Experimental and Theoretical Mechanistic Study on the Thermal Decomposition of 3,3-diphenyl-4-(trichloromethyl)-5-nitropyrazoline

The present paper is a continuation of comprehensive study regarding to synthesis and properties of pyrazoles and their derivatives. In its framework an experimental and theoretical studies of thermal decomposition of the 3,3-diphenyl-4-(trichloromethyl)-5-nitropyrazoline were performed. It was found, that the decompositions of the mentioned pyrazoline system in the solution and at the melted state proceed via completely different molecular mechanisms. These mechanisms have been explained in the framework of the Molecular Electron Density Theory (MEDT) with the computational level of B3LYP/6-31G(d). A Bonding Evolution Theory (BET) examination of dehydrochlorination of the 3,3-diphenyl-4-(trichloromethyl)-5-nitropyrazoline permits elucidation of the molecular mechanism. It was found, that on the contrary for most known HCl extrusion processes in solution, this reaction is realised via single-step mechanism.     

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。     

On the output function in a Ginsburg’s machine

We give the form of the output function in Ginsburg’s machine in which the input and output dictionaries are abelian groups and the transition function is of a special form.