2023
DOI: 10.56530/spectroscopy.yx1073b8
|View full text |Cite
|
Sign up to set email alerts
|

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

Di Guo,
Xianjing Chen,
Mengli Lu
et al.

Abstract: 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 o… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
references
References 85 publications
0
0
0
Order By: Relevance