Using the integration of high-powered X-rays, machine learning and phase capture algorithms, Cornell scientists demonstrate complex nanotextures in thin-film materials, giving researchers a new, simplified method for examining possible candidates for microelectronics and quantum computing, among other applications.
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The researchers are particularly interested in nanotextures that are dispersed unevenly in thin films because they can impart materials with novel properties. The most efficient way to study nanotextures is to visualize them, a challenge that usually requires complex electron microscopy and does not directly preserve samples.
The new imaging technique was described on 6 Julyth2023, in Proceedings of the National Academy of Sciences overcome this difficulty by using machine learning and phase capture to invert traditionally collected X-ray diffraction data—such as that generated at the Cornell High Energy Synchrotron Source, where information for research is collected—into real-space visualizations of matter at the nanoscale.
The application of X-ray diffraction makes the technique more available to researchers and allows for imaging a larger portion of the sample, said Andrej Singer, Assistant Professor of Materials Science and Engineering and David Croll Sesquicentennial Faculty Fellow in Cornell Engineering, who led the study with doctoral students. Ziming Shao.
“Large area imaging is important because it represents the true state of the material,” singer stated. “The nano-texture measured by the local probe can depend on the choice of site being examined.”
Another benefit of the novel approach is that it does not require the sample to be broken up, facilitating thin-film dynamic studies, such as displaying a light to observe how structures appear.
“This method can be easily applied to study in-situ or operando dynamics,” Shao added. “For example, we plan to use methods to study how structures change in picoseconds after excitation with short laser pulses, which will enable new concepts for terahertz technology in the future..”
This technique was assessed on two thin films, the first having a recognized nano texture used to confirm the imaging results. After testing a second thin film—a Mott insulator with physics related to superconductivity—the scientists discovered a new type of morphology never before seen in matter—a strain-induced nanopattern that is created simultaneously during cooling to cryogenic temperatures.
“Images extracted without prior knowledge, potentially setting new benchmarks and informing new physical hypotheses in phase field modeling, molecular dynamics simulations, and quantum mechanics calculationsShao added.
The co-authors include the late Lena Kourkoutis, Associate Professor of Applied And Engineering Physics; Kyle Shen, James A. Weeks Professor of Physical Sciences in the College of Arts and Sciences; Darrell Schlom, Herbert Fisk Johnson Professor of Industrial Chemistry and Tisch University Professor in the Department of Materials Science and Engineering; and Hari Nair, Research Assistant Professor of Materials Science and Engineering.
This study was supported financially by the US Department of Energy and the National Science Foundation.
Syl Kacapyr is Associate Director of Marketing and Communications for Cornell Engineering.
Shao, Z. et al. (2023) Real-space imaging of periodic nanotextures in thin films by phasing diffraction data. Proceedings of the National Academy of Sciences. doi.org/10.1073/pnas.2303312120.