论文成果

Review and Prospect: Applications of Exponential Signals with Machine Learning in Nuclear Magnetic Resonance

摘要:Nuclear magnetic resonance (NMR) spectroscopy presents an important analytical tool for composition analysis, molecular structure elucidation, and dynamic study in the fields of chemistry, biomedicine, food science, energy and more. As a basic function, exponential functions can be applied to model NMR signals of free induction decay, relaxation, and diffusion. In this paper, we will review Fourier and Laplace NMR exponential signals separately, as well as the performance of state-of-the-art machine learning on NMR applications.

ISSN号:0887-6703

卷、期、页:卷: 38期: 9页: 22-32

发表日期:2023-09-01

影响因子:0.000000

期刊分区(SCI为中科院分区):四区

收录情况:SCI(科学引文索引印刷版),SCIE(科学引文索引网络版)

发表期刊名称:SPECTROSCOPY

参与作者:Guo, Di,Chen, Xianjing,Lu, Mengli,He, Wangfeng,Lin, Yanqin,Huang, Yuqing,Qu, Xiaobo

第一作者:罗嗣慧,肖立志

论文类型:期刊论文

论文概要:Guo, Di,Chen, Xianjing,Lu, Mengli,He, Wangfeng,罗嗣慧,Lin, Yanqin,Huang, Yuqing,肖立志,Qu, Xiaobo,Review and Prospect: Applications of Exponential Signals with Machine Learning in Nuclear Magnetic Resonance,SPECTROSCOPY,2023,卷: 38期: 9页: 22-32

论文题目:Review and Prospect: Applications of Exponential Signals with Machine Learning in Nuclear Magnetic Resonance