collaboration for innovation: enriching the knowledge pool
TRANSCRIPT
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Collaboration for InnovationEnriching the Knowledge Pool
15 September 2010
Dr. Lalitha Subramanian, Accelrys Fellow Senior Director Contract Research & Scientific Consulting Services
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Sectors we serve
Chemicals
Pharmaceutical and Biotech
Energy Automotive
Personal & Home Care
MicroelectronicsAerospace & Defense Technology
Contract Research Services
p
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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
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Innovate to stay ahead …
Innovation = Conversion of knowledge into new &
profitable products, solutions and services.
Enrich Integrate Mine Analyze Innovate
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Process of InnovationAnalysis Association
Wet Lab Data
Literature Data
Problem
KnowledgeKnowledge
SynthesisSynthesis
Solution
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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
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Sectors we serve
ChemicalsPharmaceutical
and BiotechOil & Gas Automotive
Personal & Home Care
MicroelectronicsAerospace & Defense Green Industry
Contract Research Services
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PROCESS IMPROVEMENT OF HETEROGENEOUS CATALYSIS
Case Study I
Work done by: Lalitha Subramanian and Li Xiao (unpublished)
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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.
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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 ?
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Is sintering rate affected by different support formulations ?• . . . . . . . . . • . . . . . . . . .
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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
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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
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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
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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”
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Ref: Shape‐ and Size‐Specific Chemistry of Ag Nanostructures in Catalytic Ethylene Epoxidation Phillip Christopher and Suljo Linic, ChemCatChem 2010, 2, 78–83
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Process Improvements Achieved
Wet Lab Analytical Modeling dExperiments
yExperiments and
Simulation
Enriched Knowledge Pool
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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
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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
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Substrate and products readily purged thermally
– Recyclable
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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
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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
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• Path in ‐ straight channels concentrated at interval where sinusoidal pore opening occur
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Results: Para versus Ortho Proton‐to‐Cage Transfer
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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
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coworker questionable
• Steric hindrance of cage itself likely source of selectivity (as shown by this work)
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Visualization of the Reaction
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Selectivity in Green Synthesis Understood
Wet Lab Analytical Modeling dExperiments
yExperiments and
Simulations
Enriched Knowledge Pool
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Sectors we serve
Chemicals
Pharmaceutical and Biotech
Energy Automotive
Personal & Home Care
MicroelectronicsAerospace & Defense Technology
Contract Research Services
p
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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)
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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%.
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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
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pure water flux (J), rejection (R),porosity (e), and mean pore radius (rm).
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Challenge and Strategy:
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
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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
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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%
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Labelled with Vol% of total box content that is occupied by nonsolvent (water)
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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%
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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%
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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 %
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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
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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
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complicated systems (three solvents etc)
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Membrane Design Enabled
Wet Lab Analytical Modeling dExperiments
yExperiments and
Simulations
Enriched Knowledge Pool
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Sectors we serve
Chemicals
Pharmaceutical and Biotech
Energy Automotive
Personal & Home Care
MicroelectronicsAerospace & Defense Technology
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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
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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
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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
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Challenge and Strategy:
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
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Si/SiO2/HfO2 stack morphology
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Focus
Si MeOx Me
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Si/SiOx/HfO2: Projected density of states
J. Gavartin et al Microelectronics Eng. 200715.5 Å
~5 Å
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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
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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
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Rational Design Achieved
Wet Lab Analytical Modeling dExperiments
yExperiments and
Simulations
Enriched Knowledge Pool
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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
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Enrich Integrate Mine Analyze Innovate
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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
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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/
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http://accelrys.com/events/conferences/
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Dr. Lalitha SubramanianAccelrys FellowSenior DirectorAccelrys, [email protected]@accelrys.com858‐799‐5340
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