The rapid development of wearable electronic devices and virtual reality technology has revived interest in flexible sensing and control devices. Here, we report an ionic hydrogel (PTSM) prepared from polypropylene amine (PAM), tannic acid (TA), sodium alginate (SA), and MXene. Based on the multiple weak H-bonds, this hydrogel exhibits excellent stretchability (strain >4600%), adhesion, and self-healing. The introduction of MXene nanosheets endows the hydrogel sensor with a high gauge factor (GF) of 6.6. Meanwhile, it also enables triboelectric nanogenerators (PTSM-TENGs) fabricated from silicone rubber-encapsulated hydrogels to have excellent energy harvesting efficiency, with an instantaneous output power density of 54.24 mW/m 2 . We build a glove-based human−computer interaction (HMI) system using PTSM-TENGs. The multidimensional signal features of PTSM-TENG are extracted and analyzed by the HMI system, and the functions of gesture visualization and robot hand control are realized. In addition, triboelectric signals can be used for object recognition with the help of machine learning techniques. The glove based on PTSM-TENG achieves the classification and recognition of five objects through contact, with an accuracy rate of 98.7%. Therefore, strain sensors and triboelectric nanogenerators based on hydrogels have broad application prospects in man−machine interface, intelligent recognition systems, auxiliary control systems, and other fields due to their excellent stretchable and high self-healing performance.
The era of AI has prompted the continuous advancement of research on flexible electronic materials. Flexible sensors not only have good wearable performance, but also have high accuracy in acquiring...
Flexible strain sensors have significant progress in
the fields
of human–computer interaction, medical monitoring, and handwriting
recognition, but they also face many challenges such as the capture
of weak signals, comprehensive acquisition of the information, and
accurate recognition. Flexible strain sensors can sense externally
applied deformations, accurately measure human motion and physiological
signals, and record signal characteristics of handwritten text. Herein,
we prepare a sandwich-structured flexible strain sensor based on an
MXene/polypyrrole/hydroxyethyl cellulose (MXene/PPy/HEC) conductive
material and a PDMS flexible substrate. The sensor features a wide
linear strain detection range (0–94%), high sensitivity (gauge
factor 357.5), reliable repeatability (>1300 cycles), ultrafast
response–recovery
time (300 ms), and other excellent sensing properties. The MXene/PPy/HEC
sensor can detect human physiological activities, exhibiting excellent
performance in measuring external strain changes and real-time motion
detection. In addition, the signals of English words, Arabic numerals,
and Chinese characters handwritten by volunteers measured by the MXene/PPy/HEC
sensor have unique characteristics. Through machine learning technology,
different handwritten characters are successfully identified, and
the recognition accuracy is higher than 96%. The results show that
the MXene/PPy/HEC sensor has a significant impact in the fields of
human motion detection, medical and health monitoring, and handwriting
recognition.
Flexible pressure sensors with excellent performance have broad application potential in wearable devices, motion monitoring, and human−computer interaction. In this paper, a flexible pressure sensor with a porous structure is proposed by coating molybdenum disulfide (MoS 2 ) and hydroxyethyl cellulose (HEC) on a polyurethane (PU) sponge skeleton. The obtained sensor has excellent sensitivity (0.746 kPa −1 ), a wide detection range (250 kPa), fast response (120 ms), and outstanding repeatability over 2000 cycles. It is proven that the sensor can realize human motion detection and distinguish the touch of varying strength. In addition, a pressure sensing array was fabricated to reflect the pressure distribution and recognize the writing of Arabic numerals. Finally, the sensor performs speech detection through throat muscle movements, and high-accuracy (97.14%) speech recognition for seven words was achieved by a machine learning algorithm based on the support vector machine (SVM). This work provides an opportunity to fabricate simple flexible pressure sensors with potential applications in next-generation electronic skin, health detection, and intelligent robotics.
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