No abstract

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.

In this work, the guar gum (GG) and the electrospinned ethyl cellulose-polyvinyl pyrrolidone (EC-PVP) nanofibers were used as humidity-sensitive materials for fabricating a quartz crystal microbalance (QCM) sensor. Fourier transform infrared spectroscopy, scanning electron microscopy, water contact angle test, and X-ray photoelectron spectra were used to characterize the synthesized GG/EC-PVP composite material, confirming its successful preparation and good hydrophilicity. The humidity sensitivity experiments were performed at room temperature. The GG/EC-PVP-coated QCM sensor has high sensitivity (55.72 Hz/%RH) and low hysteresis (2.8% RH) in a wide relative humidity range (0–97% RH), short response/recovery time (26/2 s), excellent selectivity, good repeatability, and stability. The combined action of hydrophilic groups and porous structure enhances the humidity sensitivity. The GG/EC-PVP sensor can be used to capture and measure typical breathing patterns in different human basic emotions due to its good performance. Furthermore, a lie-detector system was also designed for judging the lying through detecting the emotional breathing pattern of the subjects.