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DC Field | Value | Language |
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dc.citation.startPage | e12676 | - |
dc.citation.title | ENERGY & ENVIRONMENTAL MATERIALS | - |
dc.contributor.author | Sanimu, Siti Norhasanah | - |
dc.contributor.author | Yang, Hwa-Young | - |
dc.contributor.author | Kandel, Jeevan | - |
dc.contributor.author | Moon, Ye-Chong | - |
dc.contributor.author | Sharma Gaudel, Gangasagar | - |
dc.contributor.author | Yu, Seung-Ju | - |
dc.contributor.author | Kim, Yong Ju | - |
dc.contributor.author | Kim, Sejung | - |
dc.contributor.author | Jun, Bong-Hyun | - |
dc.contributor.author | Rho, Won-Yeop | - |
dc.date.accessioned | 2024-01-19T12:05:26Z | - |
dc.date.available | 2024-01-19T12:05:26Z | - |
dc.date.created | 2024-01-15 | - |
dc.date.issued | 2023-11 | - |
dc.description.abstract | To unlock the full potential of PSCs, machine learning (ML) was implemented in this research to predict the optimal combination of mesoporous-titanium dioxide (mp-TiO2) and weight percentage (wt%) of phenyl-C-61-butyric acid methyl ester (PCBM), along with the current density (J(sc)), open-circuit voltage (V-oc), fill factor (ff), and energy conversion efficiency (ECE). Then, the combination that yielded the highest predicted ECE was selected as a reference to fabricate PCBM-PSCs with nanopatterned TiO2 layer. Subsequently, the PCBM-PSCs with nanopatterned TiO2 layers were fabricated and characterized to further understand the effects of nanopatterning depth and wt% of PCBM on PSCs. Experimentally, the highest ECE of 17.338% is achieved at 127 nm nanopatterning depth and 0.10 wt% of PCBM, where the J(sc), V-oc, and ff are 22.877 mA cm(-2), 0.963 V, and 0.787, respectively. The measured J(sc), V-oc, ff, and ECE values show consistencies with the ML prediction. Hence, these findings not only revealed the potential of ML to be used as a preliminary investigation to navigate the research of PSCs but also highlighted that nanopatterning depth has a significant impact on J(sc), and the incorporation of PCBM on perovskite layer influenced the V-oc and ff, which further boosted the performance of PSCs. | - |
dc.identifier.bibliographicCitation | ENERGY & ENVIRONMENTAL MATERIALS, pp.e12676 | - |
dc.identifier.doi | 10.1002/eem2.12676 | - |
dc.identifier.issn | 2575-0356 | - |
dc.identifier.scopusid | 2-s2.0-85178358031 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/68066 | - |
dc.identifier.wosid | 001112824900001 | - |
dc.language | 영어 | - |
dc.publisher | WILEY | - |
dc.title | Machine Learning-Assisted Fabrication of PCBM-Perovskite Solar Cells with Nanopatterned TiO2 Layer | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | nanopatterning | - |
dc.subject.keywordAuthor | PCBM | - |
dc.subject.keywordAuthor | perovskite solar cells | - |
dc.subject.keywordAuthor | prediction | - |
dc.subject.keywordPlus | EFFICIENCY | - |
dc.subject.keywordPlus | TRANSPORT | - |
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