English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Functional properties of transition-metal oxides strongly depend oncrystallographic defects. In transition-metal-oxide electrocatalysts such asSrIrO3 (SIO), crystallographic lattice deviations can affect ionic diffusionand adsorbate binding energies. Scanning x-ray nanodiffraction enables imagingof local structural distortions across an extended spatial region of thinsamples. Line defects remain challenging to detect and localize usingnanodiffraction, due to their weak diffuse scattering. Here we apply anunsupervised machine learning clustering algorithm to isolate the low-intensitydiffuse scattering in as-grown and alkaline-treated thin epitaxially strainedSIO films. We pinpoint the defect locations, find additional strain variationin the morphology of electrochemically cycled SIO, and interpret the defecttype by analyzing the diffraction profile through clustering. Our findingsdemonstrate the use of a machine learning clustering algorithm for identifyingand characterizing hard-to-find crystallographic defects in thin films ofelectrocatalysts and highlight the potential to study electrochemical reactionsat defect sites in operando experiments.

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