Engineered Microenvironment of Imidazolium Salts by Multi-scale Modeling and Machine Learning Algorithms for Enhanced Electrocatalytic CO2 Reduction to Ethylene and Acetamide via Targeted Delivery of CuAg Alloy Tandem Catalyst Based on Cu(111) Facet

Engineered Microenvironment of Imidazolium Salts by Multi-scale Modeling and Machine Learning Algorithms for Enhanced Electrocatalytic CO2 Reduction to Ethylene and Acetamide via Targeted Delivery of CuAg Alloy Tandem Catalyst Based on Cu(111) Facet | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Engineered Microenvironment of Imidazolium Salts by Multi-scale Modeling and Machine Learning Algorithms for Enhanced Electrocatalytic CO 2 Reduction to Ethylene and Acetamide via Targeted Delivery of CuAg Alloy Tandem Catalyst Based on Cu(111) Facet Yi Xiao, Yunhua Xu, Yingchun Ding, Haiqiang Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6647633/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Electrocatalytic CO 2 reduction (eCO 2 R) to high-value multicarbon (C 2+ ) hydrocarbons such as ethylene (CH 2 = CH 2 ) and acetamide (CH 3 CONH 2 ) via C–C/N coupling is an attractive and effective technique for achieving zero carbon emissions and advancing renewable energy. Recent studies report the use of engineered microenvironments formed via imidazolium salts (ionic liquid) to establish an electric double-layer (EDL) interfacial Helmholtz layer at the Cu-based catalyst interface. However, monometallic Cu catalysts exhibit low activity and poor Faradaic efficiency (selectivity) for hydrocarbon products, limiting their commercial application. Herein, we demonstrated targeted delivery of CuAg alloy nanoparticles (NPs) and Ag single atoms (SA) tandem on Cu(111) facets. The improved chemical properties of bimetallic nanocrystals arise from the synergistic interaction between Cu and Ag metals, enabling this tandem catalyst system (with Ag active sites) to catalyze CO 2 to CO and subsequently convert CO into (C 2+ ) hydrocarbon intermediates via C–C coupling on Cu sites. Furthermore, CO 2 reduction to CCO on Cu sites and subsequent conversion to acetamide via C–N coupling between CCO and NH 3 on Ag sites are achieved. We modeled EDL at the Cu-based tandem catalyst interface, which was modulated using the electric field–controlling constant potential (EFC–CP) method, including a series of imidazolium salts (both anions and cations) and hydronium ions (H 3 O +δ ) to study their induced electrode potentials. Imidazolium (cation) play a crucial role in increasing local CO 2 enrichment and stabilizing carbon-based intermediates. The EFC–CP method within the Helmholtz layer allows explicit consideration of solvent and induced electric field on reaction intermediates. Our results suggest that C–C couplings between *CO and *CO or *CH and *CH are the most favorable for the formation of ethylene. Specifically, 1-butyl-3-methylimidazolium tetrafuoroborate (B2195) and 1-butyl-3-methylimidazolium hexafuorophosphate (B2320) salts decrease the maximum limiting potential ( U max (η)) to − 0.84 and − 1.00 V, respectively, positioning them as promising ionic liquid for eCO 2 R. To understand EDL effects in eCO 2 R at the molecular scale, we employed ab initio molecular dynamics simulation, focusing on hydrogen-bond networks and cation effects through a multiscale approach. Furthermore, this design strategy incorporated regression machine learning (ML) using the extreme gradient boosting regression model and the sure independence screening and sparsifying operator approach to identify key features influencing the target property U max (η) serving as the ML input data. Results show that coupling energy ( E cplg ) and the average deviation in ground-state band gaps of constituent elements are the most important features for both ethylene and acetamide synthesis, with B2195 and B2320 imidazolium salts efficiently activating CO 2 and driving electroreduction to ethylene with optimized U max (η). Physical sciences/Chemistry/Catalysis/Catalyst synthesis Physical sciences/Energy science and technology/Energy modelling CuAg NPs and Ag SA tandem catalyst Ethylene and acetamide synthesis eCO2 reduction Imidazolium salts Multi-scale modeling Machine learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files MLdata.zip Dataset 1, 2, 3 and4 SupportingInformation.docx Supporting Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6647633","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":457372198,"identity":"abbab477-0006-4790-859c-884c4a2b8dff","order_by":0,"name":"Yi 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Facet\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CuAg NPs and Ag SA tandem catalyst, Ethylene and acetamide synthesis, eCO2 reduction, Imidazolium salts, Multi-scale modeling, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6647633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6647633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectrocatalytic CO\u003csub\u003e2\u003c/sub\u003e reduction (eCO\u003csub\u003e2\u003c/sub\u003eR) to high-value multicarbon (C\u003csub\u003e2+\u003c/sub\u003e) hydrocarbons such as ethylene (CH\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;CH\u003csub\u003e2\u003c/sub\u003e) and acetamide (CH\u003csub\u003e3\u003c/sub\u003eCONH\u003csub\u003e2\u003c/sub\u003e) via C\u0026ndash;C/N coupling is an attractive and effective technique for achieving zero carbon emissions and advancing renewable energy. Recent studies report the use of engineered microenvironments formed via imidazolium salts (ionic liquid) to establish an electric double-layer (EDL) interfacial Helmholtz layer at the Cu-based catalyst interface. However, monometallic Cu catalysts exhibit low activity and poor Faradaic efficiency (selectivity) for hydrocarbon products, limiting their commercial application. Herein, we demonstrated targeted delivery of CuAg alloy nanoparticles (NPs) and Ag single atoms (SA) tandem on Cu(111) facets. The improved chemical properties of bimetallic nanocrystals arise from the synergistic interaction between Cu and Ag metals, enabling this tandem catalyst system (with Ag active sites) to catalyze CO\u003csub\u003e2\u003c/sub\u003e to CO and subsequently convert CO into (C\u003csub\u003e2+\u003c/sub\u003e) hydrocarbon intermediates via C\u0026ndash;C coupling on Cu sites. Furthermore, CO\u003csub\u003e2\u003c/sub\u003e reduction to CCO on Cu sites and subsequent conversion to acetamide via C\u0026ndash;N coupling between CCO and NH\u003csub\u003e3\u003c/sub\u003e on Ag sites are achieved. We modeled EDL at the Cu-based tandem catalyst interface, which was modulated using the electric field\u0026ndash;controlling constant potential (EFC\u0026ndash;CP) method, including a series of imidazolium salts (both anions and cations) and hydronium ions (H\u003csub\u003e3\u003c/sub\u003eO\u003csup\u003e+δ\u003c/sup\u003e) to study their induced electrode potentials. Imidazolium (cation) play a crucial role in increasing local CO\u003csub\u003e2\u003c/sub\u003e enrichment and stabilizing carbon-based intermediates. The EFC\u0026ndash;CP method within the Helmholtz layer allows explicit consideration of solvent and induced electric field on reaction intermediates. Our results suggest that C\u0026ndash;C couplings between *CO and *CO or *CH and *CH are the most favorable for the formation of ethylene. Specifically, 1-butyl-3-methylimidazolium tetrafuoroborate (B2195) and 1-butyl-3-methylimidazolium hexafuorophosphate (B2320) salts decrease the maximum limiting potential (\u003cem\u003eU\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e(η)) to \u0026minus;\u0026thinsp;0.84 and \u0026minus;\u0026thinsp;1.00 V, respectively, positioning them as promising ionic liquid for eCO\u003csub\u003e2\u003c/sub\u003eR. To understand EDL effects in eCO\u003csub\u003e2\u003c/sub\u003eR at the molecular scale, we employed ab initio molecular dynamics simulation, focusing on hydrogen-bond networks and cation effects through a multiscale approach. Furthermore, this design strategy incorporated regression machine learning (ML) using the extreme gradient boosting regression model and the sure independence screening and sparsifying operator approach to identify key features influencing the target property \u003cem\u003eU\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e(η) serving as the ML input data. Results show that coupling energy (\u003cem\u003eE\u003c/em\u003e\u003csub\u003ecplg\u003c/sub\u003e) and the average deviation in ground-state band gaps of constituent elements are the most important features for both ethylene and acetamide synthesis, with B2195 and B2320 imidazolium salts efficiently activating CO\u003csub\u003e2\u003c/sub\u003e and driving electroreduction to ethylene with optimized \u003cem\u003eU\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e(η).\u003c/p\u003e","manuscriptTitle":"Engineered Microenvironment of Imidazolium Salts by Multi-scale Modeling and Machine Learning Algorithms for Enhanced Electrocatalytic CO2 Reduction to Ethylene and Acetamide via Targeted Delivery of CuAg Alloy Tandem Catalyst Based on Cu(111) Facet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-19 02:48:51","doi":"10.21203/rs.3.rs-6647633/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0aebad83-8ddd-43e1-89da-0d16b5388a94","owner":[],"postedDate":"May 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48696853,"name":"Physical sciences/Chemistry/Catalysis/Catalyst synthesis"},{"id":48696854,"name":"Physical sciences/Energy science and technology/Energy modelling"}],"tags":[],"updatedAt":"2025-06-30T15:15:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-19 02:48:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6647633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6647633","identity":"rs-6647633","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}