Publication:
Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning

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dc.citedby8
dc.contributor.authorZhong S.en_US
dc.contributor.authorYap B.K.en_US
dc.contributor.authorZhong Z.en_US
dc.contributor.authorYing L.en_US
dc.contributor.authorid57439185300en_US
dc.contributor.authorid26649255900en_US
dc.contributor.authorid56206350500en_US
dc.contributor.authorid24825925500en_US
dc.date.accessioned2023-05-29T09:38:19Z
dc.date.available2023-05-29T09:38:19Z
dc.date.issued2022
dc.description.abstractNon-fullerene acceptors are promising to achieve high efficiency in organic solar cells (OSCs). Y6-based acceptors, one group of new n-type semiconductors, have triggered tremendous attention when they reported a power-conversion efficiency (PCE) of 15.7% in 2019. After that, scientists are trying to improve the efficiency in different aspects including choosing new donors, tuning Y6 structures, and device engineering. In this review, we first summarize the properties of Y6 materials and the seven critical methods modifying the Y6 structure to improve the PCEs developed in the latest three years as well as the basic principles and parameters of OSCs. Finally, the authors would share perspectives on possibilities, necessities, challenges, and potential applications for designing multifunctional organic device with desired performances via machine learning. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo168
dc.identifier.doi10.3390/cryst12020168
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85123990435
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123990435&doi=10.3390%2fcryst12020168&partnerID=40&md5=6ad33a4223b9204592244fc35bbaa84d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26976
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleCrystals
dc.titleReview on Y6-Based Semiconductor Materials and Their Future Development via Machine Learningen_US
dc.typeReviewen_US
dspace.entity.typePublication
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