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X-ray nano-imaging of defects in thin film catalysts via cluster analysis

Cornell Affiliated Author(s)

Author

A. Luo
O.Y. Gorobtsov
J.N. Nelson
D.-Y. Kuo
T. Zhou
Z. Shao
R. Bouck
M.J. Cherukara
M.V. Holt
K.M. Shen
D.G. Schlom
J. Suntivich
A. Singer

Abstract

Functional properties of transition-metal oxides strongly depend on crystallographic defects; 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. Yet, localized lattice distortions 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 SrIrO3 films. We pinpoint the defect locations, find additional strain variation in the morphology of electrochemically cycled SrIrO3, 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. © 2022 Author(s).

Date Published

Journal

Applied Physics Letters

Volume

121

Issue

15

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139948210&doi=10.1063%2f5.0125268&partnerID=40&md5=8b8bb62d354907333928a8a91b0b87e2

DOI

10.1063/5.0125268

Group (Lab)

Kyle Shen Group

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