Halide perovskites are promising candidates for optoelectronic devices,but their solution-processed films frequently suffer from solute aggregation,crystalline disorder,and random orientation,that is,defects that sign...Halide perovskites are promising candidates for optoelectronic devices,but their solution-processed films frequently suffer from solute aggregation,crystalline disorder,and random orientation,that is,defects that significantly compromise device performance.These issues arise from complex multiscale interactions during film formation,where macroscopic mechanical forces could hypothetically influence atomic-scale assembly.This review elucidates the critical role of multiscale mechanical coupling(linking surface/interface mechanics,fluid dynamics,and crystallization kinetics)in governing perovskite film quality,which ultimately enhanced the uniformity of film thickness,photoelectric performance,and long-term stability.We first analyze how solvent evaporation,interfacial energy,and flow-induced shear shape the spatiotemporal evolution of solute distribution and crystal growth.Building on this understanding,we present mechanical regulation strategies aimed at directing film formation:modifying wettability through surfactants and surface energy modulators;manipulating flow behavior via Marangoni convection and shear stress;and mitigating interfacial stress through lattice and thermal expansion matching.To further enhance structural control,we highlight fabrication techniques that couple multiple external fields(e.g.,thermal,electrical,mechanical,and chemical fields)and leverage in situ characterization for real-time feedback.Finally,we explore machine learning-assisted multiscale modeling as a powerful tool to connect atomic interactions with macroscopic processing variables,enabling predictive optimization.By integrating these insights,this review establishes a unified framework for guiding high-quality,scalable perovskite film fabrication toward industrial deployment.展开更多
The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes...The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.展开更多
基金funded by the Zhejiang Provincial Natural Science Foundation of China(Grant LR25A020002,K.W.)the National Local Joint Laboratory for Advanced Textile Processing and Clean Production,Wuhan Textile University(Grant FX20240005,L.R.)the National Natural Science Foundation of China(Grant 12125205,J.Q.)。
文摘Halide perovskites are promising candidates for optoelectronic devices,but their solution-processed films frequently suffer from solute aggregation,crystalline disorder,and random orientation,that is,defects that significantly compromise device performance.These issues arise from complex multiscale interactions during film formation,where macroscopic mechanical forces could hypothetically influence atomic-scale assembly.This review elucidates the critical role of multiscale mechanical coupling(linking surface/interface mechanics,fluid dynamics,and crystallization kinetics)in governing perovskite film quality,which ultimately enhanced the uniformity of film thickness,photoelectric performance,and long-term stability.We first analyze how solvent evaporation,interfacial energy,and flow-induced shear shape the spatiotemporal evolution of solute distribution and crystal growth.Building on this understanding,we present mechanical regulation strategies aimed at directing film formation:modifying wettability through surfactants and surface energy modulators;manipulating flow behavior via Marangoni convection and shear stress;and mitigating interfacial stress through lattice and thermal expansion matching.To further enhance structural control,we highlight fabrication techniques that couple multiple external fields(e.g.,thermal,electrical,mechanical,and chemical fields)and leverage in situ characterization for real-time feedback.Finally,we explore machine learning-assisted multiscale modeling as a powerful tool to connect atomic interactions with macroscopic processing variables,enabling predictive optimization.By integrating these insights,this review establishes a unified framework for guiding high-quality,scalable perovskite film fabrication toward industrial deployment.
基金Key Research and Development Program of Hubei Province,China(Grant No.2022BAA096)Zhejiang Provincial Natural Science Foundation of China(This material is based upon work funded by Zhejiang Provincial Natural Science Foundation of China under Grant No.LR25A020002)support of the Center for Materials Analysis and Characterization,Material Characterization Lab,and Nanofabrication Lab at Hubei University。
文摘The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing,particularly by better controlling multiple high-dimensional process parameters.This study proposes a machine learning(ML)approach to efficiently predict and analyze perovskite film fabrication processes.By evaluating five classic ML algorithms on 130 experimental data sets from blade-coating parameters,the Random Forest(RF)model was identified as the most effective,enabling rapid prediction of over 100,000 parameter sets in just 10 min-equivalent to 3 years of manual experimentation.The RF model demonstrated strong predictive accuracy,with an R^(2) close to 0.8.This approach led to the identification of optimal process parameter combinations,significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold,thereby advancing the development of scalable manufacturing processes.
