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Encapsulating nanomaterials in carbon is one of the main ways to increase the cathode stability, but it is difficult to simultaneously optimize the rate capacity and enhance durability derived from the insufficient ion transport channels and deficient ion adsorption sites that constipate the ion transport and pseudocapacitive reaction. Herein, we develop the ligand-confined growth strategy to encapsulate the nano-Na 3 V 2 (PO 4 ) 3 cathode material in various carbon channels (microporous, mesoporous, and macroporous) to discriminate the optimal carbon channels for synchronously improving rate capacity and holding the high-rate cycle stability. Benefiting from the unobstructed ion/charge transport channels and flexible maskant created by the interconnected mesoporous carbon channels, the prepared Na 3 V 2 (PO 4 ) 3 nanoparticles confined in mesoporous carbon channel (Mes-NVP/C) achieve a discharge-specific capacity of 70 mAh g -1 even at the ultrahigh rate of 100 C, higher than those of the Na 3 V 2 (PO 4 ) 3 nanoparticles confined in microporous and macroporous carbon channel (Micr-NVP/C and Macr-NVP/C), respectively. Significantly, the capacity retention rate of Mes-NVP/C after 5000 cycles at 20 C is as high as 90.48%, exceeding most of the reported work. These findings hold great promise for traditional cathode materials to synergistically realize fast charging ability and long cycle life.

Neuron is the basic unit of the biological neural system. The Hodgkin-Huxley (HH) model is one of the most realistic neuron models on the electrophysiological characteristic description of neuron. Hardware implementation of neuron could provide new research ideas to clinical treatment of spinal cord injury, bionics and artificial intelligence. Based on the HH model neuron and the DSP Builder technology, in the present study, a single HH model neuron hardware implementation was completed in Field Programmable Gate Array (FPGA). The neuron implemented in FPGA was stimulated by different types of current, the action potential response characteristics were analyzed, and the correlation coefficient between numerical simulation result and hardware implementation result were calculated. The results showed that neuronal action potential response of FPGA was highly consistent with numerical simulation result. This work lays the foundation for hardware implementation of neural network.