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Qiang Liu Xinyu Mao Xiaoying Li Penghui Chen Xin Liu Ziyu Liu Danyang Zhu Haohong Chen Tengfei Xie Jiang Li 《Journal of the American Ceramic Society》2021,104(10):4927-4931
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. 相似文献
83.
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。 相似文献
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Dr. Maria L. Di Paolo Dr. Michael S. Christodoulou Dr. Alessandra M. Calogero Dr. Luca Pinzi Prof. Giulio Rastelli Prof. Daniele Passarella Prof. Graziella Cappelletti Prof. Lisa Dalla Via 《ChemMedChem》2019,14(18):1641-1652
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 (Ki) values in the sub-micromolar range and a good selectivity index (Ki MAO-A/Ki 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. 相似文献
87.
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. 相似文献
88.
The aim of this study was to investigate the structure and corrosion resistance of amorphous, amorphous‐crystalline, and crystalline Mg67Zn29Ca4 alloy for biodegradable applications. This paper presents a preparation method and results of the structural characterization and corrosion resistance analysis of the material. Samples were prepared in the form of 3 mm diameter rods. The structure of the alloy was examined with the use of X‐ray diffractometry and scanning electron microscopy. The thermal properties of the samples were examined with differential scanning calorimetry (DSC). Results of DSC analysis were used to determine heat treatment temperatures, allowing to obtain different fractures of crystalline phase in the material. Corrosion resistance of heat‐treated samples was investigated by immersion tests and electrochemical measurements performed in the simulated body fluid. The X‐ray diffraction results confirmed that the prepared Mg67Zn29Ca4 alloy's structure is fully amorphous. After heat treatment, samples with different fractions of amorphous phase in the structure were obtained. Immersion tests of the samples showed that the structure significantly influenced corrosion resistance in examined materials. It should be pointed out, that certain amounts of crystalline phase in amorphous matrix can greatly improve the corrosion resistance of Mg67Zn29Ca4 alloy. 相似文献
89.
Electrochemical impedance spectroscopy (EIS), anodic polarization and scanning electron microscopy techniques were used to investigate the damage mechanism in the transpassive potential region of AISI ... 相似文献