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 comprehensive investigations on defects’ stability caused lateral charge loss are investigated in Ti-doped 3D NAND. The detailed study shows that the Ti-doping strategy could reduce pre-existing shallow traps in pure Si₃N₄. The stability of Ti_Si and Ti_i defects are observed under program/erase stress, but unstable Ti_N defects with shallow-trap levels (0.41 eV) also occur. Based on unstable defects, the device simulations are carried out revealing its effects on lateral charge loss, which indicates that Ti_N defects should be reduced for reliability. Our results strongly suggest the stability of Ti-related defects could be key points for lateral charge loss.