As the development of single-junction solar cells reaches a bottleneck,tandem solar cells have emerged as a critical pathway to further enhance power conversion efficiency.Among them,monolithic perovskite/silicon hete...As the development of single-junction solar cells reaches a bottleneck,tandem solar cells have emerged as a critical pathway to further enhance power conversion efficiency.Among them,monolithic perovskite/silicon heterojunction tandem solar cells are currently the fastest-growing technology,achieving the highest efficiencies at relatively low costs.The intercon-necting layer,which connects the two sub-cells,plays a crucial role in tandem cell performance.It collects electrons and holes from the respective sub-cells and facilitates recombination and tunneling at the interface.Therefore,the properties of the inter-connecting layer are pivotal to the overall device performance.In this work,we applied statistical analysis and machine learn-ing algorithms to systematically analyze the interconnecting layer.A comprehensive dataset on interconnecting layer parame-ters was established,and predictive modeling was performed using Lasso linear regression,random forest,and multilayer per-ceptron(a type of neural network).The analysis revealed key feature importance for experimental parameters,providing valu-able insights into the application of interconnecting layers in perovskite/silicon heterojunction tandem solar cells.The final opti-mized interconnecting layer can achieve a proof-of-concept efficiency of 38.17%,providing guidance and direction for the devel-opment of monolithic perovskite/silicon tandem solar cells.展开更多
基金support of the National Key Research and Development Program of China(Grant No.2023YFB4202503)Tianjin Science and Technology Project(Grant No.24ZXZSSS00120)+4 种基金the Joint Funds of the National Natural Science Foundation of China(Grant No.U21A2072)Yunnan Provincial Science and Technology Project at Southwest United Graduate School(Grant No.202302A0370009)the Overseas Expertise Introduction Project for Discipline Innovation of Higher Education of China(Grant No.B16027)the project of high-efficiency heterojunction solar cell technology and equipment industrialization(Grant No.TC220A04A-159)TCL science and technology innovation fund.Financial support was provided by the Haihe Laboratory of Sustainable Chemical Transformations,and the Fundamental Research Funds for the Central Universities,Nankai University.
文摘As the development of single-junction solar cells reaches a bottleneck,tandem solar cells have emerged as a critical pathway to further enhance power conversion efficiency.Among them,monolithic perovskite/silicon heterojunction tandem solar cells are currently the fastest-growing technology,achieving the highest efficiencies at relatively low costs.The intercon-necting layer,which connects the two sub-cells,plays a crucial role in tandem cell performance.It collects electrons and holes from the respective sub-cells and facilitates recombination and tunneling at the interface.Therefore,the properties of the inter-connecting layer are pivotal to the overall device performance.In this work,we applied statistical analysis and machine learn-ing algorithms to systematically analyze the interconnecting layer.A comprehensive dataset on interconnecting layer parame-ters was established,and predictive modeling was performed using Lasso linear regression,random forest,and multilayer per-ceptron(a type of neural network).The analysis revealed key feature importance for experimental parameters,providing valu-able insights into the application of interconnecting layers in perovskite/silicon heterojunction tandem solar cells.The final opti-mized interconnecting layer can achieve a proof-of-concept efficiency of 38.17%,providing guidance and direction for the devel-opment of monolithic perovskite/silicon tandem solar cells.
