With nanostructured materials such as catalytic heterostructures projected to play a critical role in applications ranging from water splitting to energy harvesting, tailoring their properties to specific tasks requires an increasingly comprehensive characterization of their local chemical and electronic landscape. Although aberration-corrected electron spectroscopy currently provides sufficient spatial resolution to study this space, an approach to concurrently dissect both the electronic structure and full composition of buried metal/oxide interfaces remains a considerable challenge. In this manuscript, we outline a statistical methodology to jointly analyze simultaneously-acquired STEM EELS and EDX datasets by fusing them along their shared spatial factors. We show how this procedure can be used to derive a rich descriptive model for estimating both transition metal valency and full chemical composition from encapsulated morphologies such as core-shell nanoparticles. We demonstrate this on a heterogeneous Co-P thin film catalyst, concluding that this system is best described as a multi-shell phosphide structure with a P-doped metallic Co core.

中文翻译：

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通过多模态数据融合与像差校正 STEM 光谱分析复杂的纳米颗粒异质结构

随着纳米结构材料（如催化异质结构）预计将在从水分解到能量收集的应用中发挥关键作用，根据特定任务定制它们的特性需要对其局部化学和电子景观进行越来越全面的表征。尽管像差校正电子能谱目前提供足够的空间分辨率来研究该空间，但同时剖析埋藏金属/氧化物界面的电子结构和完整组成的方法仍然是一个相当大的挑战。在这份手稿中，我们概述了一种统计方法，通过将它们沿着共享的空间因素融合来联合分析同时获得的 STEM EELS 和 EDX 数据集。我们展示了如何使用此过程来推导出丰富的描述性模型，以从包封的形态（如核壳纳米粒子）中估计过渡金属化合价和完整化学成分。我们在多相 Co-P 薄膜催化剂上证明了这一点，得出的结论是，该系统最好描述为具有 P 掺杂金属 Co 核的多壳磷化物结构。