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Collaboration for Innovation
Enriching the Knowledge Pool


15 September 2010

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




    Chemicals

                         Energy                          Pharmaceutical
                                                                           Automotive
                                                           and Biotech




Personal & Home Care
                       Microelectronics
                                                  Aerospace & Defense
                                                       p                  Technology



                                  Contract Research Services
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 resources
 ‐   the cost of experiment is rising
     the cost of experiment is rising

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

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 Innovation
             Analysis           Association



                                                               Literature Data

                                                Wet Lab Data

   Problem

                                                         Knowledge


                    Synthesis




                                     Solution
Accelrys Contract Research Services Provide…
Lower product lead times
      A large UK based catalyst company used simulation to narrow 
      down the options for novel fuel cell catalysts
      down the options for novel fuel cell catalysts
Reduced costs
      US based Aerospace/Defense contractor used this service to 
      screen battery materials reducing experimental time
Improved product and process quality
      US based plastics and resins distributors used this service to 
      US 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 problems
      Swiss based dermatology company used this service to solve 
      critical scientific problem in the area of controlled drug release
      critical scientific problem in the area of controlled drug release
Sectors we serve




    Chemicals
                         Oil & Gas                    Pharmaceutical
                                                                         Automotive
                                                        and Biotech




Personal & Home Care
                       Microelectronics
                                                Aerospace & Defense
                                                     p                 Green Industry



                               Contract Research Services
Case Study I
PROCESS IMPROVEMENT OF HETEROGENEOUS CATALYSIS


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




                          Contract Research Services
Some “Is it …” questions:
 Deactivation of Alumina supported Silver Catalyst
•    Is the selectivity varying in consort with activity ?
•    Is activity or selectivity different for different silver surfaces ?
     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 ?
•   . . . . . . . . . 
•    . . . . . . . . . 
                                   Contract Research Services
Strategy: Focus on Surface Morphology Behavior




  Strategy:             Temp. effects:                Software used:
  Study differences     • Calculated Ag               • DMol3 code for 
  in reaction energy      surface energies              Energy 
  on various Ag           at different                  calculations
  surfaces                temperatures                • Morphology  
                                                             p    gy
  Ethylene and O2       • Model the                     code for growth 
  on Ag(100),             growth                        of surfaces
  Ag(111), Ag(110)
  Ag(111) Ag(110)         morphology of 
                          morphology of               • Materials Studio
                                                        Materials Studio
                          Ag catalyst



                         Contract Research Services
Results 

• The reaction mostly occurs 
  on open surfaces
  on open surfaces
• O2 adsorption via different 
  mechanisms on the Ag 
  mechanisms on the Ag
  surfaces
• Ethylene attaching to form a
  Ethylene attaching to form a 
  metallacycle
• Ag(100) has the lowest
  Ag(100) has the lowest 
  energy barrier for 
  epoxidation

                      Contract Research Services
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 
    surface.
    surface




        T1                    T2




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


                                               Contract Research Services
Process Improvements Achieved




                                           Modeling 
    Wet Lab          Analytical 
                         y
                                             and 
                                               d
  Experiments       Experiments
                                          Simulation

                Enriched Knowledge Pool
Case Study II
 GREEN ALTERNATIVE FOR CHEMICAL SYNTHESIS


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

                                   Contract Research Services
Challenge and Strategy:


  Challenge:
          g
  High para              Strategy:                     Software used:
  selectivity unique     • Need to have an             • DMol3 code for 
  to beta Zeolite
  to beta Zeolite          in‐depth                      energy 
  Experiments with         understanding                 calculations
  Mordenite, ZSM‐          of the                      • Sorption code 
                                                             p
  5, and Y did not         mechanism of                  for toluene 
  yield as high a          nitration of                  location sites
  para selectivity as      toluene in Beta             • Materials Studio
                                                         Materials Studio
  observed for beta        Zeolite
  Zeolite



                          Contract Research Services
Results: Toluene occupation in beta




   •   Toluene diffuses readily in the two 12‐T straight channels, but not in the 
       tortuous channel in the third direction
   •   Path in ‐
       P h i straight channels concentrated at interval where sinusoidal pore 
                    i h h       l              d i         l h      i    id l
       opening occur
                                Contract Research Services
Results:  Para versus Ortho Proton‐to‐Cage Transfer




                       Contract Research Services
Conclusions
• Origins of para‐selectivity combination of many factors
           f        l            b         f      f
   – 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 (tetrahedraloctahedral) allow for
     Flexibility of aluminum sites (tetrahedraloctahedral) allow for 
     targeted placement of nitrate ions and ultimately acetyl nitrate
   – Steric hindrance from zeolite framework most likely cause for
            hindrance from zeolite framework most likely cause for 
     para‐selectivity
       • Steric hindrance of ortho attack by acetyl group of AcNO3 proposed by Prins and 
         coworker questionable
                k       ti   bl
       • Steric hindrance of cage itself likely source of selectivity (as shown by this work)
                                     Contract Research Services
Visualization of the Reaction




                    Contract Research Services
Selectivity in Green Synthesis Understood




                                                   Modeling 
     Wet Lab          Analytical 
                          y
                                                     and 
                                                        d
   Experiments       Experiments
                                                  Simulations
                 Enriched Knowledge Pool



                     Contract Research Services
Sectors we serve




    Chemicals

                         Energy                          Pharmaceutical
                                                                           Automotive
                                                           and Biotech




Personal & Home Care
                       Microelectronics
                                                  Aerospace & Defense
                                                       p                  Technology



                                  Contract Research Services
Case Study III
CONTROL OF POLYMER MEMBRANE MORPHOLOGY


Work done by: James Wescott and Abhijit Chatterjee (unpublished)
Work done by: James Wescott and Abhijit Chatterjee (unpublished)
Membrane Morphology
•   Formation of a porous polymer membrane by precipitating 
    F    ti    f            l        b     b      i it ti
    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%. 




                                   Contract Research Services
PVDF Membranes
                                               Effects of Mixed Solvents and PVDF 
                                               Types on Performances of PVDF 
                                               Microporous Membranes
                                               Journal of Applied Polymer Science
                                               DOI 10.1002/app
                                               DOI 10 1002/

                                                 •     0wt% TMP = pure DMAc
                                                       (MTMP0) shows sponge 
                                                       like morphology
                                                       like morphology
                                                 •     100wt% TMP (MTMP100) 
                                                       also shows sponge like 
                                                       morphology
                                                            p     gy
                                                 •     60 wt% TMP in the 
                                                       TMP/DMAc solvent mix 
                                                       (MTMP60) provides finger 
                                                       like morphology, largest 
                                                       lk        h l      l
                                                       porosity, fastest 
                                                       precipitation rate
                                              pure water flux (J), rejection (R),
                                              porosity (e), and mean pore radius (rm).



                 Contract Research Services
Challenge and Strategy:



                       Strategy:                     Software used:
  Challenge:           • Understand                  • Meoscite DPD 
  Enable control of      reasons for                   for mesoscale
  pore size and          markedly                      predictions
  shape of polymer 
  shape of polymer       different                   • Solubility 
                                                                y
  membranes via          membrane                      parameter 
  rational design        morphologies                  through Pipeline 
                         and phase 
                              p                        Pilot & Synthia
                                                       Pilot & Synthia
                         separation 
                         process



                        Contract Research Services
Materials
                                                    POLYMER
            Polyvinylidene fluoride 

                                                   DP = 150
                                                   Vol% = 20%
                                                   Vol% 20%


                                                    SOLVENTS

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

                                                   Solvent composition 
                                                   • 0wt%TMP / 100wt%DMAc
                                                   • 60wt%TMP / 40wt%DMAc
            Dimethylacetamide                      • 100wt%TMP / 0wt%DMAc


                                                     NONSOLVENT

            Water                                   Start with 4 vol% and increase



                                  Contract Research Services
Morphology Prediction PVDF/TMP/DMAc/Water
    20vol% polymer

                                                                   Final model where water creates a 
                                                                   Final model here ater creates a
•        0wt%TMP / 100% DMAc                                       primary  spherical cluster which is 
                                                                   strongly phase separated from both the 
    4%                          6%
                                                                   polymer and the DMAc
                                                                   12%




    8%                          10%




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

                                            Contract Research Services
Morphology Prediction PVDF/TMP/DMAc/Water
20vol% polymer

                                                  •    Final model where there is very weak 
                                                       Final model where there is very weak
• 100wt%TMP / 0% DMAc                                  segregation of polymer
                                                  •    TMP delays the strong tendency of 
 4%                     6%                             polymer‐water phase separation to higher 
                                                       water concentrations by being reasonably 
                                                                             y      g         y
                                                       compatible with the water

                                                      12%




 8%                     10%




                              Contract Research Services
Morphology Prediction PVDF/TMP/DMAc/Water
20vol% polymer


• 60wt%TMP / 40% DMAc                                  Polymer very quickly phase 
                                                       separates (note the lower vol% 
 4%                     4.2%                           labels). Lamellar morphology at low 
                                                       nonsolvent content is formed
                                                                   content is formed.

                                                            6.4%




 4.6%                   5.4%




                               Contract Research Services
Morphology Predicted from Simulation
       100% DMAc                       60wt%TMP / 40% DMAc
                                       60 t%TMP / 40% DMA                    100wt%TMP
       Precipitation Point = ~4%       Precipitation Point = ~3.3%           Precipitation Point > 10%




19nm




   •     Experimentally – “... the precipitation rate decreases with the increase of TMP content from 60 to 
         100 wt %. Therefore, the precipitation rate is the fastest during immersion process with 60 wt % 
         100 t % Therefore the precipitation rate is the fastest d ring immersion process ith 60 t %
         TMP in the mixed solvent, which favors the formation of a finger‐like membrane structure.”.  
         Journal of Applied Polymer Science DOI 10.1002/app
                                            Contract Research Services
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 
                       p      ,            p g              g            p   gy
    alone is unable to predict, whether a sponge‐like or finger like morphology 
    will result.
•   However, the mesoscale simulation predicts
     – Micellar morphology for PVDF in DMAc solvent (=> sponge‐like)
                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 TMP
       A 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 where 
    Suggests 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) 
                                   Contract Research Services
Membrane Design Enabled




                                                  Modeling 
    Wet Lab          Analytical 
                         y
                                                    and 
                                                       d
  Experiments       Experiments
                                                 Simulations
                Enriched Knowledge Pool



                    Contract Research Services
Sectors we serve




    Chemicals

                         Energy                          Pharmaceutical
                                                                           Automotive
                                                           and Biotech




Personal & Home Care
                       Microelectronics
                                                  Aerospace & Defense
                                                       p                  Technology



                                  Contract Research Services
Case Study IV
RATIONAL DESIGN OF ADVANCED GATE STACK MATERIALS 


Work done by: Jacob Gavartin
Defects in CMOS Gate Dielectrics. In Defects in Microelectronics Materials and 
Defects 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 materials 
  gate stack: high mobility materials
  for channels, high capacitance for 
  dielectric stack and compatible 
  metallic gates.
  metallic gates.


 •Integrate new materials and 
  processes i  into existing technologies 
                      i i       h l i
  without compromising device 
  reliability 


                                             Nehalem wafer, Intel  Technology 2009 
Knowledge from Experiments




• I‐V, C‐V measurement experiments
• Leakage current measurement experiments
         g                         p
• Analytical Experiments: EELS, ESR, EXAFS, 
  FTIR, internal photoemission, HRTEM, 
  STM/AFM
Challenge and Strategy:



                        Strategy:                     Software:
  Challenge:            • Determine 
  Compatibility           valence band                • CASTEP
  between dielectric      offsets and                 • DMol3
  material and the 
  material and the        conduction 
                          band offsets for            • Materials St dio
                                                        Materials Studio
  semiconductor 
  layer                   the materials
                        • Follow changes
                          Follow changes 
                          in the interface 
                          region


                         Contract Research Services
Si/SiO2/HfO2 stack morphology
Focus




        Si   MeOx   Me
Si/SiOx/HfO2: Projected density of states

          15.5 Å
                                 J. Gavartin et al Microelectronics Eng. 2007




                          ~5 Å
Si/SiOx/HfO2: calculated band offsets


                                   •Calculations reveal relation between 
                                   structure and stoichiometry of the 
                                   structure 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 potential
                                   current and threshold potential 
                                   instability.  


  HfO2     SiOx      Si     J. Gavartin et al Microelectronics Eng. 2007 84 2412
                            M. Hakala et al JAP 2006 100 043708
                             J. Gavartin et al Microelectronics Eng. 2005 80C 412
Conclusions
• Ultrathin HfO2 layers on silicon are
  strongly disordered.

• O
  Oxygen       deficiency
               d fi i       at    the
                                   h
  Si/SiO2/HfO2 interface detrimentally
  affects device performance – large
  interface scattering reduces carrier
  mobility in the MOSFET channel

• Oxygen        excess     leads      to
  uncontrollable growth of the
  interfacial SiOx sub‐oxide layer, thus
  reducing capacitance density of the
          g p                  y
  gate.

• Calculations suggest practical steps
  of controlling oxygen stoichiometry
  at the interface
Rational Design Achieved




                                                   Modeling 
     Wet Lab          Analytical 
                          y
                                                     and 
                                                        d
   Experiments       Experiments
                                                  Simulations
                 Enriched Knowledge Pool



                     Contract Research Services
Mine the Enriched Knowledge Pool 




                                                            Modeling 
     Wet Lab                Analytical 
                                                              and 
   Experiments             Experiments
                                                           Simulations
        Mi th E i h d K       l d P l f N Id
        Mine the Enriched Knowledge Pool for New Ideas


   Enrich      Integrate             Mine               Analyze   Innovate


                           Contract Research Services
Contract Research Webinar Series
Past Webinars
• “Fuel Cell Catalyst Discovery with the Materials Studio Collection” 
   by Dr Misbah Sarwar, Research Scientist, Johnson Matthey 
   by Dr Misbah Sarwar, Research Scientist, Johnson Matthey
• To learn more and register visit: 
   http://accelrys.com/events/webinars/contract‐research‐
   fall2010/index.html
   fall2010/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/


                                Contract Research Services
Thank You
 Thank You

For more information contact …

Dr. Lalitha Subramanian
Accelrys Fellow
Senior Director
Accelrys, Inc.
lalitha@accelrys.com
858‐799‐5340


                                 www.accelrys.com
                                 www accelrys com

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Collaboration for Innovation: Enriching the Knowledge Pool

  • 2. Sectors we serve Chemicals Energy Pharmaceutical Automotive and Biotech Personal & Home Care Microelectronics Aerospace & Defense p Technology Contract Research Services
  • 3. 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 resources ‐ the cost of experiment is rising the cost of experiment is rising Optimized products and processes ‐ optimization often relies on new insight i i i f li i i h Solutions to critical research problems ‐ solutions often come from adapting or controlling molecular level  behavior
  • 4. Innovate to stay ahead … Innovation = Conversion  of knowledge into new &  profitable products,  solutions and services.  Enrich Integrate Mine Analyze Innovate
  • 5. Process of Innovation Analysis Association Literature Data Wet Lab Data Problem Knowledge Synthesis Solution
  • 6. Accelrys Contract Research Services Provide… Lower product lead times A large UK based catalyst company used simulation to narrow  down the options for novel fuel cell catalysts down the options for novel fuel cell catalysts Reduced costs US based Aerospace/Defense contractor used this service to  screen battery materials reducing experimental time Improved product and process quality US based plastics and resins distributors used this service to  US 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 problems Swiss based dermatology company used this service to solve  critical scientific problem in the area of controlled drug release critical scientific problem in the area of controlled drug release
  • 7. Sectors we serve Chemicals Oil & Gas Pharmaceutical Automotive and Biotech Personal & Home Care Microelectronics Aerospace & Defense p Green Industry Contract Research Services
  • 9. Epoxidation Catalysis – Process Improvement • Improve performance of the ethylene epoxidation catalyst by  increasing longevity of the catalyst • As the catalyst ages, the temperature is increased to increase  h l h d performance.  However experimentally it was seen that after a  certain temperature, the catalyst performance dramatically  p y p y dropped.    Contract Research Services
  • 10. Some “Is it …” questions: Deactivation of Alumina supported Silver Catalyst • Is the selectivity varying in consort with activity ? • Is activity or selectivity different for different silver surfaces ? 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 ? • . . . . . . . . .  • . . . . . . . . .  Contract Research Services
  • 11. Strategy: Focus on Surface Morphology Behavior Strategy: Temp. effects: Software used: Study differences  • Calculated Ag  • DMol3 code for  in reaction energy  surface energies  Energy  on various Ag  at different  calculations surfaces temperatures • Morphology   p gy Ethylene and O2 • Model the  code for growth  on Ag(100),  growth  of surfaces Ag(111), Ag(110) Ag(111) Ag(110) morphology of  morphology of • Materials Studio Materials Studio Ag catalyst Contract Research Services
  • 12. Results  • The reaction mostly occurs  on open surfaces on open surfaces • O2 adsorption via different  mechanisms on the Ag  mechanisms on the Ag surfaces • Ethylene attaching to form a Ethylene attaching to form a  metallacycle • Ag(100) has the lowest Ag(100) has the lowest  energy barrier for  epoxidation Contract Research Services
  • 13. 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  surface. surface T1 T2 Contract Research Services
  • 14. 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  exposed Ag surface facets; Ag nanocubes and nanowires are dominated by (100)  d f f b d d db ( ) 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 Contract Research Services
  • 15. Process Improvements Achieved Modeling  Wet Lab  Analytical  y and  d Experiments Experiments Simulation Enriched Knowledge Pool
  • 17. 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 nitration Unfavorable 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  Contract Research Services
  • 18. Challenge and Strategy: Challenge: g High para Strategy: Software used: selectivity unique  • Need to have an  • DMol3 code for  to beta Zeolite to beta Zeolite in‐depth  energy  Experiments with  understanding  calculations Mordenite, ZSM‐ of the  • Sorption code  p 5, and Y did not  mechanism of  for toluene  yield as high a  nitration of  location sites para selectivity as  toluene in Beta  • Materials Studio Materials Studio observed for beta  Zeolite Zeolite Contract Research Services
  • 19. Results: Toluene occupation in beta • Toluene diffuses readily in the two 12‐T straight channels, but not in the  tortuous channel in the third direction • Path in ‐ P h i straight channels concentrated at interval where sinusoidal pore  i h h l d i l h i id l opening occur Contract Research Services
  • 21. Conclusions • Origins of para‐selectivity combination of many factors f l b f f – 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 (tetrahedraloctahedral) allow for Flexibility of aluminum sites (tetrahedraloctahedral) allow for  targeted placement of nitrate ions and ultimately acetyl nitrate – Steric hindrance from zeolite framework most likely cause for hindrance from zeolite framework most likely cause for  para‐selectivity • Steric hindrance of ortho attack by acetyl group of AcNO3 proposed by Prins and  coworker questionable k ti bl • Steric hindrance of cage itself likely source of selectivity (as shown by this work) Contract Research Services
  • 22. Visualization of the Reaction Contract Research Services
  • 23. Selectivity in Green Synthesis Understood Modeling  Wet Lab  Analytical  y and  d Experiments Experiments Simulations Enriched Knowledge Pool Contract Research Services
  • 24. Sectors we serve Chemicals Energy Pharmaceutical Automotive and Biotech Personal & Home Care Microelectronics Aerospace & Defense p Technology Contract Research Services
  • 26. Membrane Morphology • Formation of a porous polymer membrane by precipitating  F ti f l b b i it ti 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%.  Contract Research Services
  • 27. PVDF Membranes Effects of Mixed Solvents and PVDF  Types on Performances of PVDF  Microporous Membranes Journal of Applied Polymer Science DOI 10.1002/app DOI 10 1002/ • 0wt% TMP = pure DMAc (MTMP0) shows sponge  like morphology like morphology • 100wt% TMP (MTMP100)  also shows sponge like  morphology p gy • 60 wt% TMP in the  TMP/DMAc solvent mix  (MTMP60) provides finger  like morphology, largest  lk h l l porosity, fastest  precipitation rate pure water flux (J), rejection (R), porosity (e), and mean pore radius (rm). Contract Research Services
  • 28. Challenge and Strategy: Strategy: Software used: Challenge: • Understand  • Meoscite DPD  Enable control of  reasons for  for mesoscale pore size and  markedly  predictions shape of polymer  shape of polymer different  • Solubility  y membranes via  membrane  parameter  rational design morphologies  through Pipeline  and phase  p Pilot & Synthia Pilot & Synthia separation  process Contract Research Services
  • 29. Materials POLYMER Polyvinylidene fluoride  DP = 150 Vol% = 20% Vol% 20% SOLVENTS Gradually reduced from total of 76vol%  Trimethyl phosphate in proportion to starting composition Solvent composition  • 0wt%TMP / 100wt%DMAc • 60wt%TMP / 40wt%DMAc Dimethylacetamide • 100wt%TMP / 0wt%DMAc NONSOLVENT Water Start with 4 vol% and increase Contract Research Services
  • 30. Morphology Prediction PVDF/TMP/DMAc/Water 20vol% polymer Final model where water creates a  Final model here ater creates a • 0wt%TMP / 100% DMAc primary  spherical cluster which is  strongly phase separated from both the  4% 6% polymer and the DMAc 12% 8% 10% Labelled with Vol% of total box content that is occupied by nonsolvent (water) Contract Research Services
  • 31. Morphology Prediction PVDF/TMP/DMAc/Water 20vol% polymer • Final model where there is very weak  Final model where there is very weak • 100wt%TMP / 0% DMAc segregation of polymer • TMP delays the strong tendency of  4% 6% polymer‐water phase separation to higher  water concentrations by being reasonably  y g y compatible with the water 12% 8% 10% Contract Research Services
  • 32. Morphology Prediction PVDF/TMP/DMAc/Water 20vol% polymer • 60wt%TMP / 40% DMAc Polymer very quickly phase  separates (note the lower vol%  4% 4.2% labels). Lamellar morphology at low  nonsolvent content is formed content is formed. 6.4% 4.6% 5.4% Contract Research Services
  • 33. Morphology Predicted from Simulation 100% DMAc 60wt%TMP / 40% DMAc 60 t%TMP / 40% DMA 100wt%TMP Precipitation Point = ~4% Precipitation Point = ~3.3% Precipitation Point > 10% 19nm • Experimentally – “... the precipitation rate decreases with the increase of TMP content from 60 to  100 wt %. Therefore, the precipitation rate is the fastest during immersion process with 60 wt %  100 t % Therefore the precipitation rate is the fastest d ring immersion process ith 60 t % TMP in the mixed solvent, which favors the formation of a finger‐like membrane structure.”.   Journal of Applied Polymer Science DOI 10.1002/app Contract Research Services
  • 34. 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  p , p g g p gy alone is unable to predict, whether a sponge‐like or finger like morphology  will result. • However, the mesoscale simulation predicts – Micellar morphology for PVDF in DMAc solvent (=> sponge‐like) 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 TMP A 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 where  Suggests 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)  Contract Research Services
  • 35. Membrane Design Enabled Modeling  Wet Lab  Analytical  y and  d Experiments Experiments Simulations Enriched Knowledge Pool Contract Research Services
  • 36. Sectors we serve Chemicals Energy Pharmaceutical Automotive and Biotech Personal & Home Care Microelectronics Aerospace & Defense p Technology Contract Research Services
  • 37. Case Study IV RATIONAL DESIGN OF ADVANCED GATE STACK MATERIALS  Work done by: Jacob Gavartin Defects in CMOS Gate Dielectrics. In Defects in Microelectronics Materials and  Defects in CMOS Gate Dielectrics In Defects in Microelectronics Materials and Devices–ISBN 978‐1‐42004‐376‐1, Taylor & Francis 2008, p. 341‐358
  • 38. Microelectronics: Critical Challenges in CMOS technology •Sustain aggressive scaling of metal‐ oxide‐semiconductor transistors  •Introduce new materials into the  gate stack: high mobility materials  gate stack: high mobility materials for channels, high capacitance for  dielectric stack and compatible  metallic gates. metallic gates. •Integrate new materials and  processes i into existing technologies  i i h l i without compromising device  reliability  Nehalem wafer, Intel  Technology 2009 
  • 39. Knowledge from Experiments • I‐V, C‐V measurement experiments • Leakage current measurement experiments g p • Analytical Experiments: EELS, ESR, EXAFS,  FTIR, internal photoemission, HRTEM,  STM/AFM
  • 40. Challenge and Strategy: Strategy: Software: Challenge: • Determine  Compatibility  valence band  • CASTEP between dielectric  offsets and  • DMol3 material and the  material and the conduction  band offsets for  • Materials St dio Materials Studio semiconductor  layer the materials • Follow changes Follow changes  in the interface  region Contract Research Services
  • 42. Focus Si MeOx Me
  • 43. Si/SiOx/HfO2: Projected density of states 15.5 Å J. Gavartin et al Microelectronics Eng. 2007 ~5 Å
  • 44. Si/SiOx/HfO2: calculated band offsets •Calculations reveal relation between  structure and stoichiometry of the  structure 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 potential current and threshold potential  instability.   HfO2 SiOx Si J. Gavartin et al Microelectronics Eng. 2007 84 2412 M. Hakala et al JAP 2006 100 043708 J. Gavartin et al Microelectronics Eng. 2005 80C 412
  • 45. Conclusions • Ultrathin HfO2 layers on silicon are strongly disordered. • O Oxygen deficiency d fi i at the h Si/SiO2/HfO2 interface detrimentally affects device performance – large interface scattering reduces carrier mobility in the MOSFET channel • Oxygen excess leads to uncontrollable growth of the interfacial SiOx sub‐oxide layer, thus reducing capacitance density of the g p y gate. • Calculations suggest practical steps of controlling oxygen stoichiometry at the interface
  • 46. Rational Design Achieved Modeling  Wet Lab  Analytical  y and  d Experiments Experiments Simulations Enriched Knowledge Pool Contract Research Services
  • 47. Mine the Enriched Knowledge Pool  Modeling  Wet Lab  Analytical  and  Experiments Experiments Simulations Mi th E i h d K l d P l f N Id Mine the Enriched Knowledge Pool for New Ideas Enrich Integrate Mine Analyze Innovate Contract Research Services
  • 48. Contract Research Webinar Series Past Webinars • “Fuel Cell Catalyst Discovery with the Materials Studio Collection”  by Dr Misbah Sarwar, Research Scientist, Johnson Matthey  by Dr Misbah Sarwar, Research Scientist, Johnson Matthey • To learn more and register visit:  http://accelrys.com/events/webinars/contract‐research‐ fall2010/index.html fall2010/index html
  • 49. 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/ Contract Research Services