predictive multiscale modeling of nanocellulose based ......predictive multiscale modeling of...

Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

Andriy Kovalenko

2nd International Conference on Structural Nano Composites – NANOSTRUC 2014

Consejo Superior de Investigaciones Científicas (CSIS), Madrid, Spain. 20-21 May 2014

A Platform of Theory, Modeling, and Simulation

on Multiple Space and Time Scales

Nanoscale

Continuum

Methods Mesoscale

methods Lattice Monte Carlo

Brownian dynamics

Dissipative particle

dynamics

Molecular

Simulations

MM

MD

MC

Semi-empirical

Methods

Ab initio

Methods 10-15

10-12

10-9

10-6

10-3

100

10-10 10-9 10-8 10-7 10-6 10-5 10-4 (nm) (mm)

(fs)

(ps)

(ns)

(ms)

(ms)

LENGTH

[meters]

TIME

[sec] Equilibrium & Nonequilibrium Statistical Mechanics

Integral Equation Theory of Molecular Liquids / Molecular Theory of Solvation

(including 3D-RISM, replica RISM, ss-GLE)

nanodevices,

nanomaterials,

biomolecular

systems Nanoscale physics

chemistry

www.cein.ualberta.ca / research / kovalenko

The Kovalenko Group research program:

3D-RISM-KH molecular theory of solvation – predictive method for REAL systems

)'()'(')( vvuvuv rcdh

rrrr

• three-dimensional reference interaction site model (3D-RISM) integral equation:

- direct correlation function with asymptotics of site interaction potential (force field)

where

1)()( uvuv rr hg - distribution function / total correlation function

)/()()( B

uvuv Tkuc rr

)()()( vvvvvvv rhrr - susceptibility of solvent

molecular geometry

solvent density

intermolecular correlations

• Kovalenko-Hirata closure relation

1)(for )()()/()(1

1)(for )()()/()(exp)(uvuvuv

B

uv

uvuvuv

B

uvuv

rrrr

rrrrr

gchTku

gchTkug

)(uvru

• Solvation structure and thermodynamics in closed analytical form

m )()()())(())(( uvuvuv

21uv2uv

21v

B

urrrrrr cchhhdTk

Solvation free energy:

Enthalpy-entropy Decomposition: v

u1uuvvuvu , mm TTssT VV

m

i

ii

i

i

ugd

R

Rrrr

RRf

)()()(

uvuvv

uuvSolvation forces,

analytical gradients:

Partial molar volume:

)( 1)( 1 vvvuvvv

tot

u rcdcdV rrr

Kovalenko, Hirata, J. Chem. Phys. 1999, 110, 10095 Kovalenko, in: Molecular Theory of Solvation, Kluwer, 2003 Gusarov, Pujari, Kovalenko, J. Comput. Chem. 2012, 33, 1478 Kovalenko, Pure Appl. Chem. 2013, 85, 159

Water around a NaCl ion pair in the CIP and SSIP arrangements

PMFs of NaCl in aqueous solution

Kovalenko and Hirata, JCP, 112, 10391; 10403 (2000)

Cluster arrangement

of Na+ and Cl- ions

and water molecules

in 1 mol/l solution

Potential of mean force between simple ions in aqueous

solution by 3D-RISM molecular theory of solvation

Desolvation

Contact

Ionic Pair Solvent

Separated

Ionic Pair

Yoshida, Yamaguchi, Kovalenko, and Hirata, JPCB (2002)

Neutron diffraction and

compressibility

experiment

vs

RISM-KH theory

Water-rich mixture: Tetrahedral

hydrogen bonding cage

Alcohol-rich mixture:

Zigzag hydrogen bonding chains

Omelyan, Kovalenko, and Hirata, JTCC (2003)

Structural transitions in tert-butanol-water mixture

by RISM-KH molecular theory of solvation

Salt concentration effects in aqueous solutions

≈500 experimental compounds

Solvation free energy: experiment vs 3D-RISM-KH

ΔG

exp

(kcal/m

ol)

Δμ+PMV correction, 3D-RISM-KH (kcal/mol)

D. Palmer et al, J.Phys: Condens.

Matt. 22 ,492101 (2010) J.-F. Truchon

Role for confined water in chaperonin function [Jeremy L. England, Del Lucent, and Vijay S. Pande, JACS 130, 11838 (2008)]

Chaperonin nanocavity’s capacity for accumulating water near its surface

may correlate with its success in catalyzing folding

Cut-away view of solvation structure

of wild type GroEL+ES. The waters

counted as near the surface are

colored red and white.

Gray bars: the experimentally measured folding rates of DM-MBP

when encapsulated inside different mutants of GroEL. Blue bars:

the best linear fit of the number of surface waters to the measured

refolding rate

Computational challenge:

Large protein ~70,000 atoms (up to 1,000,000 atoms with solvent),

internal solvation is essential

R≈0.78

Water in internal cavity of Chaperonin mutants: Molecular Dynamics simulation vs 3D-RISM-KH

Layers used to count water molecules

in the internal cavity of the GroEL/ES

Occupation numbers of water in the internal cavity

of the WT GroEL and GroEL mutants

3D-RISM-KH speeds up calculation of the occupation numbers in the internal cavity

of GroEL/ES by two order of magnitude, compared to explicit solvent MD

R=0.98

M. Stumpe, N. Blinov, D. Wishart, A. Kovalenko, and V. Pande, J. Chem. Phys. B 115, 205 (2011)

Convergence of MD water density distributions

4Å

2 Å

1 Å

0.5 Å

5 ns De

nsity c

orr

ela

tion

fu

nctio

ns

M. Stumpe, N. Blinov, D. Wishart, A. Kovalenko, and V. Pande,

J. Chem. Phys. B 115, 205 (2011)

Effects of conformation flexibility and ion

distribution on folding rates of chaperonins

3D

-RIS

M-K

H/N

aC

l 0.2

M

3D

-RIS

M-K

H/e

xp

licit io

ns

MD

, explic

it ions

Single snapshot Multiple snapshots

3D-RISM-KH MD

Integration volume for

occupation number calculations

0 5 10 15 20

-80

-40

0

40

Distance (Å)

E (

kJ/m

ol)

msol

(B)

msol

(Q)

Esol

(B)

Esol

(Q)

Thiophene

disaggregation

path

Optimized

geometry

0 5 10 15 20-200

-150

-100

-50

0

50

100

E (

kJ/m

ol)

Distance (Å)

PMF3D-RISM-KH+PW91

(B)

PMF3D-RISM-KH+PW91

(Q)

ECOSMO+PW91

(B)

ECOSMO+PW91

(Q)

Bdisag

Bsol. exp.

Bitumen Fragment Adsorption on Chabazite: 3D-RISM-KH vs COSMO

COSMO:

• Continuum solvation method

• Accounts for solvent properties

such as dielectric screening

3D-RISM-KH molecular

theory of solvation:

• Accounts for solvation

with accuracy comparable to

explicit solvation model

• Predicts desolvation barriers

that have to be overcome by

bitumen molecules

approaching solvated surface

• Predicts monolayer adsorption

)()()()()PMF(

solvPW91solvPW91dμdEdμdEd

Stoyanov, S.R.; Gusarov, S.; Kovalenko, A. Multiscale Modeling of the Adsorption Interaction Between Model Bitumen

Compounds and Zeolite Nanoparticles in Gas and Liquid Phase, in: Industrial Applications of Molecular Simulations,

Meunier, M. (ed.); Taylor and Francis Books, Boca Raton, FL, USA, 2011, pp. 203-230.

Adsorption of indole on kaolinite surface

from solution in toluene and in n-heptane

Aluminum hydroxide face • Indole HN is oriented b/w two or one OH groups

• Orientation of toluene molecules is parallel

Silicon oxide face • Indole molecules are almost parallel to the surface

• Toluene molecules are perpendicular to the surface, pointing

with CH3 site or ring H atoms towards SiO hexagon center

Toluene prefers

Silica face

Indole prefers

Alumina face

guv

Distance, Å

N H

Fafard, J.; Lyubimova, O.; Stoyanov, S. R.; Kenne, G.; Gusarov, S.;

Cuervo, J.; Kovalenko, A; Detellier, C. J. Phys. Chem. C, 2013, 117, 18556

Multilayer adsorption of indole on kaolinite in toluene / n-heptane solution

Results

•

predicts adsorption and depletion layers

• Organic mass loading shows multilayer

formation in organoclays

• Aromatic and hydrogen-bonding solvents

leave only a monolayer of indole

Prospectives:

o Rational design of additives for selective

desorption of bitumen from kaolinite

o Selection of green solvents for

non-aqueous extraction of bitumen

surface

aver ),,( dd)( zyxgyxzg

Fafard, J.; Lyubimova, O.; Stoyanov, S. R.; Kenne, G.;

Gusarov, S.; Cuervo, J.; Kovalenko, A; Detellier, C.

J. Phys. Chem. C, 2013, 117, 18556

Self-assembly, conformational transitions, and functions

of synthetic organic supramolecular nanoarchitectures

GC (Guanine-Cytosine)

motif

rosette ring

rosette nanotube

self-assembly in solution

self-assembly in solution

Inner and outer hydration structure of rosette nanotube

wetting monolayer in rosette nanotube

channel

structural water

Interior shell water stabilizes rosette nanotube

Water binding strength: 4.1 kcal/mol per molecule

J. G. Moralez, J. Raez, T. Yamazaki, R. K. Motkuri, A. Kovalenko, H. Fenniri, J. Am. Chem. Soc. Communications 2005, 127, 8307

R. S. Johnson, T. Yamazaki, A. Kovalenko, H. Fenniri, J. Am. Chem. Soc., 2007, 129, 5735

G. Tikhomirov, T. Yamazaki, A. Kovalenko, H. Fenniri, Langmuir, 2008, 24, 4447

T. Yamazaki, H. Fenniri, A. Kovalenko, ChemPhysChem. 2010, 11, 361

Self-assembly, conformational transitions, and functions

of synthetic organic supramolecular nanoarchitectures

One-pot fabrication

of gold nanoparticles

Au NP 1.4 nm

most stable conformation nucleation pocket

most stable De

xam

eth

aso

ne

initial relaxed initial relaxed

Ta

mo

xif

en

Drug delivery carrier

3D-RISM-KH molecular theory of solvation: an essential part of

multiscale modeling of nanochemical and biomolecular systems

3D-RISM-KH yields the solvation structure and thermodynamics,

and provides self-consistent field effective potentials and solvation forces

in solutions of various composition in a wide range of thermodynamic conditions

)}]({),([)]([)}]({),([ solvationsolute rrrrr m eee nnEnA

N. Blinov et al., Biophys. J. 98, 282 (2010);

S. Genheden et al., An MM/3D-RISM

approach for ligand binding affinities

J.Phys. Chem. B 114, 8505 (2010)

• Ab initio KS-DFT / 3D-RISM-KH molecular theory of solvation;

Implemented in the ADF computational chemistry package:

S. Gusarov, T. Ziegler, and A. Kovalenko,

J. Phys. Chem. A, 110, 6083 (2006);

D. Casanova, S. Gusarov, A. Kovalenko,

and T. Ziegler, J. Chem. Theory Comput. 3,

458 (2007)

i

iii

KH

i

UV

R

RrurgRRf

)()(/)( solvation

m

• Multiple Time Step Molecular Dynamics of a biomolecule

steered with 3D-RISM-KH molecular theory of solvation

Implemented in the Amber molecular modeling package:

• Combined with Molecular Mechanics in a MM/3D-RISM-KH

method for free energy calculations

• As a standalone program available for community free of

charge as a part of the AmberTools

3D molecular theory of solvation coupled

with MD in Amber, T. Luchko et al., J. Chem.

Theory Comput. 6, 607 (2010);

I. P. Omelyan and A. Kovalenko, J. Chem.

Phys. 139, 244106 (2013)

http://ambermd.org/#AmberTools

[T. Luchko, D. Case, S. Gusarov, A. Kovalenko]

Predictive multiscale modeling of nanochemistry and nanocatalysis in solution by ab initio methods coupled with molecular theory of solvation

Reactions: Potential Energy States analysis

Solvation Structure and Thermodynamics

Analytical gradients: Gas phase + Solvation

RRR

solvel)( mEA

Implemented in

ADF computational chemistry package

• Optimal structure

• Properties (NMR,CD,IR,…)

• Reactions / Nanocatalysis

• Spectroscopy/Photochemistry

• ab initio CASSCF, KS-DFT, OFE-DFT coupled with 3D-RISM-KH theory of molecular solvation

• Works at the Car-Parrinello level

• 3D maps of solvation shells at MD level, without noise

• Solvation of macromolecules including inner spaces

• Analytical gradients in solution: geometry optimization, transition states, reactions, nanocatalysis in solution

A. Kovalenko, F. Hirata, J. Chem. Phys. 1999, 110, 10095. H. Sato, A. Kovalenko, F. Hirata, J. Chem. Phys. 2000, 112, 9463. S. Gusarov, T. Ziegler, A. Kovalenko, J. Phys. Chem. A 2006, 110, 6083 D. Casanova, S. Gusarov, A. Kovalenko, T. Ziegler, J. Chem. Theory Comput. 2007, 3, 458 J. Kaminski, S. Gusarov, T. Wesolowski, A. Kovalenko, J. Phys. Chem. A 2010, 114, 6082

Ionic Liquid

Carbon Nanotube

Glycine

Cellulose Nano Crystals

We employ state-of-the-art multiscale theory modeling and simulations

to develop quantitative structure-property relationships (QSPR)

between surface chemical modifications and properties

for rational design of CNC-based materials

CNC modifications so as to: • Tune surface properties

• Retain hydrogen bonding network

• Preserve mechanical properties

• Control colloidal properties (dispersion)

• Control polymer melt / solution flow

Cellulose Nano Crystals (CNC or NCC): • Reinforcing additive in high-value products

• Allows the deepest level of structure control

• Has reduced defect occurrence

• Easy to functionalize

• Need tuning of hydrophilicity/hydrophobicity

Wadood, H. Can. J. Chem. Eng. 2006, 84, 513

Beck-Candanedo, et. al.

Biomacromol. 2005, 6, 1048.

3 –

5 n

m

Cellulose Nano Crystals Cellulose polymers

Acid hydrolysis

Native

cellulose

Crystalline regions

Amorphous region

100 – 300 nm

CNC from wood

CNC particle model and methods

Iα cellulose unit cell Quantum Chemistry Methods (HF, DFT)

o Geometrical and electronic structure

o Missing force field parameters

Molecular Dynamics (Amber11 software)

o GLYCAM06 (carbohydrate force field)

o SPC/E water model

3D-RISM-KH solvation structure and thermodynamics

of nanoparticles in solution

I cellulose crystal structure has been used to build the initial

fibril: 34 chains (every chain consists of 16 glucose units)

Sulfate concentration:

Boluk, Y. Elemental analysis

NCC surface

modified with

SO3– groups

S. R. Stoyanov, S. Gusarov, and A. Kovalenko, in: Production and Application of Cellulose Nanoparticles, M. T. Postek, R. J. Moon, A. Rudie, M. Bilodeau (Eds.), (TAPPI Press, 2013), pp. 147-150.

S. R. Stoyanov, O. Lyubimova, S. Gusarov, and A. Kovalenko, Nordic Pulp & Paper Res. J., 29. 144 (2014).

top view side view

S. R. Stoyanov, O. Lyubimova, S. Gusarov, and A. Kovalenko, Nordic Pulp & Paper Res. J., 29. 144 (2014)

Relaxation and twisting of CNC particle

CNC particles twisting: Analysis

CNC surface modified with SO3

– groups

TM-AFM images of microfibrils of Micrasterias denticulata. In both (a) and (b) the microfibrils can be seen to undergo right-handed twist. S.J. Hanley, J-F. Revol, L. Godbout, D. G. Gray. Cellulose (1997), 4, 209-220

Short particle (shown above)

Long particle Short particle (shown above)

Long particle



O6d

O5

O3d

O5

O3d

O5

Experimental data: hydrogen bonding in Iα cellulose

1.60Å, α=49°

1.84Å, α=68°

α

O6a

O2

O2

O6d

O2

C6d

O2 2.55Å, α=36°

2.34Å, α=4°

2.60Å, α=29° 3.0Å, α=17°

CH…O

contacts

Hydrogen

bonds

Hydrogen bonding parameters:

o r(D-A) 3.0 Å

o A-D-H angle cutoff of 20

Reorganization of CNC intramolecular hydrogen bonds

O6d-O4

O3d-O5 Bonds parameters are r(D-A) 3.0 Å

A-D-H angle cutoff is 20

NCC

O6d-O3

O6a-O3

O6a-O2

O6d-O2

Hydrogen bonds stabilize twisted CNC structure.


CNC…water solvent hydrogen bonding

Towards understanding:

Hydrogen bonding, counterion and solvent effects

CNC drying and re-dispersion behavior

Beck, S.; Bouchard, J.; Berry, R.

Biomacromolecules, 2012, 13, 1486


Charge redistribution upon relaxation of neutral CNC

unrelaxed

CNC

relaxed

CNC

Sections of 3D maps of Na+ distributions

gNa+(r) in solution around CNC particle

gNa+(r)=3.0 (in yellow)

gCl-(r)=3.0 (in gray) 0.030 mol/kg NaCl

Before relaxation:

Thick interfacial layer

of Na+ counterions

After relaxation:

Thin interfacial layer

of Na+ counterions

Even distribution

of Na+ and Cl– ions


Driving forces of twisting of CNC particles

Driving forces of the twisting of CNC in solution, revealed by MD/3D-RISM-KH modeling:

• Chirality of the sugar polymer (precursor)

• Reorganization of the intramolecular hydrogen bonds

• CNC – water hydrogen bonding

• Relaxation of the surface charge macrodipole and of the interfacial screening charge

Time correlation function


<- r ->

Solvent mediated effective Interaction between parallel

CNC particles vs distance Spin angle 0°

Pristine particles in water: Attractive interactions at 3-15 Å

Pristine particles in aq. NaCl: Stronger attraction than in water

Sulfated particles in aq. NaCl: Aggregates disturbed by

electrostatic repulsion and stabilized by Na+

Fine structure suggests strong specific solvent-solute interactions.



Effective Interaction of CNC rods bridged by Na+ counterions

• 3D distribution functions at isoelectric point show

formation of Na+ bridges stabilizing NCC aggregates

S O O

O

O

O

O

O

O S

S

Isovalue for Na+ ~ 8 Isovalue for Na+ ~ 4

Na+

Hw Ow

6 Å

Towards understanding and control of: cholesteric ordering NCC dispersion in various solvents



Dispersion of modified CNC in water, benzene, and ionic liquid

64.0×64.0x20.76 Å

Distance

pristine esterified

0 5 10 15 20

-40

-20

0

20

40

60

80

100

Distance, Å

E,

kc

al/

mo

l

water

benzene

IL

0 5 10 15 20

-40

-20

0

20

40

60

80

100

Distance, Å

E,

kc

al/

mo

l

water

benzene

IL

Structures are relaxed using PCFF,

with CNC backbone atoms fixed,

in Accelrys Materials Studio®

No solvent expulsion barrier and

a lower disaggregation barrier

in benzene or in ionic liquid

for esterified CNC

Disaggregation barriers in

benzene and IL are higher

than in water for pristine CNC

Stoyanov, S. R.; Gusarov, S., Kovalenko, A.

in: Production and Application of Cellulose Nanoparticles,

Postek, M. T.; Moon, R. J.; Rudie, A.; Bilodeau, M., Eds.

TAPPI Press, 2013, pp. 147-150.

Cl–

mmim+

gO = 5.0

gH = 2.8

gNa = 2.0

gCl = 2.4

H C O

H Cl– O Na+

CNC-water hydrogen bonds replace

CNC-CNC hydrogen bonding network

gNa = 2.0, gCl = 2.4

Effective interaction between CNC particles vs torsion angle

in NaCl aqueous solution

Torsion angle

• Asymmetry about 90°

highlights torsional anisotropy

and cholesteric ordering

• Anisotropy decreases as NaCl

concentration is increased

Towards enabling:

Understanding and control of chiro-nematic ordering

Rational design of security inks

S. R. Stoyanov, O. Lyubimova, S. Gusarov, and A. Kovalenko, (in preparation).

The Plant Cell Wall

• Cellulose is embedded in a matrix of hemicellulose and lignin

• The non-cellulosic compounds are responsible for providing the cohesive forces that stabilize the cell wall structure

Adapted from: Annu. Rev. Chem. Biomol. Eng. (2011) 2, 121-45

Overcoming plant biomass recalcitrance

A molecular model of hemicellulose Hemicellulose is a highly branched heteropolysaccharide

For example, Xylan occurs in sugar cane, corn and other crops

All of the chemical changes in the xylan structure regard the branches, mainly arabinose and glucuronic acid.

Our cell wall model

Two 4-chain cellulose fibrils immersed in a hydrogel of hemicellulose branches, e.g., arabinose and glucuronic acid monomers, which are considered as the solvent for the 3D-RISM-KH calculations

Glucuronic acid

Arabinose

Cellulose fibril

xylose

glucuronic acid

arabinose

Silveira, R. L.; Stoyanov, S. R.; Gusarov, S.; Skaf, M. S.; Kovalenko, A. J. Am. Chem. Soc. 203, 135, 19048

Effective interactions (PMF): hydrophobic faces

(A) arabinose (B) glucuronic acid (C) glucuronate

Separation of the

hydrophobic faces of

two cellulose fibrils

Different compositions and concentrations of the hemicellulose branches


Effective interactions (PMF): hydrophilic faces

Separation of the

hydrophilic faces of two

cellulose fibrils

(A) arabinose

(B) glucuronic acid

(C) glucuronate


Cost of deconstructing the cell wall

Hemicellulose has a strong impact on the stability of the cell wall

The kinetic barrier for aggregation decreases as the hemicellulose concentration is increased

Hydrophobic faces Hydrophilic faces

Cellulose + glucuronate


Changes in hemicellulose composition affect cell wall stability

Glucuronic acid & glucuronate are mainly responsible for the recalcitrance

Hydrophobic faces Hydrophilic faces

The hemicellulose composition strongly modulates the cell wall recalcitrance

Aggregation Free Energy

Hydrophilic and basic interactions have comparable contributions to recalcitrance


Primary alcohol

Glucuronate and acetate bind the H-bond donors on the cellulose surface (primary alcohols)

Site-specific cellulose-hemicellulose interactions

Arabinose and glucuronic acid bind the H-bond acceptors on the cellulose surface (secondary alcohols)

Secondary alcohol


)()()(

2

1)(

2

1)( 2

rrrrr γγγγBγγ cchhTkρ=Φ

γ

γΦ=Δμ )(d rr

Large negative SFED

Smaller negative SFED

hydrogen bonds first layer 3D-SFED isosurfaces:

• are highly localized around polar sites on the cellulose microfiber surface

• indicate hemicellulose-cellulose H-bonding

• show diffuse second layer of hemicellulose monomers stacking over cellulose surface

• show weaker stacking interactions in the second layer, less specific than H-bonding

• show stacking due to hydrophobic and enhanced intermolecular C––H……O interactions

Intensity of solute-solvent effective adhesion over 3D spatial regions of the solvation shells

Φγ(r) < 0 - adhesion Φγ(r) > 0 - expulsion

Cellulose-hemicellulose interactions: 3D solvation free energy density


CONCLUSIONS

Molecular theory of solvation – an essential part of the platform of multiscale modeling

Statistical-mechanical, 3D-RISM-KH molecular theory of solvation

Solvation structure, thermodynamics, electrochemistry, and photochemistry of liquid and

solution systems of a given composition, thermodynamic conditions, and local environment

non-polar and polar solvents, co-solvents, ionic liquids, electrolyte solutions,

solid-liquid interfaces, macro- and supramolecules, biomolecules, ligands, soft matter,

nanoparticles, and nanoporous materials

Kohn-Sham DFT / 3D-RISM-KH for electronic structure, nanochemistry,

and nanocatalysis in solution

Implemented in Amsterdam Density Functional (ADF) computational chemistry package

Multiple time step MD / Optimized isokinetic Nóse-Hoover thermostat / 3D-RISM-KH

for molecular dynamics of biomolecules steered by effective solvation forces

Implemented in Amber Molecular Dynamics package and Amber Tools

Application to various chemical and biomolecular nanosystems and nanobiomaterials.

including:

Adsorption of organic species on clays

Synthetic organic supramolecular nanoarchitectures (rosette nanotubes)

Protein nanostructures (“nanomachines”)

Celulose nanocrystals (CNC) based nanosystems and nanocomposites

Cellulosic biomass pretreatment

FUNDING

• National Research Council (NRC) of Canada

• Natural Sciences and Engineering Research

Council (NSERC)

• Natural Resources Canada (NRCan),

Energy Research and Development,

Clean Energy Fund

• Alberta Innovates Bio Solution

• Alberta Prion Research Institute

• Alberta Innovates Technology Futures

• PrioNet

• Bill and Melinda Gates Foundation

• West Grid – Compute Canada

• NAREGI Supercomputing Project, Japan

• HPC Cluster, Integrated Nanotools Research

Facility, U of Alberta, Canada

• ArboroNano Forest Nanoproducts Network

• Centre for Oil Sands Innovation (COSI)

• Imperial Oil Research

• Ballard Power Systems

• Automotive Fuel Cell Cooperation (AFCC)

• Xerox Research Centre Canada (XRCC)

• AIST – National Institute of Advanced Industrial

Science and Technology, Japan

• MEXT – Ministry of Education, Culture, Sports,

Science, and Technology of Japan

• CAPES – Brazilian Federal Agency for the

Support and Evaluation of Graduate Education

• CNPq – National Council for Scientific and

Technological Development, Brazil


Acknowledgements

http://www.ualberta.ca/

COLLABORATORS

• M. Brett (NINT / U of Alberta)

• H. Fenniri (NINT / U of Alberta)

• R. McCreery, (NINT / U of Alberta)

• L. Unsworth (NINT / U of Alberta)

• D. Wishart (NINT / U of Alberta)

• V. Neburchilov (NRC-IFCI)

• G. Lopinski (NRC-SIMS)

• S. Dew (U of Alberta)

• M. Gray (U of Alberta)

• J. Stryker (U of Alberta)

• J. Tushynski (U of Alberta)

• R. Tykwinski (U of Alberta)

• T. Ziegler (U of Calgary)

• G. Patey (U of British Columbia)

• C. Detellier (U of Ottawa)

• D. Case (Rutgers U)

• V. Pande (Stanford U)

• T. Wesolowski (U of Geneva)

• C. Chiappe (U of Pisa)

• Yu. Dmitriev (U of St. Petersburg)

• M. Holovko (ICMP, Natl. Acad. Sci. Ukraine)

• A. Trokhymchuk (ICMP, Natl. Acad. Sci. Ukraine)

• I. Omelyan (ICMP, Natl. Acad. Sci. Ukraine)

• C. Breitkopf (Technical U of Dresden)

• F. Hirata (Insti. for Molecular Science, Ritsumeikan U)

• M. Kinoshita (Kyoto U)

• H. Sato (Kyoto U)

• T. Imai (RIKEN, Japan)

• M. Yoshida (AIST - Nanosystems Research Institute)

• T. Takanohashi (AIST - Energy Research Institute)

• J. W. M. Carneiro (Federal U of Fluminense)

• L. M. Costa (Federal U of Fluminense)

• M. S. Skaf (U of Campinas)

• P. R. Seidl (Federal U of Rio de Janeiro)

• R. Veregin (Xerox Research Centre Canada)

• K. Moffat (Xerox Research Centre Canada)

• MOE, Chemical Computing Group (J. F. Truchon, P. Labute)

• ADF, Scientific Computing and Modeling (S. van Gisbergen)

• MOLCAS (B.Roos, R.Lindt, P.A.Malmquist, V.Veryazov)

• AMBER (D. Case, T. Luchko)


Acknowledgements


Ack

now

ledg

men

ts Sergey Gusarov

Alex Kobryn

Nikolay Blinov

Maria Stepanova

Stan Stoyanov

Piotr Drabik

Kengo Ichiki

Qingbin Li

Lucian Livadaru

Dragan Nilkolic

Kyrylo Tabunshchyk

Takeshi Yamazaki

Yuri Yu Dmitriev

Ihor Omelyan

Andrij Trokhymchuk

Oh, Canada!..

Lyudmyla Dorosh

Wenjuan Huang

Yansen Lauw

Xianjun Liu

Olga Liubimova

Radha K Motkuri

Nataraj S Pagadala

Bhalchandra S Pujari

Andrey Tokarev

Oleksandr Zelyak

Leonardo M da Costa

Seigo Hayaki

Jakub Kaminski

Viktor Leontyev

Siriporn Pansri

Jackeline Oliveira

Rodrigo Silveira

Joshy Yesudas

Daisuke Yokogawa


predictive multiscale modeling of nanocellulose based ......predictive multiscale modeling of...

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