silica aerogel: Application in carbon capture

7/29/2019 silica aerogel: Application in carbon capture

http://slidepdf.com/reader/full/silica-aerogel-application-in-carbon-capture 1/20

Contents

1 Introduction 1

2 Motivation 2

3 Objectives 3

4 Model and Methodology 4

4.1 Vertex displacement . . . . . . . . . . . . . . . . . . . . . . . . . 5

4.1.1 Metropolis acceptance criteria . . . . . . . . . . . . . . . 5

4.2 Tetrahedral displacement . . . . . . . . . . . . . . . . . . . . . . 6

4.3 Tetrahedral rotation (Random) . . . . . . . . . . . . . . . . . . . 6

4.4 Tetrahedral rotation (Directional) . . . . . . . . . . . . . . . . . . 84.5 Cluster displacement . . . . . . . . . . . . . . . . . . . . . . . . 8

4.6 Cluster rotation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4.7 Reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4.8 Simulation details . . . . . . . . . . . . . . . . . . . . . . . . . . 11

5 Results and Discussion 11

6 Future plans 17

I



1 Introduction

We are witnessing some of hottest days of this millennium and this increase in

global temperature, termed global warming, has caused various climate changes,

such as rise in sea level, change in precipitation pattern, increase in frequency of

heat shock, hurricane, floods, etc. 1 Global warming is mainly caused by increase

in emission of green house gases (GHGs) (such as water vapour, carbon dioxide

(CO2), methane, chlorofluorocarbon, nitrogen oxides etc). From 1970 onwards

there has been an increase of 70% in GHGs, since 1970. Among them, amongst

them, 77% is contributed by CO2. The CO2 release is majorly due to human ac-

tivities and major sources of emission are, energy (25.9%), industry (19.4%) and

transport sectors (13.1%). 2 In order to pacify the adverse eff ects of climate change,

CO2 emission has to be attenuated.

The treatment of CO2 involves capture of CO2 from flue stream, transporta-

tion of CO2 and storing it in isolation. At current technology treatment for CO2

costs 51 − 330 US $ per tonnes of CO2. 2 To mitigate eff ect of CO2, it is esti-

mated that, by 2030, 5.6 giga tonnes of CO2 per year should be treated at a cost

< 100 US$ per tonnes of CO2. 1 Techniques available for removal of CO2 are, ab-

sorption in suitable solvent, conversion into metal carbonate and adsorption on suit-

able porous medium. Most widely used technology for CO2 removal is absorption

of CO2 in organic solvent like monoethanolamine (MEA), however, organic sol-

vents are hazardous and expensive to use.The CO2 can be converted into solid inor-

ganic carbonate by treating with metal oxides such as MgO, CaO, which avoid us-

age of organic solvents, however, this technique have high energy requirement 1.6-2.8 times of current requirement). The CO2 could be adsorbed over suitable porous

adsorbent. Adsorbent with large surface area, high pore volume and high affinity to

CO2 is preferred. Activated carbon which is commonly used as adsorbent in chem-

ical industry has adsorption capacity of 48.4-52.8 mg of CO2 per g of adsorbent.3

The adsorption capacity decreases in presence of water and regeneration of adsor-

bent is difficult. Zeolites, which are mesoporous material of alumina and silica,

have adsorption capacity of 53 mg of CO2 per g of adsorbent with good regen-

eration capacities. The adsorption capacity of zeolites has poor dependency on

pressure at high pressure region (from 2 to 20 bars) although, it has significant

increase in capacity at lower pressure region (upto 1.4 bars). 4 Molecular sieves,

which separate molecules based on its size, have adsorption capacity of 264-308mg of CO2 per g of adsorbent, with increase in adsorption at higher pressure. Sil-

ica aerogel, which is a mesoporous, material of silica has adsorption capacity of

135 mg of CO2 per g of adsorbent, which decreases in presence of water. Mod-

ification of silica aerogel with polyethylenimine (PEI), increases the adsorption

1



capacity up to 245 mg of CO2 per g of adsorbent. 5 This suggest that silica aerogel

is potential material in CO2 capture application. In general adsorption capacity of porous material depends on various factors such as pore size, fractional free area

and specific surface area. Study in silica system would help to synthesis silica

based materials of desired properties. The goal of this project is to utilize silica

based product for CO2 capture and hence there is a need to study silica systems.

We begin with simple system of silica, which is silica aerogel.

2 Motivation

Silica aerogel is a coherent aggregate of sub-micron particles of colloidal silica,

forming three dimensional network in mesoporous range. Majority of its pores liesin the range of 2-50 nm, as a result has high surface area of 500-12000 m 2 / g. 6

Aerogels are low density materials (having density of around 0.003 g / cm3), where

its interstitial space is occupied by air. 7 Availability of large specific surface area

makes it a potential material for catalyst and catalyst support. This was recently

demonstrated in synthesis of methanol from carbon monoxide, where silica aerogel

was used to support Cu-Zr catalyst. 8 Silica aerogel absorbs radiation in IR region

and has extremely low thermal conductivity (<3 W /mK ). Hence, can be used as a

promising material for transparent window insulator. 9 Silica aerogel has also been

used in trapping high velocity cosmic particle in space. 10

Properties of silica aerogel are due to silica linkage and network at molecular

level, which depends on synthesis route, suggesting, heart of expressing the finalproperties in aerogels lies in its synthesis protocol. Conventionally silica aerogel is

prepared using sol-gel process, which is influenced by various parameters, such as,

concentration of precursor, pH, gelation time etc. 6 Still at molecular level how the

structure is evolved is not clearly understood. Such understanding will have sig-

nificant impact in controlling structure and designing properties in aerogel. Insight

on polymerization pathway are not only limited to silica aerogel alone and can be

used to study other porous and ordered mesoporous materials like zeolite.

Selective adsorption depends on interaction between guest molecule and host

molecules. This interaction largely depends on three dimensional network of ad-

sorbent material, affinity towards guest molecules. Choice of molecular arrange-

ment depends on protocol of synthesis, which, can be used to desired orientation

at molecular level.

2



3 Objectives

The objective of this study are. To,

• understand the silica system using suitable model.

• device a protocol to synthesis aerogel of desired properties.

• enhance aerogel’s CO2 adsorption capacity.

Silica aerogel materials are obtained using condensation and hydrolysis reac-

tion of silica precursor, termed as silica polymerization. In experimental studies,

usually, kinetics of polymerization are obtained using nuclear magnetic resonance

(NMR) and structural morphology are predicted using small angle x-ray scattering

(SAXS). However, experimental techniques are bounded by physical limitationof respective resolution. Further, results are inferred from measurable quantites,

which are averaged values. Nevertheless, in principle, employing appropriate mod-

els, simulations can evaluate actual system, at multiple length and time scale.

Major challenges in modeling silica system are large system sizes and long time

required to achieve gelation and developing appropriate interactive potential. 11 Ini-

tial approach to simulate silica system was to perform reaction kinetics. However,

silicon been capable of forming four bonds, has 15 distinguishable reaction en-

vironments and 165 rates constant. 12 Since, all the rate constants are determined

using experiments, it is near impossible to isolate each species and obtain all the

rate constants. By assuming only five distinguishable Si species, based on num-

ber of bridging oxygen, simulations were performed using bimolecular reaction.Although, such simulation agrees with initial evolution of silica species ( Q0, Q1

and Q2), shows deviation from experimental values at longer time. In attempt, to

successfully predict Qn evolution, Monte Carlo moves were performed with lat-

tice restrictions. Lattice restriction confines displacement of atoms to grids, which

lowers the degree of freedom. Such simulations are computationally less expen-

sive, however, conveys limited information. To answer such difficulty, Feuston and

Garofalini13 studied silica system using molecular dynamics by elevating temper-

ature and concentration to achieve gelation in accessible time scale of few nano

seconds. Due to elevation in temperature, their Qn distribution does not agree

with experimental distribution. Tetrahedral model using Reaction ensemble Monte

Carlo simulation covers most of drawback of aforementioned methodologies. It

tries to mimic real system by anticipating possible moves by tetrahedron. In this

report we have tried to cover the major aspects to REMC and tried to reproduce the

results of previous study 11.

3



4 Model and Methodology

To understand mechanical behaviour of silica at molecular level, silica units can

be represented by tetrahedral geometry. 14 Each of the tetrahedral units comprises

of hard sphere at center, representing silicon atom and vertex consisting of four

point particles representing hydroxyl group (OH) or bridging oxygen (BO). At this

moment there is no distinction between OH / BO.

Although corners of tetrahedral can be shared by more than one tetrahedron,

overlapping of centres is prevented by maintaining hard sphere interaction between

two silicon atoms. As represented in Equation 1. Where, r is distance between two

silica atoms.

U HS =∞, r ≤ σ0, r ≥ σ

(1)

To include bond vibrations in the model, tetrahedron is treated as flexible struc-

ture by imposing harmonic spring between OH / BO groups. The bond distortion

potential U 1 is represented by Equation 2. Where, r i is position of OH / BO and r 0is equilibrium distance.

U 1 =

4i=1

3 j=i+1

k s

2(|ri − r j| − r 0)2 (2)

Reactions occurring in silica polymerization are generalized in Equation 3.

≡ S i − OH + HO − S i ≡ ≡ S i − O − S i ≡ + H 2O (3)

Where, forward reaction is condensation and backward reaction is hydroly-

sis. Condensation reaction leads to connection of two tetrahedron via BO. Angle

potential U 2, for S i − O − S i angle is evaluated using harmonic spring potential

represented by Equation 4. Where θ is angle formed between S i − O − S i bonds

and θ 0 is equilibrium angle.

U 2 =k A

2(cosθ − cosθ 0)2 (4)

The parameters for hard sphere, corner sharing flexible tetrahedral model are

obtained from Density functional theory (DFT) studies and are reported in litera-ture. 14 These parameters are tabulated in Table 1.

In sampling of silica polymerization there are various events which are required

to simulate so that simulation closely mimic the real system. Objective of simula-

tion is to study the evolution of silica structure,in addition, also to understand the

4



Table 1: Parameters of tetrahedral model

Parameter Value

σSi−Si, (A) 2

k S,(kJ mol−1 A−2) 851

k A,(kJ mol−1 ) 226.74

r 0, (A) 2.61

θ 0, (deg) 155

pathway to silica polymerization. Hence, modelling of various moves are essential.

Such moves are explained in subsequent subsections.

4.1 Vertex displacement

At molecular length scale, every particle is set into the motion, depending on its

thermal energy. In addition, there are intra molecular motions which also accounts

for system energy. To anticipate the movement of OH / BO, vertex displacement

move is invoked. In vertex displacement move, randomly selected vertex (OH / BO)

is displaced randomly within predefined distance, dr. Displacement of vertex move,

incorporates the distortion of tetrahedron. Such distortions, results into, shift of

center by forth of the total displacement. Move is initial checked for its hard sphere

overlapping with centres of other tetrahedrons. Followed by, bond distortion, U 1and angle distortion, U 2 energies. The moved is accepted / rejected as per Metropo-

lis acceptance criteria (see sub section 4.1.1). Detail of algorithm is explained in

flowchart, Figure 1a.

4.1.1 Metropolis acceptance criteria

Metropolis acceptance criteria is a scheme to accept / reject the move based on prob-

ability of two ensemble. Probability of an ensemble is given by equation 5 where

U i is probability of existing of ith ensemble, β is Boltzmann parameter and Z is

partition function.

P(i) = e− βU i/ Z , Z = N i=1

e− βU i (5)

Metropolis scheme enable us to accept / reject the move based on the ratio of two

probabilities of two (initial and final) configurations. To manifest lowering of total

5



system energy, move is accepted for system directing towards low energy. How-

ever, move can be accepted, even when, move leads to higher energy. The accep-tance of such move depends on probability of configuration to exist. This ensures

Boltzmann distribution in an ensemble. Ratio of two probabilities, e− β(U i+1−U i),

gives Boltzmann factor, which is probability of accepting the move. With an uni-

form random number (occurrence of numbers between 0 and 1 is equal) Boltz-

mann factor is compared. If Boltzmann factor is more than Random number then

the move is accepted, else, rejected. Higher the diff erence in energies (for new

configuration with higher energy), less likely it would be occurred. Thus, without

obtaining partition function, simply by knowing two energies of diff erent configu-

ration, decision of accepting / rejecting the move could be executed. This scheme

is know as Metropolis acceptance criteria.

4.2 Tetrahedral displacement

Tetrahedral displacement move anticipate the displacement of entire tetrahedron.

In real system every silica unit is moves, depending on how they are bonded with

other tetrahedron. In this move, randomly selected tetrahedral unit is displaced

within predefined distance, dr. The selected tetrahedral can be free or bounded, as

in reality, leading to calculation of additional U 1 and U 2. Prior to energy diff er-

ence, hard sphere interaction is calculated.The final acceptance of move depends

on Metropolis acceptance criteria (see subsection 4.1.1). For each accepted move

new energy is updated and for each rejected move initial configuration is restored

back. Algorithm is explained in flow chart Figure 1b.

4.3 Tetrahedral rotation (Random)

Many a times, molecules retains its position but changes its orientation, this de-

pends on how a molecule orients with its surrounding molecules / environment.

Orientation also facilitates reaction as two reacting group may come closer by

rotation moves. With this move, more configuration can be covered for a given

ensemble, for a fixed number of simulation. In this move entire of tetrahedral is

rotated around its center, with maximum of predefined angle, d θ . At this moment

axis of rotation passes through the center, although, it is not necessary. In ran-

dom rotation, the tetrahedral is rotated around any random axis. With every such

move, U 1

and U 2

are evaluated for bonded tetrahedral (for tetrahedral there is no

such energies associated). U HS interaction is used to find overlapping and move

is accepted for all not overlapped molecules, as per metropolis acceptance criteria

(see subsection 4.1.1). The algorithm for tetrahedral rotation in random direction

is illustrated in flowchart, Figure 2a.

6



(a) Vertex

(b) Tetrahedral

Figure 1: Flowchart for displacement moves

7



4.4 Tetrahedral rotation (Directional)

This is move is similar to above mentioned move, however, has little diff erence. In

real system, sometimes rotation about S i − O bond is more favourable. This will

happen when a selected tetrahedral unit is bonded from one vertex and free to rotate

along selected bond. Without this move it would have taken many moves of random

rotation to get same orientation of tetrahedral. This move can also occur when

selected tetrahedron is connected to more than one tetrahedron, this incorporates

expenditure of energy, thereby lowering the probability. The move is accepted

based on U HS interaction and Metropolis acceptance criteria (see subsection 4.1.1).

The detail of algorithm is illustrated in flowchart in Figure 2b.

4.5 Cluster displacement

Cluster displacement move mimics the displacement of entire aggregate of silica.

The movement of aggregate depends on diff usivity of aggregate, which in turns de-

pends on size of aggregate. Larger the size of cluster, more would be its resistance

to move and lesser would be extend of its displacement. In this move, cluster of

randomly selected tetrahedral is displaced with maximum of predefined distance,

dr. Only hard sphere overlapping is considered, as there is no distortion in bond

nor in angle. Based on overlapping move is accepted or rejected. The detailed

description of algorithm is illustrated in flowchart, Figure 3.

4.6 Cluster rotation

Cluster rotation move is means to encompass possible orientation of cluster. Clus-

ters may rotate and orient as per their favourable configuration. Simulation of this

move involves bond stretching and eventually leads to displacement of center of

tetrahedral and angle distortion. Hence, in this move along with U HS ,U 1 and U 2are calculated. Hard sphere overlapping and metropolis acceptance criteria are

used to accept / reject the move. The detailed description of algorithm is illustrated

in flowchart, Figure 4.

4.7 Reaction

According to Equation 3 there are two reactions occurring simultaneously in the

system. Forward reaction is condensation reaction, involves clubbing of two OHgroups. Where as, backward reaction (hydrolysis reaction) involves splitting of

BO into two OH group. Thus this move resemble the reactions occurring in real

system. In simulation, the probability of occurrence of such moves depends on

8



(a) Random (b) Directional

Figure 2: Tetrahedral rotation

9



Figure 3: Flowchart for Cluster displace-

ment

Figure 4: Flowchart for Cluster rotation

10



equilibrium rate constant and standard Gibbs energy of reaction. Although poly-

merization involves multicomponent system, equilibrium rate constant is considerto be same for all reactions. The approximation of K eq = 500 is supported by DFT

studies reported in literature 15.

In this move two randomly selected OH groups are replaced by BO. This mani-

fests in increase in reaction rate. To set back to more realistic move, the probability

of occurrence of such move is kept very low. Reaction can occur between two OH

group of diff erent cluster or within same cluster. Inter cluster reaction leads to

increase in cluster size whereas, intra cluster reaction leads to sever distortion of

cluster. This distortion can be mollified by introducing system relaxation. System

relaxation is implemented by using force-bias Monte Carlo steps. Force bias MC

involves displacement of all vertex of a cluster in the direction of distorting force.

A set of 500 MC steps are carried after such intra cluster distortion. The detailed

description of algorithm is illustrated in flowchart, Figure 5.

4.8 Simulation details

In each simulation an ensemble of 1000 randomly configured monomers were

taken in cubic box of 104 A at 300 K. Equilibrium moved was necessary to gener-

ate random configuration of monomer. In equilibrium move all but reaction moves

were performed. Total of 1 × 105 equilibrium steps were performed. Wherein, 1

step is defined as set of moves to displace all of its particle once, which is the size of

the system, N ( 1 MC step = 1000 MC moves). Reaction moves were included after

equilibration of system was completed. All moves with predefined probability of

occurrence were considered. Probability of carrying out particular move dependson two main factor. Firstly, since movement of entire tetrahedral is more important

than individual atoms, tetrahedral moves has highest probability. Secondly, system

was diluted (5% mole) to compare with experimental outcomes, leading to low

probability of reaction to occur. Hence, probability of reaction moves were kept

very low. Detailed configuration of system and probabilities of diff erent moves are

tabulated in Table 2.

5 Results and Discussion

Although the purpose of this report was to reproduce the simulation results of work

of Malani et al. 11, it also includes the comparison with experimental work by De-vreux et al. 16 Conventionally, Qn distribution is mapped to probe silica polymer-

ization, which is performed by S i29 NMR study for each sampling. The work

presented 11, relates physical time with MC step, starting from common reference.

11



Figure 5: Flowchart for reaction move

12



Table 2: Simulation details

Parameter / Probabilities Value

Box size (lx,ly,lz Angstrom) 104,104,104

Temperature (K) 300

Equilibrium rate constant, K eq 500

Vertices displacement 0.2

Tetrahedra displacement (0.1+0.24)=0.34

Tetrahedra rotation (Random) (0.05+0.12)=0.17

Tetrahedra rotation (Directional) (0.05+0.12)=0.17

Cluster displacement 0.05

Cluster rotation 0.05

Reaction moves (0.01+0.01)=0.02

The common point considered was crossing of Q0 and Q1 profiles, which occurs

around 0.1 hr., experimentally and 148 MC in simulation. This point occurs at

165 MC in current reproduction of work. Subsequent crossing over points are

compared to establish relationship between physical time and MC steps. The com-

parison is illustrated on Figure 6. Initial crossing over of Q0 and Q1 is slower

than original work but after generation of Q2, the entire polymerization process

is accelerated. The last crossing over point between Q2 and Q4 was obtained be-

fore 104 MC steps, suggesting accelerated rate kinetics. The explanation for such

behaviour would be in distribution of probabilities for various moves. The total

probability of tetrahedral displacement was considered as 0.34, which is split into

0.1 for free tetrahedron and 0.24 for bonded tetrahedron. Same is true for rotation

moves. Suggesting, free tetrahedron, which were initially present had a less prob-

ability for moves than in latter stage, where, tetrahedron are bonded. This explain

why first cross over point was obtained late and subsequent points were obtained

early.

To avoid issue related to diff erent MC steps and physical time of the system,

Qn distribution against degree of condensation was plotted, illustrated in Figure 7.

The crossing over points, obtained in this work, takes place at higher degree of con-densation. This suggest faster reaction in this work. Apart from this discordance,

overall trend agrees with original work.

The evaluation of cluster size, gives insight about stage of polymerization. The

13



Figure 6: Evaluation of Qn during polymerization, a) Current work, b) Original

simulation 11, c) Experiments of Devreux et al. 1614



Figure 7: Variation of Qn as a function of degree of condensation, a) Current work,

b) Original simulation 11, c) Experiments of Devreux et al. 16

15



average and maximum cluster size distribution are plotted in Figure 8. The trend

can be divided into three main parts based on change in slopes of the trends. Thisdivision leads to diff erent stages of silica polymerization.

Figure 8: Evaluation of average and maximum cluster size during MC simulation.

Region I, II, III corresponds to oligomerization, ring formation and cluster aggre-

gation stages of polymerization, respectively.

1. Oligomerization (0-1000 MC steps)

The initial part of polymerization leads to formation of various oligomerssuch as dimer, dimer-monomer, dimer-dimer etc. The oligomerization is fol-

lowed by cluster-cluster aggregation or cross linking.

2. Ring formation (1000-3400 MC steps)

Small change in slope can be observed, in this region Q1 decreases where

as Q2and Q3 increases, indicates that cross linking is taking place. This also

leads to formation of ring structure.

3. Cluster-cluster aggregation (3400- 6000 MC steps)

Sharp change in the trend is observed in this region. Average and maximumcluster size both increases indicates drastic agglomeration of cluster. At this

stage cluster-cluster aggregation takes place until one single cluster is ob-

tained.

16



4. Gel ageing (≥ 6000 MC steps)

This stage occurs after one single cluster is obtained. In this stage, degree of condensation obtained is 0.8 and not completely 1. Hence, there is scope of

forming cross-linking bonds among species. This leads to strengthening of

overall silica structure.

6 Future plans

Since, this model mimics the experimentally obtained data, could be used to study

the silica system. This model can be further develop to explore parameters influ-

encing nano structure arrangement of silica aerogel. This can be used to control the

silica polymerization and to obtain a desire property of aerogel such as, enhancingCO2 capture capacity. Therefore future plans for this research can be formulated

as; To,

• understand the silica polymerization system with parameter aff ecting various

polymerization pathway.

• device a protocol to synthesis aerogel of desired properties by optimizing

over desired pathway.

• enhance aerogel capacity to capture CO2.

17



References

[1] P.R. Bosch R. Dave L.A. Meyer B. Metz, O.R. Davidson. Ipcc 2007:cli-

mate change 2007 mitigation, ar4. Technical report, IPCC Working Group III

Netherlands Environmental Assessment Agency, 2007.

[2] O. Davidson H. C. de Coninck M. Loos Metz, B. and L. A. Meyer. Ipcc, 2005:

Ipcc special report on carbon dioxide capture and storage. Technical report,

Working Group III of the Intergovernmental Panel on Climate Change, 2005.

[3] R.V. Siriwardane. Solid sorbents for removal of carbon dioxide from low

temperature gas streams.

[4] Mark E. Davis. Zeolites and molecular sieves: not just ordinary catalysts.

Industrial Engineering Chemistry Research, 30(8):1675–1683, 1991.

[5] Sanjay Kamble Rajnish Kuma Tushar Sakpal, Asheesh Kumar. Carbon diox-

ide capture using amine functionalized silica gel. Indian Journal of Chem-

istry, 51A(1):1214–1222, 2012.

[6] R.K. Iler. The Chemistry of Silica: Solubility, Polymerization, Colloid and

Surface Properties and Biochemistry of Silica. A Wiley-Interscience publi-

cation. Wiley, 1979.

[7] A. Soleimani Dorcheh and M.H. Abbasi. Silica aerogel; synthesis, prop-

erties and characterization. Journal of Materials Processing Technology,

199(13):10 – 26, 2008.

[8] Park Hyung-Ho Kang Eul Son Gurav Jyoti L., Jung In-Keun and

Nadargi Digambar Y. Silica aerogel: Synthesis and applications. Journal

of Nanomaterials, 115(32):15988–15999, 2010.

[9] Lawrence W. Hrubesh. Aerogel applications. Journal of Non-Crystalline

Solids, 225(0):335 – 342, 1998.

[10] Aerogel. http: // www.aerogel.org / , August 2013.

[11] Ateeque Malani, Scott M. Auerbach, and Peter A. Monson. Monte carlo

simulations of silica polymerization and network formation. The Journal of

Physical Chemistry C , 115(32):15988–16000, 2011.

[12] Roger A. Assink and Bruce D. Kay. Current issues in sol-gel reaction kinetics.

MRS Proceedings, 180, 1 1990.

18



[13] B. P. Feuston and S. H. Garofalini. Oligomerization in silica sols. The Journal

of Physical Chemistry, 94(13):5351–5356, 1990.

[14] R Astala, Scott M Auerbach, and PA Monson. Density functional theory

study of silica zeolite structures: Stabilities and mechanical properties of sod,

lta, cha, mor, and mfi. The Journal of Physical Chemistry B, 108(26):9208–

9215, 2004.

[15] J.C.G. Pereira, C.R.A. Catlow, G.D. Price, and R.M. Almeida. Atomistic

modeling of silica based sol-gel processes. Journal of Sol-Gel Science and

Technology, 8(1-3):55–58, 1997.

[16] F. Devreux, J. P. Boilot, F. Chaput, and A. Lecomte. Sol-gel condensation of

rapidly hydrolyzed silicon alkoxides: A joint 29Si nmr and small-angle x-ray

scattering study. Phys. Rev. A, 41:6901–6909, Jun 1990.

19

Documents

silica aerogel: Application in carbon capture