<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Moutzouri, Pinelopi</dc:creator> <dc:creator>Cordova, Manuel</dc:creator> <dc:creator>Simões de Almeida, Bruno</dc:creator> <dc:creator>Torodii, Daria</dc:creator> <dc:creator>Emsley, Lyndon</dc:creator> <dc:date>2023-03-10</dc:date> <dc:description>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.</dc:description> <dc:identifier>https://archive.materialscloud.org/record/2023.41</dc:identifier> <dc:identifier>doi:10.24435/materialscloud:xj-5f</dc:identifier> <dc:identifier>mcid:2023.41</dc:identifier> <dc:identifier>oai:materialscloud.org:1687</dc:identifier> <dc:language>en</dc:language> <dc:publisher>Materials Cloud</dc:publisher> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:subject>MARVEL/DD1</dc:subject> <dc:subject>machine learning</dc:subject> <dc:subject>NMR</dc:subject> <dc:subject>resolution</dc:subject> <dc:title>Two-dimensional pure isotropic proton solid state NMR</dc:title> <dc:type>Dataset</dc:type> </oai_dc:dc>