PCB废水总氮处理达标的工艺探讨

对PCB废水总氮的处理,从源头削减、分质收集、分质预处理到末端处理提出了多种方法,着重分析了MAP+管式微滤膜处理氨氮废水、多级组合式RO处理硝态氮废水和A2O+MBR处理综合废水,提出PCB废水总氮达标的组合工艺,项目建成后达到预期效果。     

Microwave dielectric properties of SrLa[Ga1−x(Mg0.5Ti0.5)x]O4 and SrLa[Ga1−x(Zn0.5Ti0.5)x]O4 (x = 0.2-0.8) ceramics

SrLa[Ga_1−x(R_0.5Ti_0.5)_x]O₄ (R = Mg, Zn) ceramics were prepared by a standard solid state sintering method. The single-phase ceramics with K₂NiF₄-type layered perovskite structure and I4/mmm space group were obtained, indicating that SrLa(R_0.5Ti_0.5) and SrLaGaO₄ can form the unlimited solid solutions. With increasing x for R = Mg and Zn, ε_r increases monotonously, the Qf value first increases and then decreases, while τ_f increases from a negative to a positive value. The optimized microwave dielectric properties were obtained as following: ε_r = 23.3, Qf = 89 400 GHz, τ_f = −0.8 ppm/°C for SrLa[Ga_0.6(Mg_0.5Ti_0.5)_0.4]O₄ and ε_r = 23.3, Qf = 76 200 GHz, τ_f = 0.2 ppm/°C for SrLa[Ga_0.7(Zn_0.5Ti_0.5)_0.3]O₄, indicating that the present solid solution ceramics are the promising candidates as microwave resonator materials for the telecommunication applications.     

Electrocatalytic Reduction of CO2 to Ethylene by Molecular Cu‐Complex Immobilized on Graphitized Mesoporous Carbon

The electrochemical reduction of carbon dioxide (CO₂) to hydrocarbons is a challenging task because of the issues in controlling the efficiency and selectivity of the products. Among the various transition metals, copper has attracted attention as it yields more reduced and C2 products even while using mononuclear copper center as catalysts. In addition, it is found that reversible formation of copper nanoparticle acts as the real catalytically active site for the conversion of CO₂ to reduced products. Here, it is demonstrated that the dinuclear molecular copper complex immobilized over graphitized mesoporous carbon can act as catalysts for the conversion of CO₂ to hydrocarbons (methane and ethylene) up to 60%. Interestingly, high selectivity toward C2 product (40% faradaic efficiency) is achieved by a molecular complex based hybrid material from CO₂ in 0.1 m KCl. In addition, the role of local pH, porous structure, and carbon support in limiting the mass transport to achieve the highly reduced products is demonstrated. Although the spectroscopic analysis of the catalysts exhibits molecular nature of the complex after 2 h bulk electrolysis, morphological study reveals that the newly generated copper cluster is the real active site during the catalytic reactions.     

Approximate cutting pattern optimization of frame-supported and pneumatic membrane structures

International Journal of Mechanics and Materials in Design - A simple iterative method is presented for cutting pattern optimization of frame-supported and pneumatic membrane structures for...     

Within-class multimodal classification

Multimedia Tools and Applications - In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is...     

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the ‘understanding the brain by creating the brain’ approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain–machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.