Decision tree analysis for efficient CO2 utilization in electrochemical systems

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dc.authoridGünay, M. Erdem/0000-0003-1282-718X|TAPAN, N. Alper/0000-0001-8599-0450
dc.authorwosidTAPAN, Niyazi A/H-6416-2013
dc.authorwosidGünay, M. Erdem/I-1564-2019
dc.contributor.authorGunay, M. Erdem
dc.contributor.authorTurker, Lemi
dc.contributor.authorTapan, N. Alper
dc.date.accessioned2024-07-18T20:42:46Z
dc.date.available2024-07-18T20:42:46Z
dc.date.issued2018
dc.departmentİstanbul Bilgi Üniversitesien_US
dc.description.abstractIn this work, a database of 471 experimental data points excerpted from 34 different publications on electro-catalytic reduction of CO2 was formed. Firstly, the database was examined by exploratory data analysis using box and whiskers plots. Then, decision tree analysis was applied to determine the significance of the variables and to reveal the conditions leading to higher faradaic efficiency, production rate and product selectivity. It was found that Cu content smaller than 71% resulted high faradaic efficiencies depending on the amount of Sn, catholyte type, applied potential and pH of electrolyte. In this case, applied potential and Cu content were found to have the highest significance among all the input variables. On the other hand, the most generalizable combination of variables leading to high level of rate occurred when the Cu content being less than 13%, using a membrane other than Selemion AMV, employing a backing layer such as TGP-H-60 and keeping the applied potential between -1.5 and -2.6 V; for which the applied potential and CO2 flow rate were determined as the highest significant variables. Finally, the most generalizable path for the case of selectivity was obtained with Sn content higher than 15% and Cu content less than 52%, which leaded to formic acid production having the highest production rates. It was then concluded that, exploratory data analysis and decision trees can provide useful information to determine the conditions leading to higher CO2-electroreduction performance that may guide the future studies in this area.en_US
dc.identifier.doi10.1016/j.jcou.2018.09.011
dc.identifier.endpage95en_US
dc.identifier.issn2212-9820
dc.identifier.issn2212-9839
dc.identifier.scopus2-s2.0-85054270265en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage83en_US
dc.identifier.urihttps://doi.org/10.1016/j.jcou.2018.09.011
dc.identifier.urihttps://hdl.handle.net/11411/7414
dc.identifier.volume28en_US
dc.identifier.wosWOS:000451088900009en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofJournal of Co2 Utilizationen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectExploratory Data Analysisen_US
dc.subjectDecision Treesen_US
dc.subjectBox And Whisker Ploten_US
dc.subjectCo2en_US
dc.subjectElectroreductionen_US
dc.subjectGas-Diffusion Electrodesen_US
dc.subjectCarbon-Dioxideen_US
dc.subjectFormic-Aciden_US
dc.subjectHigh-Performanceen_US
dc.subjectFaradaic Efficiencyen_US
dc.subjectPast Publicationsen_US
dc.subjectMetal-Electrodesen_US
dc.subjectCu Electrodesen_US
dc.subjectReductionen_US
dc.subjectCopperen_US
dc.titleDecision tree analysis for efficient CO2 utilization in electrochemical systemsen_US
dc.typeArticleen_US

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