Detection of subpercent local strain is important toward a further understanding of nanomaterials. Conventional strain analysis has been conducted by measuring interatomic distances in scanning transmission electron microscopy (STEM) images. However, it has been still a challenge to detect subpercent strain because the STEM images are often influenced by disturbing effects, such as random noise and image distortion. Here, we utilized an advanced data-driven analysis, Gaussian process regression, to predict the true strain distribution. By combining this with the conventional atom-resolved STEM, we achieved the precision of 0.2 % in strain detection. The proposed method was applied for gold nanoparticles to reveal the relationship between the strain distribution in the nanoparticle and shape. A spherical nanoparticle showed a contraction of ~1% near the surface owing to surface relaxation. By contrast, nanorods showed nonuniform strain distributions like lattice expansions of ~0.5% near hemispherical caps of both ends. Our results demonstrate that the local strain is affected by the shape of nanomaterials, indicating the importance to consider the shape effect on strain distribution in nanomaterials. Also, the proposed analysis provides a convenient and powerful way to measure strain with high precision at the atomic scale in various materials.

Subpercent Local Strains in Gold Nanorods Detected by Data-Driven Analysis

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