Halbach permanent magnets hold significant promise for low-field nuclear magnetic resonance (LF-NMR) applications, such as rock core analysis, owing to their yoke-free design and low external stray fields. Compared with small rock cores, large rock cores better preserve the original internal structure and fluid distribution, yet they demand a larger homogeneous region. However, the complex structure of Halbach magnets inherently yields inadequate initial homogeneity, making it difficult to directly obtain a sufficiently large homogeneous region. In this study, we defined a 100-mm-diameter spherical region of interest (ROI) and optimized the magnet structure using Halbach magnet theory and finite-element simulations. The final designed magnet provides a field strength of 158.4 mT and an initial homogeneity of 22 502 ppm (1 ppm=10-6). We applied an improved harmonic-based passive shimming method and enhanced the field homogeneity to 1 496 ppm. After passive shimming, we acquired 1H free induction decay (FID) signals from an aqueous CuSO4 sample (Φ 100 mm × H 100 mm), and Carr-Purcell-Meiboom-Gill (CPMG) signals from a two-component CuSO4 aqueous solution, and distinguished different samples based on their T2. These results demonstrate that the proposed magnet design and passive shimming method are effective for constructing large-bore, high homogeneity Halbach magnets.
Low-field nuclear magnetic resonance (LF-NMR) relaxation time spectrum analysis has been widely applied in petroleum engineering and geological exploration. However, conventional NMR relaxation time spectrum inversion algorithms require time-domain signals with high signal-to-noise ratios (SNRs), which are often difficult to obtain in practical scenarios such as on-site core analysis. To achieve accurate and stable inversion of low-SNR NMR signals, this paper proposes a deep learning-based inversion network named UDMCA (U-Net Denoising and Multi-scale Cross Attention Inversion Network), which incorporates a multi-scale cross-attention mechanism to extract and fuse relaxation features at multiple scales according to the decay characteristics of NMR signals, thereby improving inversion accuracy for low-SNR multi-component signals. In addition, a U-Net denoising module is introduced to further enhance noise robustness. Experimental results show that the proposed method achieves accurate and stable inversion for low-SNR signals and demonstrates good applicability in core NMR signal inversion, providing an effective solution for low-SNR LF-NMR data processing and inversion.
Radio frequency receivers are critical to the quality of magnetic resonance imaging (MRI). To address the key challenge in receiver design—namely, that the high sampling rate required for analog-to-digital converters (ADC) leads to high power consumption and hardware complexity—this paper proposes a signal reception scheme based on high bit width and low sampling rate. By enhancing the quantization dynamic range, this approach reduces the reliance on high sampling rates, thereby significantly lowering system power consumption. At the digital signal processing level, the lower sampling rate offers greater flexibility for filter design, enabling excellent filtering performance with lower-order filters and reduced hardware resource requirements. Guided by the RF direct bandpass sampling theory, a low-power and low-sampling-rate receiver for 0.5 T MRI systems was designed in this study. Experimental results show that the proposed design improves signal dynamic range while ensuring MRI signal quality, and simultaneously achieves significant reductions in power consumption, hardware complexity, and system cost.
Pulsed dipolar electron paramagnetic resonance (PD-EPR) is a powerful biophysical technique for probing the dynamic structures and interactions of biomacromolecules such as proteins. By measuring dipolar interactions between unpaired electrons, PD-EPR directly yields nanometer-scale distance distributions between electron spins, from which conformational transitions can be inferred by comparison under different experimental conditions. This review summarizes three major PD-EPR techniques, including double electron-electron resonance, double quantum coherence, and relaxation-induced dipolar modulation enhancement, together with commonly used spin labels and protein spin-labeling strategies, highlighting recent advances in the study of biomacromolecules and complexes.
Ensifentrine was approved for marketing by the U.S. Food and Drug Administration (FDA) in June 2024. To date, however, the literature has provided only the raw NMR spectral data of this drug without detailed signal assignments, which hinders impurity identification and quality control for ensifentrine. Furthermore, the compound contains multiple quaternary carbon and nitrogen atoms, presenting challenges in assigning NMR signals. In this study, we employed a Bruker Avance III 400 MHz NMR spectrometer to acquire 1H, 13C, and two-dimensional NMR spectra of ensifentrine, and fully assigned all 1H and 13C signals. Notably, unlike previous reports, we utilized DMSO-d6 as the solvent, which enhanced sample solubility, permitted unambiguous identification of the coupling relationships of active hydrogen signals, and yielded well-resolved two-dimensional NMR spectra. This work provides a reference for NMR-based structural analysis, content determination, and quality control of the active pharmaceutical ingredient (API) ensifentrine.
