High-Sensitivity Wearable Sensor Based On a MXene Nanochannel Self-Adhesive Hydrogel

Fast gelling, high performance MXene hydrogels for wearable sensors

Hydrogel-based functional materials had attracted great attention in the fields of artificial intelligence, soft robotics, and motion monitoring. However, the gelation of hydrogels induced by free radical polymerization typically required heating, light exposure, and other conditions, limiting their practical applications and development in real-life scenarios. In this study, a simple and direct method was proposed to achieve rapid gelation at room temperature by incorporating reductive MXene sheets in conjunction with metal ions into the chitosan network and inducing the formation of a polyacrylamide network in an extremely short time (10 s). This resulted in a dual-network MXene-crosslinked conductive hydrogel composite that exhibited exceptional stretchability (1350 %), remarkably low dissipated energy (0.40 kJ m-3 at 100 % strain), high sensitivity (GF = 2.86 at 300-500 % strain), and strong adhesion to various substrate surfaces. The study demonstrated potential applications in the reliable detection of various motions, including repetitive fine movements and large-scale human body motions. This work provided a feasible platform for developing integrated wearable health-monitoring electronic systems.

Sodium carboxymethyl cellulose and MXene reinforced multifunctional conductive hydrogels for multimodal sensors and flexible supercapacitors

With the growing demand for eco-friendly materials in wearable smart electronic devices, renewable, biocompatible, and low-cost hydrogels based on natural polymers have attracted much attention. Cellulose, as one of the renewable and degradable natural polymers, shows great potential in wearable smart electronic devices. Multifunctional conductive cellulose-based hydrogels are designed for flexible electronic devices by adding sodium carboxymethyl cellulose and MXene into polyacrylic acid networks. The multifunctional hydrogels possess excellent mechanical property (stress: 310 kPa; strain: 1127 %), toughness (206.67 KJ m-3), conductivity (1.09 ± 0.12 S m-1) and adhesion (82.19 ± 3.65 kPa). The multifunctional conductive hydrogels serve as strain sensors (Gauge Factor (GF) = 5.79, 0-700 % strain; GF = 14.0, 700-900 % strain; GF = 40.36, 900-1000 % strain; response time: 300 ms; recovery time: 200 ms) and temperature sensors (Temperature coefficient of resistance (TCR) = 2.5755 °C-1 at 35 °C- 60 °C). The sensor detects human activities with clear and steady signals. A distributed array of flexible sensors is created to measure the magnitude and distribution of pressure and a hydrogel-based flexible touch keyboard is also fabricated to recognize writing trajectories, pressures and speeds. Furthermore, a flexible hydrogel-based supercapacitor powers the LED and exhibits good cyclic stability over 15,000 charge-discharge cycles.

MXene-based electrochemical devices applied for healthcare applications

The initial part of the review provides an extensive overview about MXenes as novel and exciting 2D nanomaterials describing their basic physico-chemical features, methods of their synthesis, and possible interfacial modifications and techniques, which could be applied to the characterization of MXenes. Unique physico-chemical parameters of MXenes make them attractive for many practical applications, which are shortly discussed. Use of MXenes for healthcare applications is a hot scientific discipline which is discussed in detail. The article focuses on determination of low molecular weight analytes (metabolites), high molecular weight analytes (DNA/RNA and proteins), or even cells, exosomes, and viruses detected using electrochemical sensors and biosensors. Separate chapters are provided to show the potential of MXene-based devices for determination of cancer biomarkers and as wearable sensors and biosensors for monitoring of a wide range of human activities.

Machine Learning-Enhanced Biomass Pressure Sensor with Embedded Wrinkle Structures Created by Surface Buckling

Flexible piezoresistive sensors are core components of many wearable devices to detect deformation and motion. However, it is still a challenge to conveniently prepare high-precision sensors using natural materials and identify similar short vibration signals. In this study, inspired by microstructures of human skins, biomass flexible piezoresistive sensors were prepared by assembling two wrinkled surfaces of konjac glucomannan and k-carrageenan composite hydrogel. The wrinkle structures were conveniently created by hardness gradient-induced surface buckling and coated with MXene sheets to capture weak pressure signals. The sensor was applied to detect various slight body movements, and a machine learning method was used to enhance the identification of similar and short throat vibration signals. The results showed that the sensor exhibited a high sensitivity of 5.1 kPa-1 under low pressure (50 Pa), a fast response time (104 ms), and high stability over 100 cycles. The XGBoost machine learning model accurately distinguished short voice vibrations similar to those of individual English letters. Moreover, experiments and numerical simulations were carried out to reveal the mechanism of the wrinkle structure preparation and the excellent sensing performance. This biomass sensor preparation and the machine learning method will promote the optimization and application of wearable devices.