Vertical Graphene Canal Mesh for Strain Sensing with a Supereminent Resolution

Multiscale Structural Control by Matrix Engineering for Polydimethylsiloxane Filled Graphene Woven Fabric Strain Sensors

Elastomer cure shrinkage during composite fabrication often induces wrinkling in conductive networks, significantly affecting the performance of flexible strain sensors, yet the specific roles of such wrinkles are not fully understood. Herein, a highly sensitive polydimethylsiloxane-filled graphene woven fabric (PDMS-f-GWF) strain sensor by optimizing the PDMS cure shrinkage through careful adjustment of the base-to-curing-agent ratio is developed. This sensor achieves a gauge factor of ∼700 at 25% strain, which is over 6 times higher than sensors using commercially formulated PDMS. This enhanced sensing performance is attributed to multiscale structural control of the graphene network, enabled by precisely tuned cure shrinkage of PDMS. Using in situ scanning electron microscopy, X-ray scattering, and Raman spectroscopy, an optimized PDMS base-to-curing-agent ratio of 10:0.8 is show that enables interconnected structural changes from atomic to macroscopic scales, including larger "real" strain within the graphene lattice, enhanced flattening of graphene wrinkles, and increased crack density. These findings highlight the critical role of elastomer shrinkage in modulating the multiscale structure of conductive networks, offering new insights into matrix engineering strategies that advance the sensing performance of elastomer-based flexible strain sensors.

Development and Verification of a Vertical Graphene Sensor for Tunnel Leakage Monitoring

To achieve accurate monitoring of water leakage in tunnels, a new vertical graphene sensor is designed and developed. The sensor operates on the principle that the superabsorbent polymer (SAP) swells dramatically upon water absorption. This swelling induces deformation in the vertical graphene (VG) thin film, highly sensitive to such changes. Consequently, water leakage is detected through the resistance alterations in the VG thin film. In this study, the structure, the preparation process, and a simplified theoretical sensing model of the VG thin film are introduced, followed by the structural design, working principle, and calibration experiments of the developed water leakage sensor. Experimental results show that the sensor exhibits ultrahigh sensitivity, with a linearity error of 10.8%, and can detect water leakages as small as 50 μL. To verify the sensor's feasibility, it was deployed in the cross-sea tunnel between the Dayang Station and the Qingdao North Station on Qingdao Metro Line 8, monitoring water leakage of the tunnel surface. Monitoring results indicated that the maximum leakage volume at the leakage location reached 19 mL.

Flexible Sensors with a Multilayer Interlaced Tunnel Architecture for Distinguishing Different Strains

The diversity of body joints and the complexity of joint motions cause flexible strain sensors to undergo complex strains such as stretching, compression, bending, and extrusion, which results in sensors that do not recognize different strains, facing great challenges in detecting the true motion characteristics of joints. Here, the monitoring of body joints' real motion characteristics has been realized by the sensor that can output response signals with different resistance trends for different strains. The sensor prepared by the sacrificial template method is characterized by a multilayered interlaced tunnel architecture and carbon black embedded in the inner wall of the tunnel. Stretching, compressive, and bending strains result in increasing, decreasing, and increasing resistance, followed by a decrease in resistance of the sensor, respectively. The sensor can still output distinguishable response signals, even in the presence of complex strains induced by squeezing. Low strain detection limits (0.03%) and wide detection ranges (>600%) are achieved due to the localized strain enhancement caused by the unique structure. The sensor can detect the motion characteristics of different joints in flexion-extension, abduction-adduction, and internal-external rotation, which, in turn, can be used for real-time monitoring of complex joint motions involved in limb rehabilitation. In addition, the sensor recognizes the 26 letters of the alphabet represented by sign language gestures. The above studies demonstrate the potential application of our prepared sensors in flexible, wearable devices.

Skin-Inspired Ultra-Tough Supramolecular Multifunctional Hydrogel Electronic Skin for Human-Machine Interaction

Multifunctional supramolecular ultra-tough bionic e-skin with unique durability for human-machine interaction in complex scenarios still remains challenging. Herein, we develop a skin-inspired ultra-tough e-skin with tunable mechanical properties by a physical cross-linking salting-freezing-thawing method. The gelling agent (β-Glycerophosphate sodium: Gp) induces the aggregation and binding of PVA molecular chains and thereby toughens them (stress up to 5.79 MPa, toughness up to 13.96 MJ m-3). Notably, due to molecular self-assembly, hydrogels can be fully recycled and reprocessed by direct heating (100 °C for a few seconds), and the tensile strength can still be maintained at about 100% after six recoveries. The hydrogel integrates transparency (> 60%), super toughness (up to 13.96 MJ m-3, bearing 1500 times of its own tensile weight), good antibacterial properties (E. coli and S. aureus), UV protection (Filtration: 80%-90%), high electrical conductivity (4.72 S m-1), anti-swelling and recyclability. The hydrogel can not only monitor daily physiological activities, but also be used for complex activities underwater and message encryption/decryption. We also used it to create a complete finger joint rehabilitation system with an interactive interface that dynamically presents the user's health status. Our multifunctional electronic skin will have a profound impact on the future of new rehabilitation medical, human-machine interaction, VR/AR and the metaverse fields.

Pressure Sensor Based on a Lumpily Pyramidal Vertical Graphene Film with a Broad Sensing Range and High Sensitivity

Wearable sensors are vital for the development of electronic skins to improve health monitoring, robotic tactile sensing, and artificial intelligence. Active materials and the construction of microstructures in the sensitive layer are the dominating approaches to improve the performance of pressure sensors. However, it is still a challenge to simultaneously achieve a sensor with a high sensitivity and a wide detection range. In this work, using three-dimensional (3D) vertical graphene (VG) as an active material, in combination with micropyramid arrays and lumpy holders, the stress concentration effects are generated in nano-, micro-, and macroscales. Therefore, the lumpily pyramidal VG film-based pressure sensor (LPV sensor) achieves an ultrahigh sensitivity (131.36 kPa^-1) and a wide response range (0.1-100 kPa). Finite element analysis demonstrates that the stress concentration effects are enhanced by the micropyramid arrays and lumpy structures in micro- and macroscales, respectively. Finally, the LPV pressure sensors are tested in practical applications, including wearable health monitoring and force feedback of robotic tactile sensing.

Soft Electronics for Health Monitoring Assisted by Machine Learning

Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.