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

Non-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 t...

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Main Authors: Zhong S., Yap B.K., Zhong Z., Ying L.
Other Authors: 57439185300
Format: Review
Published: MDPI 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-269762023-05-29T17:38:19Z Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning Zhong S. Yap B.K. Zhong Z. Ying L. 57439185300 26649255900 56206350500 24825925500 Non-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. Final 2023-05-29T09:38:19Z 2023-05-29T09:38:19Z 2022 Review 10.3390/cryst12020168 2-s2.0-85123990435 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123990435&doi=10.3390%2fcryst12020168&partnerID=40&md5=6ad33a4223b9204592244fc35bbaa84d https://irepository.uniten.edu.my/handle/123456789/26976 12 2 168 All Open Access, Gold MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Non-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.
author2 57439185300
author_facet 57439185300
Zhong S.
Yap B.K.
Zhong Z.
Ying L.
format Review
author Zhong S.
Yap B.K.
Zhong Z.
Ying L.
spellingShingle Zhong S.
Yap B.K.
Zhong Z.
Ying L.
Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning
author_sort Zhong S.
title Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning
title_short Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning
title_full Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning
title_fullStr Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning
title_full_unstemmed Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning
title_sort review on y6-based semiconductor materials and their future development via machine learning
publisher MDPI
publishDate 2023
_version_ 1806423462701432832