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Pure Isotropic Proton NMR Spectra in Solids using Deep Learning
The resolution of proton solid‐state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic‐angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10107932/ https://www.ncbi.nlm.nih.gov/pubmed/36562545 http://dx.doi.org/10.1002/anie.202216607 |
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author | Cordova, Manuel Moutzouri, Pinelopi Simões de Almeida, Bruno Torodii, Daria Emsley, Lyndon |
author_facet | Cordova, Manuel Moutzouri, Pinelopi Simões de Almeida, Bruno Torodii, Daria Emsley, Lyndon |
author_sort | Cordova, Manuel |
collection | PubMed |
description | The resolution of proton solid‐state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic‐angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two‐dimensional set of magic‐angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high‐resolution (1)H solid‐state NMR spectra with isotropic linewidths in the 50–400 Hz range. |
format | Online Article Text |
id | pubmed-10107932 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101079322023-04-18 Pure Isotropic Proton NMR Spectra in Solids using Deep Learning Cordova, Manuel Moutzouri, Pinelopi Simões de Almeida, Bruno Torodii, Daria Emsley, Lyndon Angew Chem Int Ed Engl Research Articles The resolution of proton solid‐state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic‐angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two‐dimensional set of magic‐angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high‐resolution (1)H solid‐state NMR spectra with isotropic linewidths in the 50–400 Hz range. John Wiley and Sons Inc. 2023-01-13 2023-02-13 /pmc/articles/PMC10107932/ /pubmed/36562545 http://dx.doi.org/10.1002/anie.202216607 Text en © 2022 The Authors. Angewandte Chemie International Edition published by Wiley-VCH GmbH https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Research Articles Cordova, Manuel Moutzouri, Pinelopi Simões de Almeida, Bruno Torodii, Daria Emsley, Lyndon Pure Isotropic Proton NMR Spectra in Solids using Deep Learning |
title | Pure Isotropic Proton NMR Spectra in Solids using Deep Learning |
title_full | Pure Isotropic Proton NMR Spectra in Solids using Deep Learning |
title_fullStr | Pure Isotropic Proton NMR Spectra in Solids using Deep Learning |
title_full_unstemmed | Pure Isotropic Proton NMR Spectra in Solids using Deep Learning |
title_short | Pure Isotropic Proton NMR Spectra in Solids using Deep Learning |
title_sort | pure isotropic proton nmr spectra in solids using deep learning |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10107932/ https://www.ncbi.nlm.nih.gov/pubmed/36562545 http://dx.doi.org/10.1002/anie.202216607 |
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