There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 3446 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 3438 | - |
dc.citation.title | ENERGY & ENVIRONMENTAL SCIENCE | - |
dc.citation.volume | 14 | - |
dc.contributor.author | An, Na Gyeong | - |
dc.contributor.author | Kim, Jin Young | - |
dc.contributor.author | Vak, Doojin | - |
dc.date.accessioned | 2023-12-21T15:44:19Z | - |
dc.date.available | 2023-12-21T15:44:19Z | - |
dc.date.created | 2021-06-02 | - |
dc.date.issued | 2021-06 | - |
dc.description.abstract | The discovery of high-performance non-fullerene acceptors and ternary blend systems has resulted in a breakthrough in the efficiency of organic photovoltaics (OPVs) and has created new opportunities for commercialization. However, manufacturing technology has remained far behind expectations. Here we show a new research approach to develop OPVs via industrial roll-to-roll (R2R) slot die coating in conjunction with the in situ formulation technique and machine learning (ML) technology. The formulated PM6:Y6:IT-4F ternary blends deposited on continuously moving substrates resulted in the high-throughput fabrication of OPVs with various compositions. The system was used to produce training data for ML prediction. The composition/deposition parameters, referred to as deposition densities, and the efficiencies of 2218 devices were used to screen ML algorithms and to train an ML model based on a Random Forest regression algorithm. The generated model was used to predict high-performance formulations and the prediction was experimentally validated by fabricating 10.2% efficiency devices, the highest efficiency for R2R-processed OPVs so far. | - |
dc.identifier.bibliographicCitation | ENERGY & ENVIRONMENTAL SCIENCE, v.14, no.6, pp.3438 - 3446 | - |
dc.identifier.doi | 10.1039/d1ee00641j | - |
dc.identifier.issn | 1754-5692 | - |
dc.identifier.scopusid | 2-s2.0-85108577980 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/52963 | - |
dc.identifier.url | https://pubs.rsc.org/en/content/articlelanding/2021/EE/D1EE00641J#!divAbstract | - |
dc.identifier.wosid | 000650213500001 | - |
dc.language | 영어 | - |
dc.publisher | ROYAL SOC CHEMISTRY | - |
dc.title | Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary; Energy & Fuels; Engineering, Chemical; Environmental Sciences | - |
dc.relation.journalResearchArea | Chemistry; Energy & Fuels; Engineering; Environmental Sciences & Ecology | - |
dc.type.docType | Article; Early Access | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | POLYMER SOLAR-CELLS | - |
dc.subject.keywordPlus | FABRICATION | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.