Kavli Affiliate: Darrell G. Schlom, Jin Suntivich
| First 5 Authors: Aileen Luo, Oleg Yu. Gorobtsov, Jocienne N. Nelson, Ding-Yuan Kuo, Ziming Shao
| Summary:
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.
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