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Kumar A, Chatterjee A. Probabilistic microkinetic modeling: Species balance equations for a catalyst surface containing multiple short-range order parameters to capture spatial correlations. J Chem Phys 2024; 160:204107. [PMID: 38780385 DOI: 10.1063/5.0209343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Adsorbed molecules on a catalyst almost always arrange themselves in a manner that is far from perfectly random, which gives rise to spatial correlations. These correlations are a result of the interactions between the adsorbed species (adspecies) as well as elementary processes such as diffusion and reaction events that shape the adspecies arrangements. Despite their importance, spatial correlations are usually ignored while writing species balance equations for the modeling of heterogeneous catalytic systems. Recently, we have introduced a probabilistic microkinetic modeling (p-MKM) framework that aims at incorporating spatial correlations in the form of a short-ranged order (SRO) parameter into species balance equations. Here, we extend the approach to catalytic systems of higher complexity, namely, longer interactions and multiple species. This is made possible by including multiple pair probabilities in the p-MKM model for the first time. The interplay between different SRO parameters is probed. An important consideration is how many pair probabilities should be included to capture the underlying complexity with sufficient accuracy.
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Affiliation(s)
- Aditya Kumar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Abhijit Chatterjee
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
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2
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Prats H, Stamatakis M. Transition Metal Carbides as Supports for Catalytic Metal Particles: Recent Progress and Opportunities. J Phys Chem Lett 2024; 15:3450-3460. [PMID: 38512338 PMCID: PMC10983064 DOI: 10.1021/acs.jpclett.3c03214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 02/15/2024] [Accepted: 02/16/2024] [Indexed: 03/22/2024]
Abstract
Transition metal carbides (TMCs) constitute excellent alternatives to traditional oxide-based supports for small metal particles, leading to strong metal-support interactions, which drastically modify the catalytic properties of the supported metal atoms. Moreover, they possess extremely high melting points and good resistance to carbon deposition and sulfur poisoning, and the catalytic activities of some TMCs per se have been shown to be similar to those of Pt-group metals for a considerable number of reactions. Therefore, the use of TMCs as supports can give rise to bifunctional catalysts with multiple active sites. However, at present, only TiC and MoxC have been tested experimentally as supports for metal particles, and it is largely unclear which combinations may best catalyze which chemical reactions. In this Perspective, we review the most significant works on the use of TMCs as supports for catalytic applications, assess the current status of the field, and identify key advances being made and challenges, with an eye to the future.
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Affiliation(s)
- Hector Prats
- Department
of Chemical Engineering, University College
London, Roberts Building Torrington Place, London WC1E 7JE, U.K.
| | - Michail Stamatakis
- Department
of Chemistry, Inorganic Chemistry Lab, University
of Oxford, Oxford OX1 3QR, U.K.
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3
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Nicolle A, Deng S, Ihme M, Kuzhagaliyeva N, Ibrahim EA, Farooq A. Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview. J Chem Inf Model 2024; 64:597-620. [PMID: 38284618 DOI: 10.1021/acs.jcim.3c01633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.
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Affiliation(s)
- Andre Nicolle
- Aramco Fuel Research Center, Rueil-Malmaison 92852, France
| | - Sili Deng
- Massachusetts Institute of Technology, Cambridge 02139, Massachusetts, United States
| | - Matthias Ihme
- Stanford University, Stanford 94305, California, United States
| | | | - Emad Al Ibrahim
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
| | - Aamir Farooq
- King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
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4
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Benson RL, Yadavalli SS, Stamatakis M. Speeding up the Detection of Adsorbate Lateral Interactions in Graph-Theoretical Kinetic Monte Carlo Simulations. J Phys Chem A 2023; 127:10307-10319. [PMID: 37988475 PMCID: PMC11065322 DOI: 10.1021/acs.jpca.3c05581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023]
Abstract
Kinetic Monte Carlo (KMC) has become an indispensable tool in heterogeneous catalyst discovery, but realistic simulations remain computationally demanding on account of the need to capture complex and long-range lateral interactions between adsorbates. The Zacros software package (https://zacros.org) adopts a graph-theoretical cluster expansion (CE) framework that allows such interactions to be computed with a high degree of generality and fidelity. This involves solving a series of subgraph isomorphism problems in order to identify relevant interaction patterns in the lattice. In an effort to reduce the computational burden, we have adapted two well-known subgraph isomorphism algorithms, namely, VF2 and RI, for use in KMC simulations and implemented them in Zacros. To benchmark their performance, we simulate a previously established model of catalytic NO oxidation, treating the O* lateral interactions with a series of progressively larger CEs. For CEs with long-range interactions, VF2 and RI are found to provide impressive speedups relative to simpler algorithms. RI performs best, giving speedups reaching more than 150× when combined with OpenMP parallelization. We also simulate a recently developed methane cracking model, showing that RI offers significant improvements in performance at high surface coverages.
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Affiliation(s)
- Raz L. Benson
- Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.
| | - Sai Sharath Yadavalli
- Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, U.K.
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5
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Yadavalli SS, Jones G, Benson RL, Stamatakis M. Assessing the Impact of Adlayer Description Fidelity on Theoretical Predictions of Coking on Ni(111) at Steam Reforming Conditions. THE JOURNAL OF PHYSICAL CHEMISTRY. C, NANOMATERIALS AND INTERFACES 2023; 127:8591-8606. [PMID: 37197383 PMCID: PMC10184169 DOI: 10.1021/acs.jpcc.3c02323] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 04/13/2023] [Indexed: 05/19/2023]
Abstract
Methane steam reforming is an important industrial process for hydrogen production, employing Ni as a low-cost, highly active catalyst, which, however, suffers from coking due to methane cracking. Coking is the accumulation of a stable poison over time, occurring at high temperatures; thus, to a first approximation, it can be treated as a thermodynamic problem. In this work, we developed an Ab initio kinetic Monte Carlo (KMC) model for methane cracking on Ni(111) at steam reforming conditions. The model captures C-H activation kinetics in detail, while graphene sheet formation is described at the level of thermodynamics, to obtain insights into the "terminal (poisoned) state" of graphene/coke within reasonable computational times. We used cluster expansions (CEs) of progressively higher fidelity to systematically assess the influence of effective cluster interactions between adsorbed or covalently bonded C and CH species on the "terminal state" morphology. Moreover, we compared the predictions of KMC models incorporating these CEs into mean-field microkinetic models in a consistent manner. The models show that the "terminal state" changes significantly with the level of fidelity of the CEs. Furthermore, high-fidelity simulations predict C-CH island/rings that are largely disconnected at low temperatures but completely encapsulate the Ni(111) surface at high temperatures.
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Affiliation(s)
- Sai Sharath Yadavalli
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, U.K.
| | - Glenn Jones
- Johnson
Matthey Technology Centre, Sonning Common, Reading RG4 9NH, U.K.
| | - Raz L. Benson
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, U.K.
| | - Michail Stamatakis
- Thomas
Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, U.K.
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6
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Xu L, Papanikolaou KG, Lechner BAJ, Je L, Somorjai GA, Salmeron M, Mavrikakis M. Formation of active sites on transition metals through reaction-driven migration of surface atoms. Science 2023; 380:70-76. [PMID: 37023183 DOI: 10.1126/science.add0089] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2023]
Abstract
Adopting low-index single-crystal surfaces as models for metal nanoparticle catalysts has been questioned by the experimental findings of adsorbate-induced formation of subnanometer clusters on several single-crystal surfaces. We used density functional theory calculations to elucidate the conditions that lead to cluster formation and show how adatom formation energies enable efficient screening of the conditions required for adsorbate-induced cluster formation. We studied a combination of eight face-centered cubic transition metals and 18 common surface intermediates and identified systems relevant to catalytic reactions, such as carbon monoxide (CO) oxidation and ammonia (NH3) oxidation. We used kinetic Monte Carlo simulations to elucidate the CO-induced cluster formation process on a copper surface. Scanning tunneling microscopy of CO on a nickel (111) surface that contains steps and dislocations points to the structure sensitivity of this phenomenon. Metal-metal bond breaking that leads to the evolution of catalyst structures under realistic reaction conditions occurs much more broadly than previously thought.
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Affiliation(s)
- Lang Xu
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | | | - Barbara A J Lechner
- Department of Chemistry and Catalysis Research Center, School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
- Division of Materials Science, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Lisa Je
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Gabor A Somorjai
- Division of Materials Science, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Chemistry, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Miquel Salmeron
- Division of Materials Science, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
- Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Manos Mavrikakis
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
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7
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Kumar A, Chatterjee A. A probabilistic microkinetic modeling framework for catalytic surface reactions. J Chem Phys 2023; 158:024109. [PMID: 36641399 DOI: 10.1063/5.0132877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
We present a probabilistic microkinetic modeling (MKM) framework that incorporates the short-ranged order (SRO) evolution for adsorbed species (adspecies) on a catalyst surface. The resulting model consists of a system of ordinary differential equations. Adsorbate-adsorbate interactions, surface diffusion, adsorption, desorption, and catalytic reaction processes are included. Assuming that the adspecies ordering/arrangement is accurately described by the SRO parameters, we employ the reverse Monte Carlo (RMC) method to extract the relevant local environment probability distributions and pass them to the MKM. The reaction kinetics is faithfully captured as accurately as the kinetic Monte Carlo (KMC) method but with a computational time requirement of few seconds on a standard desktop computer. KMC, on the other hand, can require several days for the examples discussed. The framework presented here is expected to provide the basis for wider application of the RMC-MKM approach to problems in computational catalysis, electrocatalysis, and material science.
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Affiliation(s)
- Aditya Kumar
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Abhijit Chatterjee
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India
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8
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Ping L, Zhang Y, Wang B, Fan M, Ling L, Zhang R. Unraveling the Surface State Evolution of IrO 2 in Ethane Chemical Looping Oxidative Dehydrogenation. ACS Catal 2023. [DOI: 10.1021/acscatal.2c05770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Lulu Ping
- State Key Laboratory of Clean and Efficient Coal Utilization, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
- College of Chemical Engineering and Technology, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
| | - Yuan Zhang
- State Key Laboratory of Clean and Efficient Coal Utilization, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
| | - Baojun Wang
- State Key Laboratory of Clean and Efficient Coal Utilization, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
- College of Chemical Engineering and Technology, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
| | - Maohong Fan
- Department of Chemical and Petroleum Engineering, University of Wyoming, Laramie, Wyoming 82071, United States
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- School of Energy Resources, University of Wyoming, Laramie, Wyoming 82071, United States
| | - Lixia Ling
- College of Chemical Engineering and Technology, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
| | - Riguang Zhang
- State Key Laboratory of Clean and Efficient Coal Utilization, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
- College of Chemical Engineering and Technology, Taiyuan University of Technology, Taiyuan, Shanxi 030024, P. R. China
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9
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Pedersen JK, Clausen CM, Skjegstad LEJ, Rossmeisl J. A Mean Field‐Model for Oxygen Reduction Electrocatalytic Activity on High‐Entropy Alloys. ChemCatChem 2022. [DOI: 10.1002/cctc.202200699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Jack K. Pedersen
- University of Copenhagen: Kobenhavns Universitet Department of Chemistry Universitetsparken 5 2100 Copenhagen DENMARK
| | - Christian M. Clausen
- University of Copenhagen: Kobenhavns Universitet Department of Chemistry DENMARK
| | | | - Jan Rossmeisl
- University of Copenhagen Chemistry Universitetsparken 5 2100 københavn ø DENMARK
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10
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Mou T, Han X, Zhu H, Xin H. Machine learning of lateral adsorbate interactions in surface reaction kinetics. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2022.100825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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11
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12
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Pineda M, Stamatakis M. Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges. J Chem Phys 2022; 156:120902. [DOI: 10.1063/5.0083251] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.
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Affiliation(s)
- M. Pineda
- Thomas Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
| | - M. Stamatakis
- Thomas Young Centre and Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
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13
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Streibel V, Aljama HA, Yang AC, Choksi TS, Sánchez-Carrera RS, Schäfer A, Li Y, Cargnello M, Abild-Pedersen F. Microkinetic Modeling of Propene Combustion on a Stepped, Metallic Palladium Surface and the Importance of Oxygen Coverage. ACS Catal 2022. [DOI: 10.1021/acscatal.1c03699] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Verena Streibel
- Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, United States
- SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Hassan A. Aljama
- Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, United States
- SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - An-Chih Yang
- Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, United States
| | - Tej S. Choksi
- Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, United States
- SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | | | - Ansgar Schäfer
- BASF SE, Quantum Chemistry, Carl-Bosch-Straße 38, 67056 Ludwigshafen, Germany
| | - Yuejin Li
- BASF Corporation, Environmental Catalysis R&D and Application, 25 Middlesex-Essex Turnpike, Iselin, New Jersey 08830, United States
| | - Matteo Cargnello
- Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, California 94305, United States
| | - Frank Abild-Pedersen
- SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, Menlo Park, California 94025, United States
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14
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Pablo-García S, Sabadell-Rendón A, Saadun AJ, Morandi S, Pérez-Ramírez J, López N. Generalizing Performance Equations in Heterogeneous Catalysis from Hybrid Data and Statistical Learning. ACS Catal 2022. [DOI: 10.1021/acscatal.1c04345] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Sergio Pablo-García
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
| | - Albert Sabadell-Rendón
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
| | - Ali J. Saadun
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zürich, Switzerland
| | - Santiago Morandi
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
| | - Javier Pérez-Ramírez
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zürich, Switzerland
| | - Núria López
- Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology ICIQ, Av. Països Catalans 16, 43007, Tarragona, Spain
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15
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Goswami A, Ma H, Schneider WF. Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions. J Catal 2022. [DOI: 10.1016/j.jcat.2021.12.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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16
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Kim CA, Ricke ND, Van Voorhis T. Machine learning dynamic correlation in chemical kinetics. J Chem Phys 2021; 155:144107. [PMID: 34654306 DOI: 10.1063/5.0065874] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Lattice models are a useful tool to simulate the kinetics of surface reactions. Since it is expensive to propagate the probabilities of the entire lattice configurations, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation. Unfortunately, simple closures, such as the mean-field and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics using the lattice Lotka-Volterra model as a model system. We trained feedforward neural networks on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the ML moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pretrained closures that would enable researchers to extract useful insight at a fraction of the computational cost.
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Affiliation(s)
- Changhae Andrew Kim
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Nathan D Ricke
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Troy Van Voorhis
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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17
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Huš M, Kopač D, Bajec D, Likozar B. Effect of Surface Oxidation on Oxidative Propane Dehydrogenation over Chromia: An Ab Initio Multiscale Kinetic Study. ACS Catal 2021; 11:11233-11247. [PMID: 34513204 PMCID: PMC8422962 DOI: 10.1021/acscatal.1c01814] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 07/27/2021] [Indexed: 11/28/2022]
Abstract
An increasingly utilized way for the production of propene is propane dehydrogenation. The reaction presents an alternative to conventional processes based on petroleum resources. In this work, we investigate theoretically how Cr2O3 catalyzes this reaction in oxidative and reducing environments. Although previous studies showed that the reduced catalyst is selective for the non-oxidative dehydrogenation of propane, real operating conditions are oxidative. Herein, we use multiscale modeling to investigate the difference between the oxidized and reduced catalyst and their performance. The complete reaction pathway for propane dehydrogenation, including C-C cracking, formation of side products (propyne, ethane, ethylene, acetylene, and methane), and catalyst coking on oxidized and reduced surfaces of α-Cr2O3(0001), is calculated using density functional theory with the Hubbard correction. Parameters describing adsorption, desorption, and surface reactions are used in a kinetic Monte Carlo simulation, which employed industrially relevant conditions (700-900 K, pressures up to 2 bar, and varying oxidants: N2O, O2, and none). We observe that over the reduced surface, propene and hydrogen form with high selectivity. When oxidants are used, the surface is oxidized, which changes the reaction mechanism and kinetics. During a much faster reaction, H2O forms as a coproduct in a Mars-van Krevelen cycle. Additionally, CO2 is also formed, which represents waste and adversely affects the selectivity. It is shown that the oxidized surface is much more active but prone to the formation of CO2, while the reduced surface is less active but highly selective toward propene. Moreover, the effect of the oxidant used is investigated, showing that N2O is preferred to O2 due to higher selectivity and less catalyst coking. We show that there exists an optimum degree of surface oxidation, where the yield of propene is maximized. The coke, which forms during the reaction, can be burnt away as CO2 with oxygen.
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Affiliation(s)
- Matej Huš
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
- Association for Technical Culture of Slovenia (ZOTKS), Zaloška 65, SI-1000 Ljubljana, Slovenia
| | - Drejc Kopač
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - David Bajec
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Blaž Likozar
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
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18
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Dickbreder T, Bechstein R, Kühnle A. Crucial impact of exchange between layers on temperature programmed desorption. Phys Chem Chem Phys 2021; 23:18314-18321. [PMID: 34357364 DOI: 10.1039/d1cp01924d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Desorption of molecules from surfaces constitutes an elementary process that is fundamental in both natural and application-oriented fields, including dewetting, weathering and catalysis. A powerful method to investigate desorption processes is temperature-programmed desorption (TPD), which offers the potential to provide mechanistic insights into the desorption kinetics. Using TPD, the desorption order, the energy barrier as well as the entropy change upon desorption can be accessed. In the past, several analysis methods have been developed for TPD data. These methods have in common that they rely on the Polanyi-Wigner equation, which requires proposing a desorption mechanism with a single (or at least dominating) desorption path. For real systems, however, several coupled desorption paths can be easily envisioned, which cannot be disentangled. Here, we analyse the influence of exchange between the first and the second adsorbate layer on the desorption process. We present a kinetic model, in which molecules can desorb directly from the first layer or change into the second layer and desorb from there. Interestingly, considering this additional desorption pathway alters the desorption spectrum considerably, even if the transient second-layer occupation remains as small as 4 × 10-6 monolayers. We show that the impact of this layer exchange can be described by a modified Polanyi-Wigner equation. Our study demonstrates that layer exchange can crucially impact the TPD data.
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Affiliation(s)
- Tobias Dickbreder
- Physical Chemistry I, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany.
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19
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Abstract
We demonstrate that the Langmuir-Hinshelwood formalism is an incomplete kinetic description and, in particular, that the Hinshelwood assumption (i.e., that adsorbates are randomly distributed on the surface) is inappropriate even in catalytic reactions as simple as A + A → A2 The Hinshelwood assumption results in miscounting of site pairs (e.g., A*-A*) and, consequently, in erroneous rates, reaction orders, and identification of rate-determining steps. The clustering and isolation of surface species unnoticed by the Langmuir-Hinshelwood model is rigorously accounted for by derivation of higher-order rate terms containing statistical factors specific to each site ensemble. Ensemble-specific statistical rate terms arise irrespective of and couple with lateral adsorbate interactions, are distinct for each elementary step including surface diffusion events (e.g., A* + * → * + A*), and provide physical insight obscured by the nonanalytical nature of the kinetic Monte Carlo (kMC) method-with which the higher-order formalism quantitatively agrees. The limitations of the Langmuir-Hinshelwood model are attributed to the incorrect assertion that the rate of an elementary step is the same with respect to each site ensemble. In actuality, each elementary step-including adsorbate diffusion-traverses through each ensemble with unique rate, reversibility, and kinetic-relevance to the overall reaction rate. Explicit kinetic description of ensemble-specific paths is key to the improvements of the higher-order formalism; enables quantification of ensemble-specific rate, reversibility, and degree of rate control of surface diffusion; and reveals that a single elementary step can, counter intuitively, be both equilibrated and rate determining.
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20
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Kopač D, Jurković DL, Likozar B, Huš M. First-Principles-Based Multiscale Modelling of Nonoxidative Butane Dehydrogenation on Cr 2O 3(0001). ACS Catal 2020; 10:14732-14746. [PMID: 33362945 PMCID: PMC7754517 DOI: 10.1021/acscatal.0c03197] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/16/2020] [Indexed: 11/29/2022]
Abstract
Propane (C3H8) and butane (C4H10) are short straight-chain alkane molecules that are difficult to convert catalytically. Analogous to propane, butane can be dehydrogenated to butenes (also known as butylenes) or butadiene, which are used industrially as raw materials when synthesizing various chemicals (plastics, rubbers, etc.). In this study, we present results of detailed first-principles-based multiscale modelling of butane dehydrogenation, consisting of three size- and time-scales. The reaction is modelled over Cr2O3(0001) chromium oxide, which is commonly used in the industrial setting. A complete 108-step reaction pathway of butane (C4H10) dehydrogenation was studied, yielding 1-butene (CH2CHCH2CH3) and 2-butene (CH3CHCHCH3), 1-butyne (CHCCH2CH3) and 2-butyne (CH3CCCH3), butadiene (CH2CHCHCH2), butenyne (CH2CHCCH), and ultimately butadiyne (CHCCCH). We include cracking and coking reactions (yielding C1, C2, and C3 hydrocarbons) in the model to provide a thorough description of catalyst deactivation as a function of the temperature and time. Density functional theory calculations with the Hubbard U model were used to study the reaction on the atomistic scale, resulting in the complete energetics and first-principles kinetic parameters for the dehydrogenation reaction. They were cast in a kinetic model using mean-field microkinetics and kinetic Monte Carlo simulations. The former was used to obtain gas equilibrium conditions in the steady-state regime, which were fed in the latter to provide accurate surface kinetics. A full reactor simulation was used to account for the macroscopic properties of the catalytic particles: their loading, specific surface area, and density and reactor parameters: size, design, and feed gas flow. With this approach, we obtained first-principles estimates of the catalytic conversion, selectivity to products, and time dependence of the catalyst activity, which can be paralleled to experimental data. We show that 2-butene is the most abundant product of dehydrogenation, with selectivity above 90% and turn-over frequency above 10-3 s-1 at T = 900 K. Butane conversion is below 5% at such low temperature, but rises above 40% at T > 1100 K. Activity starts to drop after ∼6 h because of surface poisoning with carbon. We conclude that the dehydrogenation of butane is a viable alternative to conventional olefin production processes.
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Affiliation(s)
- Drejc Kopač
- Department
of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Damjan Lašič Jurković
- Department
of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Blaž Likozar
- Department
of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Matej Huš
- Department
of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
- Association
for Technical Culture of Slovenia (ZOTKS), SI-1000 Ljubljana, Slovenia
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21
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Chen B, Xiong C, Jiang DE, Savara A. Ethanol Conversion over La 0.7Sr 0.3MnO 3–x(100): Autocatalysis, Adjacent O-Vacancies, Disproportionation, and Dehydrogenation. ACS Catal 2020. [DOI: 10.1021/acscatal.0c03241] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Bo Chen
- Chemical Science Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37831-6201, United States
| | | | | | - Aditya Savara
- Chemical Science Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37831-6201, United States
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22
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Ding C, Weng J, Shen T, Xu X. The enhanced extended phenomenological kinetics method to deal with timescale disparity problem among different reaction pathways. J Comput Chem 2020; 41:2115-2123. [PMID: 32618018 DOI: 10.1002/jcc.26374] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 11/10/2022]
Abstract
Kinetic Monte Carlo method can provide valuable mechanistic insights for catalytic systems. Nonetheless, it suffers from the notorious problem of timescale disparity due to the existence of the complex catalytic network that consists of fast events and slow events. Previously, we have proposed the extended phenomenological kinetics (XPK) method that effectively deals with the timescale disparity problem between diffusion and reaction. However, it remains a great challenge to simulate systems with timescale disparity among different reaction pathways, which is important when selectivity is the major concern. In this study, we implement the enhanced XPK method to address this problem. The new algorithm works by identifying states connected through fast transitions and compressing them into a "superstate" when the chosen states satisfy a local steadystate condition. This state compression algorithm simplifies the reaction network by concealing the fast transitions. The accuracy and efficiency of the algorithm are demonstrated by two model systems: selective catalytic hydrogenation and selective catalytic decomposition. The enhanced XPK method is expected to be beneficial to the kinetic simulations of catalytic systems, especially those with complex reaction networks.
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Affiliation(s)
- Chen Ding
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, China
| | - Jingwei Weng
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, China
| | - Tonghao Shen
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, China
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, China
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23
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Bhandari S, Rangarajan S, Mavrikakis M. Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the In Situ Nature of the Active Site in Catalysis. Acc Chem Res 2020; 53:1893-1904. [PMID: 32869965 DOI: 10.1021/acs.accounts.0c00340] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Microkinetic modeling based on density functional theory (DFT) derived energetics is important for addressing fundamental questions in catalysis. The quantitative fidelity of microkinetic models (MKMs), however, is often insufficient to conclusively infer the mechanistic details of a specific catalytic system. This can be attributed to a number of factors such as an incorrect model of the active site for which DFT calculations are performed, deficiencies in the hypothesized reaction mechanism, inadequate consideration of the surface environment under reaction conditions, and intrinsic errors in the DFT exchange-correlation functional. Despite these limitations, we aim at developing a rigorous understanding of the reaction mechanism and of the nature of the active site for heterogeneous catalytic chemistries under reaction conditions. By achieving parity between experimental and modeling outcomes through robust parameter estimation and by ensuring coverage-consistency between DFT calculations and MKM predictions, it is possible to systematically refine the mechanistic model and, thereby, our understanding of the catalytic active site in situ.Our general approach consists of developing ab initio informed MKM for a given active site and then re-estimating the energies of the transition and intermediate states so that the model predictions match quantities measured in reaction kinetics experiments. If (i) model-experiment parity is high, (ii) the adjustments to the DFT-derived energetics for a given model of the active site are rationalized within the errors of standard DFT exchange-correlation functionals, and (iii) the resultant MKM predicts surface coverages that are consistent with those assumed in the DFT calculations used to initialize the MKM, we conclude that we have correctly identified the active site and the reaction mechanism. If one or more of these requirements are not met, we iteratively refine our model by updating our hypothesis for the structure of the active site and/or by incorporating coverage effects, until we obtain a high-fidelity coverage-self-consistent MKM whose final kinetic and thermodynamic parameters are within error of the values derived from DFT.Using the catalytic reaction of formic acid (FA, HCOOH) decomposition over transition-metal catalysts as an example, here we provide an account of how we applied this algorithm to study this chemistry on powder Au/SiC and Pt/C catalysts. For the case of Au catalysts, on which the FA decomposition occurred exclusively through the dehydrogenation reaction (HCOOH → CO2+H2), our approach was used to iteratively refine the model starting from the (111) facet until we found that specific ensembles of Au atoms present in sub-nanometer clusters can describe the active site for this catalysis. For the case of Pt catalysts, wherein both dehydrogenation (HCOOH → CO2 + H2) and dehydration (HCOOH → CO + H2O) reactions were active, our approach identified that a partially CO*-covered (111) surface serves as the active site and that CO*-assisted steps contributed substantially to the overall FA decomposition activity. Finally, we suggest that once the active site and the mechanism are conclusively identified, the model can subsequently serve as a high-quality basis for designing specific goal-oriented experiments and improved catalysts.
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Affiliation(s)
- Saurabh Bhandari
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
| | - Srinivas Rangarajan
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
| | - Manos Mavrikakis
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
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24
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Ravipati S, d'Avezac M, Nielsen J, Hetherington J, Stamatakis M. A Caching Scheme To Accelerate Kinetic Monte Carlo Simulations of Catalytic Reactions. J Phys Chem A 2020; 124:7140-7154. [PMID: 32786994 DOI: 10.1021/acs.jpca.0c03571] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Kinetic Monte Carlo (KMC) simulations have been instrumental in advancing our fundamental understanding of heterogeneously catalyzed reactions, with particular emphasis on structure sensitivity, ensemble effects, and the interplay between adlayer structure and adsorbate-adsorbate lateral interactions in shaping the observed kinetics. Yet, the computational cost of KMC remains high, thereby motivating the development of acceleration schemes that would improve the simulation efficiency. We present an exact such scheme, which implements a caching algorithm along with shared-memory parallelization to improve the computational performance of simulations incorporating long-range adsorbate-adsorbate lateral interactions. This scheme is based on caching information about the energetic interaction patterns associated with the products of each possible lattice process (adsorption, desorption, reaction etc.). Thus, every time a reaction occurs ("ongoing reaction"), it enables fast updates of the rate constants of "affected reactions", i.e., possible reactions in the region of influence of the "ongoing reaction". Benchmarks on KMC simulations of NOx oxidation/reduction, yielded acceleration factors of up to 20, when comparing single-thread runs without caching to runs on 16 threads with caching, for simulations with a cluster expansion Hamiltonian that incorporates up to 8th-nearest-neighbor interactions.
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Affiliation(s)
- Srikanth Ravipati
- Thomas Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Mayeul d'Avezac
- Research Software Development Group, Research IT Services, University College London, Torrington Place, London WC1E 6BT, United Kingdom
| | - Jens Nielsen
- Research Software Development Group, Research IT Services, University College London, Torrington Place, London WC1E 6BT, United Kingdom
| | - James Hetherington
- Research Software Development Group, Research IT Services, University College London, Torrington Place, London WC1E 6BT, United Kingdom
| | - Michail Stamatakis
- Thomas Young Centre and Department of Chemical Engineering, University College London, Torrington Place, London WC1E 7JE, United Kingdom
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25
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Kinetics of non-oxidative propane dehydrogenation on Cr2O3 and the nature of catalyst deactivation from first-principles simulations. J Catal 2020. [DOI: 10.1016/j.jcat.2020.03.037] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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26
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Kopač D, Likozar B, Huš M. How Size Matters: Electronic, Cooperative, and Geometric Effect in Perovskite-Supported Copper Catalysts for CO 2 Reduction. ACS Catal 2020; 10:4092-4102. [PMID: 32953235 PMCID: PMC7493227 DOI: 10.1021/acscatal.9b05303] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 02/05/2020] [Indexed: 11/28/2022]
Abstract
In heterogeneous catalysis, bifunctional catalysts often outperform one-component catalysts. The activity is also strongly influenced by the morphology, size, and distribution of catalytic particles. For CO2 hydrogenation, the size of the active copper area on top of the SrTiO3 perovskite catalyst support can affect the activity, selectivity, and stability. Here, a detailed theoretical study of the effect of bifunctionality on an important chemical CO2 transformation reaction, the reverse water gas shift (RWGS) reaction, is presented. Using density functional theory computation results for the RWGS pathway on three surfaces, namely, Cu(111), SrTiO3, and the Cu/SrTiO3 interface between both solid phases, we construct the energy landscape of the reaction. The adsorbate-adsorbate lateral interactions are taken into account for catalytic surfaces, which show a sufficient intermediate coverage. The mechanism, combining all three surfaces, is used in mesoscale kinetic Monte Carlo simulations to study the turnover and yield for CO production as a function of particle size. It is shown that the reaction proceeds faster at the interface. However, including copper and the support sites in addition to the interface accelerates the conversion even further, showing that the bifunctionality of the catalyst manifests in a more complex interplay between the phases than just the interface effect, such as the hydrogen spillover. We identify three distinct effects, the electronic, cooperative, and geometric effects, and show that the surrounded smaller Cu features on the set of supporting SrTiO3 show a higher CO formation rate, resulting in a decreasing RWGS model trend with the increasing Cu island size. The findings are in parallel with experiments, showing that they explain the previously observed phenomena and confirming the size sensitivity for the catalytic RWGS reaction.
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Affiliation(s)
- Drejc Kopač
- Department of Catalysis and Chemical
Reaction Engineering, National Institute
of Chemistry, Hajdrihova
19, SI-1001 Ljubljana, Slovenia
| | - Blaž Likozar
- Department of Catalysis and Chemical
Reaction Engineering, National Institute
of Chemistry, Hajdrihova
19, SI-1001 Ljubljana, Slovenia
| | - Matej Huš
- Department of Catalysis and Chemical
Reaction Engineering, National Institute
of Chemistry, Hajdrihova
19, SI-1001 Ljubljana, Slovenia
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27
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Bhandari S, Rangarajan S, Maravelias CT, Dumesic JA, Mavrikakis M. Reaction Mechanism of Vapor-Phase Formic Acid Decomposition over Platinum Catalysts: DFT, Reaction Kinetics Experiments, and Microkinetic Modeling. ACS Catal 2020. [DOI: 10.1021/acscatal.9b05424] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Saurabh Bhandari
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
| | - Srinivas Rangarajan
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
| | - Christos T. Maravelias
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
| | - James A. Dumesic
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
| | - Manos Mavrikakis
- Department of Chemical and Biological Engineering, University of Wisconsin—Madison, Madison, Wisconsin 53706, United States
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28
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Kim M, Franklin AD, Martin R, Bian Y, Weaver JF, Asthagiri A. Kinetics of low-temperature methane activation on IrO2(1 1 0): Role of local surface hydroxide species. J Catal 2020. [DOI: 10.1016/j.jcat.2020.01.027] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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29
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Dynamic vs static behaviour of a supported nanoparticle with reaction-induced catalytic sites in a lattice model. Sci Rep 2020; 10:2882. [PMID: 32076083 PMCID: PMC7031362 DOI: 10.1038/s41598-020-59739-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 02/03/2020] [Indexed: 11/08/2022] Open
Abstract
Modern literature shows a rapidly growing interest to the supported nanocatalysts with dynamic behaviour under reaction conditions. This new frontier of heterogeneous catalysis is recognized as one of the most challenging and worthy of consideration from all possible angles. In this context, a previously suggested lattice model is used to get an insight, by means of kinetic Monte Carlo, into the influence of the mobility of reaction-induced catalytic sites of a two-dimensional supported nanoparticle on the system behaviour. The results speak in favour of feasibility of dynamic nanocatalysts with self-organized structures capable of robust functioning. This approach, from the macroscopic end, is believed to be a useful complement to ever developing experimental and first principle approaches.
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30
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Bajpai A, Frey K, Schneider WF. Comparison of Coverage-Dependent Binding Energy Models for Mean-Field Microkinetic Rate Predictions. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2020; 36:465-474. [PMID: 31841619 DOI: 10.1021/acs.langmuir.9b03563] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The binding energies of adsorbates at catalytic surfaces are in general functions of adsorbate coverage, with corresponding consequences for equilibrium surface coverages and reaction rates under relevant conditions. This coverage dependence is commonly incorporated into mean-field microkinetic models by writing adsorption energies as an algebraic function of coverage and parametrizing against density functional theory models. In this work, we compare the performance of three different analytical coverage-dependent forms, including linear and piecewise models and a logarithmic form inspired by Wilson's activity model, against accurate results obtained from a lattice-based cluster expansion (CE) representation of adsorbate interactions combined with a Monte Carlo evaluation of reaction rates. We take as a model system O2 dissociation-limited NO oxidation to NO2 over Pt(111), parametrize all models against the same set of previously reported coverage-dependent NO and O binding energies, and solve kinetic models under the same set of assumptions. Steady-state coverages from the analytical models are similar to each other and the ensemble-averaged CE result, other than the discontinuities in O and NO coverages that appear in the piecewise model. Predicted steady-state rates differ more substantially, reflecting the sensitivity of the O2 dissociation activation energy to coverage-dependent binding energies. The activity model predicts reaction rates reliably at low temperatures and systematically deviates from CE rates at high temperatures, where minority surface sites, having low local coverage around vacant pairs, dominate overall reaction rates. The results highlight the challenges of developing coverage-dependent microkinetic models that are reliable across a range of conditions.
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Affiliation(s)
- Anshumaan Bajpai
- Chemical and Biomolecular Engineering , University of Notre Dame , Notre Dame , Indiana 46556 , United States
| | - Kurt Frey
- Chemical and Biomolecular Engineering , University of Notre Dame , Notre Dame , Indiana 46556 , United States
| | - William F Schneider
- Chemical and Biomolecular Engineering , University of Notre Dame , Notre Dame , Indiana 46556 , United States
- Chemistry and Biochemistry , University of Notre Dame , Notre Dame , Indiana 46556 , United States
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31
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Huš M, Grilc M, Pavlišič A, Likozar B, Hellman A. Multiscale modelling from quantum level to reactor scale: An example of ethylene epoxidation on silver catalysts. Catal Today 2019. [DOI: 10.1016/j.cattod.2019.05.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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32
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Affiliation(s)
- Mikkel Jørgensen
- Department of Physics and Competence Centre for Catalysis, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Henrik Grönbeck
- Department of Physics and Competence Centre for Catalysis, Chalmers University of Technology, 412 96 Göteborg, Sweden
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33
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Matera S, Schneider WF, Heyden A, Savara A. Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis. ACS Catal 2019. [DOI: 10.1021/acscatal.9b01234] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Sebastian Matera
- Fachbereich Mathematik and Informatik, Freie Universität, 14195 Berlin, Germany
| | - William F. Schneider
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Andreas Heyden
- Department of Chemical Engineering, University of South Carolina, Columbia, South Carolina 29208, United States
| | - Aditya Savara
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, United States
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34
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Andersen M, Panosetti C, Reuter K. A Practical Guide to Surface Kinetic Monte Carlo Simulations. Front Chem 2019; 7:202. [PMID: 31024891 PMCID: PMC6465329 DOI: 10.3389/fchem.2019.00202] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 03/15/2019] [Indexed: 11/26/2022] Open
Abstract
This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g., fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here.
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Affiliation(s)
- Mie Andersen
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
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35
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Abstract
The experimentally determined temperature programmed desorption profile of CO from Fe(100) is characterized by four maxima, i.e., α1-CO, α2-CO, α3-CO, and β-CO (see e.g., Moon et al., Surf. Sci. 1985, 163, 215). The CO-TPD profile is modeled using mean-field techniques and kinetic Monte Carlo to show the importance of lateral interactions in the appearance of the CO-TPD-profile. The inclusion of lateral interactions results in the appearance of a new maximum in the simulated CO-TPD profile if modeled using the mean-field, quasi-chemical approach or kinetic Monte Carlo. It is argued that α2-CO may thus originate from lateral interactions rather than a differently bound CO on Fe(100). A detailed sensitivity analysis of the effect of the strength of the lateral interactions between the species involved (CO, C, and O), and the choice of the transition state, which affects the activation energy for CO dissociation, and the energy barrier for diffusion on the CO-TPD profile is presented.
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36
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Su YQ, Wang Y, Liu JX, Filot IA, Alexopoulos K, Zhang L, Muravev V, Zijlstra B, Vlachos DG, Hensen EJ. Theoretical Approach To Predict the Stability of Supported Single-Atom Catalysts. ACS Catal 2019. [DOI: 10.1021/acscatal.9b00252] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ya-Qiong Su
- Laboratory of Inorganic Materials & Catalysis, Schuit Institute of Catalysis, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Yifan Wang
- Department of Chemical and Biomolecular Engineering, Catalysis Center for Energy Innovation, University of Delaware, 221 Academy Street, Newark, Delaware 19716, United States
| | - Jin-Xun Liu
- Laboratory of Inorganic Materials & Catalysis, Schuit Institute of Catalysis, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Ivo A.W. Filot
- Laboratory of Inorganic Materials & Catalysis, Schuit Institute of Catalysis, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Konstantinos Alexopoulos
- Department of Chemical and Biomolecular Engineering, Catalysis Center for Energy Innovation, University of Delaware, 221 Academy Street, Newark, Delaware 19716, United States
| | - Long Zhang
- Laboratory of Inorganic Materials & Catalysis, Schuit Institute of Catalysis, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Valerii Muravev
- Laboratory of Inorganic Materials & Catalysis, Schuit Institute of Catalysis, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Bart Zijlstra
- Laboratory of Inorganic Materials & Catalysis, Schuit Institute of Catalysis, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
| | - Dionisios G. Vlachos
- Department of Chemical and Biomolecular Engineering, Catalysis Center for Energy Innovation, University of Delaware, 221 Academy Street, Newark, Delaware 19716, United States
| | - Emiel J.M. Hensen
- Laboratory of Inorganic Materials & Catalysis, Schuit Institute of Catalysis, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
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37
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Huš M, Hellman A. Ethylene Epoxidation on Ag(100), Ag(110), and Ag(111): A Joint Ab Initio and Kinetic Monte Carlo Study and Comparison with Experiments. ACS Catal 2018. [DOI: 10.1021/acscatal.8b04512] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Matej Huš
- Chalmers University of Technology, Department of Physics, Fysikgränd 3, SE-41296 Gothenburg, Sweden
- National Institute of Chemistry, Department of Catalysis and Chemical Reaction Engineering, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Anders Hellman
- Chalmers University of Technology, Department of Physics, Fysikgränd 3, SE-41296 Gothenburg, Sweden
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38
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Huš M, Kopač D, Likozar B. Catalytic Hydrogenation of Carbon Dioxide to Methanol: Synergistic Effect of Bifunctional Cu/Perovskite Catalysts. ACS Catal 2018. [DOI: 10.1021/acscatal.8b03810] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Matej Huš
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
- Department of Physics, Chalmers University of Technology, Fysikgränd 3, SE-41296 Gothenburg, Sweden
| | - Drejc Kopač
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
| | - Blaž Likozar
- Department of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia
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39
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Papanikolaou KG, Darby MT, Stamatakis M. Adlayer structure and lattice size effects on catalytic rates predicted from KMC simulations: NO oxidation on Pt(111). J Chem Phys 2018; 149:184701. [DOI: 10.1063/1.5048787] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Affiliation(s)
- Konstantinos G. Papanikolaou
- Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
| | - Matthew T. Darby
- Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
| | - Michail Stamatakis
- Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, United Kingdom
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40
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Grajciar L, Heard CJ, Bondarenko AA, Polynski MV, Meeprasert J, Pidko EA, Nachtigall P. Towards operando computational modeling in heterogeneous catalysis. Chem Soc Rev 2018; 47:8307-8348. [PMID: 30204184 PMCID: PMC6240816 DOI: 10.1039/c8cs00398j] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Indexed: 12/19/2022]
Abstract
An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.
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Affiliation(s)
- Lukáš Grajciar
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Christopher J. Heard
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Anton A. Bondarenko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Mikhail V. Polynski
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Jittima Meeprasert
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Evgeny A. Pidko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Petr Nachtigall
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
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41
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Sutton JE, Lorenzi JM, Krogel JT, Xiong Q, Pannala S, Matera S, Savara A. Electrons to Reactors Multiscale Modeling: Catalytic CO Oxidation over RuO2. ACS Catal 2018. [DOI: 10.1021/acscatal.8b00713] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jonathan E. Sutton
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Juan M. Lorenzi
- Theoretical Chemistry and Catalysis Research Center, Technische Universität München, 85748 Garching, Germany
| | - Jaron T. Krogel
- Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Qingang Xiong
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sreekanth Pannala
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sebastian Matera
- Fachbereich Mathematik & Informatik, Free University, 14195 Berlin, Germany
| | - Aditya Savara
- Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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42
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Chen Z, Wang H, Su NQ, Duan S, Shen T, Xu X. Beyond Mean-Field Microkinetics: Toward Accurate and Efficient Theoretical Modeling in Heterogeneous Catalysis. ACS Catal 2018. [DOI: 10.1021/acscatal.8b00943] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Zheng Chen
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - He Wang
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Neil Qiang Su
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Sai Duan
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Tonghao Shen
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai, Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
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43
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Schaefer C. Structuring of Fluid Adlayers upon Ongoing Unimolecular Adsorption. PHYSICAL REVIEW LETTERS 2018; 120:036001. [PMID: 29400489 DOI: 10.1103/physrevlett.120.036001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Revised: 10/27/2017] [Indexed: 06/07/2023]
Abstract
Fluids with spatial density variations of single or mixed molecules play a key role in biophysics, soft matter, and materials science. The fluid structures usually form via spinodal decomposition or nucleation following an instantaneous destabilization of the initially disordered fluid. However, in practice, an instantaneous quench is often not viable, and the rate of destabilization may be gradual rather than instantaneous. In this work we show that the commonly used phenomenological descriptions of fluid structuring are inadequate under these conditions. We come to that conclusion in the context of surface catalysis, where we employ kinetic Monte Carlo simulations to describe the unimolecular adsorption of gaseous molecules onto a metal surface. The adsorbates diffuse at the surface and, as a consequence of lateral interactions and due to an ongoing increase of the surface coverage, phase separate into coexisting low- and high-density regions. The typical size of these regions turns out to depend much more strongly on the rate of adsorption than predicted from recently reported phenomenological models. We discuss how this finding contributes to the fundamental understanding of the crossover from liquid-liquid to liquid-solid demixing of solution-cast polymer blends.
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Affiliation(s)
- C Schaefer
- Department of Physics, Durham University, South Road DH1 3LE, United Kingdom
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