Fluorescent Properties of Polymer Systems with Negative Photochromism Based on Complexes of Spiropyran with Metal Ions

Russian Journal of Applied Chemistry - A spectral-kinetic investigation was carried out to study the fluorescent properties of complexes of molecules of photochromic nitro-substituted spiropyran...     

Investigation of the Effect of Artificial Internal Defects on the Tensile Behavior of Laser Powder Bed Fusion 17–4 Stainless Steel Samples: Simultaneous Tensile Testing and X-Ray Computed Tomography

Experimental Mechanics - Insufficient data are available to fully understand the effects of metal additive manufacturing (AM) defects for widespread adoption of the emerging technology....     

Astrocyte and ions metabolism during epileptogenesis:A review for modeling studies

As a large group of cells in a central nervous system, astrocytes have a great influence on ion and energy metabolism in a nervous system. Disorders of neuronal ion and energy metabolism caused by impaired astrocytes play a key role in the pathogenesis of epilepsy. This paper reviews the existing computational models of epileptogenesis resulting from impaired astrocytes and presents several open perspectives with regard to ion and energy metabolism-induced epileptogenesis in a neuron-astrocyte-capillary coupled model.     

A model for crack formation during active solid pyrolysis of a char-forming solid: crack patterns; surface area generation; volatile mass efflux

A model is developed for the formation and propagation of cracks in a material sample that is heated at its top surface, pyrolyses, and then thermally degrades to form char. In this work the sample is heated uniformly over its entire top surface by a hypothetical flame (a heat source). The pyrolysis mechanism is described by a one-step overall reaction that is dependent nonlinearly on the temperature (Arrhenius form). Stresses develop in response to the thermal degradation of the material by means of a shrinkage strain caused by local mass loss during pyrolysis. When the principal stress exceeds a prescribed threshold value, the material forms a local crack. Cracks are found to generally originate at the surface in response to heating, but occasionally they form in the bulk, away from ever-changing material boundaries. The resulting cracks evolve and form patterns whose characteristics are described. Quantities examined in detail are: the crack spacing in the pyrolysis zone; the crack length evolution; the formation and nature of crack loops which are defined as individual cracks that have joined to form loops that are disconnected from the remaining material; the formation of enhanced pyrolysis area; and the impact of all of the former quantities on mass flux. It is determined that the mass flux from the sample can be greatly enhanced over its nominal (non-cracking) counterpart. The mass efflux profile qualitatively resembles those observed in Cone Calorimeter tests.     

Methyl orange degradation enhanced by hydrogen spillover onto platinum nanocatalyst surface

Following a thermal reduction method, platinum nanoparticles were synthesized and stabilized by polyvinylpyrrolidone. The colloidal platinum nanoparticles were stable for more than 3 months. The micrograph analysis unveiled that the colloidal platinum nanoparticles were well dispersed with an average size of 2.53 nm. The sol–gel‐based inverse micelle strategy was applied to synthesize mesoporous iron oxide material. The colloidal platinum nanoparticles were deposited on mesoporous iron oxide through the capillary inclusion method. The small‐angle X‐ray scattering analysis indicated that the dimension of platinum nanoparticles deposited on mesoporous iron oxide (Pt‐Fe₂O₃) was 2.64 nm. X‐ray photoelectron spectroscopy (XPS) data showed that the binding energy on Pt‐Fe₂O₃ surface decreased owing to mesoporous support–nanoparticle interaction. Both colloidal and deposited platinum nanocatalysts improved the degradation of methyl orange under reduction conditions. The activation energy on the deposited platinum nanocatalyst interface (2.66 kJ mol^?1) was significantly lowered compared with the one on the colloidal platinum nanocatalyst interface (40.63 ± 0.53 kJ mol^?1).     

基于深度学习的ELM实时识别研究

基于深度学习的方法，在HL-2A装置上开发出了一套边缘局域模(ELM)实时识别算法。算法使用5200次放电数据(约24.19万数据切片)进行学习，得到一个深度为22层的卷积神经网络。为衡量算法的识别能力，识别了HL-2A装置自2009年实现稳定ELMy H模放电以来所有历史数据(约26000次放电数据)，共识别出1665次H模放电，其中误识别35次，误报率为2.10%。在实际的1634次H模放电中，漏识别4次，漏识别率为0.24%。该误报率和漏报率可以满足ELM实时识别的精度要求。识别算法在实时控制环境下，对单个时间点的平均计算时间为0.46ms，可以满足实时控制的计算速度要求。