A study on multi-exponential inversion of nuclear magnetic resonance relaxation data using deep learning

Easy-to-implement passive shimming approach of Halbach magnet for low-field NMR measurement

Halbach magnets have been widely employed to NMR instruments due to their low weight, low cost, and minimal leakage of magnetic field. However, field inhomogeneity remains challenge due to discrete magnet rings and manufacturing deviations of the magnetic elements. This paper aims to address this limitation through an effective passive shimming approach, which is considered the first step toward constructing high-homogeneity magnets because of its non-powered and inherently stable characteristics. We focus on the transverse dipole field generated by Halbach magnets and develop an easily implementable linear programming-genetic algorithm (LP-GA) hybrid optimization approach for passive shimming. Our methodology first employs an equivalent magnetic dipole model to calculate the sensitivity matrix of the shim pieces in the Region of Interest (ROI). Then, the LP-GA hybrid optimization algorithm determines the optimal position, number, and thickness of the shim pieces. By combining shim pieces of three different thicknesses (1 mm, 1.5 mm, and 2 mm), we significantly reduce the field inhomogeneity of a 48 mT Halbach magnet system. The effectiveness of our approach is validated through NMR measurements using water samples with copper sulfate at different concentrations, demonstrating an improvement in field homogeneity from approximately 1229 ppm to 320 ppm. The experimental results confirm that the proposed approach effectively enhances magnetic field homogeneity of low-field Halbach magnet systems and could be applied to shimming various Halbach-like magnet arrays.

Effect of Rice Protein on the Gelatinization and Retrogradation of Rice Starch with Different Moisture Content

Rice protein and moisture content are pivotal in the gelatinization and retrogradation processes of rice starch. This study aimed to explore the influence of rice protein on these processes by preparing rice starch gels with varying moisture levels and incorporating rice protein. At a high moisture content of 1:6, rice protein exhibited a minimal effect on the gelatinization properties of rice starch but significantly retarded the retrogradation of the starch gel. At intermediate moisture levels of 1:4 and 1:2, the rice starch gels showed pronounced retrogradation. However, rice protein was effective in inhibiting this retrogradation at a 1:4 moisture content, while its inhibitory effect diminished at a 1:2 moisture content. Under low moisture conditions of 1:1, the gelatinization of rice starch was markedly constrained by the limited water availability, but rice protein mitigated this constraint. Conversely, at this moisture level, rice protein promoted the retrogradation of the rice starch gel during the retrogradation process. The findings of this study offer a theoretical foundation that could inform the production of rice-based products.

Two-Dimensional Laplace NMR Reconstruction through Deep Learning Enhancement

Laplace NMR is a powerful tool for studying molecular dynamics and spin interactions, providing diffusion and relaxation information that complements Fourier NMR used for composition determination and structure elucidation. However, Laplace NMR demands sophisticated signal processing algorithms such as inverse Laplace transform (ILT). Due to the inherently ill-posed nature of ILT problems, it is generally challenging to perform satisfactory Laplace NMR processing and reconstruction, particularly for two-dimensional Laplace NMR. Herein, we propose a proof-of-concept approach that blends a physics-informed strategy with data-driven deep learning for two-dimensional Laplace NMR reconstruction. This approach integrates prior knowledge of mathematical and physical laws governing multidimensional decay signals by constructing a forward process model to simulate relationships among different decay factors. Benefiting from a noniterative neural network algorithm that automatically acquires prior information from synthetic data during training, this approach avoids tedious parameter tuning and enhances user friendliness. Experimental results demonstrate the practical effectiveness of this approach. As an advanced and impactful technique, this approach brings a fresh perspective to multidimensional Laplace NMR inversion.

Machine learning assisted interpretation of 2D solid-state nuclear magnetic resonance spectra

A machine learning methodology using deep neural network (DNN) for interpreting multidimensional solid-state nuclear magnetic resonance (SSNMR) of various synthetic and natural polymers is presented. The separated local field (SLF) SSNMR which correlates local well-defined heteronuclear dipolar with the tensor orientation of the chemical shift anisotropy (CSA) of spin in the solid state can provide valuable structure and molecular dynamics information of synthetic and biopolymers. Compared with the traditional linear least-square fitting, the proposed DNN-based methodology can efficiently and accurately determine the tensor orientation of CSA of both 13C and 15N in all four samples. The method achieves prediction precisions of the Euler angles with < ±5° and is characterized by low training costs and high efficiency (<1 s). The feasibility and robustness of the DNN-based analysis methodology are confirmed by comparison to reported-literature values. This strategy is expected to aid in the interpretation of complex multidimensional NMR spectra of complicated polymer system.