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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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 |
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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. |
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57439185300 |
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57439185300 Zhong S. Yap B.K. Zhong Z. Ying L. |
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Review |
author |
Zhong S. Yap B.K. Zhong Z. Ying L. |
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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 |
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MDPI |
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2023 |
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1806423462701432832 |