collaboration for innovation: enriching the knowledge pool

Collaboration for InnovationEnriching the Knowledge Pool

15 September 2010

Dr. Lalitha Subramanian, Accelrys Fellow Senior Director Contract Research & Scientific Consulting Services

Sectors we serve

Chemicals

Pharmaceutical and Biotech

Energy Automotive

Personal & Home Care

MicroelectronicsAerospace & Defense Technology

Contract Research Services

p

The challenges you face…

Faster delivery of products to market“trial and error” experiment is time‐consuming‐ trial and error experiment is time‐consuming

More efficient use of R&D resourcesthe cost of experiment is rising‐ the cost of experiment is rising

Optimized products and processesi i i f li i i h‐ optimization often relies on new insight

Solutions to critical research problems‐ solutions often come from adapting or controlling molecular level behavior

Innovate to stay ahead …

Innovation = Conversion of knowledge into new &

profitable products, solutions and services.

Enrich Integrate Mine Analyze Innovate

Process of InnovationAnalysis Association

Wet Lab Data

Literature Data

Problem

KnowledgeKnowledge

SynthesisSynthesis

Solution

Accelrys Contract Research Services Provide…

Lower product lead timesA large UK based catalyst company used simulation to narrow down the options for novel fuel cell catalystsdown the options for novel fuel cell catalysts

Reduced costsUS based Aerospace/Defense contractor used this service to screen battery materials reducing experimental time

Improved product and process qualityUS based plastics and resins distributors used this service toUS based plastics and resins distributors used this service to understand the action of compatibilizers and designed improved ones for nanoclay polymer composites

Solutions to critical research problemsSwiss based dermatology company used this service to solve critical scientific problem in the area of controlled drug releasecritical scientific problem in the area of controlled drug release

Sectors we serve

ChemicalsPharmaceutical

and BiotechOil & Gas Automotive


MicroelectronicsAerospace & Defense Green Industry


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PROCESS IMPROVEMENT OF HETEROGENEOUS CATALYSIS

Case Study I

Work done by: Lalitha Subramanian and Li Xiao (unpublished)

Epoxidation Catalysis – Process Improvement

• Improve performance of the ethylene epoxidation catalyst by increasing longevity of the catalyst

h l h d• As the catalyst ages, the temperature is increased to increase performance. However experimentally it was seen that after a certain temperature, the catalyst performance dramatically p y p ydropped.


Some “Is it …” questions:

• Is the selectivity varying in consort with activity ?• Is activity or selectivity different for different silver surfaces ?

Deactivation of Alumina supported Silver Catalyst

Is activity or selectivity different for different silver surfaces ?• Is silver morphology expression varying with time‐on‐stream ?• Is morphology controlled by low‐level additives ?• Is deactivation by surface passivity ? • Is metal agglomeration the prime deactivation mechanism ?• Is sintering half‐time governed by metal migration rate ?• Is metal migration via on‐surface diffusion of small clusters ?• Is metal migration via on‐surface diffusion of clunkers ?• Is evaporation to gas‐phase important ?• Is chemical compound formation occurring ?• Is cluster free energy affected by promoters or poisons ?• Is cluster free energy affected by promoters or poisons ? • Is metal surface diffusion moderated by surface defects ? • Is support structure affected by time‐on‐stream ?• Is sintering rate affected by different support formulations ?


Is sintering rate affected by different support formulations ?• . . . . . . . . . • . . . . . . . . .

Strategy: Focus on Surface Morphology Behavior

Strategy:

Study differences

Temp. effects:

• Calculated Ag

Software used:

• DMol3 code for in reaction energy on various Ag surfaces

surface energies at different temperatures

Energy calculations

• Morphology Ethylene and O2on Ag(100), Ag(111) Ag(110)

• Model the growth morphology of

p gycode for growth of surfaces

• Materials StudioAg(111), Ag(110) morphology of Ag catalyst

• Materials Studio


Results

• The reaction mostly occurs on open surfaceson open surfaces

• O2 adsorption via different mechanisms on the Agmechanisms on the Ag surfaces

• Ethylene attaching to form aEthylene attaching to form a metallacycle

• Ag(100) has the lowestAg(100) has the lowest energy barrier for epoxidation


Results

• The temperature has to be maintained between T1 and T2 for the optimum surface to be present.

• Beyond T2, the Ag (100) surface decreases causing a decrease in the preferred catalytic surfacesurface.

T1 T2


Theoretical Prediction Substantiated• Catalytic selectivity is dependent on the geometric structure of catalytically active

Ag particles.

• Shape and size controlled synthesis of Ag nanoparticles is used to show that silver p y g p

nanocubes exhibit higher selectivity than nanowires and nanospheres.

• The enhanced selectivity toward ethylene oxide is attributed to the nature of the

d f f b d d d b ( )exposed Ag surface facets; Ag nanocubes and nanowires are dominated by (100)

surface facet and Ag nanospheres are dominated by (111).

Ref: “Shape and Size Specific Chemistry of Ag Nanostructures in Catalytic Ethylene Epoxidation”


Ref: Shape‐ and Size‐Specific Chemistry of Ag Nanostructures in Catalytic Ethylene Epoxidation Phillip Christopher and Suljo Linic, ChemCatChem 2010, 2, 78–83

Process Improvements Achieved

Wet Lab Analytical Modeling dExperiments

yExperiments and

Simulation

Enriched Knowledge Pool

GREEN ALTERNATIVE FOR CHEMICAL SYNTHESIS

Case Study II

Work done by: Amity Andersen, Niri Govind, Lalitha Subramanianl l Si l i l 3 0 S b 2008 02 039Molecular Simulation, Volume 34, Issue 10 ‐ 15 September 2008 , pages 1025 ‐ 1039

Toluene Nitration – Green Alternative Route

• Nitroaromatics important intermediates in a wide range of industries

– Pharmaceuticals, Agrochemicals, Consumer goods, Polymers

• Conventional synthesis via concentrated nitric/sulfuric acid mixture

– para‐ versus ortho‐nitration of toluene selectivity poor

– Unfavorable side products and multiple nitrationUnfavorable side products and multiple nitration

– Acid waste solution needs expensive remediation treatment

• Recent research in alternative “green” synthesis methods:

– Nafion‐H, lanthanum triflate, supported clay, mixed metal oxides, sulfated zirconia, supported silica, zeolites

• In particular, zeolite H‐beta (Smith and coworkers, 1998) show:

– para selective

– Reduction in waste product (acetic acid)

– Substrate and products readily purged thermally


Substrate and products readily purged thermally

– Recyclable

Challenge and Strategy:

Challenge:g

High paraselectivity unique to beta Zeolite

Strategy:

• Need to have an

Software used:

• DMol3 code for to beta Zeolite

Experiments with Mordenite, ZSM‐

in‐depth understanding of the

energy calculations

• Sorption code 5, and Y did not yield as high a para selectivity as

mechanism of nitration of toluene in Beta

pfor toluene location sites

• Materials Studioobserved for beta Zeolite

ZeoliteMaterials Studio


Results: Toluene occupation in beta

• Toluene diffuses readily in the two 12‐T straight channels, but not in the tortuous channel in the third directionP h i i h h l d i l h i id l


• Path in ‐ straight channels concentrated at interval where sinusoidal pore opening occur

Results: Para versus Ortho Proton‐to‐Cage Transfer


Conclusions

f l b f f• Origins of para‐selectivity combination of many factors

– Large 12‐T pores allow for excellent diffusion• Two 12 T straight pores allow for free diffusion• Two 12‐T straight pores allow for free diffusion

• Tortuous 12‐T pore in third direction allow for longer sorbate residence around active cage areas

– Microcrystalline beta with high surface area crystals most selective

Flexibility of aluminum sites (tetrahedraloctahedral) allow for– Flexibility of aluminum sites (tetrahedraloctahedral) allow for targeted placement of nitrate ions and ultimately acetyl nitrate

– Steric hindrance from zeolite framework most likely cause forSteric hindrance from zeolite framework most likely cause for para‐selectivity

• Steric hindrance of ortho attack by acetyl group of AcNO3 proposed by Prins and k ti bl


coworker questionable

• Steric hindrance of cage itself likely source of selectivity (as shown by this work)

Visualization of the Reaction


Selectivity in Green Synthesis Understood


yExperiments and

Simulations



Sectors we serve

Chemicals


Energy Automotive




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CONTROL OF POLYMER MEMBRANE MORPHOLOGY

Case Study III

Work done by: James Wescott and Abhijit Chatterjee (unpublished)Work done by: James Wescott and Abhijit Chatterjee (unpublished)

Membrane MorphologyF ti f l b b i it ti• Formation of a porous polymer membrane by precipitating polymer from solution.

• The concentration of the polymer solution which is used for the• The concentration of the polymer solution, which is used for the precipitation process usually lies between 10 and 30%.

• By slowly adding nonsolvent (coagulent) to the polymer solution,By slowly adding nonsolvent (coagulent) to the polymer solution, an exchange between solvent and nonsolvent takes place.

• At a certain concentration of nonsolvent in the system ‐ the so‐called precipitation point ‐ the polymer system is changed from a sol to gel.

• The precipitation point is defined as weight nonsolvent/(weight solvent + weight polymer)*100%.


PVDF Membranes

Effects of Mixed Solvents and PVDF Types on Performances of PVDF Microporous MembranesJournal of Applied Polymer ScienceDOI 10 1002/

• 0wt% TMP = pure DMAc(MTMP0) shows sponge like morphology

DOI 10.1002/app

like morphology

• 100wt% TMP (MTMP100) also shows sponge like morphologyp gy

• 60 wt% TMP in the TMP/DMAc solvent mix (MTMP60) provides finger l k h l llike morphology, largest porosity, fastest precipitation rate


pure water flux (J), rejection (R),porosity (e), and mean pore radius (rm).


Challenge:Strategy:

• Understand

Software used:

• Meoscite DPD Enable control of pore size and shape of polymer

reasons for markedly different

for mesoscalepredictions

• Solubility shape of polymer membranes via rational design

membrane morphologies and phase

yparameter through Pipeline Pilot & Synthiap

separation process

Pilot & Synthia


Materials

Polyvinylidene fluoride POLYMER

DP = 150Vol% 20%

SOLVENTS

Vol% = 20%

Trimethyl phosphateGradually reduced from total of 76vol% in proportion to starting composition

Solvent composition

Dimethylacetamide

• 0wt%TMP / 100wt%DMAc• 60wt%TMP / 40wt%DMAc• 100wt%TMP / 0wt%DMAc

Water

NONSOLVENT

Start with 4 vol% and increase


Morphology Prediction PVDF/TMP/DMAc/WaterFinal model here ater creates a

20vol% polymer

• 0wt%TMP / 100% DMAc

4% 6%

Final model where water creates a primary spherical cluster which is strongly phase separated from both the polymer and the DMAc

12%

8% 10%8% 10%


Labelled with Vol% of total box content that is occupied by nonsolvent (water)

Morphology Prediction PVDF/TMP/DMAc/Water20vol% polymer

• Final model where there is very weak• 100wt%TMP / 0% DMAc

4% 6%

• Final model where there is very weak segregation of polymer

• TMP delays the strong tendency of polymer‐water phase separation to higher water concentrations by being reasonably

12%

y g ycompatible with the water

8% 10%


Morphology Prediction PVDF/TMP/DMAc/Water20vol% polymer

4% 4.2%

• 60wt%TMP / 40% DMAc Polymer very quickly phase separates (note the lower vol% labels). Lamellar morphology at low nonsolvent content is formed

6.4%

nonsolvent content is formed.

4.6% 5.4%


Morphology Predicted from Simulation60 t%TMP / 40% DMA60wt%TMP / 40% DMAc100% DMAc 100wt%TMPPrecipitation Point = ~3.3%Precipitation Point = ~4% Precipitation Point > 10%

19nm

• Experimentally – “... the precipitation rate decreases with the increase of TMP content from 60 to 100 t % Therefore the precipitation rate is the fastest d ring immersion process ith 60 t %


100 wt %. Therefore, the precipitation rate is the fastest during immersion process with 60 wt % TMP in the mixed solvent, which favors the formation of a finger‐like membrane structure.”. Journal of Applied Polymer Science DOI 10.1002/app

Summary• Membrane morphology depends strongly on the choice of solvent for the• Membrane morphology depends strongly on the choice of solvent for the

polymer.

• For just a simple two solvent mixture, analysis based on solubility parameters alone is unable to predict, whether a sponge‐like or finger like morphology p , p g g p gywill result.

• However, the mesoscale simulation predicts

– Micellar morphology for PVDF in DMAc solvent (=> sponge‐like)Micellar morphology for PVDF in DMAc solvent ( > sponge like)

– A fast evolving strongly phase separated lamellar structure for the PVDF system using mixed DMAc/TMP solvent (=> finger like morphology)

– A very slowly evolving weakly separated morphology for PDVF in pure TMPA very slowly evolving weakly separated morphology for PDVF in pure TMP solvent (may also look sponge‐like under SEM)

• In excellent agreement with experimental observations.

• Suggests morphology is driven largely by the solvent‐water repulsion whereSuggests morphology is driven largely by the solvent water repulsion where DMAc‐water) > TMP‐water).

Mechanistic insights & potential to screen more


complicated systems (three solvents etc)

Membrane Design Enabled


yExperiments and

Simulations



Sectors we serve

Chemicals


Energy Automotive




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RATIONAL DESIGN OF ADVANCED GATE STACK MATERIALS

Case Study IV

Work done by: Jacob GavartinDefects in CMOS Gate Dielectrics In Defects in Microelectronics Materials andDefects in CMOS Gate Dielectrics. In Defects in Microelectronics Materials and Devices–ISBN 978‐1‐42004‐376‐1, Taylor & Francis 2008, p. 341‐358

Microelectronics: Critical Challenges in CMOS technology

•Sustain aggressive scaling of metal‐oxide‐semiconductor transistors

•Introduce new materials into the gate stack: high mobility materialsgate stack: high mobility materials for channels, high capacitance for dielectric stack and compatible metallic gates.metallic gates.

•Integrate new materials and i i i h l iprocesses into existing technologies

without compromising device reliability

Nehalem wafer, Intel Technology 2009

Knowledge from Experiments

• I‐V, C‐V measurement experiments

• Leakage current measurement experimentsg p

• Analytical Experiments: EELS, ESR, EXAFS, FTIR, internal photoemission, HRTEM, STM/AFMSTM/AFM


Challenge:Strategy:

• Determine

Software:

Compatibility between dielectric material and the

valence band offsets and conduction

• CASTEP• DMol3

Materials St diomaterial and the semiconductor layer

band offsets for the materials

• Follow changes

• Materials Studio

Follow changes in the interface region


Si/SiO2/HfO2 stack morphology

Focus

Si MeOx Me

Si/SiOx/HfO2: Projected density of states

J. Gavartin et al Microelectronics Eng. 200715.5 Å

~5 Å

Si/SiOx/HfO2: calculated band offsets

•Calculations reveal relation between structure and stoichiometry of thestructure and stoichiometry of the interface region and electronic band Offsets. (The absolute values may not be accurate but the trend is important)

•Oxygen stoichiometry and local coordination is the strongest factor affecting Si/HfO2 band offsets.g /

•Oxygen related defects may trap electrons contributing to leakage current and threshold potentialcurrent and threshold potential instability.

HfO2 SiSiOx J. Gavartin et al Microelectronics Eng. 2007 84 2412M. Hakala et al JAP 2006 100 043708J. Gavartin et al Microelectronics Eng. 2005 80C 412

Conclusions

• Ultrathin HfO2 layers on silicon arestrongly disordered.

O d fi i h• Oxygen deficiency at theSi/SiO2/HfO2 interface detrimentallyaffects device performance – largeinterface scattering reduces carrierinterface scattering reduces carriermobility in the MOSFET channel

• Oxygen excess leads toOxygen excess leads touncontrollable growth of theinterfacial SiOx sub‐oxide layer, thusreducing capacitance density of theg p ygate.

• Calculations suggest practical stepsof controlling oxygen stoichiometryat the interface

Rational Design Achieved


yExperiments and

Simulations



Mine the Enriched Knowledge Pool

Wet Lab Experiments

Analytical Experiments

Modeling and Experiments Experiments Simulations

Mi th E i h d K l d P l f N IdMine the Enriched Knowledge Pool for New Ideas


Enrich Integrate Mine Analyze Innovate

Contract Research Webinar SeriesPast Webinars

• “Fuel Cell Catalyst Discovery with the Materials Studio Collection” by Dr Misbah Sarwar, Research Scientist, Johnson Mattheyby Dr Misbah Sarwar, Research Scientist, Johnson Matthey

• To learn more and register visit: http://accelrys.com/events/webinars/contract‐research‐fall2010/index htmlfall2010/index.html

What’s Next?

Join us for future webinars in this Contract Research series:

• Sept 28: PolyOne's Effective Use of Molecular Modeling During Nanocomposite Commercialization

• Oct 7: Unraveling the Secrets of Graphene by Multiscale Simulations

To learn more and register visit: http://accelrys.com/events/webinars/contract‐research‐fall2010/index.html

Visit us at upcoming conferences:

O 26 28 A l EUGM•Oct 26‐28: Accelrys EUGM

To see all conferences we’ll be attending visit: http://accelrys com/events/conferences/


http://accelrys.com/events/conferences/

Thank YouThank You

For more information contact …

Dr. Lalitha SubramanianAccelrys FellowSenior DirectorAccelrys, [email protected]@accelrys.com858‐799‐5340

www accelrys comwww.accelrys.com

collaboration for innovation: enriching the knowledge pool

Technology

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different silver surfaces

different support fo

h d asthecatalystages

resins distributors

free energy