Yan Li

The rapid development of portable electronic devices has created an increasingly urgent demand for flexible and portable power sources. The triboelectric nanogenerator (TENG) represents a promising technology for micro-energy harvesting, capable of efficiently converting mechanical energy from the environment into electricity. However, its output performance as a local power source is limited by insufficient contact efficiency and low charge density at triboelectric interfaces. Inspired by the nanocone array structure found on cicada wings, this study proposes a synergistic enhancement strategy that combines bioinspired cicada wing microstructures with corona discharge treatment, leading to the development of a high-performance bioinspired cicada wing TENG (BCW-TENG). Using lithography and template replication processes, micro-nano structures were fabricated on the PDMS surface, significantly increasing the effective contact area of the device and raising the open-circuit voltage by approximately 131% compared to a flat reference device. With further surface charge injection via corona discharge, the output performance was enhanced by another ∼165%, achieving an open-circuit voltage of 70 V and a peak power density of 57.38 mW/m2. By optimizing the power management circuit, the single-cycle energy storage efficiency was improved by a factor of 81. When employed as a sensor, the TENG successfully monitors various human motion postures and demonstrates the capability to power 44 LEDs in an array, a commercial thermo-hygrometer, a stopwatch, and a flexible pressure sensor. This study not only provides an effective synergistic strategy for enhancing TENG performance but also demonstrates its application potential in mechanical energy harvesting and motion monitoring.

Fiber optic pressure sensors are widely used in precise or specialized sensing applications due to their high sensitivity and strong immunity to electromagnetic interference. Traditional optimization methods for fiber optic sensors primarily rely on theoretical derivation together with trial-and-error through simulations, which is hardly competent to achieve synergistic optimization across multiple performance metrics. To address this issue, this paper proposes a machine learning-based optimization design method for a balloon-like single-mode fiber and Sagnac interferometric composite pressure sensor. Support vector regression is employed to establish predictive models to infer sensitivity and transmission loss of the optical sensor according to its fiber bending diameter and substrate elastic modulus. These models are then integrated with the NSGA-II multi-objective optimization algorithm for Pareto optimality search. Optimization results indicate that with a bending diameter of 4.0 mm and a substrate made of rubber that gives an elastic modulus at 0.1 GPa, the sensor achieves a sensitivity of 93.4 nm/N and a transmission loss of 13.2 dB, outperforming those equivalents that with substrates made of common materials. Furthermore, cascading the optimized sensing unit with a Sagnac reference sensor enables high pressure sensing sensitivity at ‑518.2 nm/N. This study demonstrates the effectiveness of machine learning in multi-objective collaborative optimization for fiber optic pressure sensors.

Capacitive flexible pressure sensors are limited by the compressibility of dielectric materials, which creates an inherent trade-off between high sensitivity and a wide measurement range. This trade-off makes it challenging to accurately capture physiological signals that require a wide dynamic range. This study presents an iontronic pressure sensor featuring an array of hollow pyramid microstructures. An electric double layer (EDL) with high interface capacitance was created at the junction of the ionic gel and the electrode, providing an intrinsic mechanism for signal amplification in response to pressure loads. The hollow pyramid microstructure array of ionic gel gives the sensor unique compression characteristics, allowing for better interface contact and more effective structural deformation under pressure. This design endows the structure with outstanding compressibility, which in turn enhances the sensor's sensitivity and broadens its linear detection range. This sensor achieves a sensitivity of 227.56 kPa⁻¹ over a range of 0–400 kPa and maintains a sensitivity of 58.64 kPa⁻¹ across a wide measurement range of 400–2000 kPa. The device also demonstrates impressive dynamic performance metrics, including rapid response and recovery times as well as excellent repeatability. It has been successfully deployed for the accurate acquisition of various physiological and movement-related signals, including radial artery pulse, biceps brachii contraction, and finger grasping movements. These results confirm the sensor's ability to serve as a high-performance measurement device for wearable activity monitoring and robotic perception. Furthermore, the hollow pyramid microstructure offers a versatile design principle that can be applied to other sensing modalities to create devices with broader response ranges and improved sensitivity.

The dynamic topological network architecture evolved over billions of years in biological nervous systems and provides an ideal paradigm for designing high-performance biomimetic sensors. Here, a ZnO composed micronetwork with three-dimensional neuronal branching structure, as the core and sensitive unit of a pressure sensor, is constructed by hydrothermal self-assembly. Its synapse-mimetic connection mechanism enables synergistic modulation of quantum tunneling and contact resistance effects. This biomimetic sensor exhibits a unique triple-stage sensitivity gradient (S₁ = 20.0 kPa^-1, S₂ = 93.1 kPa^-1, S₃ = 124.1 kPa^-1) across an ultrabroad pressure range (0.016-500 kPa). Innovatively, an opto-mechano-electronic synergistic modulation mechanism was introduced: photogenerated carriers excited by 365 nm ultraviolet light effectively compensate the interfacial recombination current loss, enhancing the sensor stability by 15.7% during prolonged pressure testing. The sensor exhibits excellent performance in applications including complex-curvature adaptive perception, human motion monitoring, and human-machine interaction. An intelligent perception system incorporating machine learning algorithms achieved accuracy of 98.3% in human action recognition and real-time accuracy of 96.8% in Morse code conversion, validating its potential for neuromorphic perception. This study, from the perspectives of biological topological network reconstruction and multiphysic fields coupling, provides an innovative design paradigm and technical pathway for developing neuromorphic electronic devices.

The fabrication of high-performance flexible humidity sensors using biocompatible materials is crucial for expanding their application scope. Herein, a humidity sensor using silk fibroin (SF) as the sensing material is proposed and fabricated, accompanying with a systematical investigation of the effects of interdigital electrode geometry (the shape and spacing) and thickness of sensing layer on sensor performance. The prepared sensors were characterized by high sensitivity (3580%) at 95% RH (Relative Humidity), with a detection limit (LOD) of 1.34 × 10⁻³ %RH and a quantification limit (LOQ) of 4.48 × 10⁻³ %RH. It features a short response/recovery time of 37.81/1.15s, excellent repeatability, outstanding flexibility, and minimal hysteresis (5.81%). The proposed flexible humidity sensors show pleasing applicability in many scenarios, amongst which there are three primary functions presented in this paper, i.e. the real-time respiratory status monitoring, skin perspiration moisture monitoring, and non-contact interactive switching. The experimental results reveal its broad application prospects in the areas of health monitoring and smart human-computer interactions. This work demonstrates a viable strategy for developing biocompatible humidity sensors with high performance and multifunctional applicability in next-generation wearable electronic devices.

Human physiological signals play a crucial role in the fields of health assessment, sports medicine, and human–computer interaction. However, human vibration signals exhibit a wide frequency range and significant amplitude variations, which impose greater demands on the sensors’ detection range and sensitivity. A Bionic Crackle Vibration Sensor holding a wide frequency range and high sensitivity, based on the highly sensitive vibration sensing mechanism of the slit sensilla in arthropod legs is designed. The sensor demonstrates exceptionally high sensitivity (GF =42755) and frequency resolution (0.1 Hz or $1\times 10^{-4}$ ) over a wide frequency range (0.1–10000 Hz). Our sensor can accurately monitor vibration signals within ground provoked by human walking and jumping, as well as the pulse and vocal cord vibrations. Gait monitoring systems that integrate our sensors with insoles are proved to be capable of monitoring walking speed. Our Bionic Crackle Vibration Sensor provides innovative technological solutions for wearable medical electronics and human–computer interaction interfaces, demonstrating significant application potential in health monitoring, sports rehabilitation assessment, and early warnings for sub-health conditions.

In recent years, optical fiber pressure sensors have been widely used in the fields like medical diagnosis and environmental monitoring, among others. However, the sensitivity of traditional optical fiber pressure sensors falls short of meeting the requirements for high-precision measurement. In this paper, a highly sensitive dual optical fiber parallel Fabry-Pérot interferometer (FPI) pressure sensor is proposed, accompanied by the theoretically analyze about its working principle, along with a simulation model for optical fiber deformation and optical field intensity analysis. Finally, an experimental platform is built and the performance of the proposed sensor is tested and evaluated. The experimental results show that, in the pressure range of 0-1N, the sensitivity is 29.91 nm/N for the single optical fiber FPI pressure sensor and 227.21 nm/N for the dual optical fiber parallel FPI pressure sensor. Additionally, the temperature sensitivity of this sensor turns out to be 0.075 nm/°C, indicating that it is almost unaffected by temperature and has great repeatability. This design provides a new solution for high-precision measurement of optical fiber pressure sensors.