Zijun Ning

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 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.

Human body movements are flexible and multidirectional. To obtain more accurate parameters during human motion monitoring and health managing, ultra-sensitive and omnidirectional sensing equipment is needed. Therefore, an ultra-sensitive omnidirectional strain sensor array, which utilizes a bionic spider crack sensing mechanism, is proposed and designed to detect the multidirectional mechanical strains over the skin generated by human activities. Holding the mechanism of the bionic crack sensing layer as foundation, the bionic crack strain sensor exhibits an ultrahigh strain sensitivity of GF =1920031. The proposed omnidirectional strain sensor array consists of three bionic crack strain sensors spaced 120° apart. This configuration enables the array to acquire and distinguish mechanical strains from any direction within its coverage skin area. The sensor array can be attached to the skin firmly to measure skin deformation caused by joint and muscles’ activity, as well as the angle of the wrist movements. The monitoring data obtained by the fabricated sensor array was used to train and test a badminton swing action recognition model, and eight types of badminton swing actions were successfully identified with an overall accuracy of 98.125%. This demonstrates that the omnidirectional strain sensor array can provide high-precision human motion posture information for intelligent health management and medical limb monitoring.

By establishing the nonlinear vibration model of a resonant accelerometer, this article analyzes the effects of mechanical nonlinearity and electrostatic nonlinearity on the output frequency offset of the resonant accelerometer. It is found that these two types of nonlinearities affect the nonlinear coefficient oppositely. Thereby, a nonlinearity compensation model is proposed and built by using electrostatic nonlinearity to counteract mechanical nonlinearity. Subsequently, polarization voltage conditions aiming for zero nonlinear coefficient are investigated, so that to decrease the output frequency offset of the resonant accelerometer, improving the stability of the output frequency. Finally, through theoretical calculations and numerical simulations, it is figured out that under the compensation voltage, the output frequency offset is less than 0.001%. According to the principles of electromechanical equivalence, the nonlinear vibration compensation model is equivalently represented by an electrical circuit model. The output frequencies of the equivalent circuit turned out to be consistent with that obtained by numerical analysis of the nonlinear vibration compensation model. The linearity of the accelerometer output curve before and after compensation is compared, and the results certify the feasibility of the proposed nonlinear vibration compensation method.

Aiming for high sensitivity and satisfying transmission loss, intelligent optimization methods involving machine learning are worthwhile to apply for optical pressure sensors development since machine learning is versatile to correlate the complex and nonlinear relationship between device performance and sensor structural parameters. This paper proposes a three-stage framework to conduct optimal design of an optical pressure sensor facing multiple optimization objectives. Firstly, a dataset consisting of 1600 samples with combinations of varying waveguide lengths and core widths is used to train and establish a prediction model, which also acts as the objective functions for subsequent optimization. Secondly, a genetic algorithm is applied to find out the Pareto front within the sample space according to optimization objectives. Thirdly, by inverse design based on the Pareto optimal set, the multi-objective optimization for sensor with optimal structural parameters is achieved. Experimental studies show that the optimized design of the graphene waveguide composite structure pressure sensor based on machine learning gives a sensitivity increase of 63.7% and a transmission loss reduction of 18.5% compared to the similar sensors designed in previous work. This effectively verifies the feasibility of the proposed optimization design method in this paper. Without large number of sensor fabrications and trial-and-error experiments, the reverse design of sensors with various performance requirements could be achieved automatically, which provides a new approach to improve the performance of sensors in the future.

A high-sensitivity optic fiber pressure sensor based on balloon-like single-mode fiber (SMF) is proposed and thoroughly investigated. Under pressure, the balloon-like SMF and the substrate collaborate, resulting in changes in the stress state of the core and cladding of the fiber in the sensor, which leads to changes in the difference between the effective refractive index of the core and cladding of the fiber, and shifts the spectra, so as to achieve pressure measurements. Firstly, the mechanical model of the proposed sensor is established, explaining the relationship between the varying of refractive index of the sensing unit involving fiber and substrate, and the pressure. Then, the effects of the substrate stiffness and fiber bending diameter on the sensor sensitivity are analyzed, so as to optimize the sensor’s structural and relevant parameters. It reveals that, under pressure, the substrate stiffness has a significant effect on the sensor sensitivity. A series of experimental tests were carried out based on the fabricated substrate using iron, caoutchouc, and PDMS, respectively, as substrates and applying different bending diameters for the SMF, it shows that when the substrate is caoutchouc and the bending diameter of the fiber is 3.8 mm, the sensitivity of the optimized balloon-like SMF optic pressure sensors can reach up to 90.3 nm/N in the range of 0-1 N. The experimental results effectively verify the feasibility of the optimized design method of the balloon-like SMF optic pressure sensors as well as the achievability of the high-sensitivity pressure detection.