Despite the increasing prevalence of wearable flexible sensors for human joint biomechanics, challenges, such as limited intelligence capacity, insufficient sensitivity, and power constraints, limit their practical application. To address these issues, this work presents a low-power and low-latency edge computing algorithm that incorporates interpretable machine learning to guide dimensionality reduction for direct on-sensor signal processing. Compared to traditional wireless transmission methods, the deployed tiny machine learning (tinyML) model achieves a prediction latency of only 9 ms and reduces power consumption by 75.6%. Furthermore, utilizing a triboelectric sensor based on MXene and featuring a micro-conical structure demonstrates excellent self-powered sensing capability, with output voltage and charge increased by 27.4% and 52.9%, respectively, and a high-sensitivity monitoring performance of 16 mV/Pa. The synergy between the efficient algorithm and the high-performance sensor is validated in knee joint biomechanics scenarios, showing advantages over conventional approaches in power consumption, cost, response speed, size, and accuracy. These combined strengths indicate broad application prospects in portable intelligent healthcare.

Driven by “More than Moore”, miniaturization and multifunctional integration of micro-energy devices are emerging as critical pathways for next-generation compact microsystems. This study proposes a sensing-in-Energy (SiE) microdevice that immerses an inertial switch in a parallel-connected supercapacitor’s electrolyte, enabling simultaneous impact sensing and stable energy supply under extremely high gravitational acceleration (high-g) shocks (over 10,000 g). The SiE microdevice can be viewed as a high-amplitude shock sensor (raw signal peak > 50 mV) under high-frequency perspective, and a shock-resistant electrochemical power source (voltage fluctuation < 2%) under low-frequency perspective, while energy consumption reduces over 99.9% compared with conventional high-g sensor due to its event-driven mechanism. Sensing performance is boosted > 50% using multiphysics model combined with machine learning algorithm. Furthermore, a fuze microsystem was built based on SiE microdevice, achieving 150 μs-level ultrafast response. Three-layer penetration experiments have verified the engineering application of SiE microdevice and its fuze microsystem in smart munitions domains, providing a novel paradigm for heterogeneous microsystem in high-dynamic environments.

Impact detecting and counting are fundamental functions of fuses used in hard target penetration weapons. However, detection failure caused by battery breakdown in high-g acceleration environments poses a vulnerability for such weapons. This paper introduces a novel supercapacitor that combines energy storage and high-g impact detection, called self-sensing supercapacitor. By deliberately inducing a transient soft short-circuit during shock in the supercapacitor, it is possible to detect external impact by its transient voltage drop. To realize this concept, firstly, by introducing the contact theory and force-induced percolation model, the electrode strength and roughness are found to have key impacts on the formation of soft circuits. Subsequently, to meet the needs for sensitivity and capacity, a high-density porous carbon (HDPC) that combines high mechanical strength and porosity, is selected as a suitable candidate based on the analysis results. Furthermore, a two-step curing method is proposed to prepare the high-roughness HDPC (HRHDPC) electrode and to assemble the self-sensing supercapacitor. Due to the rich specific surface of the electrodes and the high surface strength and roughness conducive to the formation of transient soft short circuits, the self-sensing supercapacitor not only possesses an excellent specific capacitance (171 F/g at 0.5 A/g) but also generates significant voltage response signals when subjected to high-g impacts ranging from 8000g to 31,000g. Finally, the self-sensing supercapacitor is applied to compose a successive high-g impact counting system and compared to traditional solutions (sensors and tantalum capacitors) in the military fuzes. The results show that the self-sensing supercapacitor-based system exhibits advantages in terms of size, power consumption, and counting accuracy.

Supercapacitors (SCs) have been successfully used in electric vehicles or military equipment systems for their high power density. However, the mechanical impacts from vehicle crashes and missile penetration probably cause performance fluctuations or failure of SCs, which may threaten the safety of systems using SCs. In this paper, a generalized circuit model to analyze the transient process of SCs under mechanical loads is proposed. The circuit model simultaneously takes capacitance change, internal short-circuit and resistance change into account, and an extra resistor-capacitor circuit (RCC) is added to simulate the nonlinear behavior during charging and discharging. Subsequently, the relationships between pressure and fundamental circuit parameters are determined by static methods. By taking the static test data into the circuit model, the transient response of different types of SCs under particular mechanical loading conditions is predicted. Finally, the influences of some crucial parameters on the voltage responses of SCs are revealed based on the simulations, which provide references for designing and optimizing mechanical load-resistant or self-sensing SCs in specific application scenarios.

In this study, the discharge voltage behavior of electric double-layer capacitors (EDLCs) during high-g impact is studied both theoretically and experimentally. A micro-scale dynamic mechanism is proposed to describe the physical basis of the increase in the discharge voltage during a high-g impact. Based on this dynamic mechanism, a multi-field model is established, and the simulation and experimental studies of the discharge voltage increase phenomenon are conducted. From the simulation and experimental data, the relationship between the increased voltage and the high-g acceleration is revealed. An acceleration detection range of up to 10, 000g is verified. The design of the device is optimized by studying the influences of the parameters, such as the electrode thickness and discharge current, on the outputs. This work opens up new avenues for the development of autonomous sensor systems based on energy storage devices and is significant for many practical applications such as in collision testing and automobile safety.

Parasitic capacitance is an unavoidable and usually unwanted capacitance that exists in electric circuits, and it is the most important second-order non-ideal effect that must be considered while designing a triboelectric nanogenerator (TENG) because its magnitude is comparable to the magnitude of the TENG capacitance. This paper investigates the structure and performance optimization of TENGs through modeling and simulation, taking the parasitic capacitance into account. Parasitic capacitance is generally found to cause severe performance degradation in TENGs, and its effects on the optimum matching resistance, maximum output power, and structural figures-of-merit (FOMs) of TENGs are thoroughly investigated and discussed. Optimum values of important structural parameters such as the gap and electrode length are determined for the different working modes of TENGs, systematically demonstrating how these optimum structural parameters change as functions of the parasitic capacitance. Additionally, it is demonstrated that the parasitic capacitance can improve the height tolerance of the metal freestanding-mode TENGs. This work provides a theoretical foundation for the structure and performance optimization of TENGs for practical applications and promotes the development of mechanical energy-harvesting techniques.