ACS Present a Ion in Dever

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    Molecular Sieves SAPO-34 with Chabazite (CHA)

    Structure:Theoretical Study of Silicon Incorporation and

    Structural Properties

    Hong Wang1, Yingxu Wei2, Cecil ODell1, Zhongmin Liu2 and James P. Lewis11Department of Physics, West Virginia University, Morgantown, WV2Dalian Institute of Chemical Physics, Dalian, P. R. China

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    Introduction

    Zeolite is a broad term used to describe a family ofmicroporous aluminosilicate minerals.

    Contain small pores and large surface area Constructed of AlO4-5 and SiO4-4 groups bound by oxygen Zeolites are synthesized by manipulating:

    Structure Silica-Aluminum Ratio Pore Size Density

    "Green Chemistry with Zeolite Catalysts." Chemical Engineering, The Chemical Engineers'ResourcePage, Distillation, HeatTranser, Design, Spreadsheet Solutions, Departments, Chemistry. Web. 22 Apr. 2010..

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    Application of Zeolite Materials Adsorption

    Drying, purification, and separation Carbon dioxide separation

    Catalysis Shape-selective catalyst- on the basis

    of molecular diameter Acid catalysts used in thepetrochemical industry

    Ion Exchange Detergent formulas- replace

    phosphates as water softening agents

    Exchange Na in zeolite for Ca or Mgin water

    http://s3.amazonaws.com/memebox/uploads/3562/kanatzidis-gasNorthwester.jpg

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    Zeolite Catalysis in Methanol to Olefins Conversion (MTO)

    CoalNatural

    Gas

    Synthesis Gas

    Methanol

    Olefins

    MTO

    https://mailhost-2.tamu.edu/service/home/~/Zeolite_seminar.pdf?auth=co&loc=en_US&id=46940&part=5

    Z-H +n(CH3OH) Z +H2O+olefin(C2, C3, C4)

    Z-H: ZSM-5 catalytic zeolite1977, Chang, Lang and Silvestri, US patent 4062905

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    SAPO-34:Microporous Silico-Aluminophosphate Molecular Sieve

    ZSM-5 SAPO-34

    Al P

    Al Si P

    Al

    Al

    P

    Al

    Al

    Al (Al-O)3-Si

    HO

    Al-(O-P)3

    Pore size around Strong acid sites Low selectivity in

    light olefins High selectivity

    ZSM-5

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    SAPO-34

    SAPO-34:Microporous Silicon Aluminumphospate Molecule Sieve

    Al P

    Al Si P

    Al

    Al

    P

    Al

    Al

    Al (Al-O)3-Si

    HO

    Al-(O-P)3

    Small size pores Suitable acid sites Strong stability High selectivity

    0.43n

    m

    C2H4 0.39nm

    C3H6 0.43nm

    C6H6 0.58nm

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    SAPO-34: Crystallization Mechanism

    J. Tan, Z. M. Liu, X. H. Bao, X. C. Liu, X.W. Han, C. Q. He, R. Z. Zhai,Microporous andMesoporousMateirals 53 (2002), 97 (108)

    SM1: Al Si (not yield)

    SM2: P Si, H SM3: Al,P Si,SiAcidity: Si(4Al)

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    Current Challenge of Silicon Incorporationin SAPO-34 Framework

    Substantial computational timeinvolved

    Extensive possibilities of silicon

    incorporation into a largerframework Identify optimal acid sites with a

    variety of silicon-incorporatingenvironments

    Unravel the puzzle of silicon

    incorporation trends during theSAPO-34 crystallization processand accompany the experimentaldesign with a full-knowledgeframework.

    [AlPO4]+(n+m)Si4++(m-n)H+ [(n+m)Si,(m-n)H]nAl,mP+mP5++nAl3+

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    SAPO-34 Computational Model and Goal

    Rhombohedral unit cell

    12 tetrahedral (T) sites- 6 Al & 6 PSpace Group:

    2x2x2 supercell (288 atoms total)

    96 tetrahedral (T) sites- 48 Al & 48 PSpace Group: P1 (after silicon substitution)

    [AlPO4]+(n+m)Si4++(m-n)H+ [(n+m)Si,(m-n)H]nAl,mP+mP5++nAl3+

    3R

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    We investigate the properties of a non-uniformly distributed Si in a2x2x2 AlPO-34 supercell.

    6 Sis in 288 atoms supercell - 1010 possible arrangements(C48/5*C48.)

    Randomly distribute Si throughout the supercell and see what trends wecan learn.

    Clustering Factor root mean square from perfect Si-Si distance (3.15 )in Si-O-Si configuration.

    For each Si in the supercell, sum over all Si neighbors of Si (choosing

    smallest Si cluster in supercell). Each 288 atom supercell optimization is approximately 10-12 hours. Generate high-throughput scenario from 100s of supercell calculations (a

    few 1000s are also feasible).

    High Throughput Calculations Clustering

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    Pseudopotentials, density-functional theorymulti-center approximations

    Local excited" orbitals (fireball orSankey-Niklewski orbitals)Atom in the box Fermi compressionfinite wavefunction

    Linear-scaling, massively-parallelmolecular-dynamics capabilitiessimulate 1000s of atoms

    J.P. Lewis et al., Phys. Rev. B. 64, 195103 (2001)S.D. Shellman et al.,J. Comp. Phys. 188, 1-15 (2003)P. Jelineket al.,Phys. Rev. B. 64, 235101 (2005)

    J.P. Lewis et al., Phys. Stat. Sol. B. 248, 1989-2007 (2011)

    Fast Computational Approach to Evaluation of MaterialProperties and Chemical Reactivity:Ab initio DFT (FIREBALL)

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    167

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    361

    364

    108

    Clustering/Energy Correlation of 720 individual Structures

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    structure No. 49 structure No. 167

    Low clustering factor, but different energies.

    2 (Al,P Si,Si)pair substitution Al,5P Si(4P), 4Si(4Al)all isolated

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    167

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    Clustering/Energy Correlation

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    structure No. 49 structure No. 364structure No. 213

    2 (Al,P Si,Si)two pairs close

    2 (Al,P Si,Si)

    two pairs far away

    Lower energy structure compared to smallerclustering factor

    (2Al, P) Si Si Si

    triplet

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    167

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    Clustering/Energy Correlation

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    structure No. 108structure No. 364

    4 (P Si) Si(4Al)(2Al, P) Si Si Sitriplet

    Lower energy structure compared to larger

    clustering factor

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    What we see ..

    Higher clustering factors mean thatthe silicon incorporation in theframework is spread throughoutthe entire supercell.

    Silicon substitution of Al, P pair is acommon trend for our random

    silicon incorporation calculationsand these pairs lower the bindingenergy.

    In our low concentrationincorporation (6 out of 96 possiblesites), silicon prefers to form short

    range clusters.

    structure No. 364

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    Future Steps and Outline

    Calculate the electrostatic potentialsfor our random networks todetermine the affects introduced bysilicon incorporation into the SAPO-34 framework.

    Design a computational route to alsonon-uniformly add hydrogen into theSAPO-34 framework based oncurrent data and determine trends.

    Increase silicon substitutionconcentrations and double SAPO-34

    supercell to detect siliconincorporation trends (i.e. increasingthe non-unformity)

    Determine the acid site strengths forfurther CH3OH adsorption

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    Acknowledgements

    WVU HPC Supercomputering Facility Prof. Zhongmin Liu Dr. Yingxu Wei

    Dalian Institute of Chemical Physics, P.R.C

    Thank you!