Rational Designs for Lithium-Sulfur Batteries with Low Electrolyte/Sulfur Ratio

Lithium-sulfur batteries (LSBs) are considered a promising next-generation energy storage device owing to their high theoretical energy density. However, their overall performance is limited by several critical issues such as lithium polysulfide (PS) shuttles, low sulfur utilization, and unstable Li metal anodes. Despite recent huge progress, the electrolyte/sulfur ratio (E/S) used is usually very high (≥20 µL mg⁻¹), which greatly reduces the practical energy density of devices. To push forward LSBs from the lab to the industry, considerable attention is devoted to reducing E/S while ensuring the electrochemical performance. To date, however, few reviews have comprehensively elucidated the possible strategies to achieve that purpose. In this review, recent advances in low E/S cathodes and anodes based on the issues resulting from low E/S and the corresponding solutions are summarized. These will be beneficial for a systematic understanding of the rational design ideas and research trends of low E/S LSBs. In particular, three strategies are proposed for cathodes: preventing PS formation/aggregation to avoid inadequate dissolution, designing multifunctional macroporous networks to address incomplete infiltration, and utilizing an imprison strategy to relieve the adsorption dependence on specific surface area. Finally, the challenges and future prospects for low E/S LSBs are discussed.     

TiO2/MXene-Assisted LDI-MS for Urine Metabolic Profiling in Urinary Disease

Cancer remains an intractable medical problem. Rapid diagnosis and identification of cancer are critical to differentiate it from nonmalignant diseases. High-throughput biofluid metabolic analysis has potential for cancer diagnosis. Nevertheless, the present metabolite analysis method does not meet the demand for high-throughput screening of diseases. Herein, a high-throughput, cost-effective, and noninvasive urine metabolic profiling method based on TiO₂/MXene-assisted laser desorption/ionization mass spectrometry (LDI-MS) is presented for the efficient screening of bladder cancer (BC) and nonmalignant urinary disease. Combined with machine learning, TiO₂/MXene-assisted LDI-MS enables high diagnostic accuracy (96.8%) for the classification of patient groups (including 47 BC and 46 ureteral calculus (UC) patients) from healthy controls (113 cases). In addition, BC patients can also be identified from noncancerous UC individuals with an accuracy of 88.3% in the independent test cohort. Furthermore, metabolite variations between BC and UC individuals are investigated based on relative quantification, and related pathways are also discussed. These results suggest that this method, based on urine metabolic patterns, provides a potential tool for rapidly distinguishing urinary diseases and it may pave the way for precision medicine.     

Fabrication and characterizations of Cr3+-doped ZnGa2O4 transparent ceramics with persistent luminescence

0.5 at.% Cr:ZnGa₂O₄ precursor was synthesized by the co-precipitation method with nitrates as raw materials, using ammonium carbonate as the precipitant. Low-agglomerated Cr:ZnGa₂O₄ powders with an average particle size of 43 nm were obtained by calcining the precursor at 900℃ for 4 h. Using the powders as starting materials, 0.5 at.% Cr:ZnGa₂O₄ ceramics with an average grain size of about 515 nm were prepared by presintering at 1150℃ for 5 h in air and HIP post-treatment at 1100℃ for 3 h under 200 MPa Ar. The in-line transmittance of 0.5 at.% Cr:ZnGa₂O₄ ceramics with a thickness of 1.3 mm reaches 59.5% at the wavelength of 700 nm. The Cr:ZnGa₂O₄ ceramics can be effectively excited by visible light and produce persistent luminescence at 700 nm. For Cr:ZnGa₂O₄ transparent ceramics, the brightness of afterglow was larger than 0.32 mcd/m² after 30 min, which is far superior to that of Cr:ZnGa₂O₄ persistent luminescence powders.     

径向不耦合装药爆破效果的数值研究

为了研究径向不耦合条件下爆破效果的影响因素,利用AUTODYN-2D对比不耦合系数、不耦合介质、炸药种类对爆破效果的影响规律。结果表明,与耦合装药相比,径向不耦合装药条件下炮孔压力明显降低。对比水介质和空气介质下的爆破效果可知,水介质不耦合条件下,炸药爆炸能量的传递效率更高。炮孔压力与炸药种类密切相关,TNT产生的炮孔压力最大,但作用时间较短,而ANFO和乳化炸药的作用时间较长。     

2-Phenyloxazole-4-carboxamide as a Scaffold for Selective Inhibition of Human Monoamine Oxidase B

A series of 2-phenyloxazoles bearing an amide group at position 4 were designed and synthesized for evaluation as potential inhibitors of human recombinant monoamine oxidases (hrMAOs). Results of kinetics experiments demonstrated that all compounds behave as competitive MAO inhibitors, with good selectivity toward the MAO-B isoform. The most potent and selective derivatives are characterized by inhibition constant (K_i) values in the sub-micromolar range and a good selectivity index (K_{i MAO-A}/K_{i MAO-B}>50). Some derivatives were also found to be able to inhibit MAO activity in nerve growth factor (NGF)-differentiated PC12 cells, taken as a model of neuronal cells. In particular, 2-(2-hydroxyphenyl)-N-phenyloxazole-4-carboxamide (compound 4 a ) may be a promising new scaffold, exerting the highest selectivity and inhibitory effect toward MAOs in NGF-differentiated PC12 cell lysates, without compromising cell viability. Molecular docking analysis allowed a rationalization of the experimentally observed binding affinity and selectivity.     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

基于ABAQUS的显式动力学分析方法研究

显式动力学(The Explicit Dynamic)是针对隐式求解器的一个补充,其分析方法对于求解广泛、各类非线性结构力学问题是一个非常有效的工具,显式方法中的单个增量步取决于模型的最高固有频率,与持续时间、载荷类型无关。ABAQUS/Explicit主要用于碰撞、接触以及失效分析。探讨了ABAQUS显式动力学的基本理论、求解的问题,对显示动力学分析方法中的求解算法、稳定时间极限和能量平衡问题进行研究,给出了显示动力学分析的一般性分析方法。通过案例验证了该方法的可行性,应用该方法可有效解决实际工业生产中的碰撞问题、复杂的接触问题以及复杂的后屈曲问题,可为显式动力学分析方法应用于工程实践奠定理论与方法基础。     

A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow

PIV (Particle Image Velocimetry) technique for flow field measurement has achieved popular self-identify through over ten years development, and its application range is becoming wider and wider. PIV post-processing techniques have a great influence on the success of particle-fluid two-phase flow field measurement and thus become a hot and difficult topic. In the present study, a Phase Respective Identification Algorithm (PRIA) is introduced to separate low-density solid particles or bubbles and high-density tracer particles from the PIV image of particle-fluid two-phase flow. PTV (Particle Tracking Velocimetry) technique is employed to calculate the velocity fields of low-density solid particles or bubbles. For the velocity fields of high-density solid particles or bubble phase and continuous phase traced by high-density smaller particles, based on the thought of wavelet transform and multi-resolution analysis and the theory of cross-correlation of image, a delaminated processing algorithm (MCCWM) is presented to conquer the limitation of conventional Fourier transform. The algorithm is firstly testified on synthetic two-phase flows, such as uniform steady flow, shearing flow and rotating flow, and the computational results from the simulated particle images are in reasonable agreement with the given simulated data. The algorithm is then applied to images of actual bubble-liquid two-phase flow and jet flow, and the results also confirmed that the algorithm proposed in the present study has good performance and reliability for post-processing PIV images of particle-fluid two-phase flow.     

Quantitative UPLC‐MS/MS analysis of chlorogenic acid derivatives in antioxidant fractionates from dandelion (Taraxacum officinale) root

While qualitative studies have identified chlorogenic acids in antioxidant extracts, particularly ethyl acetate‐derived extracts, of Taraxacum officinale, quantitative analysis of these phenolic compounds remains largely unreported for this species. In this study, bioactivity‐guided fractionation of an antioxidant crude ethyl acetate extract (DPPH = 295.481 ± 0.955 mg TE g^?1 extract) from T. officinale root resulted in a number of reverse‐phase fractions that demonstrated high antioxidant activity (DPPH = 1058.733–1312.136 mg TE g^?1 extract), stronger than that of the synthetic antioxidant Trolox^®. UPLC‐MS/MS screening of these fractions for the presence of selected mono‐ and di‐caffeoylquinic acids revealed large quantities of 1,5‐dicaffeoylquinic acid present in several fractions (853.052–907.324 μg mg^?1), respectively. Due to the antioxidant potency and high levels of 1,5‐dicaffeoylquinic acid observed in these fractions, it was concluded that specifically this chlorogenic acid derivative is a major contributor to the antioxidant efficacy of dandelion root.