This title appears in the Scientific Report :
2024
Please use the identifier:
http://dx.doi.org/10.1021/acsenergylett.3c02666 in citations.
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2024-02508 in citations.
Accelerating Photostability Evaluation of Perovskite Films through Intelligent Spectral Learning-Based Early Diagnosis
Accelerating Photostability Evaluation of Perovskite Films through Intelligent Spectral Learning-Based Early Diagnosis
Obtaining highly stable metal-halide perovskites is crucial for the commercialization of perovskite solar cells. However, current methods for evaluating perovskite stability mainly rely on a time-consuming and resource-intensive aging process. Here, we demonstrate a spectral learning-based methodolo...
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Personal Name(s): | Liu, Ziyi |
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Zhang, Jiyun / Rao, Gaofeng / Peng, Zijian / Huang, Yixing / Arnold, Simon / Liu, Bowen / Deng, Can / Li, Chen / Li, Heng / Zhi, Hanxiang / Zhang, Zhi / Zhou, Wenke / Hauch, Jens / Yan, Chaoyi / Brabec, Christoph / Zhao, Yicheng | |
Contributing Institute: |
Helmholtz-Institut Erlangen-Nürnberg Erneuerbare Energien; IEK-11 |
Published in: | ACS energy letters, 9 (2024) 2, S. 662 - 670 |
Imprint: |
Washington, DC
American Chemical Society
2024
|
DOI: |
10.1021/acsenergylett.3c02666 |
DOI: |
10.34734/FZJ-2024-02508 |
Document Type: |
Journal Article |
Research Program: |
Modules, stability, performance and specific applications |
Link: |
Get full text Published on 2024-01-30. Available in OpenAccess from 2025-01-30. |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2024-02508 in citations.
Obtaining highly stable metal-halide perovskites is crucial for the commercialization of perovskite solar cells. However, current methods for evaluating perovskite stability mainly rely on a time-consuming and resource-intensive aging process. Here, we demonstrate a spectral learning-based methodology that enables the prediction of perovskite stability by leveraging the features in photoluminescence and absorption spectra of fresh perovskite films. This methodology circumvents the long-term aging process by combining a custom-developed spectral feature extraction algorithm and an integrated voting machine learning model. By integration of the early diagnosis program with high-throughput experiments, the prediction accuracy for stable perovskites exceeds 86% in 160 fresh samples. The universality is further examined by another batch of 224 fresh samples fabricated through different processing conditions. Finally, the early diagnosis of perovskite films is successfully translated to enhanced stability in perovskite solar cells. Our work provides a new pathway to accelerate the discovery and development of stable perovskite films. |