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A new electrochemical sensor material has been fabricated via the non‐covalent functionalization of reduced graphene oxide (rGO) and soluble tetramino zincphthalocyanines (ZnPc‐NH2). Immobilization of uricase onto the synthesized nanohybrids can evidently improve the electrocatalytic activity and selectivity. The obtained composite membrane possesses a great enhancement of electron transfer rate and excellent synergistic electrocatalytic effect toward uric acid (UA) oxidation under the working potential at 0.620 V vs. Ag/AgCl with a scan rate of 0.125 V/s. The effects of the experimental parameters on the electrochemical oxidation responses of UA were investigated and optimized in detail. Under the optimized conditions, the peak currents were proportional to the UA concentration in a range from 0.5 to 100 μmol/L with detection limit of 0.15 μmol/L. Moreover, the developed sensor was applied for UA determination in human urine samples with high accuracy and satisfactory recovery, which is envisioned to have promising applications in monitoring UA in clinical research.

Nanomaterials derived from Prussian blue (PB) and its analogues (PBAs) possess uniform active centers and large surface areas, which can be applied in the field of electrocatalysis. In this study, nickel‐iron‐cobalt trimetallic PBAs, that is, NiFeCo(4 : 1)PBA nanocubes, were grown in situ on nickel foam (NF), which was subsequently calcined in air to obtain a composite electrode, NiO/FeOX/Co3O4@NF, with a loose porous hollow nanocube morphology. This structure remarkably enlarged the contact area between the electrolyte and the catalyst. In addition, a synergistic effect among these metallic oxides expedited oxygen evolution reaction (OER) kinetics. The Tafel slope (71.25 mV dec−1) toward OER of the constructed electrode is fairly small, much lower than that of our prepared bimetallic oxides@NF electrodes. The enduring stability is more than 22 h. This work indicates that, through morphology and composition control, the electrocatalytic performance of the designed electrode can be effectively improved.

The traditional neural network-based education platform has the problem of slow access to resources, which affects teaching efficiency. To this end, artificial intelligence technology is introduced for the design of distance multimedia sports teaching platforms. Through system hardware and software design, enhance the integration capability of the teaching platform, improve the controllability of the teaching platform, help the physical education platform to obtain more educational resources, accelerate the processing and integration analysis of educational information, and better assist teachers in developing physical education jobs. In order to verify the effectiveness of the method in this paper, the method in this paper is compared with the teaching platform based on neural network,the experimental results show that the platform can obtain resources quickly and has broad application prospects.

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