In recent years, flexible pressure sensors have been deployed in various fields due to their advantages of high sensitivity, low detection limits, wide operating range, and fast dynamic response. Introducing microstructures into the sensitive layer of a sensor is an effective method to improve its performance. However, most currently adopted microstructures are often obtained through photolithography, etching, or replication from naturally existing templates, which results in the poor designability of the proposed sensor. To address this issue, a rational design method for microstructures based on numerical simulation analysis has been reported in this paper. At first, a series of numerical simulations has been conducted to study the influence of microstructure shapes and size parameters on sensor performances. The results indicate that hemisphere structures with a radius of 500 μm exhibits the largest capacitance variation under the same pressure conditions. Then, a capacitive flexible pressure sensor with a 3D-printed hemispherical graded microstructure PDMS dielectric layer has been presented. The fabricated sensor exhibits a high sensitivity of 1.357 kPa ^-1 within the range of 0-5 kPa and a broad detection range of 0-500 kPa. Simultaneously, the sensor also demonstrates a fast dynamic response (< 60ms), a low detection limit (approximately 100 Pa), and a remarkable stability (over 3000 cycles). All these exceptional performances confirm the designed sensor with significant potential for widespread applications in the field of physiological signal monitoring.

Introducing microstructures into a flexible pressure sensor is an effective way to improve performance of the sensor. However, existing methods for preparing microstructures in sensors, such as the artificial template method, not only are time consuming and costly but also have restrictions on the diversity and complexity of the achieved microstructures. Therefore, leaves of plants that possess abundant microstructures on surfaces can be used as templates to prepare microstructures in pressure sensors. For this reason, we have developed a flexible capacitive pressure sensor with microstructured dielectric layer and electrodes by a simple and convenient way. Specifically, the polydimethylsiloxane (PDMS) electrode was obtained by transforming the microstructures and a non-flaking multi-walled carbon nanotubes (MWCNTs) conductive layer simultaneously from a indocalamus leaf template. The dielectric layer with double-sided microstructures was formed by molding two indocalamus leaves on both sides. All the microstructures of electrodes and dielectric layer are beneficial to increase the degree of deformation thus improves the sensitivity and detection range efficiently. As a result, the obtained flexible pressure sensor exhibits a high sensitivity of 2.012kPa ^-1 , a fast response time less than 20ms, good durability of 1000 cycles, and a wide detection range up to 100kPa. In addition, we demonstrated the availability of the designed sensor in the monitoring the tactile pressure signals and human movements. The experimental results confirm that the proposed capacitive pressure sensor has a great potential in applications such as electronic skin, wearable devices, and intelligent robots.

This paper presents a type of single-phase double “I” hole phononic crystal (DIH-PnC) structure, which is formed by vertically intersecting double “I” holes. By using the finite element method, the complex energy band curve, special point mode shapes, and different delay lines were calculated. Numerical results showed that DIH-PnC yielded ultra-wide band gaps with strong attenuation. The formation mechanism is attributed to the Bragg-coupled local resonance mechanism. The effects of the pore width in DIH-PnC on the band gaps were further explored numerically. Significantly, as the pore width variable, the position of the local resonance natural frequency could be modulated, and this enabled the coupling between the local resonance and the Bragg mechanism. Subsequently, we introduced this DIH-PnC into the thin-film piezoelectric-on-silicon (TPOS) resonator. The results illustrated that the anchor loss quality factor (${\mathrm{Q}}_{\mathrm{anc}}$) of the DIH-PnC resonator was 20,425.1% higher than that of the conventional resonator and 3762.3% higher than the ${\mathrm{Q}}_{\mathrm{anc}}$ of the cross-like holey PnC resonator. In addition, the effect of periodic array numbers on ${\mathrm{Q}}_{\mathrm{anc}}$ was researched. When the ${\mathrm{Q}}_{\mathrm{anc}}$ reached 1.12 × 10⁶, the number of the period array in DIH-PnC only needed to be 1/6 compared with cross-like holey PnC. Adopting the PnC based on the coupling Bragg and local resonance band gaps had a good effect on improving the ${\mathrm{Q}}_{\mathrm{anc}}$ of the resonator.

In this paper, a novel H-shaped radial phononic crystal (H-RPC) structure is proposed to suppress the anchor loss of a Lamb wave resonator (LWR), which has an ultra-high frequency (UHF) and ultra-wideband gap characteristics. Compared to previous studies on phononic crystal (PC) structures aimed at suppressing anchor loss, the radial phononic crystal (RPC) structure is more suitable for suppressing the anchor loss of the LWR. By using the finite element method, through the research and analysis of the complex energy band and frequency response, it is found that the elastic wave can generate an ultra-wideband gap with a relative bandwidth of up to 80.2% in the UHF range when propagating in the H-RPC structure. Furthermore, the influence of geometric parameters on the ultra-wideband gap is analyzed. Then, the H-RPC structure is introduced into the LWR. Through the analysis of the resonant frequency, it is found that the LWR formed by the H-RPC structure can effectively reduce the vibration energy radiated by the anchor point. The anchor quality factor was increased by 505,560.4% compared with the conventional LWR. In addition, the analysis of the LWR under load shows that the LWR with the H-RPC structure can increase the load quality factor by 249.9% and reduce the insertion loss by 93.1%, while the electromechanical coupling coefficient is less affected.

As the foremost component of wearable devices, flexible pressure sensors require high sensitivity, wide operating ranges, and great stability. In this paper, a pressure sensor comprising a regular batten microstructure active layer is presented. First, the influences of the dimensional parameters of the microstructures on the performances of the sensors were investigated by the mechanical finite element method (FEM). Then, parameters were optimized and determined based on the results of this investigation. Next, active layers were prepared by molding multiwalled carbon nanotube/polyurethane (MWCNT/PU) conductive composite using a printed circuit board template. Finally, a resistive flexible pressure sensor was fabricated by combining an active layer and an interdigital electrode. With advantages in terms of the structure and materials, the sensor exhibited a sensitivity of up to 46.66 kPa⁻¹ in the range of 0–1.5 kPa and up to 6.67 kPa⁻¹ in the range of 1.5–7.5 kPa. The results of the experiments show that the designed flexible pressure sensor can accurately measure small pressures and realize real-time human physiological monitoring. Furthermore, the preparation method has the advantages of a low cost, simple design, and high consistency. Thus, it has potential to promote the development of flexible sensors, wearable devices, and other related devices.

Semantic segmentation of underground mine roads is very important to efficiently obtain road information from images. The boundary of underground mine roads is not obvious, the environment is complex, and road identification is difficult. In order to effectively realize the accurate identification of underground mine roads, a network identification model using a deep learning technique is proposed. Choosing BiSeNet as the basic framework, adopting a unified attention fusion module, and using channel and spatial attention to enrich the fusion feature representation can effectively obtain feature information and reduce the loss of feature information. In addition, the lightweight network STDC is integrated into the backbone network to reduce computational complexity. Finally, experiments were carried out on underground mine roads. The experimental results show that the mean intersection over union and pixel accuracy of the proposed method reached 89.34% and 98.34%, respectively, and the recognition speed reached 23 f/s when identifying underground mine roads. In this study, the underground mine road recognition model trained by deep learning technology can solve the problem of underground mine road recognition with high accuracy.