Deep-Learning Enabled Active Biomimetic Multifunctional Hydrogel Electronic Skin

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.

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.

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.

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.

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.