Na4MnV(PO4)3 (NMVP) is a promising cathode material for sodium-ion batteries (SIBs) because of its extraordinary three-dimensional structure that provides plenty of channels for sodium-ion migration. However, the unsatisfied electrical conductivity of NMVP limits its utilization in SIBs. Herein, Zn-doped NMVP with a uniform carbonized polyacrylonitrile (PAN) coating layer, named NMZVP@cPAN, was synthesized via a sol–gel method, and carbonized PAN was uniformly distributed on the surface of NMVP. Therefore, the NMZVP@cPAN cathodes exhibited an outstanding discharge capacity of 70.6 mA·h·g–1 at 30 C and remarkable cycling stability with an admirable retention of 89.64% after 1000 cycles at 5 C. Rietveld refinement and ex situ X-ray diffraction analyses were performed to determine the change in the crystal structure. Density functional theory calculations were performed to determine the effects of Zn doping on the density of states and the migration energy barriers. Finally, the NMZVP@cPAN cathodes were successfully modified and could be used in SIBs as NMVP cathodes.

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To improve the error compensation precision of sensors, a method of the error compensation of sensors based on the neural network algorithm is proposed. The convergence of the algorithm is researched. The theory gist to select learning rate is provided by the convergence theorem. To validate the validity of the algorithm, the simulation example of the error compensation of sensor was given. The result shows that the approach of the error compensation of sensors using the neural network algorithm has a very high accuracy. Thus, the method proposed is effective.

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