4.8 Article

Big data driven perovskite solar cell stability analysis

期刊

NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41467-022-35400-4

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资金

  1. National Key R&D Program of China [2021YFF0501900, 2019YFE0123400]
  2. Excellent Young Scholar Fund from the National Science Foundation of China [22122903]
  3. Tianjin Distinguished Young Scholar Fund [20JCJQJC00260]
  4. Haihe Laboratory of Sustainable Chemical Transformations

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In the field of perovskite solar cells, evaluating and comparing stability has always been a challenge. This study proposes a single indicator to describe device stability and conducts a statistical analysis using data from over 7000 devices, providing important insights for achieving more stable perovskite solar cells.
During the last decade lead halide perovskites have shown great potential for photovoltaic applications. However, the stability of perovskite solar cells still restricts commercialization, and lack of properly implemented unified stability testing and disseminating standards makes it difficult to compare historical stability data for evaluating promising routes towards better device stability. Here, we propose a single indicator to describe device stability that normalizes the stability results with respect to different environmental stress conditions which enables a direct comparison of different stability results. Based on this indicator and an open dataset of heterogeneous stability data of over 7000 devices, we have conducted a statistical analysis to assess the effect of different stability improvement strategies. This provides important insights for achieving more stable perovskite solar cells and we also provide suggestions for future directions in the perovskite solar cell field based on big data utilization. A direct comparison of stability data of perovskite solar cells is challenging due to widely different measurement conditions and reporting standards. Here, the authors propose a single indicator to assess the stability under different environmental stress and analyse the data of over 7000 devices.

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