Multiple physical crosslinked highly adhesive and conductive hydrogels for human motion and electrophysiological signal monitoring.

Hydrogel-based flexible electronic devices serve as a next-generation bridge for human-machine interaction and find extensive applications in clinical therapy, military equipment, and wearable devices. However, the mechanical mismatch between hydrogels and human tissues, coupled with the failure of conformal interfaces, hinders the transmission of information between living organisms and flexible devices, which resulted in the instability and low fidelity of signals, especially in the acquisition of electromyographic (EMG) and electrocardiographic (ECG) signals. In this study, we designed an ion-conductive hydrogel (ICHgel) utilizing multiple physical interactions, successfully applied for human motion monitoring and the collection of epidermal physiological signals. By incorporating fumed silica (F-SiO2) nanoparticles and calcium chloride into an interpenetrating network (IPN) composed of polyvinyl alcohol (PVA) and polyacrylamide (AAm)/acrylic acid (AA) chains, the ICHgel exhibited exceptional tunable stretchability (>1450% strain) and conductivity (10.58 ± 0.85 S m-1). Additionally, the outstanding adhesion of the ICHgel proved to be a critical factor for effective communication between epidermal tissues and flexible devices. Demonstrating its capability to acquire stable electromechanical signals, the ICHgel was attached to different parts of the human body. More importantly, as a flexible electrode, the ICHgel outperformed commercial Ag/AgCl electrodes in the collection of ECG and EMG signals. In summary, the synthesized ICHgel with its outstanding conformal interface capabilities and mechanical adaptability paves the way for enhanced human-machine interaction, fostering the development of flexible electronic devices.

Bimodal Intelligent Electronic Skin Based on Proximity and Tactile Interaction for Pressure and Configuration Perception.

The flexible bimodal e-skin exhibits significant promise for integration into the next iteration of human-computer interactions, owing to the integration of tactile and proximity perception. However, those challenges, such as low tactile sensitivity, complex fabrication processes, and incompatibility with bimodal interactions, have restricted the widespread adoption of bimodal e-skin. Herein, a bimodal capacitive e-skin capable of simultaneous tactile and proximity sensing has been developed. The entire process eliminates intricate fabrication techniques, employing DLP-3D printing for the electrode layers and sacrificial templating for the dielectric layers, conferring high tactile sensitivity (1.672 kPa-1) and rapid response capability (∼30 ms) to the bimodal e-skin. Moreover, exploiting the "fringing electric field" effect inherent in parallel-plate capacitors has facilitated touchless sensing, thereby enabling static distance recognition and dynamic gesture recognition of varying materials. Interestingly, an e-skin sensing array was created to identify the positions and pressure levels of various objects of different masses. Furthermore, with the aid of machine learning techniques, an artificial neural network has been established to possess intelligent object recognition capabilities, facilitating the identification, classification, and training of various object configurations. The advantages of the bimodal e-skin render it highly promising for extensive applications in the field of next-generation human-machine interaction.

Physiological sensing system integrated with vibration sensor and frequency gel dampers inspired by spider.

Recent advances in bioelectronics in mechanical and electrophysiological signal detection are remarkable, but there are still limitations because they are inevitably affected by environmental noise and motion artifacts. Thus, we develop a gel damper-integrated crack sensor inspired by the vibration response of the viscoelastic cuticular pad and slit organs in a spider. Benefitting from the specific crack structure design, the sensor possesses excellent sensing behaviors, including a low detection limit (0.05% strain), ultrafast response ability (3.4 ms) and superior durability (>300 000 cycles). Such typical low-amplitude fast response properties allow the ability to accurately perceive vibration frequency and waveform. In addition, the gel damper exhibits frequency-dependent dynamic mechanical behavior that results in improved stability and reliability of signal acquisition by providing shock resistance and isolating external factors. They effectively attenuate external motion artifacts and low-frequency mechanical noise, resulting in cleaner and more reliable signal acquisition. When the gel damper is combined with the crack-based vibration sensor, the integrated sensor exhibits superior anti-interference capability and frequency selectivity, demonstrating its effectiveness in extracting genuine vocal vibration signals from raw voice recordings. The integration of damping materials with sensors offers an efficient approach to improving signal acquisition and signal quality in various applications.

Self-Powered Integrated System with a Flexible Strain Sensor and a Zinc-Air Battery.

At present, self-powered, lightweight, and flexible sensors are widely applied, especially in the fields of wearable devices and human health monitoring. Nevertheless, conventional self-powered flexible sensor systems rely on power supply components such as supercapacitors, nanofriction generators, and solar cells, which present certain limitations, such as high dependence on external environmental factors and the inability to provide long-term stable energy supply. Thus, a paramount exigency emerges for the development of wearable sensors endowed with enduring battery life to enable continuous monitoring of human motion for extended periods. In our academic study, we present an innovative self-powered sensing system that seamlessly combines a pliable zinc-air battery with a strain sensor. This approach offers a stable output signal over extended periods without an external energy device, which is crucial for long-term, continuous human motion monitoring. Through the incorporation of various carbon materials, we realized the multifunction of poly(vinyl alcohol) (PVA)/poly(acrylic acid) (PAA) dual network hydrogels and prepared zinc-air battery electrolytes and strain sensors. Notably, the batteries exhibit impressive power density (82.5 mW cm-2), high open-circuit voltage (1.42 V), and remarkable environmental stability. Even when subjected to puncture and breakage, the batteries remain operational without suffering from electrolyte leakage. Similarly, our strain sensor boasts a broad working range spanning from 0 to 1400%, coupled with a remarkable sensitivity (GF = 2.99) and exceptional capacity to accurately detect various mechanical deformations. When integrated into a single system, the integrated system can monitor human movement for up to 10 h, which has broad prospects in wearable sensor applications.

A liquid-free conducting ionoelastomer for 3D printable multifunctional self-healing electronic skin with tactile sensing capabilities.

Conductive elastomers with both softness and conductivity are widely used in the field of flexible electronics. Nonetheless, conductive elastomers typically exhibit prominent problems such as solvent volatilization and leakage, and poor mechanical and conductive properties, which limit their applications in electronic skin (e-skin). In this work, a liquid-free conductive ionogel (LFCIg) with excellent performance was fabricated by utilizing the innovative double network design approach based on a deep eutectic solvent (DES). The double-network LFCIg is cross-linked by dynamic non-covalent bonds, which exhibit excellent mechanical properties (2100% strain while sustaining a fracture strength of 1.23 MPa) and >90% self-healing efficiency, and a superb electrical conductivity of 23.3 mS m-1 and 3D printability. Moreover, the conductive elastomer based on LFCIg has been developed into a stretchable strain sensor that achieves accurate response recognition, classification, and identification of different robot gestures. More impressively, an e-skin with tactile sensing functions is produced by in situ 3D printing of sensor arrays on flexible electrodes to detect light weight objects and recognize the resulting spatial pressure variations. Collectively, the results demonstrate that the designed LFCIg has unparalleled advantages and presents wide application potential in flexible robotics, e-skin and physiological signal monitoring.

Tough and Robust Mechanically Interlocked Gel-Elastomer Hybrid Electrode for Soft Strain Gauge.

Soft strain gauges provide a flexible and versatile alternative to traditional rigid and inextensible gauges, overcoming issues such as impedance mismatch, the limited sensing range, and fatigue/fracture. Although several materials and structural designs are used to fabricate soft strain gauges, achieving multi-functionality for applications remains a significant challenge. Herein, a mechanically interlocked gel-elastomer hybrid material is exploited for soft strain gauge. Such a material design provides exceptional fracture energy of 59.6 kJ m-2 and a fatigue threshold of 3300 J m-2 , along with impressive strength and stretchability. The hybrid material electrode possesses excellent sensing performances under both static and dynamic loading conditions. It boasts a tiny detection limit of 0.05% strain, ultrafast time resolution of 0.495 ms, and high linearity. This hybrid material electrode can accurately detect full-range human-related frequency vibrations ranging from 0.5 to 1000 Hz, enabling the measurement of physiological parameters. Additionally, the patterned soft strain gauge, created through lithography, demonstrates superior signal-noise rate and electromechanical robustness against deformation. By integrating a multiple-channel device, an intelligent motion detection system is developed, which can classify six typical human body movements with the assistance of machine learning. This innovation is expected to drive advancements in wearable device technology.

Tough hydrogel with high water content and ordered fibrous structures as an artificial human ligament.

Natural biological tissues such as ligaments, due to their anisotropic across scale structure, have high water content, while still maintaining high strength and flexibility. Hydrogels are ideal artificial materials like human ligaments. However, conventional gel materials fail to exhibit high strength or fatigue resistance at high water content in human tissues. To address this challenge, we propose a simple integrated strategy to prepare an anisotropic hierarchical hydrogel architecture for artificial ligaments by combining freeze-casting assisted compression annealing and salting-out treatments. The hybrid polyvinyl alcohol hydrogels are of water content up to 79.5 wt%. Enhanced by the added carbon nanotubes, the hydrogels exhibit high strength of 4.5 MPa and a fatigue threshold of 1467 J m-2, as well as excellent stress sensitivity. The outstanding durability of the artificial ligament provides an all-around solution for biomedical applications.

Fault diagnosis method and application based on unsaturated piecewise linear stochastic resonance.

Signal detection and processing have become an important way to diagnose mechanical faults. The classical bistable stochastic resonance (CBSR) method for signal detection can become saturated, where the amplitude of the output signal gradually stabilizes at a relatively low level instead of increasing with further increases of the input signal amplitude. This leads to difficulty in extracting the weak signals from strong background noise. We studied a new mechanism based on unsaturated piecewise linear stochastic resonance (PLSR). The piecewise linear potential model has a unique structure, which can independently adjust the barrier height and potential wall inclination, so the piecewise linear potential model has a rich potential structure. The rich potential structure makes the potential model unsaturated, thus ensuring that the output signals increase as the input signals increase. In addition, according to the piecewise linear model, the output signal-to-noise ratio (SNR) of the system is deducted. Analysis of the influence of signal strength, potential parameters, and angular frequency on the SNR shows that the optimal SNR can be obtained by adjusting the potential parameters. We propose a weak signal detection method for bearing fault diagnosis. This method can effectively extract the weak fault signals from rolling bearings in a strong noise background. The simulated and experimental bearing fault signals verify that the proposed PLSR method is superior to the CBSR method.