Deep-Learning Enabled Active Biomimetic Multifunctional Hydrogel Electronic Skin

Recent advances in flexible hydrogel sensors: Enhancing data processing and machine learning for intelligent perception

With the advent of flexible electronics and sensing technology, hydrogel-based flexible sensors have exhibited considerable potential across a diverse range of applications, including wearable electronics and soft robotics. Recently, advanced machine learning (ML) algorithms have been integrated into flexible hydrogel sensing technology to enhance their data processing capabilities and to achieve intelligent perception. However, there are no reviews specifically focusing on the data processing steps and analysis based on the raw sensing data obtained by flexible hydrogel sensors. Here we provide a comprehensive review of the latest advancements and breakthroughs in intelligent perception achieved through the fusion of ML algorithms with flexible hydrogel sensors, across various applications. Moreover, this review thoroughly examines the data processing techniques employed in flexible hydrogel sensors, offering valuable perspectives expected to drive future data-driven applications in this field.

Flexible high electrochemical active hydrogel for wearable sensors and supercapacitor electrolytes

With the rapid advancement of flexible, portable devices, hydrogel electrolytes have gained considerable attention as potential replacements for conventional liquid electrolytes. A hydrogel electrolyte was synthesised by cross-linking acrylic acid (AA), acrylamide (AM), carboxymethyl cellulose (CMC), and zinc sulphate (ZnSO4). The formation of hydrogen bonds and chelate interactions between the P(AA-co-AM) polymer, CMC, and ZnSO4 created a robust network, enhancing the mechanical properties of the hydrogel electrolytes. Notably, the hydrogel electrolyte containing 0.6 % CMC demonstrated superior mechanical strength (compression strength of 1.22 MPa, tensile stress of 230 kPa, tensile strain of 424 %, adhesion strength of 1.98 MPa on wood). Additionally, the CMC/P(AA-co-AM) hydrogels exhibited commendable electrical performance (38 mS/cm) and a high gauge factor (2.9), enabling the precise detection of physiological activity signals through resistance measurements. The unique network structure of the hydrogel electrolyte also ensured a stable bonding interface between the electrode and the electrolyte. After 2000 charge-discharge cycles, the supercapacitor maintained good capacitance characteristics, with a capacitance retention rate of 71.21 % and a stable Coulombic efficiency of 98.85 %, demonstrating excellent cyclic stability. This study introduces a novel methodology for fabricating multifunctional all-solid-state supercapacitors and suggests that the hydrogel can significantly advance the development of wearable energy storage devices.

Carbon Quantum Dot-Functionalized Dermis-Derived Transparent Electronic Skin for Multimodal Human Motion Signal Monitoring and Construction of Self-Powered Triboelectric Nanogenerator

Electronic skin (e-skin) is considered as a highly promising interface for human-computer interaction systems and wearable electronic devices. Through elaborate design and assembly of various materials, it possesses multiple characteristics similar to human skin, including remarkable flexibility, stretchability, sensitivity to temperature and humidity, biocompatibility, and efficient interfacial ion/electron transport capabilities. Here, we innovatively integrate multifunctional carbon quantum dots (CQDs), which exhibit conductivity, antibacterial properties, ultraviolet absorption, and fluorescence emission, with poly(acrylic acid) and glycerin (Gly) into a three-dimensional network structure of natural goatskin collagen fibers. Through a top-down design strategy enhanced by hydrogen bond reconstruction, we successfully fabricated a novel transparent e-skin (PAC-eSkin). This e-skin exhibited significant tensile properties (4.94 MPa of tensile strength and 263.42% of a maximum breaking elongation), while also possessing Young's modulus similar to human skin (2.32 MPa). It is noteworthy that the functionalized CQDs used was derived from discarded goat hair, and the addition of Gly gave PAC-eSkin excellent antifreezing and moisturizing properties. Due to the presence of ultrasmall CQDs, which creates efficient ion/electron transport channels within PAC-eSkin, it could rapidly sense human motion and physiological signals (with a gauge factor (GF) of 1.88). Furthermore, PAC-eSkin had the potential to replace traditional electrode patches for real-time monitoring of electrocardiogram, electromyogram, and electrooculogram signals, with a higher SNR (signal-to-noise ratio) of 25.1 dB. Additionally, the customizable size and shape of PAC-eSkin offer vast possibilities for the construction of single-electrode triboelectric nanogenerator systems. We have reason to believe that the design and development of this transparent e-skin based on CQDs-functionalized dermal collagen matrices can pave a new way for innovations in human-computer interaction interfaces and their sensing application in diverse scenarios.

Flexible and wearable electronic systems based on 2D hydrogel composites

Flexible electronics is a rapidly developing field of study, which integrates many other fields, including materials science, biology, chemistry, physics, and electrical engineering. Despite their vast potential, the widespread utilization of flexible electronics is hindered by several constraints, including elevated Young's modulus, inadequate biocompatibility, and diminished responsiveness. Therefore, it is necessary to develop innovative materials aimed at overcoming these hurdles and catalysing their practical implementation. In these materials, hydrogels are particularly promising owing to their three-dimensional crosslinked hydrated polymer networks and exceptional properties, positioning them as leading candidates for the development of future flexible electronics.

Multifunctional Conductive Hydrogel Interface for Bioelectronic Recording and Stimulation

The past few decades have witnessed the rapid advancement and broad applications of flexible bioelectronics, in wearable and implantable electronics, brain-computer interfaces, neural science and technology, clinical diagnosis, treatment, etc. It is noteworthy that soft and elastic conductive hydrogels, owing to their multiple similarities with biological tissues in terms of mechanics, electronics, water-rich, and biological functions, have successfully bridged the gap between rigid electronics and soft biology. Multifunctional hydrogel bioelectronics, emerging as a new generation of promising material candidates, have authentically established highly compatible and reliable, high-quality bioelectronic interfaces, particularly in bioelectronic recording and stimulation. This review summarizes the material basis and design principles involved in constructing hydrogel bioelectronic interfaces, and systematically discusses the fundamental mechanism and unique advantages in bioelectrical interfacing with the biological surface. Furthermore, an overview of the state-of-the-art manufacturing strategies for hydrogel bioelectronic interfaces with enhanced biocompatibility and integration with the biological system is presented. This review finally exemplifies the unprecedented advancement and impetus toward bioelectronic recording and stimulation, especially in implantable and integrated hydrogel bioelectronic systems, and concludes with a perspective expectation for hydrogel bioelectronics in clinical and biomedical applications.

Polyurethane/Carbon Nanotube-Based ThermoSense Electronic Skin: Perception to Decision Making Aided by Internet of Things Brain

Human skin has several receptors collaborating with the brain to provide appropriate "decisions" when applying stimuli. Several research articles state that biomimetic electronic skin (e-skin) is reportedly used for sensor-related applications and performs similarly to natural skin. However, research reporting the capability of the e-skin to make decisions and therefore react upon exposure to adverse conditions is still in its nascent stage. Herein, we report the development of an e-skin, ThermoSense, that can thermoregulate by making appropriate decisions. Thermoplastic polyurethane and multiwalled carbon nanotubes were used as the model composite. The heating and sensing capabilities of the optimized e-skin were studied in detail. In the study window, the e-skin demonstrated excellent electrothermal conversion efficiency by generating a temperature of 192 °C, consuming a power of 2.23 W. A finite element modeling (FEM) was adopted to determine the distribution of the filler in the case of the optimized e-skin and thus was used to probe the reason for the heating across the e-skin via mapping of the internal energy across the sample. FEM results and experimental findings are in strong agreement. Additionally, the e-skin demonstrated its capability to act as a thermal sensor with a 0.947% °C-1 sensitivity. To integrate the decision-making capabilities of the e-skin, an Internet of Things (IoT) brain console was made using the e-skin and electronic chips by leveraging More than Moore's concept. The IoT brain was automated with decision-making programming that was controllable via an in-house-developed mobile application. The console worked exclusively under simulated conditions. When there was a shift from the set point temperature, it started to heat. Postusage, the e-skin matrix was recycled, and the recycled e-skin demonstrated a marginal decrement in performance attributes. This study opens new avenues for developing decision-making e-skins for next-generation human-machine interphases.

Strain-Temperature Dual Sensor Based on Deep Learning Strategy for Human-Computer Interaction Systems

Thermoelectric (TE) hydrogels, mimicking human skin, possessing temperature and strain sensing capabilities, are well-suited for human-machine interaction interfaces and wearable devices. In this study, a TE hydrogel with high toughness and temperature responsiveness was created using the Hofmeister effect and TE current effect, achieved through the cross-linking of PVA/PAA/carboxymethyl cellulose triple networks. The Hofmeister effect, facilitated by Na+ and SO42- ions coordination, notably increased the hydrogel's tensile strength (800 kPa). Introduction of Fe2+/Fe3+ as redox pairs conferred a high Seebeck coefficient (2.3 mV K-1), thereby enhancing temperature responsiveness. Using this dual-responsive sensor, successful demonstration of a feedback mechanism combining deep learning with a robotic hand was accomplished (with a recognition accuracy of 95.30%), alongside temperature warnings at various levels. It is expected to replace manual work through the control of the manipulator in some high-temperature and high-risk scenarios, thereby improving the safety factor, underscoring the vast potential of TE hydrogel sensors in motion monitoring and human-machine interaction applications.

Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing

Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and human-interactive sensing. Nevertheless, it remains a tremendous challenge to simultaneously achieve conformally bioadhesive epidermic electronics with remarkable self-adhesiveness, reliable ultraviolet (UV) protection ability, and admirable sensing performance for high-fidelity epidermal electrophysiological signals monitoring, along with timely photothermal therapeutic performances after medical diagnostic sensing, as well as efficient antibacterial activity and reliable hemostatic effect for potential medical therapy. Herein, a conformally bioadhesive hydrogel-based epidermic sensor, featuring superior self-adhesiveness and excellent UV-protection performance, is developed by dexterously assembling conducting MXene nanosheets network with biological hydrogel polymer network for conformally stably attaching onto human skin for high-quality recording of various epidermal electrophysiological signals with high signal-to-noise ratios (SNR) and low interfacial impedance for intelligent medical diagnosis and smart human-machine interface. Moreover, a smart sign language gesture recognition platform based on collected electromyogram (EMG) signals is designed for hassle-free communication with hearing-impaired people with the help of advanced machine learning algorithms. Meanwhile, the bioadhesive MXene hydrogel possesses reliable antibacterial capability, excellent biocompatibility, and effective hemostasis properties for promising bacterial-infected wound bleeding.

Design of AI-Enhanced and Hardware-Supported Multimodal E-Skin for Environmental Object Recognition and Wireless Toxic Gas Alarm

Post-earthquake rescue missions are full of challenges due to the unstable structure of ruins and successive aftershocks. Most of the current rescue robots lack the ability to interact with environments, leading to low rescue efficiency. The multimodal electronic skin (e-skin) proposed not only reproduces the pressure, temperature, and humidity sensing capabilities of natural skin but also develops sensing functions beyond it-perceiving object proximity and NO2 gas. Its multilayer stacked structure based on Ecoflex and organohydrogel endows the e-skin with mechanical properties similar to natural skin. Rescue robots integrated with multimodal e-skin and artificial intelligence (AI) algorithms show strong environmental perception capabilities and can accurately distinguish objects and identify human limbs through grasping, laying the foundation for automated post-earthquake rescue. Besides, the combination of e-skin and NO2 wireless alarm circuits allows robots to sense toxic gases in the environment in real time, thereby adopting appropriate measures to protect trapped people from the toxic environment. Multimodal e-skin powered by AI algorithms and hardware circuits exhibits powerful environmental perception and information processing capabilities, which, as an interface for interaction with the physical world, dramatically expands intelligent robots' application scenarios.

Smart Cushions with Machine Learning-Enhanced Force Sensors for Pressure Injury Risk Assessment

Prolonged sitting can easily result in pressure injury (PI) for certain people who have had strokes or spinal cord injuries. There are not many methods available for tracking contact surface pressure and shear force to evaluate the PI risk. Here, we propose a smart cushion that uses two-dimensional force sensors (2D-FSs) to measure the pressure and shear force in the buttocks. A machine learning algorithm is then used to compute the shear stresses in the gluteal muscles, which helps to determine the PI risk. The 2D-FS consists of a ferroelectret coaxial sensor (FCS) unit placed atop a ferroelectret film sensor (FFS) unit, allowing it to detect both vertical and horizontal forces simultaneously. To characterize and calibrate, two experimental approaches are applied: one involves simultaneously applying two perpendicular forces, and one involves applying a single force. To separate the two forces, the 2D-FS is decoupled using a deep neural network technique. Multiple FCSs are embedded to form a smart cushion, and a genetic algorithm-optimized backpropagation neural network is proposed and trained to predict the shear strain in the buttocks to prevent PI. By tracking the danger of PI, the smart cushion based on 2D-FSs may be further connected with home-based intelligent care platforms to increase patient equality for spinal cord injury patients and lower the expense of nursing or rehabilitation care.

Hydrogel Loaded with Extracellular Vesicles: An Emerging Strategy for Wound Healing

An increasing number of novel biomaterials have been applied in wound healing therapy. Creating beneficial environments and containing various bioactive molecules, hydrogel- and extracellular vesicle (EV)-based therapies have respectively emerged as effective approaches for wound healing. Moreover, the synergistic combination of these two components demonstrates more favorable outcomes in both chronic and acute wound healing. This review provides a comprehensive discussion and summary of the combined application of EVs and hydrogels to address the intricate scenario of wounds. The wound healing process and related biological mechanisms are outlined in the first section. Subsequently, the utilization of EV-loaded hydrogels during the wound healing process is evaluated and discussed. The moist environment created by hydrogels is conducive to wound tissue regeneration. Additionally, the continuous and controlled release of EVs from various origins could be achieved by hydrogel encapsulation. Finally, recent in vitro and in vivo studies reported on hydrogel dressings loaded with EVs are summarized and challenges and opportunities for the future clinical application of this therapeutic approach are outlined.

Degradable and Recyclable Hydrogels for Sustainable Bioelectronics

Hydrogel bioelectronics has been widely used in wearable sensors, electronic skin, human-machine interfaces, and implantable tissue-electrode interfaces, providing great convenience for human health, safety, and education. The generation of electronic waste from bioelectronic devices jeopardizes human health and the natural environment. The development of degradable and recyclable hydrogels is recognized as a paradigm for realizing the next generation of environmentally friendly and sustainable bioelectronics. This review first summarizes the wide range of applications for bioelectronics, including wearable and implantable devices. Then, the employment of natural and synthetic polymers in hydrogel bioelectronics is discussed in terms of degradability and recyclability. Finally, this work provides constructive thoughts and perspectives on the current challenges toward hydrogel bioelectronics, providing valuable insights and guidance for the future evolution of sustainable hydrogel bioelectronics.

Self-Powered Biomimetic Pressure Sensor Based on Mn-Ag Electrochemical Reaction for Monitoring Rehabilitation Training of Athletes

Self-powered pressure detection using smart wearable devices is the subject of intense research attention, which is intended to address the critical need for prolonged and uninterrupted operations. Current piezoelectric and triboelectric sensors well respond to dynamic stimuli while overlooking static stimuli. This study proposes a dual-response potentiometric pressure sensor that responds to both dynamic and static stimuli. The proposed sensor utilizes interdigital electrodes with MnO2/carbon/polyvinyl alcohol (PVA) as the cathode and conductive silver paste as the anode. The electrolyte layer incorporates a mixed hydrogel of PVA and phosphoric acid. The optimized interdigital electrodes and sandpaper-like microstructured surface of the hydrogel electrolyte contribute to enhanced performance by facilitating an increased contact area between the electrolyte and electrodes. The sensor features an open-circuit voltage of 0.927 V, a short-circuit current of 6 µA, a higher sensitivity of 14 mV/kPa, and outstanding cycling performance (>5000 cycles). It can accurately recognize letter writing and enable capacitor charging and LED lighting. Additionally, a data acquisition and display system employing the proposed sensor, which facilitates the monitoring of athletes' rehabilitation training, and machine learning algorithms that effectively guide rehabilitation actions are presented. This study offers novel solutions for the future development of smart wearable devices.

Projection Stereolithography 3D Printing High-Conductive Hydrogel for Flexible Passive Wireless Sensing

Hydrogel-based electronics have inherent similarities to biological tissues and hold potential for wearable applications. However, low conductivity, poor stretchability, nonpersonalizability, and uncontrollable dehydration during use limit their further development. In this study, projection stereolithography 3D printing high-conductive hydrogel for flexible passive wireless sensing is reported. The prepared photocurable silver-based hydrogel is rapidly planarized into antenna shapes on substrates using surface projection stereolithography. After partial dehydration, silver flakes within the circuits form sufficient conductive pathways to achieve high conductivity (387 S cm-1). By sealing the circuits to prevent further dehydration, the resistance remains stable when tensile strain is less than 100% for at least 30 days. Besides, the sealing materials provide versatile functionalities, such as stretchability and shape memory property. Customized flexible radio frequency identification tags are fabricated by integrating with commercial chips to complete the accurate recognition of eye movement, realizing passive wireless sensing.

Anti-Freezing and Ultrasensitive Zwitterionic Betaine Hydrogel-Based Strain Sensor for Motion Monitoring and Human-Machine Interaction

Ultrasensitive and reliable conductive hydrogels are significant in the construction of human-machine twinning systems. However, in extremely cold environments, freezing severely limits the application of hydrogel-based sensors. Herein, building on biomimetics, a zwitterionic hydrogel was elaborated for human-machine interaction employing multichemical bonding synergies and experimental signal analyses. The covalent bonds, hydrogen bonds, and electrostatic interactions construct a dense double network structure favorable for stress dispersion and hydrogen bond regeneration. In particular, zwitterions and ionic conductors maintained excellent strain response (99 ms) and electrical sensitivity (gauge factor = 14.52) in the dense hydrogel structure while immobilizing water molecules to enhance the weather resistance (-68 °C). Inspired by the high sensitivity, zwitterionic hydrogel-based strain sensors and remote-control gloves were designed by analyzing the experimental signals, demonstrating promising potential applications within specialized flexible materials and human-machine symbiotic systems.

AI energized hydrogel design, optimization and application in biomedicine

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and etc. Then, we focus on the various applications of hydrogels supported by AI technology in biomedicine, including drug delivery, bio-inks for advanced manufacturing, tissue repair, and biosensors, so as to provide a clear and comprehensive understanding of researchers in this field. Finally, we discuss the future directions and prospects, and provide a new perspective for the research and development of novel hydrogel materials for biomedical applications.

Polyvinyl alcohol/chitosan based nanocomposite organohydrogel flexible wearable strain sensors for sports monitoring and underwater communication rescue

Hydrogel-based flexible wearable sensors have garnered significant attention in recent years. However, the use of hydrogel, a biomaterial known for its high toughness, environmental friendliness, and frost resistance, poses a considerable challenge. In this study, we propose a stepwise construction and multiple non-covalent interaction matching strategy to successfully prepare dynamically physically crosslinked multifunctional conductive hydrogels. These hydrogels self-assembled to form a rigid crosslinked network through intermolecular hydrogen bonding and metal ion coordination chelation. Furthermore, the freeze-thawing process promoted the formation of poly(vinyl alcohol) microcrystalline domains within the amorphous hydrogel network system, resulting in exceptional mechanical properties, including a tensile strength (2.09 ± 0.01 MPa) and elongation at break of 562 ± 12 %. It can lift 10,000 times its own weight. Additionally, these hydrogels exhibit excellent resistance to swelling and maintain good toughness even at temperatures as low as -60 °C. As a wearable strain sensor with remarkable sensing ability (GF = 1.46), it can be effectively utilized in water and underwater environments. Moreover, it demonstrates excellent antimicrobial properties against Escherichia coli (Gram-negative bacteria). Leveraging its impressive sensing ability, we combine signal recognition with a deep learning model by incorporating Morse code for encryption and decryption, enabling information transmission.

Synergizing Machine Learning Algorithm with Triboelectric Nanogenerators for Advanced Self-Powered Sensing Systems

The advancement of the Internet of Things (IoT) has increased the demand for large-scale intelligent sensing systems. The periodic replacement of power sources for ubiquitous sensing systems leads to significant resource waste and environmental pollution. Human staffing costs associated with replacement also increase the economic burden. The triboelectric nanogenerators (TENGs) provide both an energy harvesting scheme and the possibility of self-powered sensing. Based on contact electrification from different materials, TENGs provide a rich material selection to collect complex and diverse data. As the data collected by TENGs become increasingly numerous and complex, different approaches to machine learning (ML) and deep learning (DL) algorithms have been proposed to efficiently process output signals. In this paper, the latest advances in ML algorithms assisting solid-solid TENG and liquid-solid TENG sensors are reviewed based on the sample size and complexity of the data. The pros and cons of various algorithms are analyzed and application scenarios of various TENG sensing systems are presented. The prospects of synergizing hardware (TENG sensors) with software (ML algorithms) in a complex environment and their main challenges for future developments are discussed.

Multifunctional actuators integrated with the function of self-powered temperature sensing made with Ti3C2Tx-bamboo nanofiber composites

In recent years, multifunctional actuators have received increasing attention and development. In particular, researchers have conducted extensive research on intelligent actuators with integrated sensing functions. Temperature is an important parameter for the deformation of bilayer thermal actuators. By obtaining the temperature information of a bilayer thermal actuator, the deformation amplitude and its state can be judged. Thus, there is an urgent need to develop a type of intelligent actuator with a self-powered temperature sensing function. Herein, Ti3C2Tx-based composites modified with bamboo nanofibers have been proposed and applied to intelligent actuators integrated with a self-powered temperature sensing function. By utilizing the coefficients of thermal expansion between Ti3C2Tx-bamboo nanofiber composites and a polyimide film, a bilayer photo/electro-driven thermal actuator is designed which shows a bending curvature as large as 1.9 cm-1. In addition, Ti3C2Tx-bamboo nanofiber composites have a Seebeck coefficient of -9.15 μV K-1, and are N-type thermoelectric materials and can be used as the component of self-powered temperature sensors. Finally, a series of practical applications were designed, including a light-driven floating actuator (with a moving speed of 5 mm s-1), biomimetic sunflowers, bionic tentacles, and a multifunctional gripper integrated with a self-powered temperature sensing function. In particular, the multifunctional grippers can output voltage signals carrying their temperature information without external complex power sources, demonstrating their potential for remote monitoring. The above results demonstrate that Ti3C2Tx-bamboo nanofiber composites have extensive practical applications in fields such as self-powered sensors, flexible thermoelectric generators, and soft actuators.

Rapid and Controllable Preparation of Multifunctional Lignin-Based Eutectogels for the Design of High-Performance Flexible Sensors

Currently, there is a limited amount of research on PEDOT:LS (poly(3,4-ethylenedioxythiophene):sulfonated lignin)-based hydrogels. While the addition of PEDOT:LS can enhance the conductivity of the gel, it unavoidably disrupts the gel network and negatively affects its mechanical properties. The preparation process and freezing resistance of the hydrogels also pose significant challenges for their practical applications. In this study, we have developed a novel self-catalytic system, PEDOT:LS-Fe3+, for the rapid fabrication of conductive hydrogels. These hydrogels are further transformed into eutectogels by immersing them in a deep eutectic solvent. Compared with conventional hydrogels, the eutectogels exhibit improved elongation, mechanical strength, and resistance to freezing. Specifically, the eutectogels containing 2 wt % PEDOT:LS as conductive fillers and catalysts demonstrate exceptional stretchability (∼460%), self-adhesion (∼14.6 kPa on paper), UV-blocking capability (∼99.9%), and ionic conductivity (∼1.2 mS cm-1) even at extremely low temperatures (-60 °C). Moreover, the eutectogels exhibit high stability and sensitivity in flexible sensing, successfully detecting various human motions. This study presents a novel approach for the rapid preparation of the hydrogels by utilizing lignin in the conductive PEDOT polymerization process and forming a self-catalytic system with metal ions. These advancements make the eutectogels a promising candidate material for flexible wearable electronics.