Highly Sensitive Flexible Capacitive Pressure Sensor Based on a Multicross-Linked Dual-Network Ionic Hydrogel for Blood Pressure Monitoring Applications.

Flexible capacitive pressure sensors based on ionic hydrogels (IHs) have garnered significant attention in the field of wearable technology. However, the vulnerability of traditional single-network hydrogels to mechanical damage and the complexity associated with preparing double-network hydrogels present challenges in developing a highly sensitive, easily prepared, and durable IH-based flexible capacitive pressure sensor. This study introduces a novel multicross-linked dual-network IH achieved through the physical and chemical cross-linking of polymers polyvinyl alcohol (PVA) and chitosan (CS), ionic solution H3PO4, and cross-linking agent gum arabic. Flexible capacitive pressure sensors, characterized by high sensitivity and a broad pressure range, are fabricated by employing mesh as templates to design cut-corner cube microstructures with high uniformity and controllability on the IHs. The sensor exhibits high sensitivity across a wide pressure range (0-290 kPa) and with excellent features such as high resolution (∼1.3 Pa), fast response-recovery time (∼11 ms), and repeatable compression stability at 25 kPa (>2000 cycles). The IHs as a dielectric layer demonstrate long-term water retention properties, enabling exposure to air for up to 100 days. Additionally, the developed sensor shows the ability to accurately measure the pulse wave within the small pressure range. By combining the pulse wave acquired by the sensor with a trained neural network model, we achieve successful blood pressure (BP) prediction, meeting the standards set by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. Ultimately, the sensor proposed in this study holds promising prospects for broad applications in high-precision wearable medical electronic devices.

Fabrication and characterization of highly sensitive flexible strain sensor based on biodegradable gelatin nanocomposites and double strain layered structures with crack for gesture recognition.

Flexible sensors have attracted extensive attention in the field of human-computer interaction. However, it is still a challenging task to realize accuracy gesture recognition with flexible sensor, which requires sensor not only have high sensitivity, but also have appropriate strain detection range. Here, a high gauge factor flexible sensor (gauge factor âˆ¼ 1296 under 12-20 % strain) based on crack structure is reported. The sensor is made of a biodegradable and stretchable gelatin composite combined with fabric bases, with good repeatability (6000 cycles) and a fast response (60 ms). Because of the double-layer structure, it has a suitable detection range (20 % strain). The sensor is manufactured by a screen-printing process, and it has been used to make data gloves and has realized 9 gestures recognition with machine learning algorithm (99.6 % accuracy). In general, this study offers a wearable gestures recognition scheme through the proposed sensor.