Classical modeling of extrinsic degradation in polycrystalline perovskite solar cells; defect induced degradation
SOLAR ENERGY MATERIALS AND SOLAR CELLS, cilt.261, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 261
- Basım Tarihi: 2023
- Doi Numarası: 10.1016/j.solmat.2023.112500
- Dergi Adı: SOLAR ENERGY MATERIALS AND SOLAR CELLS
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Environment Index, Greenfile, INSPEC, Metadex, Civil Engineering Abstracts
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Orta Doğu Teknik Üniversitesi Adresli: Hayır
Özet
In the realm of photovoltaic devices, the future appears bright for polycrystalline perovskite solar cells. How-ever, the promise of their efficiency is threatened by a myriad of degradation mechanisms. These mechanisms, like dark spots on a sunny day, create shadows of uncertainty on the performance of polycrystalline PSCs. Nonetheless, this article comprehensively explains these degradation mechanisms and their impact on grain boundaries in PSCs. The paper investigates grain boundaries' effects on carrier lifetime by employing various models, such as the Matthiessen rule and the Drude-Smith method. The findings reveal that defect density is the primary factor affecting the material's performance, and grain boundaries' size influences its changes. Drude-Smith's model provides a more precise estimation of the mobility, total scattering lifetime, and PL quantum yield in polycrystalline semiconductors with reduced scattering time. The presented method is verified by feeding extracted parameters into Drift-Diffusion equations and fitting them with reported experimental photovoltaic conversion efficiency data. Furthermore, based on the simulation results and the strong correlation between grain boundaries and the time factor, the study proposes a comprehensive model that can effectively predict PSCs' degradation time.