolution of the various bands of

FTIR and UV-visible spectrums of lignin carbonized, there was no great changes that

would indicate an important change in the lignin structure. This happen possibly due that

temperatures applied for carbonization of lignin were not high enough.

Despite efficiency of semi empirical method PM3 by the generation of UV-Vis and IR

spectra of macromolecular system of lignin, B3LYP was the best computational method

emulating the spectroscopic characteristics of lignin extracted of rose stems. Thus, it is

better work with a small molecular system of lignin using an accuracy method such as

B3LYP than with a large molecular system of lignin using semi empirical methods, which

have an erratic accuracy.

The PM3 method can provide luckily similar UV-vis spectra without solvent to the

experimental spectra, but generally, it is a limited method beyond a general structural

prediction.

48

8 Recommendations

This work shows preliminary interesting results toward a quantified spectroscopic

characterization of lignin components although could be extended in different aspects.

In the experimental aspects, the extraction process could be improved and a wider range

of spectroscopic characterization techniques could be used. For example, NMR analysis

would be a particularly interesting addition. Moreover, the carbonization process could

be done at higher temperatures in order to be able to appreciate significant structural

changes.

In the theoretical aspects, the structure of lignin could be quantified in more details by

generating spectroscopic spectra simulated by DFT method using different distributions

of lignin alcohol monomers. In this work, only one distribution of 3 monomers was

generated but the same approach could be generalized for systems of increasing size.

Also, while for lignin units generated by B3LYP functional should be satisfying, it would

be interesting to test alternative basis sets and functional to construct the simulated

spectra. Indeed, to carry out a comprehensive study of different available computational

methods, in relation to its capability of predicting lignin vibrational properties, would be

an important step towards a very precise characterization of lignin electronic structure.

49

9 References

(1) Lopez-Camas, K.; Arshad, M.; Ullah, A. Chemical Modification of Lignin by

Polymerization and Depolymerization, 1st ed.; Sharma, S., Kumar, A., Eds.;

Springer: Mohali, Punjab, India, 2020. https://doi.org/10.1007/978-3-030-40663-

9_5.

(2) Lee, Y.; Escamilla-Treviño, L.; Dixon, R. A.; Voit, E. O. Functional Analysis of

Metabolic Channeling and Regulation in Lignin Biosynthesis: A Computational

Approach. PLoS Comput. Biol. 2012, 8 (11).

https://doi.org/10.1371/journal.pcbi.1002769.

(3) Mahmood, Z.; Yameen, M.; Jahangeer, M.; Riaz, M.; Ghaffar, A.; Javid, I. Lignin

as Natural Antioxidant Capacity. Lignin - Trends Appl. 2018.

https://doi.org/10.5772/intechopen.73284.

(4) Marathe, P. S.; Westerhof, R. J. M.; Kersten, S. R. A. Fast Pyrolysis of Lignins

with Different Molecular Weight: Experiments and Modelling. Appl. Energy 2019,

236 (December 2018), 1125–1137.

https://doi.org/10.1016/j.apenergy.2018.12.058.

(5) Thakur, V. K.; Thakur, M. K. Recent Advances in Green Hydrogels from Lignin:

A Review. Int. J. Biol. Macromol. 2015, 72, 834–847.

https://doi.org/10.1016/j.ijbiomac.2014.09.044.

(6) Thulluri, C.; Pinnamaneni, S. R.; Shetty, P. R.; Addepally, U. Synthesis of Lignin-

Based Nanomaterials/Nanocomposites: Recent Trends and Future Perspectives.

Ind. Biotechnol. 2016, 12 (3), 153–160. https://doi.org/10.1089/ind.2015.0022.

(7) Figueiredo, P.; Lintinen, K.; Hirvonen, J. T.; Kostiainen, M. A.; Santos, H. A.

Properties and Chemical Modifications of Lignin: Towards Lignin-Based

Nanomaterials for Biomedical Applications. Prog. Mater. Sci. 2018, 93, 233–269.

https://doi.org/10.1016/j.pmatsci.2017.12.001.

(8) Spiridon, I. Extraction of Lignin and Therapeutic Applications of Lignin-Derived

Compounds. A Review. Environ. Chem. Lett. 2020, No. 123456789.

https://doi.org/10.1007/s10311-020-00981-3.

(9) Sangha, A. K.; Petridis, L.; Smith, J. C.; Ziebell, A.; Parks, J. M. Molecular

Simulation as a Tool for Studying Lignin. Environ. Prog. Sustain. Energy 2011,

50

31 (1), 47–54. https://doi.org/10.1002/ep.10628.

(10) Capanema, E. A.; Balakshin, M. Y.; Kadla, J. F. A Comprehensive Approach for

Quantitative Lignin Characterization by NMR Spectroscopy. J. Agric. Food Chem.

2004, 52 (7), 1850–1860. https://doi.org/10.1021/jf035282b.

(11) Chowdhury, M. A. The Controlled Release of Bioactive Compounds from Lignin

and Lignin-Based Biopolymer Matrices. Int. J. Biol. Macromol. 2014, 65, 136–

147. https://doi.org/10.1016/j.ijbiomac.2014.01.012.

(12) Kamm, B.; Gruber, P. R. Handbook of Fuels Beyond Oil and Gas : The Methanol

Economy Bailey ’ S Industrial Oil and Fat Products Oil Refineries in the 21st

Century; 2006; Vol. 1.

(13) Vaz, S. Analytical Techniques and Methods for Biomass; 2016.

https://doi.org/10.1007/978-3-319-41414-0.

(14) Witzler, M.; Alzagameem, A.; Bergs, M.; Khaldi-Hansen, B. El; Klein, S. E.;

Hielscher, D.; Kamm, B.; Kreyenschmidt, J.; Tobiasch, E.; Schulze, M. Lignin-

Derived Biomaterials for Drug Release and Tissue Engineering. Molecules 2018,

23 (8), 1–22. https://doi.org/10.3390/molecules23081885.

(15) Alqahtani, M. S.; Alqahtani, A.; Al-Thabit, A.; Roni, M.; Syed, R. Novel Lignin

Nanoparticles for Oral Drug Delivery. J. Mater. Chem. B 2019, 7 (28), 4461–4473.

https://doi.org/10.1039/c9tb00594c.

(16) Watkins, D.; Nuruddin, M.; Hosur, M.; Tcherbi-Narteh, A.; Jeelani, S. Extraction

and Characterization of Lignin from Different Biomass Resources. J. Mater. Res.

Technol. 2015, 4 (1), 26–32. https://doi.org/10.1016/j.jmrt.2014.10.009.

(17) Qian, E. W. Pretreatment and Saccharification of Lignocellulosic Biomass;

Elsevier, 2013. https://doi.org/10.1016/B978-0-12-404609-2.00007-6.

(18) Figueiredo, P.; Lintinen, K.; Kiriazis, A.; Hynninen, V.; Liu, Z.; Bauleth-Ramos,

T.; Rahikkala, A.; Correia, A.; Kohout, T.; Sarmento, B.; Yli-Kauhaluoma, J.;

Hirvonen, J.; Ikkala, O.; Kostiainen, M. A.; Santos, H. A. In Vitro Evaluation of

Biodegradable Lignin-Based Nanoparticles for Drug Delivery and Enhanced

Antiproliferation Effect in Cancer Cells. Biomaterials 2017, 121, 97–108.

https://doi.org/10.1016/j.biomaterials.2016.12.034.

(19) Krasznai, D. J.; Champagne Hartley, R.; Roy, H. M.; Champagne, P.;

51

Cunningham, M. F. Compositional Analysis of Lignocellulosic Biomass:

Conventional Methodologies and Future Outlook. Crit. Rev. Biotechnol. 2018, 38

(2), 199–217. https://doi.org/10.1080/07388551.2017.1331336.

(20) Lievonen, M.; Valle-Delgado, J. J.; Mattinen, M. L.; Hult, E. L.; Lintinen, K.;

Kostiainen, M. A.; Paananen, A.; Szilvay, G. R.; Setälä, H.; Österberg, M. A

Simple Process for Lignin Nanoparticle Preparation. Green Chem. 2016, 18 (5),

1416–1422. https://doi.org/10.1039/c5gc01436k.

(21) Huffman, F. G. Uronic Acids. Encycl. Food Sci. Nutr. 2003, No. d, 5890–5896.

https://doi.org/10.1016/b0-12-227055-x/01221-9.

(22) Xiong, F.; Han, Y.; Wang, S.; Li, G.; Qin, T.; Chen, Y.; Chu, F. Preparation and

Formation Mechanism of Renewable Lignin Hollow Nanospheres with a Single

Hole by Self-Assembly. ACS Sustain. Chem. Eng. 2017, 5 (3), 2273–2281.

https://doi.org/10.1021/acssuschemeng.6b02585.

(23) Kumar, A.; Anushree; Kumar, J.; Bhaskar, T. Utilization of Lignin: A Sustainable

and Eco-Friendly Approach. J. Energy Inst. 2020, 93 (1), 235–271.

https://doi.org/10.1016/j.joei.2019.03.005.

(24) Den, W.; Sharma, V. K.; Lee, M.; Nadadur, G.; Varma, R. S. Lignocellulosic

Biomass Transformations via Greener Oxidative Pretreatment Processes: Access

to Energy and Value Added Chemicals. Front. Chem. 2018, 6 (APR), 1–23.

https://doi.org/10.3389/fchem.2018.00141.

(25) Gellerstedt, G. L. F.; Henriksson, E. G. Lignins: Major Sources, Structure and

Properties. Monomers, Polym. Compos. from Renew. Resour. 2008, 201–224.

https://doi.org/10.1016/B978-0-08-045316-3.00009-0.

(26) Chung, H.; Washburn, N. R. Chemistry of Lignin-Based. 2012, 1, 137–160.

(27) Dong, X.; Dong, M.; Lu, Y.; Turley, A.; Jin, T.; Wu, C. Antimicrobial and

Antioxidant Activities of Lignin from Residue of Corn Stover to Ethanol

Production. Ind. Crops Prod. 2011, 34 (3), 1629–1634.

https://doi.org/10.1016/j.indcrop.2011.06.002.

(28) Gregorova, A.; Redik, S.; Sedlarik, V.; Stelzer, F. Lignin-Containing Polyethylene

Films with Antibacterial Activity. NANOCON 2011 - Conf. Proceedings, 3rd Int.

Conf. 2011, 184–189.

52

(29) Azadi, P.; Inderwildi, O. R.; Farnood, R.; King, D. A. Liquid Fuels, Hydrogen and

Chemicals from Lignin: A Critical Review. Renew. Sustain. Energy Rev. 2013, 21,

506–523. https://doi.org/10.1016/j.rser.2012.12.022.

(30) Mahmood, N.; Yuan, Z.; Schmidt, J.; Xu, C. Depolymerization of Lignins and

Their Applications for the Preparation of Polyols and Rigid Polyurethane Foams:

A Review. Renew. Sustain. Energy Rev. 2016, 60, 317–329.

https://doi.org/10.1016/j.rser.2016.01.037.

(31) Frangville, C.; Rutkevičius, M.; Richter, A. P.; Velev, O. D.; Stoyanov, S. D.;

Paunov, V. N. Fabrication of Environmentally Biodegradable Lignin

Nanoparticles. ChemPhysChem 2012, 13 (18), 4235–4243.

https://doi.org/10.1002/cphc.201200537.

(32) Chen, N.; Dempere, L. A.; Tong, Z. Synthesis of pH-Responsive Lignin-Based

Nanocapsules for Controlled Release of Hydrophobic Molecules. ACS Sustain.

Chem. Eng. 2016, 4 (10), 5204–5211.

https://doi.org/10.1021/acssuschemeng.6b01209.

(33) Dai, L.; Liu, R.; Hu, L. Q.; Zou, Z. F.; Si, C. L. Lignin Nanoparticle as a Novel

Green Carrier for the Efficient Delivery of Resveratrol. ACS Sustain. Chem. Eng.

2017, 5 (9), 8241–8249. https://doi.org/10.1021/acssuschemeng.7b01903.

(34) Rico-García, D.; Ruiz-Rubio, L.; Pérez-Alvarez, L.; Hernández-Olmos, S. L.;

Guerrero-Ramírez, G. L.; Vilas-Vilela, J. L. Lignin-Based Hydrogels: Synthesis

and Applications. Polymers (Basel). 2020, 12 (1), 1–23.

https://doi.org/10.3390/polym12010081.

(35) Larrañeta, E.; Imízcoz, M.; Toh, J. X.; Irwin, N. J.; Ripolin, A.; Perminova, A.;

Domínguez-Robles, J.; Rodríguez, A.; Donnelly, R. F. Synthesis and

Characterization of Lignin Hydrogels for Potential Applications as Drug Eluting

Antimicrobial Coatings for Medical Materials. ACS Sustain. Chem. Eng. 2018, 6

(7), 9037–9046. https://doi.org/10.1021/acssuschemeng.8b01371.

(36) Iravani, S.; Varma, R. S. Plants and Plant-Based Polymers as Scaffolds for Tissue

Engineering. Green Chem. 2019, 21 (18), 4839–4867.

https://doi.org/10.1039/c9gc02391g.

(37) Raschip, I. E.; Hitruc, E. G.; Vasile, C. Semi-Interpenetrating Polymer Networks

53

Containing Polysaccharides. II. Xanthan/lignin Networks: A Spectral and Thermal

Characterization. High Perform. Polym. 2011, 23 (3), 219–229.

https://doi.org/10.1177/0954008311399112.

(38) Quraishi, S.; Martins, M.; Barros, A. A.; Gurikov, P.; Raman, S. P.; Smirnova, I.;

Duarte, A. R. C.; Reis, R. L. Novel Non-Cytotoxic Alginate–lignin Hybrid

Aerogels as Scaffolds for Tissue Engineering. J. Supercrit. Fluids 2015, 105

(2015), 1–8. https://doi.org/10.1016/j.supflu.2014.12.026.

(39) Li, S.-M.; Sun, S.-L.; Ma, M.-G.; Dong, Y.-Y.; Fu, L.-H.; Sun, R.-C.; Xu, F.

Lignin-Based Carbon/CePO4 Nanocomposites: Solvothermal Fabrication,

Characterization, Thermal Stability, and Luminescence. BioResources 2013, 8 (3),

4155–4170. https://doi.org/10.15376/biores.8.3.4155-4170.

(40) Gupta, A. K.; Mohanty, S.; Nayak, S. K. Influence of Addition of Vapor Grown

Carbon Fibers on Mechanical, Thermal and Biodegradation Properties of Lignin

Nanoparticle Filled Bio-Poly(trimethylene Terephthalate) Hybrid

Nanocomposites. RSC Adv. 2015, 5 (69), 56028–56036.

https://doi.org/10.1039/c5ra07828h.

(41) Zhang, Y.; He, H.; Liu, Y.; Wang, Y.; Huo, F.; Fan, M.; Adidharma, H.; Li, X.;

Zhang, S. Recent Progress in Theoretical and Computational Studies on the

Utilization of Lignocellulosic Materials. Green Chem. 2019, 21 (1), 9–35.

https://doi.org/10.1039/c8gc02059k.

(42) Elder, T.; Berstis, L.; Beckham, G. T.; Crowley, M. The Application of

Computational Chemistry to Lignin. Proc. 19th Int. Symp. Wood, Fibre Pulping

Chem. 2017.

(43) Gastegger, M.; Behler, J.; Marquetand, P. Machine Learning Molecular Dynamics

for the Simulation of Infrared Spectra. Chem. Sci. 2017, 8 (10), 6924–6935.

https://doi.org/10.1039/c7sc02267k.

(44) Petridis, L.; Pingali, S. V.; Urban, V.; Heller, W. T.; O’Neill, H. M.; Foston, M.;

Ragauskas, A.; Smith, J. C. Self-Similar Multiscale Structure of Lignin Revealed

by Neutron Scattering and Molecular Dynamics Simulation. Phys. Rev. E - Stat.

Nonlinear, Soft Matter Phys. 2011, 83 (6), 4–7.

https://doi.org/10.1103/PhysRevE.83.061911.

54

(45) Christensen, A. S.; Kubař, T.; Cui, Q.; Elstner, M. Semiempirical Quantum

Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical

Applications. Chem. Rev. 2016, 116 (9), 5301–5337.

https://doi.org/10.1021/acs.chemrev.5b00584.

(46) Zhang, X.; Yang, W.; Blasiak, W. Modeling Study of Woody Biomass:

Interactions of Cellulose, Hemicellulose, and Lignin. Energy and Fuels 2011, 25

(10), 4786–4795. https://doi.org/10.1021/ef201097d.

(47) Ramachandran, K. I.; Deepa, G.; Namboori, K. Computational Chemistry and

Molecular Modeling: Principles and Applications. Comput. Chem. Mol. Model.

Princ. Appl. 2008, 1–397. https://doi.org/10.1007/978-3-540-77304-7.

(48) Christensen, A. S.; Kubař, T.; Cui, Q.; Elstner, M. Semiempirical Quantum

Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical

Applications. Chem. Rev. 2016, 116 (9), 5301–5337.

https://doi.org/10.1021/acs.chemrev.5b00584.

(49) Javier, P.; Valencia, S.; Elena, L.; Marinez, B. Lignin in the Restoration of the

Cultural Heritage. 2005, 37 (1), 106–109.

(50) Stewart, J. J. P. Optimization of Parameters for Semiempirical Methods IV:

Extension of MNDO, AM1 and PM3 to More Main Group Elements. J. Mol.

Model. 2004, 10 (2), 155–164. https://doi.org/10.1007/s00894-004-0183-z.

(51) D.Watts, H. Application of Density Functional Theory Calculations to Elucidate

the Structure of Lignin Linkages and the Intermolecular Interactions among

Proxies of Lignin, Hemicellulose,and Cellulose, The Pennsylvania State

University, 2012.

(52) Zhang, I. Y.; Wu, J.; Xu, X. Extending the Reliability and Applicability of B3LYP.

Chem. Commun. 2010, 46 (18), 3057–3070. https://doi.org/10.1039/c000677g.

(53) Lu, L.; Hu, H.; Hou, H.; Wang, B. An Improved B3LYP Method in the Calculation

of Organic Thermochemistry and Reactivity, Elsevier B.V., 2013, Vol. 1015.

https://doi.org/10.1016/j.comptc.2013.04.009.

(54) Haensel, T.; Reinmöller, M.; Lorenz, P.; Beenken, W. J. D.; Krischok, S.; Ahmed,

S. I. U. Valence Band Structure of Cellulose and Lignin Studied by XPS and DFT.

Cellulose 2012, 19 (3), 1005–1011. https://doi.org/10.1007/s10570-012-9681-9.

55

(55) Karmanov, A. P.; Ishankhodzhaeva, M. M.; Derkacheva, O. Y. Experimental and

Quantum Chemical Study of Biosynthetic Lignin Models — Dehydropolymers.

Russ. Chem. Bull. 2017, 66 (4), 643–647. https://doi.org/10.1007/s11172-017-

1785-9.

(56) Erickson, H. P. Size and Shape of Protein Molecules at the Nanometer Level

Determined by Sedimentation, Gel Filtration, and Electron Microscopy. Biol.

Proced. Online 2009, 11 (1), 32–51. https://doi.org/10.1007/s12575-009-9008-x.

(57) Mao, H.; Chen, X.; Huang, R.; Chen, M.; Yang, R.; Lan, P.; Zhou, M.; Zhang, F.;

Yang, Y.; Zhou, X. Fast Preparation of Carbon Spheres from Enzymatic

Hydrolysis Lignin: Effects of Hydrothermal Carbonization Conditions. Sci. Rep.

2018, 8 (1), 2–11. https://doi.org/10.1038/s41598-018-27777-4.

(58) Neese, F. The ORCA Program System. Wiley Interdiscip. Rev. Comput. Mol. Sci.

2012, 2 (1), 73–78. https://doi.org/10.1002/wcms.81.

(59) Neese, F.; Wennmohs, F.; Becker, U.; Riplinger, C. The ORCA Quantum

Chemistry Program Package. J. Chem. Phys. 2020, 152 (22), 224108.

https://doi.org/10.1063/5.0004608.

(60) Sadeghifar, H.; Ragauskas, A. Lignin as a UV Light Blocker-a Review. Polymers

(Basel). 2020, 12 (5), 1–10. https://doi.org/10.3390/POLYM12051134.

(61) You, T.; Xu, F. Applications of Molecular Spectroscopic Methods to the

Elucidation of Lignin Structure. Appl. Mol. Spectrosc. to Curr. Res. Chem. Biol.

Sci. 2016. https://doi.org/10.5772/64581.

(62) Tesfaw, B.; Chekol, F.; Mehretie, S.; Admassie, S. Adsorption of pb(II) Ions from

Aqueous Solution Using Lignin from Hagenia Abyssinica. Bull. Chem. Soc.

Ethiop. 2016, 30 (3), 473–484. https://doi.org/10.4314/bcse.v30i3.16.

(63) Zhao, J.; Ou, S.; Ding, S.; Wang, Y.; Wang, Y. Effect of Activated Charcoal

Treatment of Alkaline Hydrolysates from Sugarcane Bagasse on Purification of P-

Coumaric Acid. Chem. Eng. Res. Des. 2011, 89 (10), 2176–2181.

https://doi.org/10.1016/j.cherd.2011.02.010.

(64) Traoré, M.; Kaal, J.; Martínez Cortizas, A. Application of FTIR Spectroscopy to

the Characterization of Archeological Wood. Spectrochim. Acta - Part A Mol.

Biomol. Spectrosc. 2016, 153, 63–70. https://doi.org/10.1016/j.saa.2015.07.108.

56

(65) El Marouani, M.; El Hrech, N.; El Jastimi, J.; El Hajji, A.; Rghioui, L.; Sebbahi,

S.; El Hajjaji, S.; Kifani-Sahban, F. Lignin and Derivative Charcoals: Functional

Groups Involved in the Adsorption Phenomenon. J. Mater. Environ. Sci. 2017, 8

(12), 4313–4322. https://doi.org/10.26872/jmes.2017.8.12.454.

(66) Boeriu, C. G.; Bravo, D.; Gosselink, R. J. A.; Van Dam, J. E. G. Characterisation

of Structure-Dependent Functional Properties of Lignin with Infrared

Spectroscopy. Ind. Crops Prod. 2004, 20 (2), 205–218.

https://doi.org/10.1016/j.indcrop.2004.04.022.

(67) Abdelaziz, O. Y.; Hulteberg, C. P. Physicochemical Characterisation of Technical

Lignins for Their Potential Valorisation. Waste and Biomass Valorization 2017, 8

(3), 859–869. https://doi.org/10.1007/s12649-016-9643-9.

(68) Bykov, I. Characterization of Natural and Technical Lignins Using FTIR

Spectroscopy. Construction 2008, 43.

(69) Hazwan, M.; Bowra, S.; Cox, P. Purity and Structural Composition of Lignin

Isolated from Miscanthus X Giganteus by Sub-Critical Water Extraction with

Associated Modifiers. J. Agric. Food Eng. 2020, 1 (1), 1–12.

https://doi.org/10.37865/jafe.2020.0010.

(70) L.D.S.Yadav. Organic Spectroscopy; 2005; Vol. 1. https://doi.org/10.1007/978-1-

4020-2575-4.

(71) Cao, J.; Xiao, G.; Xu, X.; Shen, D.; Jin, B. Study on Carbonization of Lignin by

TG-FTIR and High-Temperature Carbonization Reactor. Fuel Process. Technol.

2013, 106, 41–47. https://doi.org/10.1016/j.fuproc.2012.06.016.

(72) Qian, Y.; Qiu, X.; Zhu, S. Lignin: A Nature-Inspired Sun Blocker for

Broadspectrum Sunscreens. Green Chem. 2015, 17 (1), 320–324.

https://doi.org/10.1039/c4gc01333f.

(73) Gordobil, O.; Olaizola, P.; Banales, J. M.; Labidi, J. Lignins from Agroindustrial

by-Products as Natural Ingredients for Cosmetics: Chemical Structure and in Vitro

Sunscreen and Cytotoxic Activities. Molecules 2020, 25 (5).

https://doi.org/10.3390/molecules25051131.

(74) Schmidt, J. A. Mechanical Pulps, Ultraviolet/Visible Spectroscopy of

Chromophores in. Encycl. Anal. Chem. 2006, 1–20.

57

https://doi.org/10.1002/9780470027318.a2205.

(75) Jablonský, M.; Kočiš, J.; Ház, A.; Šima, J. Characterization and Comparison by

UV Spectroscopy of Precipitated Lignins and Commercial Lignosulfonates.

Cellul. Chem. Technol. 2015, 49 (3–4), 267–274.

(76) Kondo, T.; Watanabe, T.; Ohshita, T.; Kyuma, T. Physico-Chemical

Characteristics of Soluble Lignin Fractions Released from Forage Grasses by

Ruminant Digestion. Japan Agric. Res. Q. 1998, 32 (3), 187–195.

(77) Wang, K.; Xu, F.; Sun, R. Molecular Characteristics of Kraft-AQ Pulping Lignin

Fractionated by Sequential Organic Solvent Extraction. Int. J. Mol. Sci. 2010, 11

(8), 2988–3001. https://doi.org/10.3390/ijms11082988.

(78) Kang, S.; Li, X.; Fan, J.; Chang, J. Characterization of Hydrochars Produced by

Hydrothermal Carbonization of Lignin, Cellulose, D-Xylose, and Wood Meal. Ind.

Eng. Chem. Res. 2012, 51 (26), 9023–9031. https://doi.org/10.1021/ie300565d.

(79) El-Hendawy, A. N. A. Variation in the FTIR Spectra of a Biomass under

Impregnation, Carbonization and Oxidation Conditions. J. Anal. Appl. Pyrolysis

2006, 75 (2), 159–166. https://doi.org/10.1016/j.jaap.2005.05.004.

(80) Yanai, T.; Tew, D. P.; Handy, N. C. A New Hybrid Exchange-Correlation

Functional Using the Coulomb-Attenuating Method (CAM-B3LYP). Chem. Phys.

Lett. 2004, 393 (1–3), 51–57. https://doi.org/10.1016/j.cplett.2004.06.011.

(81) Wu, X. F.; Li, S. X.; Li, M. F. Effect of Temperature on the Properties of Charcoal

Prepared from Carbonization of Biorefinery Lignin. BioResources 2019, 13 (3),

6736–6745. https://doi.org/10.15376/biores.13.3.6736-6745.

(82) Gorelsky, S. I. Ab Initio and Semiempirical Methods. Encycl. Inorg. Bioinorg.

Chem. 2011. https://doi.org/10.1002/9781119951438.eibc0377.

(83) Repasky, M. P.; Chandrasekhar, J.; Jorgensen, W. L. PDDG/PM3 and

PDDG/MNDO: Improved Semiempirical Methods. J. Comput. Chem. 2002, 23

(16), 1601–1622. https://doi.org/10.1002/jcc.10162.

58

ANNEXES

Table A 1.Physical and chemical properties of reagents used in the extraction and analysis of lignin.

Compound M. Weight

(g/mol)

Density

(g/cm3)

Boiling

P. ( ºC)

Melting P.

(ºC)

Hazards Statements

Formic acid

(CH2O2)

46.025 1.22 100.8 8.4

Acetic acid

(CH3COOH)

60.021129372 1.049 118 17

Hydrogen

Peroxide (H2O2)

34.0147 1,4 150 -1

Ethanol

(C2H5OH)

46.07 78 -114 0.785

Table A 2.Details of Materials used in the extraction and analysis of lignin.

Name Details

Beakers 10 mL,25 mL,100 mL,150 mL,250 mL

Test tubes 100 mL, 200 mL

Dialysis bag From 12 000 to 14 000 Daltons

Kitasato flask 1000 mL

Magnetic stir PTFE stirrer bars

Droppers 1 mL, 2 mL

Table A 3.Details of equipment used in the extraction and analysis of lignin.

For the lignin extraction process

Equipment

name

Details

Soxhlet

Equipment -------------------

59

Heating mantle -------------------

Rotary

evaporator

equipment

BUCHI RotavaporTM R-210 Rotary Evaporator System

w/Glassware A,C,CR,E,S or V,w/o Display

Analytical

Balance: Balanza analítica HR-150A. Capacity (max): 152g; Accuracy (de): 0.1

mg Electric Oven POL-EKO APARATURA SP.J. Type SLW 115 STD. Temperature max

300 °C, Voltage 230V 50Hz

Coffee Grinder DAEWOO DCG-362, 120V-60Hz, 150W

Hot plate stirrer Hot plate & stirrer Mtops MS300HS

Max 380℃, stirring speed is among 100~1500rpm Vacuum system

filtration. ------------------

For hydrothermal treatment:

Hydrothermal

Reactor

100ml,Teflon lined Hydrothermal Synthesis Autoclave

Reactor,PTFE lined vessel

Centrifuge ELMI СМ-09M Fugamix. Maximum stability even at its

maximum speed of 9,000 RPM and maximum centrifugal force of

5,200 RCF (G-Force)

For the characterization / analysis:

Equipment

Name

Details

FT-IR: Agilent Cary 630 FTIR spectrometer equipped with a single

reflection, diamond attenuated total reflectance(ATR). Also, this

has a 25 mm interferometer permanently working Michelson 45°.

UV-VIS-NIR UV/VIS/NIR PerkinElmer Lambda 1050 equipped with Tungsten-

halogen and deuterium lamps operating from 175-3300 nm with a

detector photomultiplier R6872 for high energy in the UV/VIS

wavelength range, controlled by PerkinElmer WinLab software.

Table A 4.Details of equipment used in the extraction and analysis of lignin.

Molecule SMILES Code

Organosolv lignin CC(C(C1=CC2=C(C(=C1)OC)OC(C2CO)C3=CC(=C(C=

C3)OC(CO)C(C4=CC(=C(C=C4)O)OC)OC5=C(C=C(C=

C5OC)C(C(CO)OC6=C(C=C(C=C6)C7C8COCC8C(O7)C

9=CC(=C(C(=C9)OC)O)OC)OC)OC1=C(C=C(C=C1)C(C

(CO)OC1=C(C=C(C=C1)C=CCO)OC)O)OC)O)OC)O)O

Coniferyl alcohol Oc1ccc(cc1OC)/C=C/CO

Sinapyl alcohol OC/C=C/c1cc(OC)c(O)c(OC)c1

Coumaryl alcohol OC/C=C/c1ccc(O)cc1

Table A 5.IR Signals of constructed spectra through a combination of monomer alcohols

Wavenumber (cm-1)

Lignin Construction of

Basic unit

B3LYP

Construction of

Basic Unit

B3LYP

Construction of

Basic unit

PM3

Construction of

Basic Unit

PM3

60

(solvent) (solvent)

3250 3771 3750 3905 3462

2920 3138 3729 3147 3397

2955 3007 3144 3042 3312

2871 1642 3007 2944 3194

2829 1547 1632 1891 2280

2730 1329 1527 1596 2169

2100 1247 1315 1491 2090

1726 1147 1216 1392 1875

1572 1010 1125 1232 1810

1460 928 977 1133 1627

1375 834 829 1035 1581

1340 740 731 888 1457

1250 640 618 789 1255

1144 533 527 677 1110

1032 558 882

842 621

764 568

526

Table A 6.IR signals of basic unit spectra by different computational methods

Wavenumber (cm-1)

Lignin Basic unit

B3LYP

Basic Unit

B3LYP

(solvent)

Basic unit

PM3

Basic Unit

PM3

(solvent)

3250 3716 3734 3919 3452

2920 3209 3637 3814 3371

2955 3139 3234 3154 3251

2871 3009 3154 3065 2284

2829 1743 3032 2960 2106

2730 1621 1728 1889 2002

2100 1533 1615 1768 1873

1726 1367 1526 1663 1752

1572 1254 1357 1559 1655

1460 1148 1268 1389 1518

1375 1036 1139 1320 1413

1340 914 1003 1148 1171

1250 841 922 1067 648

1144 769 793 873

1032 656 640 761

842 505 455 631

764 433 527

526 414

61

Table A 7. IR signals of macromolecular lignin obtained experimentally and by PM3 method

Wavenumber (cm-1)

Experimental

Lignin

Lignin(SMILES)

PM3

3250 3149

2920 3028

2955 2910

2871 2771

2829 2708

2730 1883

2100 1768

1726 1467

1572 1379

1460 1282

1375 1138

1340 1030

1250 914

1144 777

1032

842

764

526