Artificial intelligence for high-resolution nuclear MRS under inhomogeneous magnetic fields
Qiu Wenqi1, Wei Zhiliang1, Ye Qimiao1, Chen Youhe2, Lin Yulan1, and Chen Zhong1
1Department of Electronic Engineering, Xiamen University, Xiamen, China, People's Republic of, 2Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, China, People's Republic of
High-resolution multi-dimensional nuclear magnetic
resonance (NMR) spectroscopy serves as an irreplaceable and versatile tool in
various chemical investigations. In this study, a method based on the concept
of partial homogeneity is developed to offer two-dimensional (2D)
high-resolution NMR spectra under inhomogeneous fields. Oscillating gradients
are exerted to encode the high-resolution information, and a
field-inhomogeneity correction algorithm based on pattern recognition is designed
to recover high-resolution spectra. The proposed method improves performances
of 2D NMR spectroscopy under inhomogeneous fields without increasing the
experimental duration or significant loss in sensitivity, and thus may open
important perspectives for studies of inhomogeneous chemical systems.
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