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Meng Y, Liu X, Ma Y, Gao X, Wen X. Investigation of water gas shift reactivity on Fe5C2 (111): A DFT study. MOLECULAR CATALYSIS 2022. [DOI: 10.1016/j.mcat.2022.112538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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2
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Alioui O, Badawi M, Erto A, Amin MA, Tirth V, Jeon BH, Islam S, Balsamo M, Virginie M, Ernst B, Benguerba Y. Contribution of DFT to the optimization of Ni-based catalysts for dry reforming of methane: a review. CATALYSIS REVIEWS 2022. [DOI: 10.1080/01614940.2021.2020518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Oualid Alioui
- Laboratoire de génie des procédés chimiques, LGPC, Université Ferhat ABBAS Sétif-1 19000 Sétif, Algeria
| | - Michael Badawi
- Laboratoire de Physique et Chimie Théoriques, UMR CNRS 7019, Université de Lorraine, 54000 Nancy, France
| | - Alessandro Erto
- Dipartimento di Ingegneria Chimica, dei Materiali e Università degli Studi di Napoli, P.leTecchio, 80, 80125, Napoli, Italy
| | - Mohammed A. Amin
- Department of Chemistry, College of Science, Taif University, Taif 21944, Saudi Arabia
| | - Vineet Tirth
- Mechanical Engineering Department, College of Engineering, King Khalid University, Abha 61411, Asir, Kingdom of Saudi Arabia
- Research Center for Advanced Materials Science (RCAMS), King Khalid University Guraiger, Abha, Asir, Kingdom of Saudi Arabia
| | - Byong-Hun Jeon
- Department of Earth Resources and Environmental Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Saiful Islam
- Civil Engineering Department, College of Engineering, King Khalid University, Abha-61411, Asir, Kingdom of Saudi Arabia
| | - Marco Balsamo
- Dipartimento di Scienze Chimiche, Università degli Studi di Napoli Federico II, Complesso Universitario di Monte Sant’Angelo, 80126 Napoli, Italy
| | - Mirella Virginie
- Univ. Lille, CNRS, Centrale Lille, ENSCL, Uni. Artois, UMR 8181 –UCCS – Unité de Catalyse et de Chimie du Solide, F-59000 Lille, France
| | - Barbara Ernst
- Université de Strasbourg, CNRS, IPHC UMR 7178, Laboratoire de Reconnaissance et Procédés de Séparation Moléculaire (RePSeM), ECPM 25 rue Becquerel, Université de Strasbourg, Strasbourg, France
| | - Yacine Benguerba
- Department of Chemistry, College of Science, Taif University, Taif 21944, Saudi Arabia
- Department of process engineering, Faculty of Technology, Ferhat ABBAS Sétif 1 University, 19000 Setif, Algeria
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Schumann J, Bao Y, Hannagan RT, Sykes ECH, Stamatakis M, Michaelides A. Periodic Trends in Adsorption Energies around Single-Atom Alloy Active Sites. J Phys Chem Lett 2021; 12:10060-10067. [PMID: 34632767 DOI: 10.1021/acs.jpclett.1c02497] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Single-atom alloys (SAAs) make up a special class of alloy surface catalysts that offer well-defined, isolated active sites in a more inert metal host. The dopant sites are generally assumed to have little or no influence on the properties of the host metal, and transport of chemical reactants and products to and from the dopant sites is generally assumed to be facile. Here, by performing density functional theory calculations and surface science experiments, we identify a new physical effect on SAA surfaces, whereby adsorption is destabilized by ≤300 meV on host sites within the perimeter of the reactive dopant site. We identify periodic trends for this behavior and demonstrate a zone of exclusion around the reactive sites for a range of adsorbates and combinations of host and dopant metals. Experiments confirm an increased barrier for diffusion of CO toward the dopant on a RhCu SAA. This effect offers new possibilities for understanding and designing active sites with tunable energetic landscapes surrounding them.
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Affiliation(s)
- Julia Schumann
- Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, U.K
- Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, U.K
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Yutian Bao
- Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, U.K
| | - Ryan T Hannagan
- Department of Chemistry, Tufts University, 62 Talbot Avenue, Medford, Massachusetts 02155, United States
| | - E Charles H Sykes
- Department of Chemistry, Tufts University, 62 Talbot Avenue, Medford, Massachusetts 02155, United States
| | - Michail Stamatakis
- Department of Chemical Engineering, University College London, Roberts Building, Torrington Place, London WC1E 7JE, U.K
| | - Angelos Michaelides
- Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, U.K
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
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Zhou C, Zhao JY, Liu PF, Chen J, Dai S, Yang HG, Hu P, Wang H. Towards the object-oriented design of active hydrogen evolution catalysts on single-atom alloys. Chem Sci 2021; 12:10634-10642. [PMID: 34447556 PMCID: PMC8356813 DOI: 10.1039/d1sc01018b] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Accepted: 07/01/2021] [Indexed: 11/22/2022] Open
Abstract
Given a desired property, locating relevant materials is always highly desired but very challenging in a range of areas, including heterogeneous catalysis. Obviously, object-oriented design/screening is an ideal solution to this problem. Herein, we develop an inverse catalyst design workflow in Python (CATIDPy) that utilizes a genetic-algorithm-based global optimization method to guide on-the-fly density functional theory calculations, successfully realizing the highly accelerated location of active single-atom alloy (SAA) catalysts for the hydrogen evolution reaction (HER). 70 binary and 752 ternary SAA candidate catalysts are identified for the HER. Furthermore, via considering the segregation stability and cost of materials, we extracted 6 binary and 142 ternary SAA candidate catalysts that are recommended for experimental synthesis. Remarkably, guided by these theoretical identifications, homogeneously dispersed Ni-based bimetallic catalysts (e.g., NiMo, NiAl, Ni3Al, NiGa, and NiIn) were synthesized experimentally to test the reliability of the CATIDPy workflow, and they showed superior HER performance to bare Ni foam, indicating huge potential for use in real-world water electrolysis techniques. Perhaps more importantly, these results demonstrate the capacity of such a proposed approach for investigating unexplored chemical spaces to efficiently design promising catalysts without knowledge from the expert domain, which has far-reaching implications. An inverse catalyst design workflow in Python (CATIDPy) for discovering unexplored chemical spaces successfully realized the highly accelerated location of active single-atom alloy (SAA) catalysts for the hydrogen evolution reaction (HER).![]()
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Affiliation(s)
- Chuan Zhou
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry, Research Institute of Industrial Catalysis, East China University of Science and Technology Shanghai 200237 China
| | - Jia Yue Zhao
- Key Laboratory for Ultrafine Materials of Ministry of Education, Shanghai Engineering Research Center of Hierarchical Nanomaterials, East China University of Science and Technology Shanghai 200237 China
| | - Peng Fei Liu
- Key Laboratory for Ultrafine Materials of Ministry of Education, Shanghai Engineering Research Center of Hierarchical Nanomaterials, East China University of Science and Technology Shanghai 200237 China
| | - Jianfu Chen
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry, Research Institute of Industrial Catalysis, East China University of Science and Technology Shanghai 200237 China
| | - Sheng Dai
- Key Laboratory for Advanced Materials, Feringa Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals, East China University of Science and Technology Shanghai 200237 China
| | - Hua Gui Yang
- Key Laboratory for Ultrafine Materials of Ministry of Education, Shanghai Engineering Research Center of Hierarchical Nanomaterials, East China University of Science and Technology Shanghai 200237 China
| | - P Hu
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry, Research Institute of Industrial Catalysis, East China University of Science and Technology Shanghai 200237 China .,School of Chemistry and Chemical Engineering, The Queen's University of Belfast Belfast BT9 5AG UK
| | - Haifeng Wang
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry, Research Institute of Industrial Catalysis, East China University of Science and Technology Shanghai 200237 China
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Xu J, Cao XM, Hu P. Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis. Phys Chem Chem Phys 2021; 23:11155-11179. [PMID: 33972971 DOI: 10.1039/d1cp01349a] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.
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Affiliation(s)
- Jiayan Xu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China. and School of Chemistry and Chemical Engineering, Queen's University Belfast, Belfast BT9 5AG, UK
| | - Xiao-Ming Cao
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China.
| | - P Hu
- Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Frontiers Science Center for Materiobiology and Dynamic Chemistry, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, School of Chemistry and Molecular Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, P. R. China. and School of Chemistry and Chemical Engineering, Queen's University Belfast, Belfast BT9 5AG, UK
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6
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Xie W, Xu J, Ding Y, Hu P. Quantitative Studies of the Key Aspects in Selective Acetylene Hydrogenation on Pd(111) by Microkinetic Modeling with Coverage Effects and Molecular Dynamics. ACS Catal 2021. [DOI: 10.1021/acscatal.0c05345] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Wenbo Xie
- School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast BT9 5AG, U.K
| | - Jiayan Xu
- School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast BT9 5AG, U.K
| | - Yunxuan Ding
- School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast BT9 5AG, U.K
| | - P. Hu
- School of Chemistry and Chemical Engineering, The Queen’s University of Belfast, Belfast BT9 5AG, U.K
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7
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Chen J, Jia M, Hu P, Wang H. CATKINAS: A large-scale catalytic microkinetic analysis software for mechanism auto-analysis and catalyst screening. J Comput Chem 2021; 42:379-391. [PMID: 33315262 DOI: 10.1002/jcc.26464] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 12/19/2022]
Abstract
As an effective method to analyze complex catalytic reaction networks, microkinetic modeling is gaining increasing popularity in the catalytic activity evaluation and rational design of heterogeneous catalysts. An automated simulator with stable and reliable performance is especially useful and in great request. Here we introduce the CATKINAS package developed for large-scale microkinetic modeling and analysis. Featuring with a multilevel solver and a multifunctional analyzer, CATKINAS can provide both accurate solutions and various quantitative and automatic analysis for a wide range of catalytic systems. The structure and the basic workflow are overviewed with the multilevel solver particularly illustrated. Also, we take the CO methanation reaction as an example to illustrate the application and efficiency of the CATKINAS package.
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Affiliation(s)
- Jianfu Chen
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Menglei Jia
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
| | - Peijun Hu
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China.,School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast, BT9 5AG, UK
| | - Haifeng Wang
- Key Laboratory for Advanced Materials, Centre for Computational Chemistry and Research Institute of Industrial Catalysis, East China University of Science and Technology, 130 Meilong Road, Shanghai, 200237, China
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8
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Wang Z, Hu P. Rational catalyst design for CO oxidation: a gradient-based optimization strategy. Catal Sci Technol 2021. [DOI: 10.1039/d0cy02053b] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In this work, we proposed a gradient-based optimization strategy for rational catalyst design.
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Affiliation(s)
- Ziyun Wang
- School of Chemistry and Chemical Engineering
- The Queen's University of Belfast
- Belfast BT9 5AG
- UK
| | - P. Hu
- School of Chemistry and Chemical Engineering
- The Queen's University of Belfast
- Belfast BT9 5AG
- UK
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9
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Cheng X, Jiang X, Tao K, Su Q, Wang Y, Xie E. Microscopic Nature of Gas Adsorption on WO 3 Surfaces: Electron Interaction and Localization. J Phys Chem Lett 2020; 11:9070-9078. [PMID: 33047959 DOI: 10.1021/acs.jpclett.0c02020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Even though WO3 has been used in gas-sensors for many years, little is known about the gas-sensing mechanism. Using first-principles density functional theory and experimental methods, we study the adsorption of CO, H2, NH3, and NO2 on the surface of WO3 (001). The results indicate that the surface undergoes reconstruction after gas adsorption, which inevitably causes the localization of surface electrons and a change of the electric (sensing) signal. Through the analysis of atomic orbital and molecular orbital, the adsorption mechanism can be effectively predicted. The above analysis confirms that the resistance change is only related to the electronic behavior of the surface and the gas. We corrected problems associated with adsorption energy to characterize the adsorption strength of a gas on a surface, and we investigated the effect of the test temperature and test environment on both electronic interaction and the final electric sensing signal.
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Affiliation(s)
- Xu Cheng
- School of Physical Science and Technology, Lanzhou University, 730000 Lanzhou, China
| | - Xiao Jiang
- School of Physical Science and Technology, Lanzhou University, 730000 Lanzhou, China
| | - Kun Tao
- Key Lab for Magnetism and Magnetic Materials of Ministry of Education, Lanzhou University, 730000 Lanzhou, China
| | - Qing Su
- School of Physical Science and Technology, Lanzhou University, 730000 Lanzhou, China
| | - Yanrong Wang
- School of Physical Science and Technology, Lanzhou University, 730000 Lanzhou, China
| | - Erqing Xie
- School of Physical Science and Technology, Lanzhou University, 730000 Lanzhou, China
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Ding Y, Shen Y, Peng C, Huang M, Hu P. Unraveling the Photogenerated Electron Localization on the Defect-Free CH 3NH 3PbI 3(001) Surfaces: Understanding and Implications from a First-Principles Study. J Phys Chem Lett 2020; 11:8041-8047. [PMID: 32893641 DOI: 10.1021/acs.jpclett.0c02105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The localization of photogenerated electrons in photovoltaic and photocatalytic materials is crucial for reducing the electron-hole recombination rate. Here, the photogenerated electron localization is systematically investigated on the CH3NH3PbI3 (MAPbI3) perovskite using first-principles calculations. It is found that under vacuum conditions, the photogenerated electron is delocalized in the MAPbI3 bulk as well as on the stochiometric MAPbI3(001) surface with the CH3NH3I (MAI) termination, while it is trapped on the defect-free PbI2-terminated surface. Our ab initio molecular dynamics simulations reveal that the introduction of solutions will prompt the formation of localized electronic states. The photogenerated electron is discovered to be localized on both the MAI- and PbI2-terminated surfaces in the presence of solutions with different concentrations of HI, from pure water to the saturated solution. We demonstrate that the Pb-I bond weakening or breaking resulting in an unsaturated coordination of a Pb site is the prerequisite to trap the photogenerated electron.
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Affiliation(s)
- Yunxuan Ding
- School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, U.K
| | - Yujie Shen
- School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, U.K
| | - Chao Peng
- School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, U.K
| | - Meilan Huang
- School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, U.K
| | - P Hu
- School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, U.K
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