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...     

Decorating surface charge of graphite nanoplate using an electrostatic coupling agent for 3-dimensional polymer nanocomposite

Here, we report a facile approach to electrostatically couple the surface charges of graphite nanoplate (GNP) fillers and poly(methyl methacrylate) (PMMA) polymer particles using ethylene maleic anhydride (EMA) copolymer as an electrostatic coupling agent. Our strategy involved switching the intrinsic repulsive electrostatic interactions between the directly exfoliated GNPs fillers and the PMMA particles to attractive electrostatic surface interactions for preparing core(PMMA)-shell (GNP) precursor in order to optimizing 3-dimensionally dispersed polymer nanocomposite. As a result, the electrical conductivity of the composites dramatically increased by a factor of 16.7 in the EMA-coupled GNP/PMMA composites compared with that of the EMA-free GNP/PMMA composites. In addition, the percolation threshold was also notably reduced from 0.32 to 0.159 vol% after electrostatic coupling of the GNPs fillers and PMMA particles. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48390.     

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.