Yanqing Zhu, Yuxi Zhang, Min Hu, Li Wan, Wenchao Huang, Jingyuan Chu, Yuying Hao, Yi-Bing Cheng, Alexandr N Simonov, Jianfeng Lu
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引用次数: 0
Abstract
Upscaling the perovskite solar cell (PSC) while avoiding losses in the power conversion efficiency presents a substantial challenge, especially when transitioning from ≤1 cm2 cells to ≥10 cm2 modules. In addition to the fabrication of key functional layers, scalable technologies for surface passivation, considered indispensable for achieving high-performance PSCs, are urgently required. However, studies on this topic remain limited. In this study, an industry-ready slot-die coating method for the effective passivation of perovskite films as a practical alternative is developed to the spin-coating procedures commonly used in research. The coating conditions and molecular structure of the passivation agent are systematically optimized to achieve high-quality film morphology and substantially suppress interface recombination. 2-chloro-5-(trifluoromethyl)-phenylammonium bromide exhibited the best results, improving the open-circuit voltage of cells and subcells in a module by 80 ± 4 and 72 ± 10 mV, respectively. Correspondingly, the larger-area (active area: 10 cm2) modules sustained the highest efficiency of 21.9% under simulated 1-sun irradiation. The encapsulated devices retained 94% of their initial performances after 750 h of continuous operation. The proposed surface-passivation slot-die technology is compatible with high-throughput processes and is employable for large-scale PSC fabrication.
Small MethodsMaterials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍:
Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques.
With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community.
The online ISSN for Small Methods is 2366-9608.