Pinelopi Moutzouri¹, Manuel Cordova^1*, Bruno Simões de Almeida¹, Daria Torodii¹, Lyndon Emsley^1*

1 Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

* Corresponding authors emails: manuel.cordova@epfl.ch, lyndon.emsley@epfl.ch

DOI10.24435/materialscloud:xj-5f [version v1]

Publication date: Mar 10, 2023

How to cite this record

Pinelopi Moutzouri, Manuel Cordova, Bruno Simões de Almeida, Daria Torodii, Lyndon Emsley, Two-dimensional pure isotropic proton solid state NMR, Materials Cloud Archive 2023.41 (2023), https://doi.org/10.24435/materialscloud:xj-5f

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.

Materials Cloud sections using this data

Files

File name	Size	Description
code.zip MD5md5:5c30816b8ac9b873259b62dcff1bd991	48.3 MiB	Python code used to train and use the model and pre-trained model
experiments.zip MD5md5:b3daa989fb87d227a24b741be038f675	4.0 GiB	Experimental datasets on which the model is applied

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

MARVEL/DD1 machine learning NMR resolution