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Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset

Author

Listed:
  • Noor Titan Putri Hartono

    (Helmholtz-Zentrum Berlin für Materialien und Energie)

  • Hans Köbler

    (Helmholtz-Zentrum Berlin für Materialien und Energie)

  • Paolo Graniero

    (Helmholtz-Zentrum Berlin für Materialien und Energie
    Freie Universität Berlin)

  • Mark Khenkin

    (Helmholtz-Zentrum Berlin für Materialien und Energie)

  • Rutger Schlatmann

    (Helmholtz-Zentrum Berlin für Materialien und Energie)

  • Carolin Ulbrich

    (Helmholtz-Zentrum Berlin für Materialien und Energie)

  • Antonio Abate

    (Helmholtz-Zentrum Berlin für Materialien und Energie)

Abstract

While perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under controlled conditions (continuous illumination, controlled temperature and atmosphere) from devices comprising various lead-halide perovskite absorbers, charge selective layers, contact layers, and architectures. In a high-level statistical analysis, we find a correlation between the maximum reached power conversion efficiency (PCE) and the relative PCE loss observed after 150-hours of ageing, with more efficient cells statistically also showing higher stability. Additionally, using the unsupervised machine learning method self-organising map, we cluster this dataset based on the degradation curve shapes. We find a correlation between the frequency of particular shapes of degradation curves and the maximum reached PCE.

Suggested Citation

  • Noor Titan Putri Hartono & Hans Köbler & Paolo Graniero & Mark Khenkin & Rutger Schlatmann & Carolin Ulbrich & Antonio Abate, 2023. "Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40585-3
    DOI: 10.1038/s41467-023-40585-3
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    References listed on IDEAS

    as
    1. Konrad Domanski & Essa A. Alharbi & Anders Hagfeldt & Michael Grätzel & Wolfgang Tress, 2018. "Systematic investigation of the impact of operation conditions on the degradation behaviour of perovskite solar cells," Nature Energy, Nature, vol. 3(1), pages 61-67, January.
    2. Yuze Lin & Bo Chen & Yanjun Fang & Jingjing Zhao & Chunxiong Bao & Zhenhua Yu & Yehao Deng & Peter N. Rudd & Yanfa Yan & Yongbo Yuan & Jinsong Huang, 2018. "Excess charge-carrier induced instability of hybrid perovskites," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    3. Zhuang Zhang & Huanhuan Wang & T. Jesper Jacobsson & Jingshan Luo, 2022. "Big data driven perovskite solar cell stability analysis," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Mark V. Khenkin & Eugene A. Katz & Antonio Abate & Giorgio Bardizza & Joseph J. Berry & Christoph Brabec & Francesca Brunetti & Vladimir Bulović & Quinn Burlingame & Aldo Di Carlo & Rongrong Cheacharo, 2020. "Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures," Nature Energy, Nature, vol. 5(1), pages 35-49, January.
    5. T. Jesper Jacobsson & Adam Hultqvist & Alberto García-Fernández & Aman Anand & Amran Al-Ashouri & Anders Hagfeldt & Andrea Crovetto & Antonio Abate & Antonio Gaetano Ricciardulli & Anuja Vijayan & Ash, 2022. "An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles," Nature Energy, Nature, vol. 7(1), pages 107-115, January.
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