Credit: Kohei Aso from JAIST.
Sometimes, a material’s property, like magnetism & catalysis, can change drastically due to nothing quite minute changes within the separation between its atoms, commonly mentioned as ‘local strains’ within the parlance of materials science. a particular measurement of such local strains is, therefore, important to materials scientists.
One powerful technique employed for this purpose is ‘high-angle annular dark-field imaging’ (HAADF), an approach within scanning transmission electron microscopy that produces images with bright spots that theoretically coincide with atomic positions. However, in practice, HAADF images are often distorted thanks to mechanical & electrical noise within the apparatus, limiting the littlest measurable local strains to slightly over 1%.
Now, a team of scientists led by professor Kohei Aso from Japan Advanced Institute of Science and Technology (JAIST), Japan, have leveraged a way from field of data science to calculate strain distribution in materials more accurately, improving the precision of HAADF imaging. This study, published in ACS Nano, was administered together with JAIST Professor Yoshifumi Oshima, then graduate student Jens Maebe, post-doctoral fellow Xuan Quy Tran, professor Tomokazu Yamamoto, & Professor Syo Matsumura from Kyushu University, Japan.
The team combined HAADF imaging with Gaussian process regression (GPR), data processing technique commonly utilized in machine learning and fields like economics & geology. In Gaussian process, truth state of data (in this case, atomic positions or displacement) is assumed to be represented by a smooth function, and random noise is added to ‘true state’ when data is observed. By reversing this process through GPR, one can more accurately estimate truth positions of the atoms, and thus calculate local strains with higher precision. Specifically, the proposed method enabled the team to calculate strain with a precision of 0.2%.
The team demonstrated the potential of their approach by measuring local strains in gold nanostructures & comparing tensile strains in gold nanosphere with those in gold nanorods (essentially cylinders with hemispherical caps) of various lengths. These comparisons revealed that strain distributions in gold nanoparticles varied depend on their shape, with nanorods exhibiting a tensile strain of about 0.5% near the region where curvature abruptly changes. Dr. Aso explains that “it is known that spherical gold nanoparticles are subjected to uniform stress over their entire surface, and this stress is proportional to surface-tension . Thus, uniform compressive strain occurs within the direction perpendicular to the surface. In contrast, in gold nanorods, the strain applied to the surface becomes non-uniform, & scientists have theorized that tensile strain should occur in certain places. However, this had not been proven experimentally, until now.”
With these findings, the team is thrilled about future’ prospects of their strain measurement strategy. “Our technique will provide detailed information on the atomic world, which is important for development of innovative materials & devices with both high performance & little size. this might be useful for development of devices & sensors employing nanoscale materials & structures. Moreover, the process requires no expensive modifications or complicated procedures and may be readily adopted,” says Dr. Aso.
The findings were published in ACS Nano.
