Defect engineering is an effective way to modulate the electric states and provide active sites for electrocatalytic reactions. However, most studied oxygen vacancies are unstable and susceptible under the oxygen circumstance. Here, we fabricated cobalt-defected Co 3−x O 4 in situ for an efficient oxygen evolution reaction (OER). XAFS and PALS characterizations show that the crystals have abundant Co vacancies and a distorted structure. DFT calculations indicate that the metal defects lead to obvious electronic delocalization, which enhances the carrier transport to participate in water-splitting reactions along the defective conducting channels and the water adsorption/activation on the catalyst surface. Therefore, cobalt-defected Co 3−x O 4 shows remarkably high OER activity by delivering a much lower overpotential of 268 mV@ 10 mA cm −2 (with a small Tafel slope of 38.2 mV/dec) for OER in KOH electrolyte, in comparison with normal Co 3 O 4 (376 mV), IrO 2 (340 mV), and RuO 2 (276 mV). This work opens up a promising approach to construct electronic delocalization structures in metal oxides for high-performance electrochemical catalysts.

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Based on analysis on properties of quantum linear superposition, to overcome the complexity of existing quantum associative memory which was proposed by Ventura, a new storage method for multiply patterns is proposed in this paper by constructing the quantum array with the binary decision diagrams. Also, the adoption of the nonlinear search algorithm increases the pattern recalling speed of this model which has multiply patterns to O(log 2 2 n−t ) = O(n − t) time complexity, where n is the number of quantum bit and t is the quantum information of the t quantum bit. Results of case analysis show that the associative neural network model proposed in this paper based on quantum learning is much better and optimized than other researchers' counterparts both in terms of avoiding the additional qubits or extraordinary initial operators, storing pattern and improving the recalling speed.

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