Ut-Battelle, Inc.

The University of Tennessee Research Foundation

Anton V Ievlev

Olga S Ovchinnikova

Matthias Lorenz

Nikolay Borodinov

Sergei V Kalinin

Rama K Vasudevan

Yongtao Liu

Steven T King

Machine learning approach can combine mass spectral imaging (MSI) techniques, one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The machine learning approach can perform transformations on the spectral image data of the two MSI techniques to reduce dimensionality, and using a correlation technique, find relationships between the transformed spectral image data. The determined relationships can be used to generate MSI spectra of desired resolution.

Correlative multimodal chemical imaging via machine learning

A method of performing time-of-flight secondary ion mass spectrometry on a sample includes the step of directing a beam of primary ions to the sample, and stimulating the migration of ions within the sample while the beam of primary ions is directed at the sample. The stimulation of the ions is cycled between a stimulation state and a lower stimulation state. Secondary ions emitted from the sample by the beam of primary ions are collected in a time-of-flight mass spectrometer. Time-of-flight secondary ion mass spectrometry is then performed on the secondary ions. A system for performing time-of-flight secondary ion mass spectrometry on a sample is also disclosed.

Time-resolved chemical studies via time-of-flight secondary ion mass spectrometry

Techniques for generating full-spatial resolution, full spectral resolution image(s) from a 3D spectral-data cube for any spectral value within a given spectral range are provided without requiring the acquisition of all full-spatial resolution, full spectral resolution data by an instrument. The 3D spectral-data cube is generated from a limited number of full-spatial resolution, sparse spectral resolution data and a sparse-spatial resolution, full-spectral resolution data of the same area of the sample. The use of the 3D spectral-data cube reduces the data acquisition time.

Pan-sharpening for microscopy

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Knoxville, TN, United States of America

Anton V Ievlev