Predicting Product Distribution of Propene Dimerization in Nanoporous Materials

被引:3
作者
Lin, Yifei Michelle [1 ]
Smit, Berend [1 ,2 ]
机构
[1] Univ Calif Berkeley, Dept Chem & Biomol Engn, Berkeley, CA 94720 USA
[2] EPFL, Inst Sci & Ingn Chim, Lab Mol Simulat, Rue Ind 17, CH-1951 Sion, Switzerland
基金
欧洲研究理事会;
关键词
propene dimerization; metal organic frameworks; zeolites; product distribution; Monte Carlo; molecular simulation; METAL-ORGANIC FRAMEWORK; FORCE-FIELD; MOLECULAR SIMULATIONS; SHAPE SELECTIVITY; BINARY-MIXTURES; ADSORPTION; OLIGOMERIZATION; SEPARATION; ZEOLITES; EQUILIBRIA;
D O I
10.1021/acscatal.7b00712
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
In this work, a theoretical framework is developed to explain and predict changes in the product distribution of the propene dimerization reaction, which yields a mixture of C-6 olefin isomers, resulting from the use of different porous materials as catalysts. The MOF-74 class of materials has shown promise in catalyzing the dimerization of propene with high selectivity for valuable linear olefin products. We show that experimentally observed changes in the product distribution can be explained in terms of the contribution of the pores to the free energy of formation, which are directly computed using molecular simulation. Our model is used to screen a library of 118 existing and hypothetical MOF and zeolite structures to study how product distribution can be tuned by changing pore size, shape, and composition of porous materials. Using these molecular descriptors, catalyst properties are identified that increase the selective reaction of linear olefin isomers, which are valued as industrial feedstocks. A pore size commensurate with the size of the desired linear products enhances linear conversion by sterically hindering the branched isomers. Another promising feature is the presence of open metal sites, which interact with the olefin bond to provide favorable binding sites for the linear isomers.
引用
收藏
页码:3940 / 3948
页数:9
相关论文
共 50 条
[21]   Fluorinated and Nanoporous Graphene Materials As Sorbents for Gas Separations [J].
Schrier, Joshua .
ACS APPLIED MATERIALS & INTERFACES, 2011, 3 (11) :4451-4458
[22]   Recent developments in the molecular modeling of diffusion in nanoporous materials [J].
Dubbeldam, D. ;
Snurr, R. Q. .
MOLECULAR SIMULATION, 2007, 33 (4-5) :305-325
[23]   The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials [J].
Witman, Matthew ;
Ling, Sanliang ;
Jawahery, Sudi ;
Boyd, Peter G. ;
Haranczyk, Maciej ;
Slater, Ben ;
Smit, Berend .
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2017, 139 (15) :5547-5557
[24]   Modeling Pure Gas Permeation in Nanoporous Materials and Membranes [J].
Bhatia, Suresh K. .
LANGMUIR, 2010, 26 (11) :8373-8385
[25]   Nanoporous carbon materials: modern production methods and applications [J].
Pavlenko, Vladimir V. ;
Zakharov, Alexander Yu. ;
Ayaganov, Zhanibek E. ;
Mansurov, Zulkhair A. .
RUSSIAN CHEMICAL REVIEWS, 2024, 93 (09)
[26]   Modeling the Adsorption of CO2/N2 Mixtures on Siliceous Nanoporous Materials [J].
Gargiulo, Nicola ;
Macario, Anastasia ;
Iucolano, Fabio ;
Giordano, Girolamo ;
Caputo, Domenico .
SCIENCE OF ADVANCED MATERIALS, 2015, 7 (02) :258-263
[27]   Assessing the influences of kinetics and intrazeolite diffusion on propene oligomerization product selectivity in MFI zeolites [J].
Rogers, Elizabeth E. Bickel ;
Gounder, Rajamani .
JOURNAL OF CATALYSIS, 2024, 438
[28]   Bayesian optimization of nanoporous materials [J].
Deshwal, Aryan ;
Simon, Cory M. ;
Doppa, Janardhan Rao .
MOLECULAR SYSTEMS DESIGN & ENGINEERING, 2021, 6 (12) :1066-1086
[29]   Microwave synthesis of nanoporous materials [J].
Tompsett, GA ;
Conner, WC ;
Yngvesson, KS .
CHEMPHYSCHEM, 2006, 7 (02) :296-319
[30]   Analytical representation of micropores for predicting gas adsorption in porous materials [J].
Thornton, Aaron W. ;
Furman, Scott A. ;
Nairn, Kate M. ;
Hill, Anita J. ;
Hill, James M. ;
Hill, Matthew R. .
MICROPOROUS AND MESOPOROUS MATERIALS, 2013, 167 :188-197