2026-06-03T14:18:42Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:2173 2024-09-17T18:10:17Z openaire_data community-mcarchive

Torodii, Daria Emsley, Lyndon Torodii, Daria Holmes, Jacob Moutzouri, Pinelopi Nilsson Lill, Sten Cordova, Manuel Pinon, Arthur Grohe, Kristof Wegner, Sebastian Putra, Okky Dwichandra Norberg, Stefan Welinder, Anette Schantz, Staffan Emsley, Lyndon 2024-09-17 The recent development of ultra-fast MAS (>100 kHz) provides new opportunities for structural characterization in solids. Here we use NMR crystallography to validate the structure of verinurad, a microcrystalline active pharmaceutical ingredient. To do this, we take advantage of 1H resolution improvement at ultra-fast MAS and use solely 1H-detected experiments and machine learning methods to assign all the experimental proton and carbon chemical shifts. This framework provides a new tool for elucidating chemical information from crystalline samples with limited sample volume and yields remarkably faster acquisition times compared to 13C-detected experiments, without the need to employ dynamic nuclear polarization. application/zip application/zip application/zip application/zip application/zip text/markdown https://doi.org/10.24435/materialscloud:qk-x9 oai:materialscloud.org:2173 mcid:2024.136 eng Materials Cloud https://doi.org/10.1021/acs.oprd.1c00284 https://doi.org/10.1039/d4fd00076e https://doi.org/10.1039/D4FD00076E https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:t6-30 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode Solid-State NMR NMR crystallography Verinurad Crystal structure validation of verinurad via proton-detected ultra-fast MAS NMR and machine learning info:eu-repo/semantics/other