Machine learning analysis on stability of perovskite solar cells
Solar Energy Materials and Solar Cells, cilt.205, 2020 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 205
- Basım Tarihi: 2020
- Doi Numarası: 10.1016/j.solmat.2019.110284
- Dergi Adı: Solar Energy Materials and Solar Cells
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Agricultural & Environmental Science Database, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Environment Index, Greenfile, INSPEC, Metadex, Civil Engineering Abstracts
- Anahtar Kelimeler: Association rule mining, Data mining, Knowledge extraction, Machine learning, Perovskite solar cells, Stability
- Boğaziçi Üniversitesi Adresli: Evet
Özet
In this work, a dataset containing long-term stability data for 404 organolead halide perovskite cells was constructed from 181 published papers and analyzed using machine-learning tools of association rule mining and decision trees; the effects of cell manufacturing materials, deposition methods and storage conditions on cell stability were investigated. For regular cells, mixed cation perovskites, multi-spin coating as one-step deposition, DMF + DMSO as precursor solution and chlorobenzene as anti-solvent were found to have positive effects on stability; SnO2 as ETL compact layer, PCBM as second ETL, inorganic HTLs or HTL-free cells, LiTFSI + TBP + FK209 and F4TCNQ as HTL additives and carbon as back contact were also found to improve stability. The cells stored under low humidity were found to be more stable as expected. The degradation was slightly faster in inverted cells under humid condition; the use of some materials (like mixed cation perovskites, PTAA and NiOx as HTL, PCBM + C60 as ETL, and BCP interlayer) were found to result in more stable cells.