Perovskite solar cells are the rising star of third-generation photovoltaic technology. With a power conversion efficiency of 25.5%, the record efficiency is close to the theoretical maximum efficiency for a...
Lead halide perovskite solar cells (PSCs) have emerged as a highly promising next‐generation photovoltaic (PV) technology that combines high device performance with ease of processing and low cost. However, the potential leaching of lead is recognized as a major environmental concern for their large‐scale commercialization, especially for application areas with significant overlap with human life. Herein, a quantitative kinetic analysis of the Pb leaching behavior of five types of benchmark PSCs, namely, MAPbI3, FA0.95MA0.05Pb(I0.95Br0.05)3, Cs0.05(FA0.85MA0.15)0.95Pb(I0.85Br0.15)3, CsPbI3, and CsPbI2Br, under laboratory rainfall conditions is reported. Strikingly, over 60% of the Pb contained in the unencapsulated perovskite devices is leached within the first 120 s under rainfall exposure, suggesting that very rapid leaching of Pb can occur when indoor and outdoor PV devices are subject to physical damage or failed encapsulation. The initial Pb leaching rate is found to be strongly dependent on the types of PSCs, pointing to a potential route toward Pb leaching reduction through further optimization of their materials design. The findings offer kinetic insights into the Pb leaching behavior of PSCs upon aqueous exposure, highlighting the urgency to develop robust mitigation methods to avoid a potentially catastrophic impact on the environment for their large‐scale deployment.
The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.
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