Abstract
Nanoparticulate electrocatalysts for the oxygen reduction reaction are structurally diverse materials. Scanning transmission electron microscopy (STEM) has long been the go-to tool to obtain high-quality information about their nanoscale structure. More recently, its four-dimensional modality has emerged as a tool for a comprehensive crystal structure analysis using large datasets of diffraction patterns. In this study, we track the alternations of the crystal structure of individual carbon-supported PtCu3 nanoparticles before and after fuel cell-relevant activation treatment, consisting of a mild acid-washing protocol and potential cycling, essential for forming an active catalyst. To take full advantage of the rich, identical location 4D-STEM capabilities, unsupervised algorithms were used for the complex data analysis, starting with k-means clustering followed by non-negative matrix factorization, to find commonly occurring signals within specific nanoparticle data. The study revealed domains with (partially) ordered alloy structures, twin boundaries, and local amorphization. After activation, specific nanoparticle surface sites exhibited a loss of crystallinity which can be correlated to the simultaneous local scarcity of the ordered alloy phase, confirming the enhanced stability of the ordered alloy during potential cycling activation conditions. With the capabilities of our in-house developed identical-location 4D-STEM approach to track changes in individual nanoparticles, combined with advanced data analysis, we determine how activation treatment affects the electrocatalysts’ local crystal structure. Such an approach provides considerably richer insights and is much more sensitive to minor changes than traditional STEM imaging. This workflow requires little manual input, has a reasonable computational complexity, and is transferrable to other functional nanomaterials.
Supplementary materials
Title
Supporting Information: Identifying Structure-Stability Relationships in Individual Pt-Alloy Nanoparticles Using Identical-Location 4D-STEM and Unsupervised Machine Learning
Description
Additional experimental SEM, STEM, EDX, and 4D-STEM data, simulated 4D-STEM data, additional methodology discussion.
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Code: Identifying Structure-Stability Relationships in Individual Pt-Alloy Nanoparticles Using Identical-Location 4D-STEM and Unsupervised Machine Learning
Description
This repository contains code to analyze 4D-STEM datasets of nanomaterials. The code was tested on images of carbon-supported platinum-copper nanoparticles. There are scripts available for preprocessing the raw data, applying virtual detectors to it, segmenting the sample region under investigation using k-means clustering, and finding common patterns within the data using non-negative matrix factorization. Additionally, it includes a script to create a core-shell nanoparticle model out of a single-domain nanoparticle model.
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