材料科学
锡
结晶
动力学
钙钛矿(结构)
化学工程
无机化学
冶金
化学
量子力学
物理
工程类
作者
Peilin Wang,Tianpeng Li,Zuoming Jin,Zhiguo Zhang,Zhiqi Chen,Bing Chen,Jia Liang
标识
DOI:10.1002/aenm.202501164
摘要
Abstract Tin (Sn)‐based perovskite solar cells (TPSCs) have garnered significant attention in the photovoltaic field due to their promising physicochemical properties. Strong Brønsted acids are demonstrated to be highly effective in regulating the crystallization kinetics, thereby reducing defect formation in Sn‐based perovskite films. However, their strong acidity increases the oxidative potential of DMSO solutions, accelerating undesirable Sn 2+ oxidation. Herein, a graph‐convolutional neural network is utilized to rapidly screen common organic acids and identified ibuprofen (IPF), a molecule with moderate acidity as a suitable additive, achieving a delicate balance between regulating crystallization kinetics and mitigating Sn 2+ oxidation. Moreover, IPF incorporation improves the morphology and crystallization of Sn‐based perovskites while optimizing their band alignment with functional layers, facilitating more efficient charge transport. As a result, TPSCs with IPF achieve an impressive PCE of 15.63%, with a substantial enhancement in V OC from 0.78 to 0.94 V. Furthermore, the hydrophobic benzene ring and carbon chains in IPF enhance moisture resistance, contributing to enhanced long‐term stability, with TPSCs maintaining 90% of their initial efficiency after 850 h. This study highlights the critical role of precisely tailoring additive properties in precursor solution to obtain high‐quality perovskite films, advancing the development of more efficient and stable TPSCs.
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