Organosilicon compounds are one of the hot topics in research on electrolyte materials, with their physical and chemical properties standing as important parameters for measuring battery performance. In this paper, the structure of CN(CH₂)₂SiCH₃(OCH₂CH₂OCH₃)₂(BNS), solvation effect, diffusion coefficient and thermal stability of LiPF₆/BNS were analyzed and evaluated by various nuclear magnetic resonance (NMR) methods (i.e., ¹H NMR, ¹³C NMR, DOSY, ⁷Li NMR and ¹⁹F NMR). It was observed that there was a solvation effect between BNS and LiPF₆. Cyano (CN) and ether bond (-O-) groups in BNS may help to form complexes with Li⁺, and the coordination ability of CN is better than that of -O-. The formation of complex bond accelerated dissociation and diffusion of LiPF₆, and also improved the thermal stability of electrolyte (LiPF₆/BNS). It was proved that decomposition of LiPF₆ at high temperature was the main cause of electrolyte failure. The results of this study provided a theoretical basis for the development of new electrolyte compounds and improvement of their performance.

The Fischer-Tropsch process produces liquid fuels. However, the process also yields Fischer-Tropsch wastewater, an acidic and corrosive byproduct enriched with various water-soluble oxygen-containing compounds such as alcohols, aldehydes, ketones and carboxylic acids, demanding careful treatment before discharge. Herein, the interactions between n-butanol and propionic acid, two main components in Fischer-Tropsch wastewater, and humic acid (HA) were studied with nuclear magnetic resonance (NMR) techniques. One-dimensional ¹H chemical shift titration and ¹H relaxation time measurements showed that: the peak width of both n-butanol and propionic acid broadened with increasing HA concentration, while the ¹H chemical shift was unaltered for n-butanol, but moved to high field for propionic acid. The ¹H spin-lattice and spin-spin relaxation time of n-butanol and propionic acid decreased and the ¹H correlation time increased with increasing HA concentration. The interactions between HA and n-butanol were enhanced by increasing the HA concentration, but less affected by solution pH. While, the interactions between HA (20 mg/mL) and propionic acid were promoted by increasing the HA concentration and decreasing the solution pH value. It also appeared that the HA concentration plays a decisive role in the interaction. The study demonstrated that using HA for treatment of Fischer-Tropsch wastewater has a great potential in industrial applications owing to its simplicity and low price.

Tetracyclines are a class of broad-spectrum antibiotics that have antibacterial activities against Gram-positive and Gram-negative bacteria, including Mycoplasma, Chlamydia, and Rickettsia. In this paper, we report the one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) studies on the exchangeable protons and hydrogen bound in tetracycline hydrochloride (TC). The NMR results were compared with the results obtained from the crystal structures of free tetracycline and RNA- and protein-bound complexes. The H-bonding frameworks in the drug and their correlation with drug activity were discussed. The¹H and ¹³C NMR signals of TC and its derivatives were fully assigned and interpreted, and the misassigned signals in previous reports were corrected. The results of this study could be conducive to further exploration of the structure-function correlation of this drug family in more complex biological and environmental systems.

The ¹H NMR spectra of C-2 monosubstituted norbornene and its derivatives are usually difficult to assign due to the anisotropy effect of norbornene ring and being a mixture of exo- and endo- isomers. Based on the preparation of configurationally pure C-2 substituted norbornene derivatives, this study assigned the ¹H and ¹³C NMR signals of exo-5-norbornene-2-carboxylic acid, endo-5-norbornene-2-carboxylic acid, exo-5-norbornene-2-methanol and endo-5-norbornene-2-methanol by combined use of ¹H NMR, DEPT135, ¹H-¹H COSY, ¹H-¹³C HMQC and ¹H-¹H NOESY spectroscopy, and corresponding coupling constants. The effects of norbornene derivatives' C-2 substituents and their configurations on ¹H NMR chemical shifts of the compunds were also investigated.

Cathepsin B (Cat B) is a lysosomal cysteine protease that plays an essential role in cellular metabolism. Cat B is overexpressed in lung cancer cells. Fluorescence imaging techniques have been developed for measuring intracellular Cat B levels, which, however, suffers from limited penetration depth and interferences from autofluorescence background. To resolve these technical difficulties, we designed a novel Cat B biosensor detected by hyperpolarized xenon magnetic resonance imaging. It consists of a cryptophane cage as a ¹²⁹Xe NMR reporter and an amide bond as a Cat B-specific cleavable group. When the biosensor interacts with Cat B, the cleavage of amide bond leads to changes in ¹²⁹Xe chemical shift. Combing with hyperpolarization-chemical exchange saturation transfer (Hyper-CEST), our biosensor provides a novel method for responsive detection of Cat B.

The aim of this study is to identify brain structures involved in the individual difference of nicotine susceptibility in rats with longitudinal magnetic resonance imaging (MRI). Adult male Sprague-Dawley rats were subjected to intermittent intraperitoneal nicotine injection for 14 days using a mini-osmotic pump, followed by forced withdrawal for 14 days. Somatic withdrawal behavioral signs test was performed on day 0, 15, and 29 to quantify nicotine dependence. Correlation analyses between the withdrawal behavior score and structural MRI indices on day 1 (baseline scan) revealed that the severity of nicotine dependence was negatively correlated with the gray matter (GM) volumes in bilateral prelimbic cortex/left granular insular cortex and the white matter volume in bilateral thalamus, but positively correlated with the GM volumes in right hippocampal CA1/left thalamus. It is proposed that the aforementioned structural indices may be used as biomarkers for nicotine susceptibility, predicting the risk of nicotine dependence before individuals are exposed to nicotine.

Accurate segmentation of prostate region is an important prerequisite to improve the accuracy of computer-aided prostate cancer diagnosis. In this work, a new and accurate prostate segmentation algorithm is proposed and tested. The new algorithm consists of 4 steps: reading T₂-weighted magnetic resonance images, calculating local binary pattern (LBP) feature map of prostate magnetic resonance images by using an 8x5 LBP feature template, segmenting the feature map with the improved distance regularization level set evolution (DRLSE) algorithm, and extracting coarse contour of the prostate. A new energy function is constructed to extract local gray scale information and gradient information, and the coarse contour is iteratively developed into the final fine prostate contour on the basis of this new energy function. The algorithm was tested with the SPIE-AAPM-NCI Prostate MR Classification Challenge Database. The segmentation results of the proposed algorithm were compared with that of manual segmentation by doctors. The results showed that the Dice coefficient obtained by using the proposed algorithm was 0.94±0.01, with a relative volume difference (RVD) of -1.21%±2.44% and a 95% Hausdorff distance (HD) of 6.15±0.66 mm. Compared with the existing segmentation algorithms, the segmentation results obtained with the algorithm proposed in this paper are closer to the manual segmentation results.

Segmentation of whole breast and fibroglandular tissue (FGT) is an important task for quantitative analysis of breast cancer risk in dynamic contrast enhanced magnetic resonance (DCE-MR) images. In this study, an automated segmentation model based on nnU-Net is proposed to segment the whole breast and FGT in 3D fat-suppressed breast DCE-MR images, taking the advantages of hierarchical image features learning, as well as the fusion of deep features and shallow features. The model could automatically perform preprocessing, data augmentation and dynamic adaptation of network configurations with respect to different imaging parameters. Experimental results show that the method could accurately and efficiently segment the whole breast and FGT in the collected dataset of 3D fat-suppressed breast DCE-MR images with variable imaging characteristics, achieving the average Dice similarity coefficients 0.969±0.007 and 0.893±0.054, respectively, for breast and FGT segmentation.

To meet the demands in surgical treatment and radiotherapy, this work combines the faster region convolutional neural network (Faster-RCNN) and Level-Set methods to segment cerebellopontine angle (CPA) tumors automatically and precisely. T₁WI-SE magnetic resonance images from 317 CPA tumor patients were collected. Features extracted by VGG16 were combined with the region proposal network (RPN) for training. A CPA tumor localization model was then established, before the Level-Set method was applied to accurately segment the tumor. The segmentation results of different CPA tumor regions were compared in terms of precision, recall, mean average precision (mAP) and Dice coefficient. The results showed that the method proposed can effectively and precisely segment CPA tumors, thereby capable of reducing the burden on clinicians and improving the treatment effect.

In this paper, a new method for fine brain functional parcellation based on resting-state functional magnetic resonance imaging (rs-fMRI) data was proposed. The method combines the t-distribution stochastic neighbor embedding (t-SNE) and automatic spectral clustering (ASC) algorithms. First, correlation analyses are conducted between the time courses of the brain region to be parcellated and the whole brain. Second, t-SNE is used to extract the high-dimensional functional connectivity patterns. Last, the number of clusters is automatically determined by the ASC algorithm, and to divide the brain region of interest to generate the fine brain subregions. The results of simulated seed regions proved that the method proposed had higher accuracy than the commonly-used spectral clustering and spectral clustering with principal component analysis. Moreover, the method was successfully applied to parcellate the parahippocampal gyrus into 3 functional subregions in the left and right hemispheres. In conclusion, the algorithm combining t-SNE and ASC is an effective method for fine brain functional parcellation and construction of functional brain atlas.

In this work, operando low-field nuclear magnetic resonance (LF-NMR) technology was used to quantitatively study the photocatalytic reduction of Cr(Ⅵ) in a real solid-liquid reaction environment. The performance of Ag/g-C₃N₄ photocatalysts with different loadings of Ag for the photocatalytic reduction of aqueous Cr(Ⅵ) to Cr(Ⅲ) under visible-light irradiation was systematically investigated. The results showed that the addition of Ag cocatalysts (Ag loading amount: 1 wt.%, 2 wt.%, 5 wt.%, and 10 wt.%) significantly enhanced the photocatalytic performance of g-C₃N₄ photocatalysts, while the optimal content of Ag loading was 5 wt.%, which was greater than that of pristine g-C₃N₄ by a factor of 4.0. Furthermore, we carried out a quantitative analysis on the concentration of paramagnetic Cr(Ⅲ) ions in solution by using the transverse relaxation time (T₂) values, and proved the viability of employing operando LF-NMR relaxometry for monitoring the photocatalytic Cr(Ⅵ) reduction performance.

Accurate measurement of soil moisture is crucial in agricultural water management. A mini inside-out nuclear magnetic resonance (NMR) sensor has been designed to measure the moisture in model soils, consisting of three cylindrical magnets magnetized in the axial direction and four arc spiral coils with the same size in series. A circumferential symmetric constant gradient field can be obtained by adjusting the radius and height between the cylinders. To improve the signal-to-noise ratio of the sensor, this a wide-band matching method was proposed in this work to expand the excitation region of the radio frequency coils. The Carr-Purcell-Meiboom-Gill (CPMG) signal was analyzed through inverse Laplace transformation. With the results obtained, moisture content of the sample was found to be positively correlated with the integral area of T₂ spectrum peak, verifying the feasibility of the sensor in soil moisture measurement.

Highly stable magnetic field is essential to nuclear magnetic resonance (NMR) spectrometers. To maintain the magnetic field stability, high-quality superconducting joints are required. Along with the development of NMR superconductor magnet, researches on superconducting joints have also attracted lots of attention from all over the world in the past decades. This review paper introduces the research progress of superconducting joint technology. The first part briefly introduces the development of NMR superconducting magnet and superconducting joint. The second part summarizes the research progresses of superconducting joint between low temperature superconductor materials. The joint between high temperature superconductor materials is demonstrated in the third part. In the fourth part, the measurement technology regarding the resistance of superconducting joints is discussed. Finally, an outlook on superconducting joint research is presented for further discussion in the community.