Machine Learning Method Reveals Hidden Strong Metal‐Support Interaction in Microscopy Datasets,Small Methods

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Machine Learning Method Reveals Hidden Strong Metal‐Support Interaction in Microscopy Datasets
Small Methods ( IF 10.7 ) Pub Date : 2021-02-25 , DOI: 10.1002/smtd.202100035
Thomas Blum ₁ , Jeffery Graves ₂ , Michael J Zachman ₃ , Felipe Polo-Garzon ₄ , Zili Wu ₄ , Ramakrishnan Kannan ₅ , Xiaoqing Pan ₁ , Miaofang Chi ₃

Affiliation

Forming an ultra‐thin, permeable encapsulation oxide‐support layer on a metal catalyst surface is considered an effective strategy for achieving a balance between high stability and high activity in heterogenous catalysts. The success of such a design relies not only on the thickness, ideally one to two atomic layers thick, but also on the morphology and chemistry of the encapsulation layer. Reliably identifying the presence and chemical nature of such a trace layer has been challenging. Electron energy‐loss spectroscopy (EELS) performed in a scanning transmission electron microscope (STEM), the primary technique utilized for such studies, is limited by a weak signal on overlayers when using conventional analysis methods, often leading to misinterpreted or missed information. Here, a robust, unsupervised machine learning data analysis method is developed to reveal trace encapsulation layers that are otherwise overlooked in STEM‐EELS datasets. This method provides a reliable tool for analyzing encapsulation of catalysts and is generally applicable to any spectroscopic analysis of materials and devices where revealing a trace signal and its spatial distribution is challenging.

中文翻译：

机器学习方法揭示了显微镜数据集中隐藏的强金属支撑相互作用

在金属催化剂表面形成超薄、可渗透的封装氧化物载体层被认为是在多相催化剂中实现高稳定性和高活性之间平衡的有效策略。这种设计的成功不仅取决于厚度，理想情况下是一到两个原子层厚，而且还取决于封装层的形态和化学性质。可靠地识别这种痕量层的存在和化学性质一直具有挑战性。在扫描透射电子显微镜 (STEM) 中进行的电子能量损失光谱 (EELS) 是用于此类研究的主要技术，但在使用传统分析方法时，会受到覆盖层上微弱信号的限制，通常会导致误解或遗漏信息。在这里，一个健壮的，开发了无监督机器学习数据分析方法，以揭示在 STEM-EELS 数据集中否则会被忽略的跟踪封装层。该方法为分析催化剂的封装提供了一种可靠的工具，通常适用于对揭示痕量信号及其空间分布具有挑战性的材料和器件的任何光谱分析。

更新日期：2021-02-25

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