High sensitivity flexible strain sensor for motion monitoring based on MWCNT@MXene and silicone rubber.

Research on flexible strain sensors has grown rapidly and is widely applied in the fields of soft robotics, body motion detection, wearable sensors, health monitoring, and sports. In this study, MXene was successfully synthesized in powder form and combined with multi-walled carbon nanotube (MWCNT) to develop MWCNT@MXene conductive network-based flexible strain sensors with silicone rubber (SR) substrate. Combining MWCNTs with MXene as a conductive material has been shown to significantly improve the sensor performance, due to MXene's high conductivity properties that strengthen the MWCNT conductive pathway, increase sensitivity, and improve sensor stability. The sensor is fabricated by a sandwich method consisting of three layers, which enables more accurate and reliable detection of strain changes. The main innovation of this research is the utilization of MWCNT@MXene as a conductive material that optimizes the performance of flexible strain sensors, overcomes the limitations of previous materials, and makes it a more effective solution for long-term applications. Furthermore, the sensor was evaluated to test its performance through sensitivity, linearity, response time, and durability tests. The results showed that the sensor exhibited excellent performance with a high sensitivity of 39.97 over a strain range of 0-100% and excellent linearity (0.99) over a strain of 0-50%. The sensor also has a fast response time of about 70 ms, it also has good stability during low (1-5%) and high (20-100%) strain cycle testing and can withstand up to 1200 loading and unloading cycles. In addition, the sensor effectively detects a wide range of body movements, including finger, wrist and knee movements. These findings show that the electromechanical properties of strain sensors are significantly improved through the use of MWCNT@MXene as a conductive material, so these sensors are considered a promising solution for applications in wearables and body motion monitoring.