A scalable, binder-free and one-step electric discharge rusting technique is proposed to fabricate high performance Fe–Fe _{1− x} O based coplanar microsupercapacitors.

Compliant constant-force mechanisms (CCFMs) are compliant mechanisms that provide nearly constant forces over prescribed deflection ranges. This study proposes a topology optimization method for designing hinge-free compliant constant-output-force mechanisms. Bifurcation cause the objective function to oscillate, leading to convergence failure. Therefore, a constraint for preventing bifurcation in constant-output-force mechanisms is proposed. Moreover, a minimum scale constraint method that extends the design domain is utilized in the proposed optimization algorithm. The relationship between the minimum length scale and the force variation in the optimized CCFM is obtained and discussed, and an optimized CCFM is fabricated and tested. The optimized CCFM exhibits a force variation of 0.7% over a constant-force stroke of 26% of the CCFM length and is thus competitive among recently proposed CCFMs.

As the thickness of an ultra-thin flattened heat pipe (UTHP) decreases, the fabrication difficulty increases exponentially, and the thermal performance deteriorates rapidly. In this study, three types of composite wicks were developed for UTHPs with a 0.6 mm thickness: copper foam and mesh wick (CFMW), two layers of different mesh wick (TDMW), and three layers of the same mesh wick (TSMW). The startup and steady-state performances of the UTHPs with liquid filling ratios of 60% to 120% were investigated. The findings indicated that the CFMW UTHP with a filling ratio of 100% exhibited the best startup performance, with the highest equilibrium temperature of 58.37 °C. The maximum heat transport capacities of the CFMW, TDMW, and TSMW UTHP samples were 9, 8, and 8.5 W, respectively, at their corresponding optimum filling ratios of 110%, 90%, and 100%. The CFMW UTHP exhibited the lowest evaporation and condensation thermal resistances of 0.151 and 0.189 K/W, respectively, which were 24.67% and 41.85% lower than those of the TSMW UTHP. CFMW can be used to improve the thermal performance of UTHPs. This study provides important guidelines for the structural design, fabrication technology, and performance improvement of high-performance UTHPs used in portable electronic devices.

Surface defect detection of steel is a critical step in ensuring the quality and safety of industrial products. In particular, detecting small-scale defects such as scratches and spots—characterized by blurred edges and low contrast—poses stringent requirements for both the accuracy and real-time performance of detection algorithms. To address the limitations of traditional methods—namely low efficiency and high cost—as well as the shortcomings of existing deep learning models in detecting small objects with high accuracy and low computational cost, this paper proposes an improved lightweight object detection model for steel surface defect detection, termed ASD-YOLO. Built upon the YOLO11 architecture, the model conducts coordinated multi-module optimization around three key aspects: small-object modeling, feature fusion, and structural lightweighting. The backbone integrates the ACmix module, which combines local convolution and global attention to enhance detail perception capabilities. A Slim-Neck module is employed in the neck to reduce parameter size while improving feature transmission efficiency. The Shape-IoU loss function is introduced to improve the model's ability to represent irregular defect boundaries. A DLGCA attention mechanism is incorporated prior to the detection head, significantly enhancing the model’s discrimination capability in complex textured backgrounds. Experimental results show that ASD-YOLO achieves a mAP@0.5 of 77.3% on the NEU-DET dataset, marking a 5.4% improvement over the baseline YOLO11 model. The model also achieves a 3.5% reduction in parameters and a 7.9% decrease in computational cost, while demonstrating strong generalization performance on the GC10-DET dataset. These results indicate that the proposed method achieves synergistic improvements in detection accuracy, model compactness, and industrial applicability, offering a reliable and efficient solution for steel defect detection.

Transition metal oxides, distinguished by their high theoretical specific capacitance values, inexpensive cost, and low toxicity, have been extensively utilized as electrode materials for high-performance supercapacitors. Nevertheless, their conductivity is generally insufficient to facilitate rapid electron transport at high rates. Therefore, research on bimetallic oxide electrode materials has become a hot spot, especially in the field of micro-supercapacitors (MSC). Hence, this study presents the preparation of bimetallic oxide electrode materials via electro-spark machining (EM), which is efficient, convenient, green and non-polluting, as well as customizable. The fabricated copper-molybdenum bimetallic oxide (MoCuO_x) device showed good electrochemical performance under the electrode system. It provided a high areal capacity of 50.2 mF cm⁻² (scan rate: 2 mV s⁻¹) with outstanding cycling retention of 94.9% even after 2000 cycles. This work opens a new window for fabricating bimetallic oxide materials in an efficient, environmental and customizable way for various electronics applications.