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The performance of supercapacitors is directly influenced by the conductivity of polypyrrole, which serves as the electrode material. In order to balance considerations of cost-effectiveness and conductivity, this study employs magnetron sputtering to fabricate a copper–tin alloy layer as the conductive layer for polypyrrole. The deposition of a copper–tin alloy film through magnetron sputtering has a significant impact on the polymerization effect of pyrrole as well as being a crucial factor influencing the performance of supercapacitors. Various parameters, including working pressure, sputtering time, and sputtering power, affect the conductivity of the copper–tin alloy film. Furthermore, the degree of influence of each parameter on the conductivity of the copper–tin alloy film varies. This study utilizes an orthogonal experimental design to investigate the impact of various factors and levels on the conductivity and uniformity of a metal film. The objective is to optimize the process parameters for the creation of a copper–tin alloy film with desirable characteristics. Experimental results indicate that the working voltage, sputtering time, and sputtering power significantly influence the coefficient of variation, deposition rate, target current, and operating voltage of the film. Furthermore, FT-IR, XRD, and SEM tests are conducted on samples prepared using the identified optimal process parameters. In addition, we demonstrate various approaches to enhance the experiment’s reliability. The findings indicate that the most favorable process parameters for achieving optimal results are a working pressure of 0.065 Pa, a sputtering time of 20 min, and a sputtering power of 70 W. It was observed that the sputtering time significantly influences the uniformity of the copper–tin alloy film, whereas the sputtering power has a minimal impact on its uniformity. The deposition rate is primarily influenced by the working pressure, with the greatest effect observed. Conversely, the sputtering time has the least impact on the deposition rate. Similarly, the target current is predominantly affected by the sputtering power, exhibiting the greatest influence, while the sputtering time has the least effect. Furthermore, the working voltage is most significantly influenced by the working pressure, whereas the sputtering time has the least impact on the working voltage.

Electrothermal actuation is widely employed in MEMS systems, and the electrothermal V-shaped actuator (referred to as EVA) has garnered attention due to its stable output force. However, current EVAs in MEMS face the challenge of limited driving displacement. To investigate the impact of metal coating on the driving displacement of silicon EVAs, a mechanical model is established, and a formula for calculating the maximum displacement is derived. The theoretical analysis results are compared between cases with and without metal coating. To validate the formula’s accuracy, the COMSOL simulation platform is utilized, employing the finite element method to model and simulate actuators with various metal coatings. The analysis demonstrates that the calculated results have a maximum error of 10% compared to the simulation results. The metal coating enhances the displacement of silicon EVAs to different degrees, with a more pronounced effect observed for metal coatings with lower resistivity. Notably, a copper metal coating doubles the displacement of silicon EVAs at a voltage of 4 V. In other words, under the same displacement, a silicon EVA with a metal coating requires a lower input voltage compared to the group without a metal coating, resulting in a significant voltage reduction of 33.75%.

In this paper, a 3D printing system for a thermal battery electrode ink film is set up and investigated based on the on-demand microdroplet ejection technology. The optimal structural dimensions of the spray chamber and metal membrane of the micronozzle are determined via simulation analysis. The workflow and functional requirements of the printing system are set up. The printing system includes a pretreatment system, piezoelectric micronozzle, motion control system, piezoelectric drive system, sealing system, and liquid conveying system. Different printing parameters are compared to obtain optimized printing parameters, which can be attributed to the optimal pattern of the film. The feasibility and controllability of 3D printing methods are verified by printing tests. The size and output speed of the droplets can be controlled by adjusting the amplitude and frequency of the driving waveform acting on the piezoelectric actuator. So, the required shape and thickness of the film can be achieved. An ink film in terms of nozzle diameter = 0.6 mm, printing height = 8 mm, wiring width = 1 mm, input voltage = 3 V and square wave signal frequency = 35 Hz can be achieved. The electrochemical performance of thin-film electrodes is crucial in thermal batteries. The voltage of the thermal battery reaches its peak and tends to flatten out at around 100 s when using this printed film. The electrical performance of the thermal batteries using the printed thin films is found to be stable. This stabilized voltage makes it applicable to thermal batteries.

Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.