Sequential spin-coating method in enhancing crystal morphology of ambient air-processed perovskite solar cells
Source abstract
Abstract
The proper control of perovskite crystal morphology is a fundamental aspect of achieving efficient perovskite solar cells (PSCs) by ensuring better film coverage on an electron transport layer (ETL). In this study, three distinct approaches to depositing perovskite were thoroughly examined using different measurement techniques such as X-ray diffraction, photoluminescence spectra, UV-visible spectroscopy, and scanning electron microscopy. The impact of these perovskite deposition methods performed in an air-processed condition on the power conversion efficiency (PCE) of PSCs was thoroughly investigated. The results highlight that the infiltration of a mesoporous TiO₂ ETL scaffold with perovskite solution yields much better coverage when employing sequential deposition processing, as opposed to single-step deposition processing. Moreover, the performance of perovskite-based photovoltaics is significantly improved to 6.8% by employing the two-step deposition dipping approach, which actively prevents the development of disorganized capping layers. This surpasses the 5.2% efficiency achieved through the one-step deposition method. Additionally, by optimizing the thickness of the mesoporous TiO₂ film and using the spin coating method for the organic precursors in the process of perovskite formation, a significant enhancement in PCE is accomplished, surpassing 13.6%. Moreover, the best performing devices without encapsulation can retain 97% of the initial PCE over 40 days, under dark, 35% relative humidity, and room temperature. Hence, in this study the ambient air fabrication capability of the sequential deposition approach is demonstrated for the efficient and stable PSCs.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Perovskite Materials and Applications · Chalcogenide Semiconductor Thin Films · Quantum Dots Synthesis And Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (52)
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