Towards computational design of zeolite catalysts for CO2 reduction

Carbon dioxide, an energy waste by-product with significant environmental consequences can be utilized and converted into useful chemical products such as formic acid, formaldehyde, methanol or methane, but more energy and cost efficient catalytic processes are required. Here we develop the methodology for the intelligent selection of porous zeolites for dual-adsorption of hydrogen and carbon dioxide as templates for preparing the optimal catalytic environment for carbon dioxide reduction. Useful zeolite catalysts were computationally screened from over 300 thousand zeolite structures using a combination of molecular simulation and machine-learning techniques. Several of the top candidates were very promising energy-efficient templates for catalysis with the potential to perform at 50% above conventional reactors. It is also found that an optimal cavity size of around 6 A is required to maximize the change in entropy–enthalpy upon adsorption with a maximum void space >30% to boost product formation per volume of material.

[1]  Ping Liu,et al.  Highly active copper-ceria and copper-ceria-titania catalysts for methanol synthesis from CO2 , 2014, Science.

[2]  Berend Smit,et al.  Understanding molecular simulation: from algorithms to applications , 1996 .

[3]  B. Smit,et al.  Evaluating mixture adsorption models using molecular simulation , 2013 .

[4]  Mark Z. Jacobson,et al.  Review of solutions to global warming, air pollution, and energy security , 2009 .

[5]  Jie Su,et al.  PKU-9: an aluminogermanate with a new three-dimensional zeolite framework constructed from CGS layers and spiro-5 units. , 2009, Journal of the American Chemical Society.

[6]  Tao Lu,et al.  An Integrated Virtual Screening Approach for VEGFR-2 Inhibitors , 2013, J. Chem. Inf. Model..

[7]  S. Sandler,et al.  Storage and separation of CO2 and CH4 in silicalite, C168 schwarzite, and IRMOF-1: a comparative study from Monte Carlo simulation. , 2007, Langmuir : the ACS journal of surfaces and colloids.

[8]  R. Crabtree.  Combinatorial and rapid screening approaches to homogeneous catalyst discovery and optimization , 1999 .

[9]  Rajamani Krishna,et al.  Computer-assisted screening of ordered crystalline nanoporous adsorbents for separation of alkane isomers. , 2012, Angewandte Chemie.

[10]  A. Myers,et al.  A comprehensive technique for equilibrium calculations in adsorbed mixtures: the generalized FastIAS method , 1988 .

[11]  T. Thonhauser,et al.  High-throughput screening of small-molecule adsorption in MOF , 2013, 1306.1873.

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  Randall Q. Snurr,et al.  Structure–property relationships of porous materials for carbon dioxide separation and capture , 2012 .

[14]  Rajamani Krishna,et al.  Transferable force field for carbon dioxide adsorption in zeolites , 2009 .

[15]  P. Dyson,et al.  Direct synthesis of formic acid from carbon dioxide by hydrogenation in acidic media , 2014, Nature Communications.

[16]  C. Wilmer,et al.  Large-scale screening of hypothetical metal-organic frameworks. , 2012, Nature chemistry.

[17]  W. Marsden.  I and J , 2012 .

[18]  Siglinda Perathoner,et al.  Catalysis for biomass and CO2 use through solar energy: opening new scenarios for a sustainable and low-carbon chemical production. , 2014, Chemical Society reviews.

[19]  David S Sholl,et al.  Accelerating applications of metal-organic frameworks for gas adsorption and separation by computational screening of materials. , 2012, Langmuir : the ACS journal of surfaces and colloids.

[20]  P. A. Cheeseman,et al.  Computational Discovery of New Zeolite-Like Materials , 2009 .

[21]  Yuhan Sun,et al.  Effect of pore structure on Ni catalyst for CO2 reforming of CH4 , 2010 .

[22]  M. Doblaré,et al.  Insights on the Molecular Mechanisms of Hydrogen Adsorption in Zeolites , 2013 .

[23]  D. Lévesque,et al.  Hydrogen adsorption in the NaA zeolite: A comparison between numerical simulations and experiments , 2000 .

[24]  Li-Chiang Lin,et al.  Optimizing nanoporous materials for gas storage. , 2014, Physical chemistry chemical physics : PCCP.

[25]  Edward J. Maginn,et al.  What to Do with CO2 , 2010 .

[26]  Li-Chiang Lin,et al.  Predicting large CO2 adsorption in aluminosilicate zeolites for postcombustion carbon dioxide capture. , 2012, Journal of the American Chemical Society.

[27]  Wei Wang,et al.  Recent advances in catalytic hydrogenation of carbon dioxide. , 2011, Chemical Society reviews.

[28]  F. Zerbetto,et al.  Atomistic molecular dynamics simulations reveal insights into adsorption, packing, and fluxes of molecules with carbon nanotubes , 2014 .

[29]  Jiahui Chen,et al.  An Ideal Absorbed Solution Theory (IAST) Study of Adsorption Equilibria of Binary Mixtures of Methane and Ethane on a Templated Carbon , 2011 .

[30]  Alan L. Myers,et al.  Thermodynamics of mixed‐gas adsorption , 1965 .

[31]  M. Rao,et al.  Isosteric Heat of Adsorption:  Theory and Experiment. , 1999, The journal of physical chemistry. B.

[32]  Yamil J. Colón,et al.  High-throughput computational screening of metal-organic frameworks. , 2014, Chemical Society reviews.

[33]  Michael W Deem,et al.  A database of new zeolite-like materials. , 2011, Physical chemistry chemical physics : PCCP.

[34]  Elsje Alessandra Quadrelli,et al.  Carbon dioxide utilisation : closing the carbon cycle , 2015 .

[35]  D. Cao,et al.  Porous covalent–organic materials: synthesis, clean energy application and design , 2013 .

[36]  David A. Winkler,et al.  Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds , 2013, J. Chem. Inf. Model..

[37]  Matthew R. Hill,et al.  Feasibility of zeolitic imidazolate framework membranes for clean energy applications , 2012 .

[38]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[39]  N. McKeown,et al.  Design principles for microporous organic solids from predictive computational screening , 2013 .

[40]  D. Musmarra,et al.  A Real Adsorbed Solution Theory model for competitive multicomponent liquid adsorption onto granular activated carbon , 2012 .

[41]  Randall Q. Snurr,et al.  Large-Scale Quantitative Structure–Property Relationship (QSPR) Analysis of Methane Storage in Metal–Organic Frameworks , 2013 .

[42]  Ying Mei,et al.  Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces. , 2012, Journal of materials chemistry.

[43]  M. Eremets,et al.  Ammonia as a case study for the spontaneous ionization of a simple hydrogen-bonded compound , 2014, Nature Communications.

[44]  Abhoyjit S Bhown,et al.  In silico screening of carbon-capture materials. , 2012, Nature materials.

[45]  A. Myers.  Characterization of nanopores by standard enthalpy and entropy of adsorption of probe molecules , 2004 .

[46]  André Bardow,et al.  Life-cycle assessment of carbon dioxide capture and utilization: avoiding the pitfalls , 2013 .

[47]  Maciej Haranczyk,et al.  Addressing Challenges of Identifying Geometrically Diverse Sets of Crystalline Porous Materials , 2012, J. Chem. Inf. Model..

[48]  Junliang Sun,et al.  A zeolite family with chiral and achiral structures built from the same building layer. , 2008, Nature materials.

[49]  Optimisation of the Fischer-Tropsch process using zeolites for tail gas separation. , 2014, Physical chemistry chemical physics : PCCP.

[50]  R. Krishna,et al.  A computational study of CO2, N2, and CH4 adsorption in zeolites , 2007 .

[51]  Robert Langer,et al.  Modelling and Prediction of Bacterial Attachment to Polymers , 2014 .

[52]  R. Weissleder,et al.  Modeling biological activities of nanoparticles. , 2012, Nano letters.

[53]  Gérard Férey,et al.  Calculating Geometric Surface Areas as a Characterization Tool for Metal−Organic Frameworks , 2007 .

[54]  Frank R Burden,et al.  Quantitative structure-property relationship modeling of diverse materials properties. , 2012, Chemical reviews.

[55]  R. Gorte,et al.  Characterization of stoichiometric adsorption complexes in H-ZSM-5 using microcalorimetry , 1992 .

[56]  Tu C Le,et al.  Aqueous solubility prediction: do crystal lattice interactions help? , 2013, Molecular pharmaceutics.

[57]  Frank R. Burden,et al.  An Optimal Self‐Pruning Neural Network and Nonlinear Descriptor Selection in QSAR , 2009 .

[58]  Maciej Haranczyk,et al.  Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials , 2012 .

[59]  Wei-Qiao Deng,et al.  Capture and conversion of CO2 at ambient conditions by a conjugated microporous polymer , 2013, Nature Communications.