Liutao Yu

A facile and mild approach was used for the controlled synthesis of 3D porous gear-like CuO on a Cu substrate (PGC) based on annealing gear-like Cu(OH)2 (GC) at 200 °C in air. There are 3–10 edges that build up a gear-like structure and a huge number of holes formed on each edge. As an integrated nanostructure, the binder-free PGC can be used as a supercapacitors electrode (SC) directly. Due to its 3D porous structure and the highly conductive Cu substrate as a current collector, the integrated electrode exhibited excellent electrochemical properties. These were demonstrated by excellent specific capacitance as high as 348 F g−1 at a discharge current density of 1 A g−1, which corresponds to the energy density of 43.5 Wh kg−1. The electrochemical tests also showed that the as-synthesized PGC exhibited excellent cycling stability.

A facile and low-cost approach has been developed to fabricate porous CuO nanobelts directly grown on a Cu substrate. The as-prepared CuO samples can be directly used as integrated electrodes for lithium-ion batteries and pseudo-supercapacitors without the addition of other ancillary materials such as carbon black or a binder to enhance electrode conductivity and cycling stability. The unique nanostructural features endow them with excellent electrochemical performance as demonstrated by high capacities of 640 mA h g(-1) after 100 cycles at 0.2 C rate and an excellent specific capacitance of 340 F g(-1), which corresponds to the energy density of 45 W h kg(-1). The cyclability of the electrode demonstrates only a 10-15% loss in capacitance over 5000 cycles.

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends.

Flexible supercapacitors have recently attracted increasing attention as they show unique promising advantages, such as flexibility and shape diversity, and they are light-weight and so on. Herein, we designed a series of 3D porous spinous iron oxide materials synthesized on a thin iron plate through a facile method under mild conditions. The unique nanostructural features endow them with excellent electrochemical performance. The electrochemical properties of the integrated electrodes as active electrode materials for supercapacitors have been investigated using different electrochemical techniques including cyclic voltammetry, and galvanostatic charge-discharge in Na2SO4 and LiPF6/EC : DEC electrolyte solutions. These integrated electrodes showed high specific capacitance (as high as 524.6 F g(-1) at the current density of 1 A g(-1)) in 1.0 M Na2SO4 (see Table S1). Moreover, the integrated electrodes also show high power densities and high energy densities in a LiPF6/EC : DEC electrolyte solution; for example, the energy densities were 319.3, 252.5, 152.1, 74.13 and 38.6 W h kg(-1) at different power densities of 8.81, 21.59, 56.65, 92.09 and 152.64 kW kg(-1), respectively. Additionally, the flexible superior electrode exhibited excellent stability with capacitance retention of 92.9% after 5000 cycles. Therefore, such flexible integrated devices might be used in smart and portable electronics.