Lin Enping, Telkki Ville-Veikko, Lin Xiaoqing, Huang Chengda, Zhan Haolin, Yang Yu, Huang Yuqing, Chen Zhong
State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen, Fujian 361005, China.
NMR Research Unit, University of Oulu, P.O. Box 3000, Oulu FIN-90014, Finland.
J Phys Chem Lett. 2021 Jun 3;12(21):5085-5090. doi: 10.1021/acs.jpclett.1c01022. Epub 2021 May 24.
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.
作为旨在阐明化学结构的传统核磁共振（NMR）的完美补充，拉普拉斯NMR是研究自旋弛豫和扩散的强大技术，可揭示分子运动和自旋相互作用的信息。与采用傅里叶变换处理采集数据的传统NMR不同，拉普拉斯NMR依赖于类似于拉普拉斯逆变换的专门设计的信号处理和重建算法，并且在需要高光谱分辨率和高光谱维度的情况下通常面临严峻挑战。在此，基于针对高维问题的张量技术和稀疏性假设，我们提出了一种用于多维拉普拉斯NMR数据高分辨率重建的通用方法。我们表明，所提出的方法可以以高分辨率方式重建多维拉普拉斯NMR光谱，用于商业NMR仪器采集的指数衰减弛豫和扩散数据。因此，它将拓宽多维拉普拉斯NMR的应用范围。
