AI Article Synopsis
- Solid-state NMR spectroscopy struggles with broad H NMR spectra due to strong dipolar couplings in organic solids, which complicates data interpretation.
- The authors proposed a method to eliminate errors that cause residual dipolar broadening by mapping them into a second dimension, resulting in pure isotropic proton spectra with enhanced resolution.
- Utilizing deep learning, they applied this PIP approach to samples like L-tyrosine hydrochloride and ampicillin, achieving high-resolution dipolar correlation and spin-diffusion spectra that revealed previously hidden isotropic correlation peaks.
Article Abstract
One key bottleneck of solid-state NMR spectroscopy is that H 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 H 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 H- H 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.
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| http://dx.doi.org/10.1002/anie.202301963 | DOI Listing |
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