2026-06-28T01:04:42Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:1687 2023-03-10T16:35:51Z openaire_data community-mcarchive

Cordova, Manuel Emsley, Lyndon Moutzouri, Pinelopi Cordova, Manuel Simões de Almeida, Bruno Torodii, Daria Emsley, Lyndon 2023-03-10 One key bottleneck of solid-state NMR spectroscopy is that ¹H NMR spectra of organic solids are often very broad due to the presence of a strong network of dipolar couplings. We have recently suggested a new approach to tackle this problem. More specifically, we parametrically mapped errors leading to residual dipolar broadening into a second dimension and removed them in a correlation experiment. In this way pure isotropic proton (PIP) spectra were obtained that contain only isotropic shifts and provide the highest ¹H NMR resolution available today in rigid solids. Here, using a deep-learning method, we extend the PIP approach to a second dimension, and for samples of L-tyrosine hydrochloride and ampicillin we obtain high resolution ¹H-¹H double-quantum/single-quantum dipolar correlation and spin-diffusion spectra with significantly higher resolution than the corresponding spectra at 100 kHz MAS, allowing the identification of previously overlapped isotropic correlation peaks. application/zip application/zip text/markdown https://doi.org/10.24435/materialscloud:xj-5f oai:materialscloud.org:1687 mcid:2023.41 eng Materials Cloud https://doi.org/10.1002/anie.202301963 https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:8g-3s info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode MARVEL/DD1 machine learning NMR resolution Two-dimensional pure isotropic proton solid state NMR info:eu-repo/semantics/other