J. Phys. Chem. Lett. 2021, 12, 21, 5085–5090, http://doi.org/10.1021/acs.jpclett.1c01022
High-resolution reconstruction for multidimensional laplace NMR
|Author:||Lin, Enping1; Telkki, Ville-Veikko2; Lin, Xiaoqing1;|
1State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, Fujian 361005, China
2NMR Research Unit, University of Oulu, Oulu FIN-90014, Finland
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021090144894
American Chemical Society,
|Publish Date:|| 2021-09-01
As a perfect complement to conventional NMR that aims for chemical structure elucidation, Laplace NMR constitutes a powerful technique to study spin relaxation and diffusion, revealing information on molecular motions and spin interactions. Different from conventional NMR adopting Fourier transform to deal with the acquired data, Laplace NMR relies on specially designed signal processing and reconstruction algorithms resembling the inverse Laplace transform, and it generally faces severe challenges in cases where high spectral resolution and high spectral dimensionality are required. Herein, based on the tensor technique for high-dimensional problems and the sparsity assumption, we propose a general method for high-resolution reconstruction of multidimensional Laplace NMR data. We show that the proposed method can reconstruct multidimensional Laplace NMR spectra in a high-resolution manner for exponentially decaying relaxation and diffusion data acquired by commercial NMR instruments. Therefore, it would broaden the scope of multidimensional Laplace NMR applications.
Journal of physical chemistry letters
|Pages:||5085 - 5090|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
114 Physical sciences
116 Chemical sciences
This work was supported by National Natural Science Foundation of China (U1805261, 11761141010, 22073078, and 61601386). E.L. thanks Dr. Bin Yuan for providing DOSY experimental data of the sample M6. V.-V.T. acknowledges the financial support of the European Research Council (ERC) under Horizon 2020 (H2020/2018-2022/ERC Grant Agreement No. 772110) and Kvantum Institute (University of Oulu).
|EU Grant Number:||
(772110) UFLNMR - Ultrafast Laplace NMR
© 2021 American Chemical Society. Published under a CC-NC-ND 4.0 License.