[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127724-en":3,"doc-seo-127724-105":32,"detail-sidebar-cat-0-en-105":76},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":29,"update_tm":30,"read_time":31},127724,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Cu-exchanged zeolites rely on mobile solvated Cu+ cations","\u003Cp>Cu-exchanged zeolites depend on mobile solvated Cu+ cations for catalytic function, yet the influence of framework composition on transport remains insufficiently clarified. Ab initio molecular dynamics can reveal atomistic mechanisms but cannot efficiently cover large length/time scales or wide compositional diversity. A machine-learning interatomic potential is developed to reproduce ab initio results and enable multinanosecond simulations across large supercells and varied chemistry. Biased and unbiased studies of [Cu(NH3)2]+ mobility show aluminum pairing in eight-membered rings enhances local hopping, while higher NH3 concentration promotes long-range diffusion. Reactivity tests on chemically controlled Cu-CHA catalysts support the simulation conclusions. \u003C/p>","\u003Cp>This article is licensed under CC-BY 4.0   \u003C/p>\u003Cp>[http://pubs.acs.org/journal/acscii](http://pubs.acs.org/journal/acscii)  Article   \u003C/p>\u003Cp>Effect of Framework Composition and NH3 on the Diffusion of Cu+ in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics  \u003C/p>\u003Cp>Reisel Millan, Estefanía Bello-Jurado, Manuel Moliner, Mercedes Boronat, * and Rafael Gomez-Bombarelli *  \u003C/p>\u003Cp> Cite This: ACS Cent. Sci. 2023, 9, 2044−2056  \u003C/p>\u003Cp>Read Online  \u003C/p>\u003Cp>Downloaded via CSIC on November 24, 2023 at 11:50:38 (UTC) . See [https://pubs.acs.org/sharingguidelines](https://pubs.acs.org/sharingguidelines) for options on how to legitimately share published articles.  \u003C/p>\u003Cp>ACCESS  \u003C/p>\u003Cp> Metrics & More  \u003C/p>\u003Cp> Article Recommendations  \u003C/p>\u003Cp>*sı   \u003C/p>\u003Cp>Supporting Information  \u003C/p>\u003Cp>ABSTRACT: Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalytic activity, but the role of the framework  \u003C/p>\u003Cp>composition in transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length and time scales or diverse compositions. We report a machine-learning interatomic potential that accurately reproduces ab initio results and effectively generalizes to allow multinanosecond simulations of large supercells and diverse chemical compositions. Biased and unbiased simulations of [Cu(NH3)2]+ mobility show that aluminum pairing in eight-membered rings accelerates local hopping and demonstrate that increased NH3 concentration enhances long-range diffusion. The probability of  \u003C/p>\u003Cp>finding two [Cu(NH3)2]+ complexes in the same cage, which is key for SCR-NOx reaction, increases with Cu content and Al content but does not correlate with the long-range mobility of Cu+ . Supporting experimental evidence was obtained from reactivity tests of Cu-CHA catalysts with a controlled chemical composition.  \u003C/p>\u003Cp>■ INTRODUCTION  \u003C/p>\u003Cp>Copper-exchanged zeolites play a crucial role as redox catalysts for some environmentally relevant processes, such as the partial methane oxidation to methanol or the selective catalytic reduction of nitrogen oxides with ammonia (NH3−SCR − NOx). In both cases, the small pore Cu-SSZ-13 zeolite with1h11e CHA structure has been reported as an efficient catalyst.  \u003C/p>\u003Cp>The NH3−SCR−NOx reaction is currently employed for the removal of nitrogen oxides (NOx) from exhaust gases in diesel vehicles and stationary plants through a redox catalytic cycle in which Cu+ is oxidized to Cu2+ by O2, NO2, or NO + O2 and then reduced to Cu+ by the reaction of NH3 and NO forming harmless N2 + H2O (Scheme 1). 12−16 This understanding of the reaction mechanism has enabled the development of optimized catalysts by tuning the framework topology, composition, and copper speciation. In the as-prepared catalysts, Cu+ and Cu2+ cations are directly coordinated to the zeolite framework forming heterogeneous active sites, while under reaction conditions NH3 solvates the Cu+ cations forming mobile [Cu(NH3)2]+ complexes that act as dynamic active sites, resembling homogeneous catalysts but within the confinement of the zeolite pores. At low temperature, that is, between 423 and 523 K, the oxidation step involves transient dimeric [Cu(NH3)2−OO−Cu(NH3)2]2+ species whose formation requires the simultaneous presence of two [Cu-  \u003C/p>\u003Cp>Scheme 1. Illustration of the Low-Temperature SCR-NOx Redox Cycle  \u003C/p>\u003Cp>| Received: July 13, 2023\u003Cbr>Published: October 18, 2023 |  |\u003C/p>\u003Cp>| --- | --- |\u003C/p>\u003Cp>\u003Cbr>\u003C/p>\u003Cp>© 2023 The Authors. Published by American Chemical Society  \u003C/p>\u003Cp>2044  \u003C/p>\u003Cp>[https://doi.org/10.1021/acscentsci.3c00870](https://doi.org/10.1021/acscentsci.3c00870)  \u003C/p>\u003Cp>ACS Cent. Sci. 2023, 9, 2044−2056  \u003C/p>\u003Cp>Figure 1. Neural network potential. (a) Illustration of the active learning cycle. At each iteration, an ensemble of NNPs is trained on the available labeled data contained in the database, initially obtained from previous PBE+D3 MD simulations. Then, biased MD trajectories are generated with these NNPs, and based on the force uncertainty of an ensemble \u003C/p>","cbCaitne0OebZinv","https://ap.wps.com/l/cbCaitne0OebZinv","pdf",7262339,11,1,13,"English","en",105,"# Abstract\n# Introduction\n## NH3-SCR-NOx catalytic cycle and mobile Cu+ species\n## Need for transport understanding and computational challenges\n## Machine-learning potential and simulation approach","","Cu-exchanged zeolites rely on mobile solvated Cu+ cations | PDF","Cu-exchanged zeolites depend on mobile solvated Cu+ cations for catalytic function, yet the influence of framework composition on transport remains insufficiently clarified. Ab initio molecular dynamics can reveal atomistic mechanisms but cannot efficiently cover large length/time scales or wide compositional diversity. A machine-learning interatomic potential is developed to reproduce ab initio results and enable multinanosecond simulations across large supercells and varied chemistry. Biased and unbiased studies of [Cu(NH3)2]+ mobility show aluminum pairing in eight-membered rings enhances local hopping, while higher NH3 concentration promotes long-range diffusion. Reactivity tests on chemically controlled Cu-CHA catalysts support the simulation conclusions. 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