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Nanoscale Chemical Imaging via Machine Learning on Coregistered Multimodal Data

Invention Reference Number


Technology Summary

Research aimed at understanding sub-cellular biological process requires chemical visualization at sub- micron spatial resolutions. This has been the impetus toward the development of mass spectrometry imaging approaches with sub-cellular resolution. However, the combination of spatial and spectral limitations of the existing techniques makes this direct measurement practically infeasible. Here, we have developed a machine-learning-enabled approach to infer intact molecular spectra with down to 100 nm spatial resolution through machine learning.


Nikolay Borodinov
Center for Nanophase Materials Sciences Division

Licensing Contact

Eugene R Cochran
(865) 576-2830