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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/12/2/168 |
| _version_ | 1797481497444220928 |
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| author | Sijing Zhong Boon Kar Yap Zhiming Zhong Lei Ying |
| author_facet | Sijing Zhong Boon Kar Yap Zhiming Zhong Lei Ying |
| author_sort | Sijing Zhong |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T22:15:30Z |
| format | Article |
| id | doaj.art-98785e4a7f174f858139459b03388d72 |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-09T22:15:30Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-98785e4a7f174f858139459b03388d722023-11-23T19:24:00ZengMDPI AGCrystals2073-43522022-01-0112216810.3390/cryst12020168Review on Y6-Based Semiconductor Materials and Their Future Development via Machine LearningSijing Zhong0Boon Kar Yap1Zhiming Zhong2Lei Ying3State Key Laboratory of Luminescent Materials and Devices, Institute of Polymer Optoelectronic Materials and Devices, South China University of Technology, Guangzhou 510640, ChinaInstitute of Sustainable Energy, Universiti Tenaga Nasional, Jalan Ikram-Uniten, Kajang 43000, MalaysiaState Key Laboratory of Luminescent Materials and Devices, Institute of Polymer Optoelectronic Materials and Devices, South China University of Technology, Guangzhou 510640, ChinaState Key Laboratory of Luminescent Materials and Devices, Institute of Polymer Optoelectronic Materials and Devices, South China University of Technology, Guangzhou 510640, ChinaNon-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.https://www.mdpi.com/2073-4352/12/2/168Y6non-fullerene acceptororganic solar cellmachine learningmulti-function |
| spellingShingle | Sijing Zhong Boon Kar Yap Zhiming Zhong Lei Ying Review on Y6-Based Semiconductor Materials and Their Future Development via Machine Learning Crystals Y6 non-fullerene acceptor organic solar cell machine learning multi-function |
| title | 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_short | 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 |
| topic | Y6 non-fullerene acceptor organic solar cell machine learning multi-function |
| url | https://www.mdpi.com/2073-4352/12/2/168 |
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