DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bang, Gi Joo | ko |
dc.contributor.author | Gu, Geun Ho | ko |
dc.contributor.author | Noh, Juhwan | ko |
dc.contributor.author | Jung, Yousung | ko |
dc.date.accessioned | 2022-08-30T03:00:14Z | - |
dc.date.available | 2022-08-30T03:00:14Z | - |
dc.date.created | 2022-08-29 | - |
dc.date.created | 2022-08-29 | - |
dc.date.created | 2022-08-29 | - |
dc.date.issued | 2022-08 | - |
dc.identifier.citation | ACS CATALYSIS, v.12, no.16, pp.10255 - 10263 | - |
dc.identifier.issn | 2155-5435 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298214 | - |
dc.description.abstract | The emission of unburned exhaust methane from natural-gas-based combustion engines is an important source of greenhouse gas to control. Rutile IrO2 has shown great potential as a methane oxidation catalyst, but further developments for practical use have been slow as the kinetic mechanism and design principles under exhaust conditions are poorly understood. Here, we demonstrate the experiment-validated first-principles-based microkinetic model (MKM) for IrO2 to elucidate the mechanistic insights and develop the descriptor-based MKM screening pipeline to discover feasible catalysts for methane complete oxidation. The framework uses a minimal number of ab initio descriptors suggested by sensitivity analysis and scaling relations, equipped further with a machine learning model to extend the search space to a larger scale. We search through hundreds of doped rutile oxides by constructing the MKM-based activity map and suggest promising Pareto-optimum candidates. The proposed workflow can be extended to explore other industrial catalysts under experimental conditions. | - |
dc.language | English | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.title | Activity Trends of Methane Oxidation Catalysts under Emission Conditions | - |
dc.type | Article | - |
dc.identifier.wosid | 000840979100001 | - |
dc.identifier.scopusid | 2-s2.0-85136072793 | - |
dc.type.rims | ART | - |
dc.citation.volume | 12 | - |
dc.citation.issue | 16 | - |
dc.citation.beginningpage | 10255 | - |
dc.citation.endingpage | 10263 | - |
dc.citation.publicationname | ACS CATALYSIS | - |
dc.identifier.doi | 10.1021/acscatal.2c00842 | - |
dc.contributor.localauthor | Jung, Yousung | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | ab initio calculations | - |
dc.subject.keywordAuthor | microkinetic model | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | high-throughput screening | - |
dc.subject.keywordAuthor | methane oxidation | - |
dc.subject.keywordAuthor | oxidative condition | - |
dc.subject.keywordAuthor | IrO2 | - |
dc.subject.keywordPlus | H BOND ACTIVATION | - |
dc.subject.keywordPlus | LOW-TEMPERATURE | - |
dc.subject.keywordPlus | SCALING RELATIONS | - |
dc.subject.keywordPlus | SURFACE | - |
dc.subject.keywordPlus | ADSORPTION | - |
dc.subject.keywordPlus | COMBUSTION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | POINTS | - |
dc.subject.keywordPlus | STATE | - |
dc.subject.keywordPlus | CH4 | - |
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