.png){kind=icon}
返回
Neural Network Method for Diffusion-Ordered NMR Spectroscopy
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.analchem.1c03883.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Diffusion-ordered NMR spectroscopy (DOSY) presents an essential tool for the analysis of compound mixtures by revealing intrinsic diffusion behaviors of the mixed components. For the interpretation of the diffusion information, intrinsically designed algorithms for a DOSY spectrum reconstruction are required. The estimated diffusion coefficients are desired to have consistency for all the spectral signals from the same molecule and good separation of signals from different molecules. For this purpose, we propose a novel method that adopts a coordinated multiexponential fitting to ensure the consistency of diffusion coefficients and apply a sparse constraint to enhance the robustness. A lightweight neural network is applied as an optimizer to solve this highly nonlinear and nonconvex optimization problem. The proposed method provides estimated diffusion coefficients with excellent distinguishment between species and outperforms the state-of-the-art reconstruction algorithms, such as the Laplacian inversion and the multivariate fitting methods.
Keyword:
REGULARIZATION
RESOLUTION
ALGORITHM
INVERSION
MODEL
期刊
IF:
6.7
论文数:
4.7W
被引数:
15.9W
机构
引用论文
Trend in age-specific cancer mortality and cancer-free life expectancy in China, 2008–17: a cross-sectional survey of nationwide data.png)
The Lancet
The LancetIF0
PREAI
分享收藏Iterative Thresholding Algorithm for Multiexponential Decay Applied to PGSE NMR DataANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
分享收藏
Pressure Tuning of Competing Charged and Neutral Exciton States in Quasi-2D Semiconductor Structures
PREAI
分享收藏分享收藏
查看更多内容