![]() ![]() The new imaging technique detailed July 6 in the Proceedings of the National Academy of Sciences overcomes these challenges by using phase retrieval and machine learning to invert conventionally-collected X-ray diffraction data – such as that produced at the Cornell High Energy Synchrotron Source, where data for the study was collected – into real-space visualization of the material at the nanoscale. The most effective way to study the nanotextures is to visualize them directly, a challenge that typically requires complex electron microscopy and does not preserve the sample. ![]() ![]() Scientists are especially interested in nanotextures that are distributed non-uniformly throughout a thin film because they can give the material novel properties. Using a combination of high-powered X-rays, phase-retrieval algorithms and machine learning, Cornell researchers revealed the intricate nanotextures in thin-film materials, offering scientists a new, streamlined approach to analyzing potential candidates for quantum computing and microelectronics, among other applications. ![]()
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