Luminescence-Based Implied Voltage Imaging of Tandem Solar Cells Using Bandpass Filters

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Soma Zandi, Shuai Nie, Yan Zhu, Thomas G. Allen, Erkan Aydin, Esma Ugur, Jianghui Zheng, Guoliang Wang, Xu Liu, Xiaojing Hao, Anita Ho-Baillie, Stefaan De Wolf, Thorsten Trupke, Ziv Hameiri
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引用次数: 0

Abstract

A luminescence-based technique is demonstrated for selectively imaging the implied voltages of tandem solar cells. The luminescence emission is captured using a narrow bandpass filter so that the detected luminescence signal is insensitive to the optical properties of the device, thus, revealing the variations in the implied voltages. The proposed method is validated through simulation and experiments conducted on two-terminal perovskite/silicon tandem solar cells with different structures, optical properties, and compositions (e.g., different bandgaps for the perovskite cells). Implied voltage images of each sub-cell can be determined with a maximum relative error of 1%. The proposed technique can also be used to obtain local current–voltage curves. The method is expected to be a valuable tool for optimizing the performance of tandem solar cells, scaling up tandem devices, investigating local defects, and predicting the ultimate device performance.

Abstract Image

利用带通滤波器对串联太阳能电池进行基于发光的隐含电压成像。
一种基于发光的技术被证明可以选择性地成像串联太阳能电池的隐含电压。使用窄带通滤波器捕获发光发射,以便检测到的发光信号对器件的光学特性不敏感,从而揭示隐含电压的变化。通过对具有不同结构、光学性质和成分(例如钙钛矿电池的不同带隙)的双端钙钛矿/硅串联太阳能电池的模拟和实验验证了所提出的方法。每个子电池的隐含电压图像可以确定,最大相对误差为1%。该方法还可用于获得局部电流-电压曲线。该方法有望成为优化串联太阳能电池性能、扩大串联器件规模、研究局部缺陷和预测最终器件性能的有价值的工具。
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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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