Quantifying Atomically Dispersed Catalysts Using Deep Learning Assisted Microscopy

Haoyang Ni, Zhenyao Wu, Xinyi Wu, Jacob G. Smith, Michael J. Zachman, Jian Min Zuo, Lili Ju, Guannan Zhang, Miaofang Chi

Research output: Contribution to journalArticlepeer-review

Abstract

The catalytic performance of atomically dispersed catalysts (ADCs) is greatly influenced by their atomic configurations, such as atom-atom distances, clustering of atoms into dimers and trimers, and their distributions. Scanning transmission electron microscopy (STEM) is a powerful technique for imaging ADCs at the atomic scale; however, most STEM analyses of ADCs thus far have relied on human labeling, making it difficult to analyze large data sets. Here, we introduce a convolutional neural network (CNN)-based algorithm capable of quantifying the spatial arrangement of different adatom configurations. The algorithm was tested on different ADCs with varying support crystallinity and homogeneity. Results show that our algorithm can accurately identify atom positions and effectively analyze large data sets. This work provides a robust method to overcome a major bottleneck in STEM analysis for ADC catalyst research. We highlight the potential of this method to serve as an on-the-fly analysis tool for catalysts in future in situ microscopy experiments.

Original languageEnglish (US)
Pages (from-to)7442-7448
Number of pages7
JournalNano letters
Volume23
Issue number16
DOIs
StatePublished - Aug 23 2023

Keywords

  • STEM
  • catalyst
  • convolutional neural network
  • deep learning
  • image analysis

ASJC Scopus subject areas

  • General Chemistry
  • Condensed Matter Physics
  • Mechanical Engineering
  • Bioengineering
  • General Materials Science

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