Characterization of anisotropic gas permeability and thermomechanical properties of highly textured porous alumina

Porous alumina with a highly textured microstructure was fabricated by pulse electric current sintering (PECS) using alumina platelets. Highly oriented porous alumina with a porosity of 3%–50% was obtained by a pressure-controlled method of PECS. The properties of the highly textured porous alumina were measured in two directions. The nitrogen gas permeance and thermal conductivity at room temperature were higher in the direction along the platelet length due to the higher continuity of pores and the connectivity of alumina platelets, respectively. The anisotropy of the thermal conductivity at room temperature was investigated and explained by the effect of grain size of platelets as well as morphology and orientation of pores. The bending strength was higher with the loading direction along the platelet thickness. The thermal shock strength was clearly different in the two directions. The difference in the thermal shock strength was investigated by the measurement of properties and thermal stress analysis.     

APP软件性能效率研究

性能效率是APP软件的重要质量属性，但目前缺乏APP软件性能效率的通用模型。分析了APP软件的性能特征，基于ISO/IEC 25010标准提出了APP软件的性能效率模型，定义了APP软件性能效率的子特性和度量指标。基于提出的APP软件性能效率模型，通过实验对APP软件的性能效率进行了度量及相关分析。     

Combustion synthesis of AlN doped with carbon and oxygen

Carbon-and-oxygen-doped AlN specimens were prepared by combustion synthesis using Al, graphite, and AlN. Graphite addition changed the product color from white to blue. By XRD, the lattice constant increased slightly with increasing carbon content. Blue AlN powder was synthesized with a molar ratio of the diluent AlN of 0.2-0.5 with a fixed graphite content of 0.05. At an AlN molar ratio exceeding 0.6, carbon was not successfully incorporated due to the lower reaction temperature. Calcination at 800°C in air removed residual graphite without changing the crystal structure or product color. Oxygen, nitrogen, and carbon analyses revealed that blue AlN powders contained 0.45-0.54 mass% carbon and 1.4-1.6 mass% oxygen, while the undoped AlN contained 0.021 mass% carbon and 0.94 mass% oxygen. The origin of the white-to-blue color change was investigated via reflection measurements. Blue AlN exhibits an absorption peak at 634 nm (1.96 eV). From first-principles electronic structure calculations, the C-doped AlN and carbon-and-oxygen-doped AlN with a 1:1 ratio could be classified as p-type, whereas the O-doped AlN and 1:3 carbon-and-oxygen-doped AlN were n-type. One reason for the absorption peak at 634 nm may be a transition from the conduction band to an upper unoccupied state. These results suggest the possible control of optical and electronic properties of AlN via carbon-and-oxygen doping.     

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.     

Migraine: Calcium Channels and Glia

Migraine is a common neurological disease that affects about 11% of the adult population. The disease is divided into two main clinical subtypes: migraine with aura and migraine without aura. According to the neurovascular theory of migraine, the activation of the trigeminovascular system (TGVS) and the release of numerous neuropeptides, including calcitonin gene-related peptide (CGRP) are involved in headache pathogenesis. TGVS can be activated by cortical spreading depression (CSD), a phenomenon responsible for the aura. The mechanism of CSD, stemming in part from aberrant interactions between neurons and glia have been studied in models of familial hemiplegic migraine (FHM), a rare monogenic form of migraine with aura. The present review focuses on those interactions, especially as seen in FHM type 1, a variant of the disease caused by a mutation in CACNA1A, which encodes the α1A subunit of the P/Q-type voltage-gated calcium channel.     

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%。