Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet-Reference-Cited by-同舟云学术

Improvement in Signal-to-Noise Ratio of Liquid-State NMR Spectroscopy via a Deep Neural Network DN-Unet

Published:2020-12-30 Issue:3 Volume:93 Page:1377-1382
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Wu Ke¹,Luo Jie¹,Zeng Qing¹^ORCID,Dong Xi¹,Chen Jinyong¹,Zhan Chaoqun¹,Chen Zhong¹^ORCID,Lin Yanqin¹^ORCID

Affiliation:

1. Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, State Key Laboratory for Physical Chemistry of Solid Surfaces, Xiamen University, Xiamen 361005, China

Funder

National Natural Science Foundation of China

Fujian Province

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.0c03087

Reference41 articles.

1. Perspectives on high-field and solid-state NMR methods of quadrupole nuclei

2. NMR spectroscopy up to 35.2 T using a series-connected hybrid magnet

3. Hyperpolarised xenon in biology

4. Increase in signal-to-noise ratio of > 10,000 times in liquid-state NMR

5. Assessing Site-Specific Enhancements Imparted by Hyperpolarized Water in Folded and Unfolded Proteins by 2D HMQC NMR

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