钙钛矿(结构)
动力学蒙特卡罗方法
光伏系统
能量转换效率
材料科学
蒙特卡罗方法
概率逻辑
载流子
电荷(物理)
光电子学
计算机科学
物理
化学
电气工程
工程类
人工智能
量子力学
统计
数学
结晶学
作者
Behzad Bahrami,Sally Mabrouk,Ashim Gurung,Khan Mamun Reza,Hytham Elbohy,Rajesh Pathak,Gopalan Saianand,Nirmal Adhikari,Ashish Dubey,Sheikh Ifatur Rahman,Qiquan Qiao
标识
DOI:10.1002/aesr.202000068
摘要
Perovskite solar cells (PSCs) have received considerable attention in recent years due to their low processing cost and high‐power conversion efficiency. However, the mechanisms of PSCs are not fully understood. A model based on a probabilistic and statistical approach needs to be developed to simulate, optimize, and predict the photovoltaic (PV) performance of PSC. Herein, the 3D model based on the kinetic Monte Carlo (KMC) approach is developed to simulate 3D morphology of perovskite‐based solar cells and predict their PV performances and charge dynamics. The developed 3D model incorporates the temporal and physical behavior of perovskites, such as charge generation, transport, and recombination. The KMC simulation results show that pin holes‐free perovskite films with a homogenous 400 nm thick perovskite capping layer achieve the highest power conversion efficiency of 20.85%. However, the shortest apparent charge transport time ( τ t ) and the longest apparent charge carrier recombination lifetime ( τ r ) are found for the champion device. PV performance from the fabricated device is used to validate this simulation model. This model can provide a significant conceptual advance in identifying bottlenecks and guiding novel device designs to further improve the performance of perovskite PVs.
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