Authors: Tomokazu Yamamoto | Atsuko Sato | Yasukazu Murakami
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Automated STEM-EDS analysis of alloy nanoparticle catalysts using object detection with deep learning <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d5816e71">Heterogeneous catalysis is currently one of the key materials in chemistry due to its high applicability in industrial processes. The activity of the supported alloy nanoparticle catalyst is strongly related to its structure and composition. Therefore, statistical analysis of particle sizes and compositions of alloy particles on oxide supports is essential. Scanning transmission electron microscopy (STEM) imaging and energy-dispersive x-ray spectroscopy (EDX) was often used for catalyst nanoparticle characterization. Automation of STEM analysis of alloy nanoparticles is desired for fast catalyst structure optimization and development. In this work, we developed automated STEM image acquisition and EDX elemental analysis for supported nanoparticle catalysts using object detection and segmentation techniques with deep learning. A custom Python code for automated STEM-EDS mapping data acquisition and analysis in Gatan Digital micrograph was developed with third-party popular Python library such as Numpy, matplotlib, OpenCV, scikit-learn, PyTorch. HyperSpy library [ <a class="xref-link" href="#r1">1</a>] was used for the compositional calculation of EDX spectrum data. Ultralytics YOLOv8 [ <a class="xref-link" href="#r2">2</a>] library was used for deep-learning object detection and segmentation of STEM and STEM-EDX images. STEM-EDS analyses of Pt/TiO <sub>2</sub> and PdRuIrPtRh/Al <sub>2</sub>O <sub>3</sub> catalysts were performed using JEM-ARM200F NEOARM (JEOL) at acceleration voltage of 200 kV. <a class="xref-link" href="#fg001">Fig.1</a> shows typical examples of STEM image of Pt/TiO <sub>2</sub> and PdRuIrPtRh/Al <sub>2</sub>O <sub>3</sub> catalysts and their segmentation result by YOLOv8. The particle segmentation is generally working well in <a class="xref-link" href="#fg001">Fig.1</a>. Statistical analysis was performed by calculating the composition of the particles based on the particle segmentation. It is confirmed that automated statistical analysis of alloy nanoparticle compositions works well using developed code. <div class="fig panel" id="fg001"> <a class="named-anchor" id="fg001"> <!-- named anchor --> </a> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/57fd405d-767e-4564-983f-8b2d7d9a9ba5/ScienceOpen/image/309_6726937664102-TomokazuYamamotoAPMC2025_fig1.jpg"/> </div> <div class="panel-content"> <div class="label">Fig. 1.</div> <div class="caption" id="d5816e107"> <p class="first" dir="auto" id="d5816e108">(a) HAADF-STEM image of the Pt/TiO2 catalyst. (b) The segmantation result for the image of (a). (c) HAADF-STEM image of PdRuIrPtRh/Al2O3 catalyst. (d) The segmantation result for the image of (d). </div> </div> </div> Nanoparticle ; Alloy ; Object detection ; Catalysis ; Deep learning ; Computer science ; Object (grammar) ; Artificial intelligence ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Pattern recognition (psychology) ; Metallurgy ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques