Analysis of CO selectivity during electroreduction of CO2 in deep eutectic solvents by machine learning

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dc.contributor.authorGuenay, M. Erdem
dc.contributor.authorTapan, N. Alper
dc.date.accessioned2024-07-18T20:40:40Z
dc.date.available2024-07-18T20:40:40Z
dc.date.issued2023
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, supervised and unsupervised machine learning approaches were applied to determine routes to high CO selectivity during the electroreduction of CO2 in deep eutectic solvents (DES) utilizing the molecular, chemical, and physical characteristics of hydrogen bond donors and acceptors, as well as the properties of different electrodes and DES solvents. In addition, effective data visualization and machine learning techniques were employed to identify relationships between descriptor variables and CO faradaic efficiency. First, SHAP (Shapley Additive exPlanations) analysis was applied to determine the positive and negative effects of the descriptor variables on the target, and it was found that urea in HBD (hydrogen bond donor) has the greatest impact on the target. Then, principal component analysis (PCA) was used to identify the combinations that lead to low, medium, and high levels of the target. PCA indicated that high-level clusters may be linked with HBA (hydrogen bond acceptor) molecular properties rather than HBD in addition to choline chloride-type HBA, HBA/HBD ratio, HBD density, HBD melting point, and urea-type HBD. Finally, decision tree classification was used to discover the variables leading to very high levels of the target. The decision tree revealed one pathway with very high CO faradaic efficiency and two pathways with high CO faradaic efficiency. To conclude, future researchers will be able to design new experiments with less effort and time while analyzing the effect of new DES components for high-performance CO2 electrolyzers as a result of the machine learning study and exploratory data analysis performed in this study.en_US
dc.identifier.doi10.1007/s10800-023-02045-0
dc.identifier.issn0021-891X
dc.identifier.issn1572-8838
dc.identifier.scopus2-s2.0-85180678385en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s10800-023-02045-0
dc.identifier.urihttps://hdl.handle.net/11411/7174
dc.identifier.wosWOS:001131810700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Applied Electrochemistryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectShapley Analysisen_US
dc.subjectDecision Treesen_US
dc.subjectCo Faradaic Efficiencyen_US
dc.subjectDeep Eutectic Solventen_US
dc.subjectData Miningen_US
dc.subjectVisualizationen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectElectrochemical Reductionen_US
dc.subjectCholine Chlorideen_US
dc.subjectCarbon-Dioxideen_US
dc.subjectSolubilityen_US
dc.subjectPredictionen_US
dc.subjectElectrocatalysten_US
dc.subjectAmmoniumen_US
dc.subjectDesignen_US
dc.subjectAlloyen_US
dc.subjectWateren_US
dc.titleAnalysis of CO selectivity during electroreduction of CO2 in deep eutectic solvents by machine learningen_US
dc.typeArticleen_US

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