The conductive hydrogel has shown extensive applications in multifunctional sensors. However, the intrinsic characteristics of conductive hydrogels, such as lacking anti-freezing, mechanical strength, and being easily dehydrated have hindered their application in wearable devices. Herein, reduced graphene oxide (rGO) and graphene oxide (GO) nanosheets were incorporated into a porous cross-linking poly(vinyl alcohol) (PVA) hydrogel network within dimethyl sulfoxide (DMSO)/H₂O binary solvent. A honeycomb-like dense micro-network and uniform dispersion conductive tough fillers endow high toughness (3.1 MPa @ 600% strain) and conductivity to the organohydrogel. With the intense interaction between water and DMSO, the organohydrogel exhibits reliable mechanical and electronic performance in a wide range of temperatures (−30 to 60 °C) and possesses excellent ambient stability. The organohydrogel had excellent sensitivity and linear repeatable response ability in both mechanical and thermal stimulation. With the integrated unique features, the sensitive organohydrogel was developed for a multifunctional sensor. As a strain/pressure sensor, it exhibited a sensitive response to the applied tensile or compressed strain with repeatability and durability. As a temperature sensor, it presented excellent linear and reliable thermal response with a high sensing accuracy. This work provides a new paradigm for designing a wearable multifunctional sensor or E-skin, which is used to detect the motion or temperature of the human in the future.

Graphene nanoplatelet (GNP) and multi-walled carbon nanotube (MWCNT) hybrid films were prepared with the aid of surfactant Triton X-100 and sonication through a vacuum filtration process. The influence of GNP content ranging from 0 to 50 wt.% on the mechanical and electrical properties was investigated using the tensile test and Hall effect measurement, respectively. It showed that the tensile strength of the hybrid film is decreasing with the increase of the GNP content while the electrical conductivity exhibits an opposite trend. The effectiveness of the MWCNT/GNP hybrid film as a strain sensor is presented. The specimen is subjected to a flexural loading, and the electrical resistance measured by a two-point probe method is found to be function of applied strain. Experimental results demonstrate that there are two different linear strain-sensing stages (0–0.2% and 0.2–1%) in the resistance of the hybrid film with applied strain. The strain sensitivity is increasing with the increase of the GNP content. In addition, the repeatability and stability of the strain sensitivity of the hybrid film were conformed through the cyclic loading–unloading tests. The MWCNT/GNP hybrid film shows promising application for strain sensing.

Intelligent flexible sensors that are comfortable to wear and have self-powered properties are primary candidates for next-generation wearable electronics. Nevertheless, most current flexible sensors cannot work independently, they have to rely on external power. Herein, we report a flexible sensing device that is able to reliably and stably monitor human motion with successive self-powering. The device consists of a supercapacitor as a power source and a strain sensor. An ionic hydrogel was used either as the electrolyte for the supercapacitor or as the functional element for the sensor. Thanks to the superior electromechanical and electrochemical properties of the hydrogel, when used as the electrolyte, the supercapacitor delivers over a wide voltage window (0–2.5 V), and exhibits superior energy density (81.46 mW h cm⁻²), and power density (2500 mW cm⁻²). The ionic hydrogel electrolyte also exhibits environmental durability and strain-resistance, demonstrating its reliability under deformation. Strain sensors based on such ionic organohydrogels operate over a wide working range (0–1000%), with high sensitivity (gauge factor = 6.04), and durability. Such a compact wearable sensing system presents potential applications in health monitoring by detecting motion precisely and rapidly. When machine learning is combined into a wearable sensing system, the intelligent device offers new prospects for tackling challenges in wearable electronics.

A flexible hybrid film consisting of graphene nanoplatelets (GNPs) and multi-walled carbon nanotubes (MWCNTs) was prepared and employed as a multifunctional sensor to monitor temperature and liquid leakage, based on the piezoresistive effect. The influences of the GNP content on the mechanical, thermal, and sensing properties were investigated. Experimental results showed that both the hardness and Young’s modulus of the hybrid film were decreased with an increasing GNP content, while the thermal conductivity exhibited in an opposite trend. The electrical resistance of the hybrid film decreased was linearly with an increase in temperature. The resistance change increased linearly with an increase of the solvent adsorption. These features demonstrated the potential applications of the hybrid film in the detection of temperature, and liquid leakage. The sensitivity of leakage detection is increasing with the increase of the GNP loading, while temperature sensitivity is in the opposite trend.

Vibration measurement serves as the basis for various engineering practices such as natural frequency or resonant frequency estimation. As image acquisition devices become cheaper and faster, vibration measurement and frequency estimation through image sequence analysis continue to receive increasing attention. In the conventional photogrammetry and optical methods of frequency measurement, vibration signals are first extracted before implementing the vibration frequency analysis algorithm. In this work, we demonstrate that frequency prediction can be achieved using a single feed-forward convolutional neural network. The proposed method is verified using a vibration signal generator and excitation system, and the result compared with that of an industrial contact vibrometer in a real application. Our experimental results demonstrate that the proposed method can achieve acceptable prediction accuracy even in unfavorable field conditions.

The cross sections of blended natural/styrene-butadiene (NSBR) composites filled with different volume fractions of carbon particles were observed using a Quanta 250 scanning electron microscope. In addition, the sizes and distributions of the carbon particles were analyzed using Nano Measurer. A two-dimensional representative volume element model (RVE) for a rubber composite reinforced with circular carbon particles was established, and the uniaxial tensile behaviors of polymer nanocomposites with different particle size distribution patterns were simulated using the ABAQUS software. The results showed the following. (1) For the random models, if the difference of particle size was larger and particle distance was closer, stress distribution would be denser as well as the stress concentration would become greater. However, if the difference of particle size was small, for the case of same particle volume fraction, the particle size has little influence on the macromechanical properties whether the average size is large or small. (2) The correlation between the volume fraction and distribution of the carbon particles revealed that when the volume fraction of carbon black particles was larger than 12%, clusters between carbon particles in the polymer nanocomposites could not be avoided and the modulus of the composites increased with an increase in the cluster number.