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Stability Follows Efficiency Based on the Analysis of a Large Perovskite Solar Cells Ageing Dataset by Noor Titan Putri Hartono; Hans Köbler; Paolo Graniero; Mark Khenkin; Rutger Schlatmann; Carolin Ulbrich; Antonio Abate is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
What is Stability Follows Efficiency Based on the Analysis of a Large Perovskite Solar Cells Ageing Dataset about?
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
Who reads Stability Follows Efficiency Based on the Analysis of a Large Perovskite Solar Cells Ageing Dataset?
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Noor Titan Putri Hartono; Hans Köbler; Paolo Graniero; Mark Khenkin; Rutger Schlatmann; Carolin Ulbrich; Antonio Abate
- Publisher
- Springer Science and Business Media LLC
- Published
- 2023
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)