Sensors play an important role in information perception during the age of intelligence, particularly in areas such as environmental monitoring and human perception. To meet the huge demands for information acquisition in the whole society, the development of elaborated sensor structures using patterned manufacturing technology is important to improve the performance of sensors. Creating patterned structures can enhance the interaction between the sensitive material and target matter, increase the contact area between the sensor and the target matter, amplify the effect of target matter on the sensor structure, and enhance the density of information sensing by building arrays. This review presents a comprehensive overview of patterned micro-nanostructure manufacturing techniques for performance enhancement of flexible sensors, including printing, exposure lithography, mould method, soft lithography, nanoimprinting lithography, and laser direct writing technology. Meanwhile, it introduces the evaluation methods of flexible sensor performance and discusses how patterned structures influence this performance. Finally, some practical application examples of patterned manufacturing techniques are introduced according to different types of flexible sensors. This review also summarises and provides an outlook on the role of these techniques in enhancing sensor performance offering valuable insights for future developments in the patterned manufacturing of flexible sensors.

Auditory systems are the most efficient and direct strategy for communication between human beings and robots. In this domain, flexible acoustic sensors with magnetic, electric, mechanical, and optic foundations have attracted significant attention as key parts of future voice user interfaces (VUIs) for intuitive human–machine interaction. This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS₂ flexible vibration sensor (FVS) with high sensitivity for acoustic recognition. The performance of the MXene/MoS₂ FVS was systematically investigated both theoretically and experimentally, and the MXene/MoS₂ FVS exhibited high sensitivity (25.8 mV/dB). An MXene/MoS₂ FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization. This study also investigated a machine learning-based speaker recognition process, for which a machine-learning-based artificial neural network was designed and trained. The developed neural network achieved high speaker recognition accuracy (99.1%).

Environmentally friendly biomimetic materials with good deformability, high pressure-sensitive performance, and excellent biocompatibility are highly attractive for health monitoring, but to simultaneously meet these requirements is a formidable challenge. In this study, biocompatible MXene quantum dot (MQD)/watermelon peel (WMP) aerogels were obtained by immersing freeze-dried fresh watermelon peel into the quantum dot dispersion. The resulting bio-aerogels with a three-dimensional (3D) porous network structure exhibited a low in elasticity modulus (0.03 MPa) and limit of detection (0.4 Pa) and it showed biocompatibility. With a maximum pressure-sensitive response of 323 kPa^－1, the 3D porous MQD/WMP aerogels exhibited good stability. In addition, the sensing signals could be displayed on mobile phones through a Bluetooth module to monitor human motion (pulse, sound, and walking) in real time. More importantly, the MQD/WMP aerogels exhibited excellent biocompatibility in a cytotoxicity test, thus decreasing the safety risk when they are applied to human skin. The finding in this study will facilitate the fabrication of high-performance biomimetic MXene active matrices, which are derived from natural biological materials, for flexible electronics.