Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials

Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these...

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Bibliographic Details
Main Authors: Shunji Yamada, Eisuke Chikayama, Jun Kikuchi
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:International Journal of Molecular Sciences
Subjects:
solid-state NMR
short-time Fourier transform
signal deconvolution
prediction
anisotropy
<i>T</i><sub>2</sub> relaxation
Online Access:https://www.mdpi.com/1422-0067/22/3/1086

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