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Development of fuel electrodes for reversible solid oxide cells (RSOCs) is important for achieving good performance and stability under both solid oxide fuel cell and electrolysis cell operation modes. In this work, CeO2-decorated La0.43Ca0.37Ti0.94Ni0.06O3−δ(denoted as LCTN-Ce) fuel electrodes were prepared via a simple hydrothermal route, where CeO2is uniformly decorated onto the external surface of the LCTN-Ce electrode with in situ exsolved Ni nanoparticles (NPs) after reduction treatment. The decorated CeO2content can be regulated by adjusting the hydrothermal treatment time and precursor solution concentration. As the decorated CeO2content increases from 0.81 to 2.05%, the conductivity of the LCTN-Ce electrode decreases while the oxygen vacancy concentration increases, comprehensively affecting the cell power output and CO2electrolysis activity. The electrolyte-supported cell assembled with LCTN-1.46Ce cell with a moderate CeO2content of 1.46% shows an excellent reversible electrochemical performance and RSOC stability in 50% H2/50% CO2at 750 °C. The hydrothermal decoration provides a facile way to modify perovskite oxides, and the LCTN-Ce with in situ exsolved Ni NPs and surface-decorated CeO2is proved to be a potential RSOC fuel electrode material.

The exploration and rational design of high-performance, durable, and non-precious-metal bifunctional oxygen electrocatalysts are highly desired for the large-scale application of overall water splitting. Herein, an effective and straightforward coupling approach was developed to fabricate high-performance bifunctional OER/HER electrocatalysts based on core–shell nanostructure comprising a Ni/Ni3N core and a NiFe(OH)xshell. The as-prepared Ni/Ni3N@NiFe(OH)x-4 catalyst exhibited low overpotentials of 57 and 243 mV at 10 mA cm–2for the HER and OER in 1.0 m KOH, respectively, superior to most bifunctional oxygen electrocatalysts reported so far. Compared to the unmodified Ni/Ni3N, the Ni/Ni3N@NiFe(OH)x-4 catalyst exhibited a 43.3-fold increase in mass activity for the OER and an 8.7-fold increase for the HER, as well as a 29.5-fold increase in intrinsic activity for the OER and a 2.6-fold increase for the HER. When employed as both the cathode and the anode of the electrolyzer for the overall water splitting reaction, its voltage was reduced to 1.58 V at 10 mA cm–2. This surface reconstruction method increased the electrochemically active surface area and enhanced the catalytic activity. Furthermore, in situ Raman spectroscopy revealed that the Fe etching reduced the onset potential for the active phase NiOOH, promoted its formation, and accelerated the reaction kinetics, thereby enhancing the overall electrocatalytic performance of the catalyst.

The state and input constraints of nonlinear systems could greatly impede the realization of their optimal control when using reinforcement learning (RL)-based approaches since the commonly used quadratic utility functions cannot meet the requirements of solving constrained optimization problems. This article develops a novel optimal control approach for constrained discrete-time (DT) nonlinear systems based on safe RL. Specifically, a barrier function (BF) is introduced and incorporated with the value function to help transform a constrained optimization problem into an unconstrained one. Meanwhile, the minimum of such an optimization problem can be guaranteed to occur at the origin. Then a constrained policy iteration (PI) algorithm is developed to realize the optimal control of the nonlinear system and to enable the state and input constraints to be satisfied. The constrained optimal control policy and its corresponding value function are derived through the implementation of two neural networks (NNs). Performance analysis shows that the proposed control approach still retains the convergence and optimality properties of the traditional PI algorithm. Simulation results of three examples reveal its effectiveness.

Automatic detection of traffic signs is crucial for Advanced Driving Assistance Systems (ADAS). Current two-stage approaches consist of a preliminary object detection step, where the traffic signs are categorized within broader families (e.g., speed limits), and then sub-classes (e.g., speed limit 40). However, these cascading methods fail to achieve satisfying performance, especially in more realistic driving scenarios where images are acquired under more challenging conditions. Under such conditions, the first-stage detection step is likely to provide inaccurate predictions, making the subsequent classification step useless. In this paper, we propose a simple yet effective two-stage fusion framework for traffic sign detection. Different from the previous cascading method, our framework directly predicts categories in the first-stage detection and fuse the two-stage category predictions to improves overall robustness. Besides, in order to filter the false detection boxes under low-resolution inputs, we also propose an effective post-processing method called Surrounding-Aware Non-Maximum Suppression (SA-NMS) as an alternative technique for the first-stage detection. After combining the above proposed methods, our framework obtains good detection performance. Experimental results on the widely used Tsinghua-Tencent 100K (TT100K) traffic sign dataset, which contains images of traffic signs collected under a variety of challenging conditions, show that the proposed framework outperforms current approaches in both accuracy and inference speed, achieving 89.7 mAP and 65 FPS for ${608\times 608}$ low resolution images.