In this work, a conductive composite film composed of multi-walled carbon nanotubes (MWCNTs) and multi-layer Ti₃C₂Tx MXene nanosheets is used to construct a strain sensor on sandpaper Ecoflex substrate. The composite material forms a sophisticated conductive network with exceptional electrical conductivity, resulting in sensors with broad detection ranges and high sensitivities. The findings indicate that the strain sensing range of the Ecoflex/Ti₃C₂Tx/MWCNT strain sensor, when the mass ratio is set to 5:2, extends to 240%, with a gauge factor (GF) of 933 within the strain interval from 180% to 240%. The strain sensor has demonstrated its robustness by enduring more than 33,000 prolonged stretch-and-release cycles at 20% cyclic tensile strain. Moreover, a fast response time of 200 ms and detection limit of 0.05% are achieved. During application, the sensor effectively enables the detection of diverse physiological signals in the human body. More importantly, its application in a data glove that is coupled with machine learning and uses the Support Vector Machine (SVM) model trained on the collected gesture data results in an impressive recognition accuracy of 93.6%.

Flexible electronics is pursuing a new generation of electronic skin and human–computer interaction. However, effectively detecting large dynamic ranges and highly sensitive human movements remains a challenge. In this study, flexible strain sensors with a self-assembled PDMS/MXene/MWCNT structure are fabricated, in which MXene particles are wrapped and bridged by dense MWCNTs, forming complex sliding conductive networks. Therefore, the strain sensor possesses an impressive sensitivity (gauge factor = 646) and 40% response range. Moreover, a fast response time of 280 ms and detection limit of 0.05% are achieved. The high performance enables good prospects in human detection, like human movement and pulse signals for healthcare. It is also applied to wearable smart data gloves, in which the CNN algorithm is utilized to identify 15 gestures, and the final recognition rate is up to 95%. This comprehensive performance strain sensor is designed for a wide array of human body detection applications and wearable intelligent systems.

Flexible pressure sensors have garnered significant attention over the past few decades owing to their indispensable role in electronic skin and health monitoring, and there is an urgent demand for high sensitivity to meet the requirements of large-scale applications. In this work, we demonstrate a resistive pressure sensor with self-assembled MXene/MWCNTs complex conductive networks, whose hollow substrate is achieved via designed molds and thermally expandable microspheres. Herein, the pressure sensor exhibits the desired performances, including a high sensitivity of 2.63 kPa⁻¹, an ultra-low detection limit of ~0.25% relative resistance change, and rapid response times of 340 ms. The high performance enables promising prospects for detecting diverse human body movements. More importantly, it has been applied in numerical classification based on machine learning with the Hidden Markov Model, achieving an impressive accuracy of ~99.2%. Our research offers novel insights for enhancing the performance of pressure sensors, which hold great potential for practical applications.

The emergence of flexible multifunctional sensing devices has attracted extensive attention from researchers in the field of wearable devices and artificial intelligence. However, the current multifunctional flexible electronic devices, with low stability and complicated fabrication process, are the key constraints to their wide application. Laser-induced graphene patterned rectangular array sensors prepared by carbonizing polyimide films are highly sensitive to external strain and pressure stimuli. With outstanding mechanical properties and heightened sensitivity, the structured pressure sensor effectively showcases its capacity to detect a broad spectrum of human activities and subtle touches. The sensor adopts a three-layered structure comprising a flexible substrate layer, a conductive sensing layer, and an encapsulation layer. Polydimethylsiloxane (PDMS) is employed as the material for both the substrate and encapsulation layers, while laser-induced graphene (LIG) serves as the material for the conductive layer. The resulting patterned pressure sensor exhibits notable sensitivity, with a value of 1.63 kPa-1 within the pressure range of 0 to 40 kPa and 3.5 kPa-1 within the pressure range of 40 to 120 kPa. Furthermore, cyclic load-unload assessments demonstrated the remarkable stability of the sensor. Undergoing compression for 3,000 cycles at a pressure of 60 kPa, the sensor maintained its capability for stable signal detection. In applications such as slide recognition and object grip recognition, the pressure sensor exhibited a noteworthy level of resolution. At the same time, the sensor additionally possesses the capability to monitor human health, and is capable of sensing large body movements and micro-motion sensing signals. Therefore, the sensor can be used for detecting external forces and grasping objects, and has great potential for development in robotics, artificial intelligence, and human-computer interaction.