Study of CuSb bimetallic flow-through gas diffusion electrodes for efficient electrochemical CO2 reduction to CO.

Electrochemical CO2 reduction (eCO2R) to industrially important feedstock has received great attention, but it faces different challenges. Among them, the poor CO2 mass transport due to low intrinsic CO2 solubility significantly limits the rate of reduction reactions, leading to lower catalytic performance; thereby, commercially relevant current densities can't be achieved. Moreover, the poor activity and selectivity of high-cost monometallic catalysts, including Cu, Zn, Ag, and Au, undermine the efficiency of eCO2R. Flow-through gas diffusion electrodes (FTGDE), a newly developed class of GDEs, can potentially solve the issue of poor CO2 mass transport because they directly feed the CO2 to the catalyst layer. In addition, abundant surface area, porous structure, and improved triple-phase interface make them an excellent candidate for extremely high rate eCO2R. Antimony, a low-cost and abundant metalloid, can be effectively tuned with Cu to produce useful products such as CO, formate, and C2H4 through eCO2R. Herein, a series of porous binary CuSb FTGDEs with different Sb compositions are fabricated for the electrocatalytic reduction of CO2 to CO. The results show that the catalytic performance of CuSb FTGDEs improved with increasing Sb content up to a certain threshold, beyond which it started to decrease. The CuSb FTGDE with 5.4 g of antimony demonstrated higher current density (206.4 mA/cm2) and faradaic efficiency (72.82 %) at relatively lower overpotentials. Compared to gas diffusion configuration, the poor catalytic activity and selectivity achieved by CuSb FTGDE in non-gas diffusion configuration signifies the importance of improved local CO2 concentration and improved triple-phase interface formation in GDE configuration. The several hours stable operation of CuSb FTGDEs during eCO2R demonstrates its potential for efficient electrocatalytic conversion applications.

Copper ferrite and cobalt oxide two-layer coated macroporous SiC substrate for efficient CO2-splitting and thermochemical energy conversion.

CO2-splitting and thermochemical energy conversion effectiveness are still challenged by the selectivity of metal/metal oxide-based redox materials and associated chemical reaction constraints. This study proposed an interface/substrate engineering approach for improving CO2-splitting and thermochemical energy conversion through CuFe2O4 and Co3O4 two-layer coating SiC. The newly prepared material reactive surface area available for gas-solid reactions is characterized by micro-pores CuFe2O4 alloy easing inter-layer oxygen micro mass exchanges across a highly stable SiC-Co3O4 layer. Through a thermogravimetry analysis, oxidation of the thermally activated oxygen carriers exhibited remarkably CO2-splitting capacities with a total CO yield of 1919.33 µmol/g at 1300 °C. The further analysis of the material CO2-splitting performance at the reactor scale resulted in 919.04 mL (788.94 µmol/g) of CO yield with an instantaneous CO production rate of 22.52 mL/min and chemical energy density of 223.37 kJ/kg at 1000 °C isothermal redox cycles. The reaction kinetic behavior indicated activation energy of 30.65 kJ/mol, which suggested faster CO2 activation and oxidation kinetic on SiC-Co3O4-CuFe2O4 O-deficit surfaces. The underlying mechanism for the remarkable thermochemical performances was analyzed by combining experiment and density functional theory (DFT) calculations. The significance of exploiting the synergy between CuFe2O4 and Co3O4 layers and stoichiometric reaction characteristics provided fundamental insights useful for the theoretical modeling and practical application of the solar thermochemical process.

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks.

Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.