P-doped CoSe2 nanoparticles embedded in 3D honeycomb-like carbon network for long cycle-life Na-ion batteries

We report for the first time a Na-ion battery anode material composed of P-doped CoSe2 nanoparticles(P-CoSe2)with the size of 5-20 nm that are uniformly embed in a 3D porous honeycomb-like carbon network.High rate capability and cycling stability are achieved simultaneously.The honeycomb-like carbon network is rationally designed to support high electrical conductivity,rapid Na-ion diffusion as well as the accommodation of the volume expansion from the active P-CoSe2 nanoparticles.In particular,heteroatom P-doping within CoSe2 introduces stronger P-Co bonds and additional P-Se bonds that signif-icantly improve the structure stability of P-CoSe2 for highly stable sodiation/desodiation over long-term cycling.P-doping also improves the electrical conductivity of the CoSe2 nanoparticles,leading to highly elevated electrochemical kinetics to deliver high specific capacities at high current densities.Benefiting from the unique nanostructure and atomic-level P-doping,the P-CoSe2(2∶1)/C anode delivers an excel-lent cycle stability with a specific capacity of 206.9 mA h g-1 achieved at 2000 mA g-1 after 1000 cycles.In addition,this material can be synthesized using a facile pyrolysis and selenization/phosphorization approach.This study provides new opportunities of heteroatom doping as an effective method to improve the cycling stability of Na-ion anode materials.     

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.     

Interfacial Crystallization of Diacylglycerols Rich in Medium‐ and Long‐Chain Fatty Acids in Water‐in‐Oil Emulsions

The effects of diacylglycerols rich in medium‐ and long‐chain fatty acids (MLCD) on the crystallization of hydrogenated palm oil (HPO) and formation of 10% water‐in‐oil (W/O) emulsion are studied, and compared with the common surfactants monostearoylglycerol (MSG) and polyglycerol polyricinoleate (PGPR). Polarized light microscopy reveals that emulsions made with MLCD form crystals around dispersed water droplets and promotes HPO crystallization at the oil‐water interface. Similar behavior is also observed in MSG‐stabilized emulsions, but is absent from emulsions made with PGPR. The large deformation yield value of the test W/O emulsion is increased four‐fold versus those stabilized via PGPR due to interfacial crystallization of HPO. However, there are no large differences in droplet size, solid fat content (SFC), thermal behavior or polymorphism to account for these substantial changes, implying that the spatial distribution of the HPO crystals within the crystal network is the driving factor responsible for the observed textural differences. MLCD‐covered water droplets act as active fillers and interact with surrounding fat crystals to enhance the rigidity of emulsion. This study provides new insights regarding the use of MLCD in W/O emulsions as template for interfacial crystallization and the possibility of tailoring their large deformation behavior. Practical Applications: MLCD is applied in preparing W/O emulsion. It is found that MLCD forms unique interfacial Pickering crystals around water droplets, which promote the surface‐inactive HPO nucleation at the oil‐water interface. Thus MLCD‐covered water droplets act as active fillers and interact with surrounding fat crystals, which can greatly enhance the rigidity of emulsion. This observation would provide a theoretical reference and practical basis for the application of the MLCD with appreciable nutritional properties in lipid‐rich products such as whipped cream, shortenings margarine, butter and ice cream, so as to substitute hydrogenated oil. MLCD‐stabilized emulsions can also be explored for the development of novel confectionery products, lipsticks, or controlled release matrices.     

酸改性CoPd/TiO2催化CH4-CO2直接合成C2含氧化合物

采用酸处理方法对CoPd/TiO₂催化剂进行改性，并将酸改性催化剂用于温和条件下CH₄-CO₂梯阶转化直接合成C₂含氧化合物（乙酸和乙醇）的反应。在150～300℃考察了浸酸方式和不同种类酸处理对催化剂活性和选择性的影响。采用X射线衍射（XRD）、X射线光电子能谱（XPS）、NH₃程序升温脱附（NH₃-TPD）和N₂吸附对催化剂进行了表征。结果表明，酸改性明显提高了CoPd/TiO₂上C₂含氧化合物的生成速率和选择性。浸酸方式对催化剂性能和结构有显著影响，先用酸浸渍载体然后再浸渍活性金属所得催化剂具有更高的活性。在H₃PO₄、HNO₃和HCl中，H₃PO₄浸渍的催化剂活性最佳，在150℃时C₂含氧化合物（乙酸和乙醇）的生成速率为3365 μg/(g·h)，选择性达到91%。     

低温水等离子体活化和表面接枝DMAE聚氯乙烯中空纤维膜研究

采用低温水等离子体技术，在三通道聚氯乙烯(PVC)膜表面接枝了甲基丙烯氧基苄基二甲基氯化铵(DMAE)单体，增强了膜亲水和抗菌性能。通过红外分析，表明DMAE成功接枝到了PVC膜上，水通量提高两倍，PVC-ir-H₂O膜(通过水等离子体处理的膜)对牛血清蛋白(BSA)的吸附能力下降67%，对BSA溶液的通量从7.7提高至40 kg?m^-2?h^-1，并且对BSA的截留能力不变。通过静态及动态抗菌实验，接枝后的PVC膜（PVC-g-PMAE膜）抗菌率达到100%，膜组件运行中的抗菌率也达到82%以上。在保证细菌截留率100%的同时，其渗透通量提高三倍。该膜表面修饰工程技术能实现膜表面的均一化改性，且绿色环保、操作简便、成本低，改性膜在饮用水处理领域，尤其是家用净水器中展现了很好的应用前景。