X-ray Nano-imaging of Defects in Thin Film Catalysts via Cluster Analysis

TL;DR

This paper introduces a machine learning clustering approach to identify and analyze crystallographic defects in thin film catalysts using X-ray nanodiffraction, enhancing defect detection in electrocatalyst research.

Contribution

The study applies an unsupervised clustering algorithm to detect low-intensity diffuse scattering, enabling precise localization and characterization of defects in transition-metal oxide thin films.

Findings

Successfully localized defects in SrIrO3 films

Revealed strain variations after electrochemical cycling

Demonstrated potential for operando defect analysis

Abstract

Functional properties of transition-metal oxides strongly depend on crystallographic defects. In transition-metal-oxide electrocatalysts such as SrIrO3 (SIO), crystallographic lattice deviations can affect ionic diffusion and adsorbate binding energies. Scanning x-ray nanodiffraction enables imaging of local structural distortions across an extended spatial region of thin samples. Line defects remain challenging to detect and localize using nanodiffraction, due to their weak diffuse scattering. Here we apply an unsupervised machine learning clustering algorithm to isolate the low-intensity diffuse scattering in as-grown and alkaline-treated thin epitaxially strained SIO films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SIO, and interpret the defect type by analyzing the diffraction profile through clustering. Our findings demonstrate the use of a machine learning clustering algorithm for identifying and characterizing hard-to-find crystallographic defects in thin films of electrocatalysts and highlight the potential to study electrochemical reactions at defect sites in operando experiments.