Pseudocapacitors store energy on/near the surface of electrode materials through redox reactions, whose capacitive activity thus depends on the electronic states of the surface and interface, and electronic conductivity of the electrode materials. Here, we report a simple strategy to prepare the hybrid nanostructure of NiCo₂O₄@rGO with the controllable composition by depositing graphene oxide (GO) on the NiCo₂O₄ scaffold combined with chemical reduction. By combining NiCo₂O₄ mesoporous nanosheets with reduced graphene oxide (rGO), the hybrid electrode shows significant improvement in electrical conductivity, as well as more electrochemically active sites and short transport path lengths for both electrons and ions. Moreover, resulting from the Schottky electric field at the interface of NiCo₂O₄ and rGO, NiCo₂O₄ exhibits superior electron collection efficiency, thus contributing to the redox reactions on/near the surface of NiCo₂O₄. Benefiting from the rational structural features and excellent electrical conductivity, the designed NiCo₂O₄@rGO hybrid electrode demonstrates high specific capacitance (3.6 F cm⁻² at a current density of 5 mA cm⁻²), good rate capability and superior cycling stability (90% of the initial capacitance can be retained after 2000 charge/discharge cycles). This work provides a new strategy for high-performance supercapacitor electrodes by combining capacitive (especially carbons) and faradaic (redox electroactive) materials.

The structural, electronic, and optical properties of a g-C₃N₄(001)/BiVO₄(010) nanocomposite have been investigated using first-principles calculations. The results indicate that g-C₃N₄(001) can stably adsorb onto the BiVO₄(010) surface, and it tends to form a regular wavy shape. The calculated band gap of the g-C₃N₄(001)/BiVO₄(010) nanocomposite is narrower compared with that of BiVO₄ or BiVO₄(010), primarily due to the introduction of N 2p states near the Fermi level. The g-C₃N₄(001)/BiVO₄(010) nanocomposite has a favorable type-II band alignment; thus, photoexcited electrons can be injected into the conduction band of g-C₃N₄(001) from the conduction band of BiVO₄(010). The proper interface charge distribution facilitates carrier separation in the g-C₃N₄(001)/BiVO₄(010) interface region. The electron injection and carrier separation can prevent the recombination of electron–hole pairs. The calculated absorption coefficients indicate an obvious redshift of the absorption edge, which is in good agreement with the experimental results. Our calculation results suggest that the g-C₃N₄(001)/BiVO₄(010) nanocomposite has significant advantages for visible-light photocatalysis.

An asymmetric supercapacitor offers opportunities to effectively utilize the full potential of the different potential windows of the two electrodes for a higher operating voltage, resulting in an enhanced specific capacitance and significantly improved energy without sacrificing the power delivery and cycle life. To achieve high energy and power densities, we have synthesized an all-solid-state asymmetric supercapacitor with a wider voltage range using Fe-doped Co₃O₄ and three-dimensional reduced graphene oxide (3DrGO) as the positive and negative electrodes, respectively. In contrast to undoped Co₃O₄, the increased density of states and modified charge spatial separation endow the Fe-doped Co₃O₄ electrode with greatly improved electrochemical capacitive performance, including high specific capacitance (1997 F g⁻¹ and 1757 F g⁻¹ at current densities of 1 and 20 A g⁻¹, respectively), excellent rate capability, and superior cycling stability. Remarkably, the optimized all-solid-state asymmetric supercapacitor can be cycled reversibly in a wide range of 0–1.8 V, thus delivering a high energy density (270.3 W h kg⁻¹), high power density (9.0 kW kg⁻¹ at 224.2 W h kg⁻¹), and excellent cycling stability (91.8% capacitance retention after 10 000 charge–discharge cycles at a constant current density of 10 A g⁻¹). The superior capacitive performance suggests that such an all-solid-state asymmetric supercapacitor shows great potential for developing energy storage systems with high levels of energy and power delivery.

This paper investigates the problem of energy-efficient trajectory optimization with wireless charging (ETWC) in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system. A UAV is dispatched to collect computation tasks from specific ground smart devices (GSDs) within its coverage while transmitting energy to the other GSDs. In addition, a high-altitude platform with a laser beam is deployed in the stratosphere to charge the UAV, so as to maintain its flight mission. The ETWC problem is characterized by multi-objective optimization, aiming to maximize both the energy efficiency of the UAV and the number of tasks collected via optimizing the UAV's flight trajectories. The conflict between the two objectives in the problem makes it quite challenging. Recently, some single-objective reinforcement learning (SORL) algorithms have been introduced to address the aforementioned problem. Nevertheless, these SORLs adopt linear scalarization to define the user utility, thus ignoring the conflict between objectives. Furthermore, in dynamic MEC scenarios, the relative importance assigned to each objective may vary over time, posing significant challenges for conventional SORLs. To solve the challenge, we first build a multi-objective Markov decision process that has a vectorial reward mechanism. There is a corresponding relationship between each component of the reward and one of the two objectives. Then, we propose a new trace-based experience replay scheme to modify sample efficiency and reduce replay buffer bias, resulting in a modified multi-objective reinforcement learning algorithm. The experiment results validate that the proposed algorithm can obtain better adaptability to dynamic preferences and a more favorable balance between objectives compared with several algorithms.