论文标题
在单粒水平上的高光谱纳米级映射
Hyperspectral nanoscale mapping of hybrid perovskite photophysics at the single grain level
论文作者
论文摘要
在过去的几年中，混合有机无机钙钛矿引起了光电子化应用的重大兴趣。尽管在理解钙钛矿的光物理学方面有快速的进展，但仍需要改善对微观结构对钙钛矿光物理过程的影响的理解。在这里，我们将无监督的机器学习和原型杂种钙钛矿膜的阴极发光显微镜结合在一起，与传统的高斯图像处理丢失的光物理过程分解光物理过程。高光谱图用非负基质分解解码，揭示了与一级带缘发射，光子回收和缺陷发射有关的组件。盲目的非负矩阵分解过程提供了对电子束暴露下中间钙钛矿相变的变化的更多了解，并说明了传统高斯技术如何隐藏相关的排放特征，这些发射特征对于开发环境强大的钙钛矿设备的开发至关重要
Hybrid organic-inorganic perovskites have drawn significant interest for applications in optoelectronics over the last few years. Despite rapid progress in understanding the photophysics of perovskite, there remains a need for improved understanding of the effect of microstructure on perovskite photophysical processes. Here, we combine unsupervised machine learning and cathodoluminescence microscopy of a prototypical hybrid perovskite film to decode photophysical processes that are otherwise lost with conventional Gaussian image processing. Hyperspectral maps are decoded with non-negative matrix factorization, revealing components relating to primary band-edge emission, photon recycling, and defect emission. A blind-spectral non-negative matrix factorization procedure provides additional understanding of changes in an intermediate perovskite phase under electron beam exposure and illustrates how traditional Gaussian techniques may hide relevant emission features that are critical to the development of environmentally robust perovskite devices
