As a typical representative of micro/nano-textured silicon, black silicon has excellent light absorption properties and is gradually surfacing as a substitute for standard silicon in photoelectric devices. Black silicon overcomes the limitations of traditional silicon-based devices, which are unable to achieve infrared light detection at wavelengths >1100 nm and have low quantum efficiency and sensitivity in ultraviolet light detection. In this article, the recent theoretical and experimental breakthroughs in near-infrared and ultraviolet detection using black silicon are summarized in detail. First, black silicon and the techniques for its fabrication are introduced. Then, the application of enhanced black silicon photodetectors within or above the bandgap limit and black silicon fabricated using different methods in infrared detection is discussed. In principle, infrared detection using black silicon is achieved by jointly utilizing element doping, localized surface plasmon resonance effect, and heterojunction formation. In addition, the application of black silicon in ultraviolet detection is also introduced. Ultraviolet detection is realized by an induced junction and the self-built electric field between black silicon and aluminum oxide. Finally, the increasingly growing potential of black silicon in near-infrared and ultraviolet detection applications, such as infrared night vision imaging, signal detection, ultraviolet light intensity monitoring, and national defense early warning, is further discussed.

High-performance medical acoustic sensors are essential in medical equipment and diagnosis. Commercially available medical acoustic sensors are capacitive and piezoelectric types. When they are used to detect heart sound signals, there is attenuation and distortion due to the sound transmission between different media. This paper proposes a new bionic acoustic sensor based on the fish ear structure. Through theoretical analysis and finite element simulation, the optimal parameters of the sensitive structure are determined. The sensor is fabricated using microelectromechanical systems (MEMS) technology, and is encapsulated in castor oil, which has an acoustic impedance close to the human body. An electroacoustic test platform is built to test the performance of the sensor. The results showed that the MEMS bionic sensor operated with a bandwidth of 20–2k Hz. Its linearity and frequency responses were better than the electret microphone. In addition, the sensor was tested for heart sound collection application to verify its effectiveness. The proposed sensor can be effectively used in clinical auscultation and has a high SNR.

This work proposes a design for a direct-reading conductivity sensor with a parallel symmetrical four-electrode structure, which integrates a silicon-based platinum thin-film strip electrode and a serpentine temperature compensation electrode. The optimal structural parameters of the electrode were determined by finite element simulations performed via COMSOL Multiphysics. Next, the designed conductivity sensor chip was fabricated using MEMS technology, and subsequently, the conductivity measurement circuit was designed to test the fabricated sensor’s performance. In laboratory tests, the optimal AC excitation frequency was observed to be 1.067 kHz, while the maximum measurement range was 0–107.41 mS/cm and the measurement precision in low concentration range (0–76.422 mS/cm) was ±0.1 mS/cm. Furthermore, the maximum measurement error of the sensor evaluated using the National Center of Ocean Standards and Metrology was ±0.073 mS/cm. The designed sensor possesses the characteristics of high accuracy, high range, and miniaturization, and enables real-time reading of conductivity value and temperature compensation, which is of great significance for the on-site observation of the physical parameters of marine environment.

Microelectromechanical system (MEMS) pressure sensors have a wide range of applications based on the advantages of mature technology and easy integration. Among them, piezoresistive sensors have attracted great attention with the advantage of simple back-end processing circuits. However, less research has been reported on the performance of piezoresistive pressure sensors in dynamic environments, especially considering the vibrations and shocks frequently encountered during the application of the sensors. To address these issues, this paper proposes a design method for a MEMS piezoresistive pressure sensor, and the fabricated sensor is evaluated in a series of systematic dynamic environmental adaptability tests. After testing, the output sensitivity of the sensor chip was 9.21 mV∙bar⁻¹, while the nonlinearity was 0.069% FSS. The sensor overreacts to rapidly changing pressure environments and can withstand acceleration shocks of up to 20× g. In addition, the sensor is capable of providing normal output over the vibration frequency range of 0–5000 Hz with a temperature coefficient sensitivity of −0.30% FSS °C⁻¹ over the temperature range of 0–80 °C. Our proposed sensor can play a key role in applications with wide pressure ranges, high-frequency vibrations, and high acceleration shocks, as well as guide MEMS-based pressure sensors in high pressure ranges and complex environmental adaptability in their design.

Continuous fetal movement monitoring in late pregnancy may improve fetal wellbeing and pregnancy outcomes. While fetal movements can be visualized with ultrasound, it is intermittent and limited to clinical settings. Inertial measurement units may enable at-home fetal monitoring but usually require a large-footprint, multisensor design. Here, we report smart, compact wearable pressure-strain combo sensors continuously monitoring fetal movements through maternal abdominal skin motions. In the 2D and 3D artificial abdomen systems, our octagonal-shaped gold nanowire–based strain sensor served as an isotropic sensor, enabling omnidirectional simulated “kicking load” detection within an area of ~77 (2D) and ~217 cm ² (3D), while an interdigitated electrode–based pressure sensor showed highly sensitive localized load detection. Building upon these findings, we designed compact pressure- and strain-sensing integrated Band-Aids and tested on 59 pregnant women. We developed machine learning models to distinguish fetal from nonfetal movements with >90% accuracy in ultrasound-based validation studies. This AI-powered, Band-Aid–like sensing system offers potential as a compact, comfortable, and accurate continuous out-of-hospital fetal movement monitoring technology.