Performance limiting structural heterogeneities in metal halide perovskites
Performance limiting structural heterogeneities in metal halide perovskites
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金属卤化物钙钛矿的性能限制结构异质性
DOI：
10.1117/12.2594861
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Doherty T
中科院分区：
文献类型：
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作者：
Doherty T
卤化物钙钛矿材料对于低成本光电应用具有有前景的性能特征。由钙钛矿吸收体制造的光伏器件的单结器件的功率转换效率超过 25%，串联器件的功率转换效率超过 28%。尽管钙钛矿被广泛认为是容错材料，但它仍然表现出相当大的深亚带隙非辐射陷阱态密度，这会产生光致发光的局部变化，从根本上限制了器件的性能。这些陷阱状态还与混合卤化物钙钛矿组合物中光诱导的卤化物偏析和局部应变有关，这两者都会对器件稳定性产生不利影响。了解这些陷阱的性质对于最终消除损耗并提高在理论性能极限下运行并具有最佳稳定性的设备至关重要。 在本次演讲中，我们概述了 (Cs0.05FA0.78MA0.17)Pb(I0.83Br0.17)3 薄膜中非辐射复合位点的分布、组成和结构起源（Doherty、Winchester 等人，Nature，2020）。通过将扫描电子和同步加速器 X 射线显微镜技术与光电发射电子显微镜 (PEEM) 测量相结合，我们揭示了纳米级陷阱簇在高性能钙钛矿薄膜的表面上非均匀分布，并且存在与这些有害位点的生成相关的独特的结构和成分指纹。最后，我们将展示如何将扫描电子衍射与卷积神经网络相结合，从而实现束敏薄膜钙钛矿中低剂量（~6 e/Å2）、高分辨率（4nm）的自动结构相识别。这种纳米级的见解将有助于回答该领域持续存在的开放性问题，例如“钙钛矿器件不稳定的纳米级起源是什么？”、“相纯度对性能有多重要？”
Halide perovskite materials have promising performance characteristics for low-cost optoelectronic applications. Photovoltaic devices fabricated from perovskite absorbers have reached power conversion efficiencies above 25 per cent in single-junction devices and 28 per cent in tandem devices. Though widely considered defect tolerant materials, perovskites still exhibit a sizeable density of deep sub-gap non-radiative trap states, which create local variations in photoluminescence that fundamentally limit device performance. These trap states have also been associated with light-induced halide segregation in mixed halide perovskite compositions and local strain, both of which can detrimentally impact device stability5. Understanding the nature of these traps will be critical to ultimately eliminate losses and yield devices operating at their theoretical performance limits with optimal stability. In this talk we outline the distribution and compositional and structural origins of non-radiative recombination sites in (Cs0.05FA0.78MA0.17)Pb(I0.83Br0.17)3 thin films (Doherty, Winchester, et al., Nature, 2020). By combining scanning electron and synchrotron X-Ray microscopy techniques with photoemission electron microscopy (PEEM) measurements we reveal that nanoscale trap clusters are distributed non-homogenously across the surface of high performing perovskite films and that there are distinct structural and compositional fingerprints associated with the generation of these detrimental sites. Finally, we will show how combining our scanning electron diffraction with convolutional neural networks can enable low-dose (~6 e/Å2), high-resolution (4nm) automated structural phase identification in beam sensitive thin-film perovskites. This nanoscale insight will help answer ongoing open questions in the field such as “What are the nanoscale origins of instability in perovskite devices?”, “how important is phase purity for performance?”