铷蒸气激光器3D理论模型的建立及阈值特性模拟

基于三能级速率方程理论,结合惠更斯-菲涅尔衍射积分公式,利用分步光束传播方法建立了半导体泵浦碱金属蒸气激光器(DPAL)端面泵浦3D理论计算模型,用于模拟研究铷蒸气激光器的泵浦阈值特性。该模型将光束传播、光与物质相互作用独立考虑,利用迭代算法求解。考虑激光器腔内模式与泵浦光模式匹配对阈值特性的影响,模拟了单端泵浦铷蒸气激光器内部的三维动力学过程。在具体算例中研究了模式匹配最佳时,在阈值工作状态下铷蒸气室中的三维粒子数分布,以及沿不同轴线的增益分布。仿真了在最佳模式匹配位置附近,泵浦光模式变化对阈值特性的影响。根据蒸气池中粒子数分布和光场分布具体分析了模式匹配影响阈值特性的机理。同时还模拟了不同长度蒸气池对阈值特性的影响。结果表明,腔参数与泵浦光模式之间存在一定的关系,对于特定的泵浦光模式,存在恰当的腔参数,使得阈值达到最小。     

Continuum model for low-frequency phonons of boron nitride nanotubes

A continuum model is employed to calculate the low-frequency phonons of boron nitride nanotubes. We find an excellent agreement of the optically active modes calculated within this approach and those from more elaborate calculations within an energy and wavelength window that can be established beforehand, from the choice of the bulk input parameters. We verify that this model describes correctly the dependence of radial breathing mode with the radius, the existence of parabolic modes at small wavevectors, and other general characteristics of the dispersion relations of these systems.     

Stage-I fatigue crack studies in order to validate the dislocation-free zone model of fracture for bulk materials

Stage-I fatigue cracks are commonly described by the model of Bilby, Cottrell and Swinden (BCS model). However, since several experimental investigations have shown a dislocation-free zone (DFZ) in front of crack-tips, it is necessary to validate the new DFZ model and to examine the deviations to the BCS model. Therefore, the dislocation density distribution is derived from height profiles of slip lines in front of stage-I fatigue cracks in CMSX4® single crystals measured by contact-mode atomic force microscopy. This is possible, because the cracks are initiated at notches milled by focused ion beam technique directly on slip planes with a high Schmid factor. Consequently, the directions of the Burgers vectors are well known; it is possible to calculate the dislocation density distributions from the height profiles. The measured distributions are compared to the calculated distribution function of the DFZ model proposed by Chang et al. The additionally measured microscopic friction stress of the dislocations is then used to calculate the influence of grain boundaries on the dislocation density distribution in front of stage-I cracks. The calculation is done by the extended DFZ model of Shiue et al. and compared with the measured distribution function in polycrystalline specimens. Finally, the crack-tip sliding displacement as a measure for the crack propagation rate is compared for the DFZ model and the BCS model with the experimentally revealed values. The important result: the often used BCS model does not reflect the experimental measurements. On the contrary, the DFZ model reflects the measurements at stage-I cracks qualitatively and quantitatively.     

Recent Clinical and Preclinical Studies of Hydroxychloroquine on RNA Viruses and Chronic Diseases: A Systematic Review

The rapid spread of the new Coronavirus Disease 2019 (COVID-19) has actually become the newest challenge for the healthcare system since, to date, there is not an effective treatment. Among all drugs tested, Hydroxychloroquine (HCQ) has attracted significant attention. This systematic review aims to analyze preclinical and clinical studies on HCQ potential use in viral infection and chronic diseases. A systematic search of Scopus and PubMed databases was performed to identify clinical and preclinical studies on this argument; 2463 papers were identified and 133 studies were included. Regarding HCQ activity against COVID-19, it was noticed that despite the first data were promising, the latest outcomes highlighted the ineffectiveness of HCQ in the treatment of viral infection. Several trials have seen that HCQ administration did not improve severe illness and did not prevent the infection outbreak after virus exposure. By contrast, HCQ arises as a first-line treatment in managing autoimmune diseases such as rheumatoid arthritis, lupus erythematosus, and Sjögren syndrome. It also improves glucose and lipid homeostasis and reveals significant antibacterial activity.     

Poly(propylene carbonate)/poly(3‐hydroxybutyrate)‐based bionanocomposites reinforced with cellulose nanocrystal for potential application as a packaging material

Poly(propylene carbonate) (PPC) is an aliphatic polycarbonate synthesized from carbon dioxide and propylene oxide. Poly(3‐hydroxybutyrate) (PHB) is a type of thermoplastic polyester produced by biological fermentation. The blending of PHB with PPC can effectively enhance the mechanical properties and barrier properties of PPC. Bionanocomposites of PPC/PHB enhanced by cellulose nanocrystal (CNC) are prepared via a two‐step process using polyethylene glycol as a carrier. Results show that the oxygen barrier properties of the composites increased with the increase of the CNC content. When the CNC content is 1 wt%, the oxygen barrier performance increases nearly 18 times. The assumed model can predict the barrier performance of composites with the combined influence of morphology and CNC distribution. This will make PPC/PHB/CNC nanocomposites a very promising degradable material for food packaging application.     

A Pressure‐Induced Inverse Order–Disorder Transition in Double Perovskites

Given the consensus that pressure improves cation ordering in most of known materials, a discovery of pressure‐induced disordering could require recognition of an order–disorder transition in solid‐state physics/chemistry and geophysics. Double perovskites Y₂CoIrO₆ and Y₂CoRuO₆ polymorphs synthesized at 0, 6, and 15 GPa show B‐site ordering, partial ordering, and disordering, respectively, accompanied by lattice compression and crystal structure alteration from monoclinic to orthorhombic symmetry. Correspondingly, the long‐range ferrimagnetic ordering in the B‐site ordered samples are gradually overwhelmed by B‐site disorder. Theoretical calculations suggest that unusual unit‐cell compressions under external pressures unexpectedly stabilize the disordered phases of Y₂CoIrO₆ and Y₂CoRuO₆.     

Crystallization Mechanism of 9,9-Diphenyl-dibenzosilole from Solids

Organic semiconductor (OSC) crystals have great potential to be applied in many fields, as they can be flexibly designed according to the demands and show an outstanding device performance. However, OSCs with the capacity of solid-state crystallization (SSC) are developing too slowly to meet demands in productions and applications, due to their difficulties in molecular design and synthesis, unclear mechanism and high dependence on experimental conditions. In this work, in order to solve the problems, we synthesized an organic semiconductor capable of SSC at room temperature by adjusting the relationship between conjugated groups and functional groups. The thermodynamic and kinetic properties have been studied to discover the model of film SSC. Moreover, it can be purposefully controlled to prepare the high-quality crystals, and their corresponding organic electronic devices were further fabricated and discussed.     

Maximization of Spatial Charge Density: An Approach to Ultrahigh Energy Density of Capacitive Charge Storage

Capacitive energy storage has advantages of high power density, long lifespan, and good safety, but is restricted by low energy density. Inspired by the charge storage mechanism of batteries, a spatial charge density (SCD) maximization strategy is developed to compensate this shortage by densely and neatly packing ionic charges in capacitive materials. A record high SCD (ca. 550 C cm^?3) was achieved by balancing the valance and size of charge‐carrier ions and matching the ion sizes with the pore structure of electrode materials, nearly five times higher than those of conventional ones (ca. 120 C cm^?3). The maximization of SCD was confirmed by Monte Carlo calculations, molecular dynamics simulations, and in situ electrochemical Raman spectroscopy. A full‐cell supercapacitor was further constructed; it delivers an ultrahigh energy density of 165 Wh L^?1 at a power density of 150 WL^?1 and retains 120 Wh L^?1 even at 36 kW L^?1, opening a pathway towards high‐energy‐density capacitive energy storage.     

Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning

In patients with depression, the use of 5-HT reuptake inhibitors can improve the condition. Machine learning methods can be used in ligand-based activity prediction processes. In order to predict SERT inhibitors, the SERT inhibitor data from the ChEMBL database was screened and pre-processed. Then 4 machine learning methods (LR, SVM, RF, and KNN) and 4 molecular fingerprints (CDK, Graph, MACCS, and PubChem) were used to build 16 prediction models. The top 5 models of accuracy (Q) in the cross-validation of training set were used to build three different ensemble learning models. In the test1 set, the VOT_CLF3 model had the largest SP (0.871), Q (0.869), AUC (0.919), and MCC (0.728). In the unbalanced test2 set, VOT_CLF3 had the largest SE (0.857), SP (0.867), Q (0.865) and MCC (0.639). VOT_CLF3 was recommended for the virtual screening process of SERT inhibitors. In addition, 12 molecular structural alerts that frequently appear in SERT inhibitors were found (P < 0.05), which provided important reference value for the design work of SERT inhibitors.