In situ solid-state nuclear magnetic resonance (ssNMR) techniques under continuous flow and batch-like conditions as well as 2D ¹³C-¹³C dipolar-based COmbined R2 Driven (CORD) spin diffusion NMR experiments were utilized to investigate the dehydration process of ethanol on molecular sieve H-SSZ-13. Kinds of intermediate species including ethanol with different adsorption orientation, diethyl ether under activated state, the surface ethoxy, triethyloxonium ion and even ethene were captured directly, and the evolution process of these intermediate species was also revealed in this paper. Moreover, it can be emphasized that the ethene species were observed in situ by ssNMR for the first time. These results enriched the fundamental research of ethanol dehydration reaction.

The selective hydrogenation of 1, 3-butadiene over Pd-Sn/Al₂O₃ bimetallic catalysts was investigated by heterogeneous parahydrogen-induced polarization (PHIP). A series of Pd₁-Sn_x/Al₂O₃ bimetallic catalysts with different Pd/Sn ratios were synthesized by incipient wetness co-impregnation method. It was observed that the Pd/Sn ratios of the catalysts had a significant effect on the reaction activity of 1, 3-butadiene and the selectivity to butene. A monotonic decrease of reaction conversion was observed as the Pd/Sn ratio decreased, while the selectivity to butene showed an opposite trend. This can be attributed to the ensemble effect and the ligand effect caused by the increasing tin component content: as the Pd/Sn ratio decreases, less palladium component was exposed on the surface, leading to low activity. The electronic properties of the Pd atoms were altered, which led to a weaker adsorption strength of the semi-hydrogenation product butene and an increased selectivity. Smaller Pd ensembles were exposed on Sn-rich catalysts surface, which favored a higher ratio of pairwise addition process, leading to a stronger PHIP effect. The isomerization measurements by PASADENA (parahydrogen and synthesis allow for dramatically enhanced nuclear alignment) showed that the isomerization process between 1-butene and 2-butene decreased with the increase of Sn content. This can be accounted for by the easier desorption of 1-butene on Sn-rich catalysts.

In this work, the motions of methylammonium cations (MA) in different phase structures of MAPbCl₃ perovskites, including orthorhombic, tetragonal and cubic phases, were studied by solid-state ²H nuclear magnetic resonance (NMR). By a combination of experimental ²H NMR and numerical simulations, the motion models in different phase structures of MAPbCl₃ were established in this work. The NMR results disclosed the characteristics of the motions of MA in different phase structures. In the orthorhombic phase, the MA cations are not completely frozen but wobble locally while rapidly rotating around the C-N bond. As the temperature increases, MA cations show various modes of motion in the perovskite lattice. The NMR results also showed that the changes of the motions of MA in different temperatures are not accompanied by the changes of phase structures. The observations in this work will deepen our understanding of the molecular mechanism of the MAPbCl₃ phase transitions.

Recent studies show that high permittivity materials (HPMs) have great application prospects in improving the performance of RF coils and enhancing magnetic resonance image signal to noise ratio (SNR) in high and ultra-high field magnetic resonance imaging (MRI). So far the research about HPMs mainly focuses on its benefit for MRI image SNR, while investigation on how its geometrical structure affects the homogeneity of transmit field ($ {B}_{\text{1}}^{\text{+}} $) is insufficient. In this study the effect of the geometrical structure of HPMs on the average transmit efficiency and $ {B}_{\text{1}}^{\text{+}} $ inhomogeneity at 1.5 T was quantitatively analyzed through electromagnetic simulation. The results indicated that for the four investigated geometrical structures of HPMs the quartered cylinder is the optimum solution, which would be valuable for the application of HPMs in MRI.

This paper introduces the design and implementation of a 5 T magnetic resonance radio frequency power amplifier (RFPA), which can be used in scan rooms without a circulator. The RFPA was designed based on laterally-diffused metal-oxide semiconductor (LDMOS) power tube with high standing wave tolerance, and it contained a three-stage amplifier circuit. The transmission line transformers with magnetic core in RFPA was designed as non-magnetic. The reflected power was monitored tensely. The nonlinear automatic correction of RFPA was realized by using analogic negative feedback technology. The results showed that the output of RFPA power is up to 2 kW, the gain linearity performance is less than or equal to 1 dB, and the phase linearity performance is less than or equal to 10 deg over 40 dB range up to rated power during the load disturbance (reflection coefficient Г < 0.5), which meets the imaging requirements. Thus the rationality and feasibility of the proposed design scheme are verified.

Two-dimensional nuclear magnetic resonance (NMR) technology can perform non-destructive, rapid and quantitative measurement and characterization of various hydrogen-containing fluids in the reservoir, but is limited by the acquisition methods and parameters of NMR equipment. In the detection of samples with ultra-fast relaxation components such as organic matter and asphalt, the problem of missing or inaccurate fluid components in two-dimensional spectrum due to incomplete signal acquisition often occurs. In this paper, a high-precision inversion method of T₂-T₁ two-dimensional spectrum based on ultrafast relaxation component compensation technology is proposed. This method extends the one-dimensional NMR front-end signal compensation technology. By compensating the components of the echo data before the inversion of two-dimensional NMR data, the problem of signal leakage at the front end of two-dimensional NMR logging can be effectively solved. The application of experiments and logging data shows that this method can obtain more accurate and complete reservoir information in shale oil and other reservoirs rich in signals of fast relaxation components.

Electromagnetic simulation using a knee model is the main method for calculating local specific absorption rate (SAR) values of knee joint. To construct a knee model, a cascaded network structure containing two convolutional neural networks, i.e. U-Net, was proposed for segmenting knee magnetic resonance images. The first network segmented tissues with large volume from the whole image, such as muscle and fat, and predicted the position information of cartilage and meniscus based on the segmentation results. The second network segmented tissues with small volume from a smaller sub-image based on the acquired position information to improve accuracy. Both networks adopted focal loss function and their segmentation results were merged to form the model. We evaluated the segmentation results of this method and 4 comparison methods, by quantitative metrics, and constructed separate knee joint models to calculate local SAR values. The results indicate that the cascaded network structure proposed in this paper can construct knee joint models for SAR simulation more accurately.

Accurate and reliable breast tumor segmentation is essential for the diagnosis, treatment and prognosis of breast cancer. To address the shortcomings of existing dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI)-based breast tumor segmentation methods, which tend to miss small tumors, we proposed a more reliable and efficient segmentation method for breast tumors in DCE-MRI based on a fully convolutional network (FCN). Firstly, the breast DCE-MRI data was preprocessed, followed by intercepting the image blocks of 128*128, and dividing the dataset into two sub-datasets according to the number of pixels in the tumor region. Secondly, the whole set was used to train CBP5-Net to obtain a classification model. Then, two sub-datasets were used to train RAU-Net to get two segmentation models. Finally, the test set was entered into the network, and the network outputs were post-processed to obtain the final segmentation results. The Dice coefficient, sensitivity, specificity and intersection over union (IoU) index of the method proposed in this paper reached 0.938 8, 0.952 3, 0.998 5 and 0.876 8, respectively. It proves that the proposed method can be used to segment DCE-MRI breast tumors effectively and accurately.

To further improve the segmentation accuracy, robustness, and training efficiency of existing articular synovium segmentation algorithms, a new deep learning network based on Dense-UNet++ was proposed. First, we inserted the DenseNet module into the UNet++ network, then applied the Swish activation function to train the model. The network was trained through 14 512 synovial images augmented from 1 036 synovial images, and tested through 68 images. The average accuracy of the model reached 0.819 9 for dice similarity coefficient (DSC), and 0.927 9 for intersection over union (IOU) index. Compared with UNet, ResUNet and VGG-UNet++, DSC coefficient and IOU index were improved, and DSC oscillation coefficient reduced. In addition, when applied in the same synovial image set and using the same network structure, the Swish function can help improve the accuracy of segmentation compared with the ReLu function. The experimental results show that the proposed algorithm performs better in segmenting articular synovium and may assist doctors in disease diagnosis.

The joint application of multiple diffusion models on single sampled dataset is becoming a hot topic in clinical research. This study investigated the influence of the three data sampling schemes on the quantification of neural diffusion models. The three sampling schemes compared were QGrid, Free and MDDW on the Siemens scanners. The diffusion models involved were diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP) models. It was demonstrated that the results of NODDI and MAP were sensitive to the sampling schemes and the set of maximum b-value, while that of DTI and DKI were comparatively not sensitive to varying configurations. It was also shown that QGrid and Free schemes provided more consistent results. Thus the sampling scheme should be carefully selected in multi-center studies and studies with large sample size. QGrid and Free schemes are recommended for their advantages demonstrated in this study.

Limit of detection (LOD), which indicates the detection ability of an analytical method, is an important parameter used in validating quantitative ¹H nuclear magnetic resonance (NMR) method. Different approaches to evaluate LOD have been reported in literature, including the calibration curve approach, regression parameters-based approach, ASTM approach (proposed by American Society of Testing Materials), EPA approach (established by the United States Environmental Protection Agency) and the signal-to-noise ratio approach. In this study, systemic analyses and summaries of all mentioned approaches were given together with their principles, equations and characteristics. A novel approach based on signal-to-ratio regression curve for determining the detection limit was proposed, which can overcome weaknesses of the signal-to-noise ratio approach. The LOD of liquid-state ¹H NMR method in the determination of sodium formate in aqueous solution was calculated using 5 different approaches. The influence of the number of scans on LOD was discussed. The results showed that the LOD was in the range of 10.4~14.4 μmol/L when the number of scans was 64 with 700 MHz NMR spectrometer. In conclusion, the study can provide a reference for determining the LOD of ¹H quantitative NMR.