Although Si nanowires (NWs) arrays are superior candidates for visible light photocatalysis, reports about the photodegradation activity of various crystal-orientated Si NWs are still insufficient. Here, light-doped hydrogen-terminated Si NWs arrays with different crystal orientations were prepared via a metal-assisted chemical etching method (MACE), which simply modulated the concentration of the oxidizer, H 2 O 2 . Their dye photodegradation activities were systematically and comprehensively investigated. When compared with Si NWs arrays with crystal orientations of (110) and (111), Si NWs arrays with (100) crystal orientation exhibit a superior photodegradation activity and stability due to the anisotropy of optical and physical properties. The n-type Si NWs arrays exhibit better photodegradation activity than the p-type Si NWs arrays of the same crystal orientation and similar length. The results provide a further understanding of the synthesis of Si NWs arrays with various orientations, and the relationships between photodegradation activity/stability and crystal orientations.

This study synthesized ultra-fine nanometer-scaled ruthenium oxide (RuO2) quantum dots (QDs) on reduced graphene oxide (rGO) surface by a facile and rapid microwave-assisted hydrothermal approach. Benefiting from the synergistic effect of RuO2 and rGO, RuO2/rGO nanocomposite electrodes showed ultra-high capacitive performance. The impact of different RuO2 loadings in RuO2/rGO nanocomposite on their electrochemical performance was investigated by various characterizations. The composite RG-2 with 38 wt.% RuO2 loadings exhibited a specific capacitance of 1120 F g−1 at 1 A g−1. In addition, it has an excellent capacity retention rate of 84 % from 1A g-1 to 10 A g−1, and excellent cycling stability of 89% retention after 10,000 cycles, indicating fast ion-involved redox reactions on the nanocomposite surfaces. These results illustrate that RuO2/rGO composites prepared by this facile process can be an ideal candidate electrode for high-performance supercapacitors.

Artificial intelligence has made people’s demands for computer computing efficiency increasingly high. The traditional hardware circuit simulation method for neural morphology computation has problems of unstable performance and excessive power consumption. This research will use non-volatile flash memory cells that are easy to read and write to build a convolutional neural network structure to improve the performance of neural morphological computing. In the experiment, floating-gate transistors were used to simulate neural network synapses to design core cross-array circuits. A voltage subtractor, voltage follower and ReLU activation function are designed based on a differential amplifier. An Iris dataset was introduced in this experiment to conduct simulation experiments on the research circuit. The IMC circuit designed for this experiment has high performance, with an accuracy rate of 96.2% and a recall rate of 60.2%. The overall current power consumption of the hardware circuit is small, and the current power consumption of the subtractor circuit and ReLU circuit does not exceed 100 µA, while the power consumption of the negative feedback circuit is about 440 mA. The accuracy of analog circuits under the IMC architecture is above 93%, the energy consumption is only about 360 nJ, and the recognition rate is about 12 μs. Compared with the classic von Neumann architecture, it reduces the circuit recognition rate and power consumption while meeting accuracy requirements.

As a typical binary transition metal oxide, ZnFe2O4 has attracted considerable attention for supercapacitor electrodes due to its high theoretical specific capacitance. However, the reported synthesis processes of ZnFe2O4 are complicated and ZnFe2O4 nanoparticles are easily agglomerated, leading to poor cycle life and unfavorable capacity. Herein, a facile microwave hydrothermal process was used to prepare ZnFe2O4/reduced graphene oxide (rGO) nanocomposites in this work. The influence of rGO content on the morphology, structure, and electrochemical performance of ZnFe2O4/rGO nanocomposites was systematically investigated. Due to the uniform distribution of ZnFe2O4 nanoparticles on the rGO surface and the high specific surface area and rich pore structures, the as-prepared ZnFe2O4/rGO electrode with 44.3 wt.% rGO content exhibits a high specific capacitance of 628 F g−1 and long cycle life of 89% retention over 2500 cycles at 1 A g−1. This work provides a new process for synthesizing binary transition metal oxide and developing a new strategy for realizing high-performance composites for supercapacitor electrodes.