Formation of Chlorotriophenoxy Radicals from Complete Series Reactions of Chlorotriophenols with H and OH Radicals

The chlorothiophenoxy radicals (CTPRs) are key intermediate species in the formation of polychlorinated dibenzothiophenes/thianthrenes (PCDT/TAs). In this work, the formation of CTPRs from the complete series reactions of 19 chlorothiophenol (CTP) congeners with H and OH radicals were investigated theoretically by using the density functional theory (DFT) method. The profiles of the potential energy surface were constructed at the MPWB1K/6-311+G(3df,2p)//MPWB1K/6-31+G(d,p) level. The rate constants were evaluated by the canonical variational transition-state (CVT) theory with the small curvature tunneling (SCT) contribution at 600–1200 K. The present study indicates that the structural parameters, thermal data, and rate constants as well as the formation potential of CTPRs from CTPs are strongly dominated by the chlorine substitution at the ortho-position of CTPs. Comparison with the study of formation of chlorophenoxy radicals (CPRs) from chlorophenols (CPs) clearly shows that the thiophenoxyl-hydrogen abstraction from CTPs by H is more efficient than the phenoxyl-hydrogen abstraction from CPs by H, whereas the thiophenoxyl-hydrogen abstraction from CTPs by OH is less impactful than the phenoxyl-hydrogen abstraction from CPs by OH. Reactions of CTPs with H can occur more readily than that of CTPs with OH, which is opposite to the reactivity comparison of CPs with H and OH.     

基于个体协同触发强化学习的多机器人行为决策方法* .txt

摘要：为了提高多机器人行为最优决策控制中强化学习的效率和收敛速度，研究了多机器人的分布式马尔科夫建模与控制策略。根据机器人有限感知能力设计了个体 协同感知触发函数，机器人个体从环境观测结果计算个体 协同触发响应概率，定义一次触发过程后开始计算联合策略，减少机器人间通讯量和计算资源。引入双学习率改进Q学习算法，并将该算法应用于机器人行为决策。仿真实验结果表明，当机器人群组数量在20左右时，本文算法的协同效率较高，单位时步比为1085 0。同时距离调节参数η对机器人协同搜索效率有影响，当η=0008时，所需的移动时步比和平均移动距离都能达到最小值。通过双学习率的引入，该算法较基于环境模型的强化学习算法具有更高的学习效率和适用性，平均性能提升35%，对于提高多机器人自主协同能力具有较高的理论意义及应用价值。 .txt     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.     

太赫兹超表面计算全息

全息术是一种三维成像技术,它已经被应用于多种实际场景。随着计算机科学与技术的迅猛发展,计算全息由于其方便和灵活的特性,已经成为一种广泛应用的全息成像方法。本文回顾了我们近期基于超表面的太赫兹计算全息研究进展。其中,作为全息板的超表面展示出了超越传统光学器件的独特性能。首先,利用超表面实现了对于全息板每个像素的相位振幅同时且独立的调控,进而实现了高质量全息成像。这种新的电磁波操控能力也带来了新的全息成像效果,如利用介质超表面实现了全息像沿传播方向上的连续变化。其次,对超表面在不同偏振态下的响应进行设计,分别实现了线偏振态与频率复用、圆偏振态复用、以及基于表面波的偏振复用超表面全息术。此外,本文提出了依赖于温度变化而主动可控的超表面全息术,为今后计算全息术的设计与实现提供了新的方案,也推动了超表面在实际应用方面的发展。     

Synthesis of Cellulose-Based Macromolecular Coupling Agent and its Utilization in Poly (butylene succinate)/Wood Flour Composites

A long fatty side chain was introduced into the macromolecule of hydroxyethyl cellulose (HEC) via esterification reaction. The hydrophobicity of hydroxyethyl cellulose lauric acid ester (HECLAE) was enhanced in comparison with HEC. The obtained HECLAE was used as macromolecular coupling agent in poly (butylene succinate)/wood flour composites and exhibited a positive influence on improving the mechanical performance of composites. Besides, HECLAE plays a role as a hydrophobic agent in composites. A significant increase in storage modulus (E’) was observed upon the incorporation of treated wood flour. SEM images showed that the dispersion of treated wood flour in PBS matrix was improved.