Strong and Tough Antifreezing Hydrogel Sensor via the Synergy of Coordination and Hydrogen Bonds

Hydrogel sensors are fascinating as flexible sensors and electronic skin due to their excellent biocompatibility and structure controllability. However, developing conductive hydrogels possessing both excellent mechanical and antifreezing properties for environmental-adaptive sensors remains a challenge. Herein, a strategy of combining betaine and metal ions to construct poly(acrylic acid) (PAA)-based high-conductive hydrogels has been reported. PAA-Al3+/betaine hydrogels with high toughness and antifreezing property were prepared by a one-step UV curing method. Their high toughness is attributed to the coordination of metal ions with the carboxylic groups in PAA, the interaction of betaine with PAA, and the formation of hydrogen bonds between them and water molecules. Moreover, the significant antifreezing property is due to the reduction of free water in the hydrogel. This, in turn, is attributed to the hydration of metal ions and the synergistic hydrogen bonding between betaine and water. The experiments demonstrate that the hydrogel has excellent mechanical property, high conductivity, superior transparency, antiswelling property, antipuncture as well as shape memory properties, and especially, low cytotoxicity. It can be used as a sensor for motion detection and information recognition. This work provides new insights into the application of flexible sensors and human-machine interfaces in multienvironmental conditions.

Reliable and Scalable Piezoresistive Sensors with an MXene/MoS2 Hierarchical Nanostructure for Health Signals Monitoring

The increased popularity of wearable electronic devices has led to a greater need for advanced sensors. However, fabricating pressure sensors that are flexible, highly sensitive, robust, and compatible with large-scale fabrication technology is challenging. This work investigates a piezoresistive sensor constructed from an MXene/MoS2 hierarchical nanostructure, which is obtained through an easy and inexpensive fabrication process. The sensor exhibits a high sensitivity of 0.42 kPa-1 (0-1.5 kPa), rapid response (∼36 ms), and remarkable mechanical durability (∼10,000 cycles at 13 kPa). The sensor has been demonstrated to be successful in detecting human motion, speech recognition, and physiological signals, particularly in analyzing human pulse. These data can be used to alert and identify irregularities in human health. Additionally, the sensing units are able to construct sensor arrays of various sizes and configurations, enabling pressure distribution imaging in a variety of application scenarios. This research proposes a cost-effective and scalable approach to fabricating piezoresistive sensors and sensor arrays, which can be utilized for monitoring human health and for use in human-machine interfaces.

All-Fabric Tactile Sensors Based on Sandwich Structure Design with Tunable Responsiveness

Capacitance tactile sensors (TSs) based on electrode distance and contact area variations have been notably employed for various purposes due to their magnificent stress sensitivity. Nevertheless, developing TSs with tunable responsiveness in a broad pressure interval is crucial owing to the trade-off between sensitivity and linear identification range. Herein, a TS including Ag-coated Velcro and spacer fabric is constructed, where its sandwich framework provides a sizable expansion in compression deformation ability. In addition, a multilayered framework composed of the stacked TS from self-adhesive Velcro provides more contact area and significant deformation for stress distribution, further balancing the sensitivity, sensing range, and linearity for smart garment application. By utilizing the overlaid selection of multilayer structures, the all-textile TS demonstrates outstanding sensitivity with a one-layer structure (0.036 kPa^-1) over a pressure range of 0.2-5 kPa and retains a sensitivity of 0.002 kPa^-1 in a four-layer structure over a wide pressure range of 0.2-110 kPa, representing a significant improvement compared to previous results. The sensor possesses excellent performance in terms of response speed (104 ms), repeatability (10,000 cycles), and flexibility. In addition, its significant applications, involving human motion detection, pliable keyboards, and human-computer interface, are successfully shown. Based on the facile and scalable manufacturing approach, a suitable procedure is presented to construct next-generation wearable electronics.

Human-Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

With the development of science and technology, human-machine interaction has brought great benefits to the society. Here, we design a voice and gesture signal translator (VGST), which can translate natural actions into electrical signals and realize efficient communication in human-machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response of 20-2000 Hz with a high sensitivity of 167 mV/dB, and the resolution of frequency detection can reach 0.1 Hz. By designing its internal structure, its resonant frequency and output voltage can be adjusted. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function of speech recognition with a high accuracy rate of 97%. It also has good antinoise performance to recognize speech correctly even in noisy environments. Meanwhile, in gesture recognition, the triboelectric translator is able to recognize simple hand gestures and to judge the distance between hand and the VGST based on the principle of electrostatic induction. This work demonstrates that triboelectric nanogenerator (TENG) technology can have great application prospects and significant advantages in human-machine interaction and high-fidelity platforms.

Flexible Triboelectric Tactile Sensor Based on a Robust MXene/Leather Film for Human-Machine Interaction

With the rapid development of Internet of Things (IoT) technology in recent years, self-actuated sensor systems without an external power supply such as flexible triboelectric nanogenerator (TENG)-based strain sensors have received wide attention due to their simple structure and self-powered active sensing properties. However, to satisfy the practical applications of human wearable biointegration, flexible TENGs impose higher requirements for establishing a balance between material flexibility and good electrical properties. In this work, the strength of the MXene/substrate interface was greatly improved by utilizing leather with a unique surface structure as the substrate material, resulting in a mechanically strong and electrically conductive MXene film. Due to the natural fiber structure of the leather surface, the surface of the MXene film with a rough structure was obtained, which improved the electrical output performance of the TENG. The electrode output voltage of MXene film on leather based on single-electrode TENG can reach 199.56 V and the maximum output power density can reach 0.469 mW/cm². Combined with laser-assisted technology, the efficient array preparation of MXene and graphene was achieved and applied to various human-machine interface (HMI) applications.

Cellulose Nanocrystal-Based All-3D-Printed Pyro-Piezoelectric Nanogenerator for Hybrid Energy Harvesting and Self-Powered Cardiorespiratory Monitoring toward the Human-Machine Interface

Biomaterials with spontaneous piezoelectric property are highly emerging in recent times for the generation of electricity from mechanical energy sources that are amply available in nature. In this context, pyroelectricity, an integral property of piezoelectric materials, might be an interesting tool in harvesting thermal energy from the fluctuations of temperature. On the other hand, respiration and heart pulse are the significant human vital signs that can be used for early detection and prevention of cardiorespiratory diseases. Here, we report an all-three-dimensional (3D)-printed pyro-piezoelectric nanogenerator (Py-PNG) based on the most abundant and completely biodegradable biopolymer on earth, i.e., cellulose nanocrystal (CNC) for hybrid (mechanical as well as thermal) energy harvesting, and interestingly, the NG could be used as an e-skin sensor for application in self-powered noninvasive cardiorespiratory monitoring for personal healthcare. Notably, the CNC-based device will be biocompatible and economically advantageous due to its biomaterial-based supremacy and huge availability. This is an original approach with 3D geometrical advancement in designing a NG/sensor, where the unique all-3D-printed manner is adopted, and certainly, it has promising potential in reducing the number of processing steps to required equipment during the multilayer fabrication. The all-3D-printed NG/sensor shows outstanding mechano-thermal energy harvesting performance along with sensitivity and is capable of accurate detection of heart pulse as well as respiration, whenever and whichever required without the need of any battery or an external power supply. In addition, we have also extended its application in demonstrating a smart mask-based breath monitoring system. Thus, the real-time cardiorespiratory monitoring provides notable and fascinating information in medical diagnosis, stepping toward biomedical device development and human-machine interface.

Soft Wireless Headband Bioelectronics and Electrooculography for Persistent Human-Machine Interfaces

Recent advances in wearable technologies have enabled ways for people to interact with external devices, known as human-machine interfaces (HMIs). Among them, electrooculography (EOG), measured by wearable devices, is used for eye movement-enabled HMI. Most prior studies have utilized conventional gel electrodes for EOG recording. However, the gel is problematic due to skin irritation, while separate bulky electronics cause motion artifacts. Here, we introduce a low-profile, headband-type, soft wearable electronic system with embedded stretchable electrodes, and a flexible wireless circuit to detect EOG signals for persistent HMIs. The headband with dry electrodes is printed with flexible thermoplastic polyurethane. Nanomembrane electrodes are prepared by thin-film deposition and laser cutting techniques. A set of signal processing data from dry electrodes demonstrate successful real-time classification of eye motions, including blink, up, down, left, and right. Our study shows that the convolutional neural network performs exceptionally well compared to other machine learning methods, showing 98.3% accuracy with six classes: the highest performance till date in EOG classification with only four electrodes. Collectively, the real-time demonstration of continuous wireless control of a two-wheeled radio-controlled car captures the potential of the bioelectronic system and the algorithm for targeting various HMI and virtual reality applications.

Highly Sensitive, Stretchable, and Robust Strain Sensor Based on Crack Propagation and Opening

Soft and stretchable strain sensors have been attracting significant attention. However, the trade-off between the sensitivity (gauge factor) and the sensing range has been a major challenge. In this work, we report a soft stretchable resistive strain sensor with an unusual combination of high sensitivity, large sensing range, and high robustness. The sensor is made of a silver nanowire network embedded below the surface of an elastomeric matrix (e.g., poly(dimethylsiloxane)). Periodic mechanical cuts are applied to the top surface of the sensor, changing the current flow from uniformly across the sensor to along the conducting path defined by the open cracks. Both experiment and finite element analysis are conducted to study the effect of the slit depth, slit length, and pitch between the slits. The stretchable strain sensor can be integrated into wearable systems for monitoring physiological functions and body motions associated with different levels of strain, such as blood pressure and lower back health. Finally, a soft three-dimensional (3D) touch sensor that tracks both normal and shear stresses is developed for human-machine interfaces and tactile sensing for robotics.

Balanced Coexistence of Reversible and Irreversible Covalent Bonds in a Conductive Triple Polymeric Network Enables Stretchable Hydrogels with High Toughness and Adhesiveness

The application of soft hydrogels to stretchable devices has attracted increasing attention in deformable bioelectronics owing to their unique characteristic, "modulus matching between materials and organs". Despite considerable progress, their low toughness, low conductivity, and absence of tissue adhesiveness remain substantial challenges associated with unstable skin-interfacing, where body movements undesirably disturb electrical signal acquisitions. Herein, we report a material design of a highly tough strain-dissipative and skin-adhesive conducting hydrogel fabricated through a facile one-step sol-gel transition and its application to an interactive human-machine interface. The hydrogel comprises a triple polymeric network where irreversible amide linkage of polyacrylamide with alginate and dynamic covalent bonds entailing conjugated polymer chains of poly(3,4-ethylenedioxythiophene)-co-(3-thienylboronic acid) are simultaneously capable of high stretchability (1300% strain), efficient strain dissipation (36,209 J/m²), low electrical resistance (590 Ω), and even robust skin adhesiveness (35.0 ± 5.6 kPa). Based on such decent characteristics, the hydrogel was utilized as a multifunctional layer for successfully performing either electrophysiological cardiac/muscular on-skin sensors or an interactive stretchable human-machine interface.

A Smart Pen Based on Triboelectric Effects for Handwriting Pattern Tracking and Biometric Identification

The rapid development of artificial intelligence places high demands on human-machine interfaces. Various types of huma-machine interfaces have been implemented, including smart keyboards, electronic skins, and wearable motion sensors. Handwriting behavior has a high degree of interaction freedom, and handwriting characteristics offer high-security standards for human-machine systems. Herein, we propose a portable smart pen integrated with triboelectric displacement vector sensors to trace handwriting trajectories for human-machine interactions and biometric identification. A triboelectric pressure sensor array is evenly distributed along the pen case to sense displacement vectors, and an additional triboelectric sensor is placed on top of the pen to detect vertical force. By leveraging the resin pen refill as a tribopositive material and a nanowired polyethylene tribonegative layer attached to a Cu electrode, triboelectric signals are generated during the writing/moving process. The calculation and analysis of the sensor signals enable the recognition of handwritten patterns, including Latin letters and Arabic numerals. Moreover, the smart pen can be used to authenticate users based on their unique handwriting patterns, which can help take human-machine interfaces and cyber security to the next level. Furthermore, a custom smart pen operation mode that enables the control of a slide presentation demonstrates the smart pen's potential for various human-machine interface applications.

3D MXene/PEDOT:PSS Composite Aerogel with a Controllable Patterning Property for Highly Sensitive Wearable Physical Monitoring and Robotic Tactile Sensing

MXene based composite conductive aerogels have been extensively investigated as sensitive materials for wearable pressure sensors owing to their effective 3D network microstructures and the excellent conductivity of MXene. In this work, we fabricated a 3D porous Ti3C2Tx MXene/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) composite aerogel (MPCA) with a controllable patterning property utilizing the Cu-assisted electrogelation method. The prepared composite aerogel can be assembled into pressure sensors for wearable physical monitoring and high-resolution sensor microarrays for robotic tactile sensing. The multi-interactions between MXene and PEDOT:PSS enable the MPCA to have a stable 3D conductive network, which consequently enhances both the mechanical flexibility and the piezoresistive property of the MPCA. Thus, the fabricated pressure sensor demonstrating high sensitivity (26.65 kPa-1 within 0-2 kPa), fast response ability (106 ms), and excellent stability can be further applied for wearable physical monitoring. Moreover, due to the controllable patterning property of the electrogelation preparation method, a high-resolution pressure sensor microarray was successfully prepared as an artificial tactile interface, which can be attached to a robotic fingertip to directly recognize the tactile stimuli from human fingers and identify braille letters like human fingers. The proposed MPCA, endowed with a remarkable comprehensive property, particularly the highly sensitive sensing performance and controllable patterning property, demonstrates an enormous advantage and a great potentiality toward wearable electronics.

Molecular Rationale for the Design of Instantaneous, Strain-Tolerant Polymeric Adhesive in a Stretchable Underwater Human-Machine Interface

Strain-tolerant reversible adhesion under harsh mechanical deformation is important for realizing long-lasting polymeric adhesives. Despite recent advances, cohesive failure within adhesives remains a critical problem that must be solved to achieve adhesion that is robust against humidity, heat, and mechanical stress. Here, we report a molecular rationale for designing an instantaneous polymeric adhesive with high strain tolerance (termed as iPASTE) even in a stretchable human-machine interface. The iPASTE consists of two biocompatible and eco-friendly polymers, linearly oligomerized green tea extracts, and poly(ethylene glycol) for densely assembled networks via dynamic and reversible hydrogen bonds. Other than the typical approach containing nanoclay or branched adhesive precursors, the linear configuration and conformation of such polymer chains within iPASTE lead to strong and moisture-resistant cohesion/adhesion. Based on the strain-tolerant adhesion of iPASTE, it was demonstrated that a subaqueous interactive human-machine interface integrated with a robot arm and a gold nanomembrane strain-sensitive electronic skin can precisely capture a slithery artificial fish by using finger gesture recognition.

Ultrastretchable, Adhesive, Fast Self-Healable, and Three-Dimensional Printable Photoluminescent Ionic Skin Based on Hybrid Network Ionogels

Developing multifunctional stretchable ionic skin (I-Skin) to mimic the sensations of the human skin is of great interest and shows promising potential in wearable sensors and human-machine interfaces (HMIs). However, common ionogels prepared with small-molecule cross-linkers and single networks can hardly satisfy the requirements of adjustable mechanical properties, strong adhesion, fast self-healability, and good stability in extreme environments. Herein, an ultrastretchable (>10,000%), ultrastrong adhesive (>6.8 MPa), ultrafast self-healable (10 s), high thermally stable (-60 to 250 °C), and three-dimensional (3D)-printable photoluminescent ionogel with shape memory properties has been designed. The ionogel consists of hyperbranched polymer covalent-cross-linked poly(zwitterionic ionic liquid)-co-poly(acrylic acid) and multiple dynamic bonding cross-linked networks. The excellent performance of the ionogel-based high-stretchable strain sensor and the triboelectric nanogenerator (TENG)-based self-powered touch sensor is further demonstrated over a wide temperature range (-40 to 150 °C). More importantly, ionogel-based I-Skin can work as an HMI for human gesture recognition and real-time wireless control of robots under extreme vacuum conditions and can also self-heal immediately along with function recovery after mechanical damage.

Finger Pad Topography beyond Fingerprints: Understanding the Heterogeneity Effect of Finger Topography for Human-Machine Interface Modeling

With the rapid development of haptic devices, there is an increasing demand to understand finger pad topography under different conditions, especially for investigation of the human-machine interface in surface haptic devices. An accurate description of finger pad topography across scales is essential for the study of the interfaces and could be used to predict the real area of contact and friction force, both of which correlate closely with human tactile perception. However, there has been limited work reporting the heterogeneous topography of finger pads across scales. In this work, we propose a detailed heterogeneous finger topography model based on the surface roughness power spectrum. The analysis showed a significant difference between the topography on ridges and valleys of the fingerprint and that the real contact area estimation could be different by a factor of 3. In addition, a spatial-spectral analysis method is developed to effectively compare topography response to different condition changes. This paper provides insights into finger topography for advanced human-machine interaction interfaces.

Printed, Wireless, Soft Bioelectronics and Deep Learning Algorithm for Smart Human-Machine Interfaces

Recent advances in flexible materials and wearable electronics offer a noninvasive, high-fidelity recording of biopotentials for portable healthcare, disease diagnosis, and machine interfaces. Current device-manufacturing methods, however, still heavily rely on the conventional cleanroom microfabrication that requires expensive, time-consuming, and complicated processes. Here, we introduce an additive nanomanufacturing technology that explores a contactless direct printing of aerosol nanomaterials and polymers to fabricate stretchable sensors and multilayered wearable electronics. Computational and experimental studies prove the mechanical flexibility and reliability of soft electronics, considering direct mounting to the deformable human skin with a curvilinear surface. The dry, skin-conformal graphene biosensor, without the use of conductive gels and aggressive tapes, offers an enhanced biopotential recording on the skin and multiple uses (over ten times) with consistent measurement of electromyograms. The combination of soft bioelectronics and deep learning algorithm allows classifying six classes of muscle activities with an accuracy of over 97%, which enables wireless, real-time, continuous control of external machines such as a robotic hand and a robotic arm. Collectively, the comprehensive study of nanomaterials, flexible mechanics, system integration, and machine learning shows the potential of the printed bioelectronics for portable, smart, and persistent human-machine interfaces.

MXene-Coated Air-Permeable Pressure-Sensing Fabric for Smart Wear

Considering the fast development of wearable electronics and soft robotics, pressure sensors with high sensitivity, durability, and washability are of great importance. However, the surface modification of fabrics with high-sensitivity active materials requires that issues associated with poor interface adhesion and stability are resolved. In this study, we explored the key factors for firmly bonding MXene to fabric substrates to fabricate wearable and washable pressure sensing fabric. The interactions between MXene and various fabrics were elucidated by investigating the adsorption and binding capacities. The natural rough surface of cotton fibers also promoted the firm adsorption of MXene. As a result, MXene was difficult to detach, even with mechanical washing and ultrasonic treatment. Further, the abundant functional groups on the MXene surface were conducive to interfacial interactions with cotton fibers. An increase in the amount of fluorine-containing functional groups also improved the hydrophobicity of the fabric surface. The good force-sensitive resistance of MXene-coated cotton allowed this pressure-sensing fabric to function as a flexible pressure sensor, which showed a high gauge factor (7.67 kPa^-1), a rapid response and relaxation speed (<35 ms), excellent stability (>2000 cycles), and good washing durability. Further, the as-fabricated flexible pressure sensor was demonstrated as a wearable human-machine interface that supported multitouch interactions and exhibited a rapid response. Thus, this work provides a new approach for developing next-generation high-sensitivity wearable pressure sensors.

Wearable Triboelectric-Human-Machine Interface (THMI) Using Robust Nanophotonic Readout

With the rapid advances in wearable electronics and photonics, self-sustainable wearable systems are desired to increase service life and reduce maintenance frequency. Triboelectric technology stands out as a promising versatile technology due to its flexibility, self-sustainability, broad material availability, low cost, and good scalability. Various triboelectric-human-machine interfaces (THMIs) have been developed including interactive gloves, eye blinking/body motion-triggered interfaces, voice/breath monitors, and self-induced wireless interfaces. Nonetheless, THMIs conventionally use electrical readout and produce pulse-like signals due to the transient charge flows, leading to unstable and lossy transfer of interaction information. To address this issue, we propose a strategy by equipping THMIs with robust nanophotonic aluminum nitride (AlN) modulators for readout. The electrically capacitive nature of AlN modulators enables THMIs to work in the open-circuit condition with negligible charge flows. Meanwhile, the interaction information is transduced from THMIs' voltage to AlN modulators' optical output via the electro-optic Pockels effect. Thanks to the negligible charge flow and the high-speed optical information carrier, stable, information-lossless, and real-time THMIs are achieved. Leveraging the design flexibility of THMIs and nanophotonic readout circuits, various linear sensitivities independent of force speeds are achieved in different interaction force ranges. Toward practical applications, we develop a smart glove to realize continuous real-time robotics control and virtual/augmented reality interaction. Our work demonstrates a generic approach for developing self-sustainable HMIs with stable, information-lossless, and real-time features for wearable systems.

3D Printed, Customizable, and Multifunctional Smart Electronic Eyeglasses for Wearable Healthcare Systems and Human-Machine Interfaces

Personal accessories such as glasses and watches that we usually carry in our daily life can yield useful information from the human body, yet most of them are limited to exercise-related parameters or simple heart rates. Since these restricted characteristics might arise from interfaces between the body and items as one of the main reasons, an interface design considering such a factor can provide us with biologically meaningful data. Here, we describe three-dimensional-printed, personalized, multifunctional electronic eyeglasses (E-glasses), not only to monitor various biological phenomena but also to propose a strategy to coordinate the recorded data for active commands and game operations for human-machine interaction (HMI) applications. Soft, highly conductive composite electrodes embedded in the E-glasses enable us to achieve reliable, continuous recordings of physiological activities. UV-responsive, color-tunable lenses using an electrochromic ionic gel offer the functionality of both eyeglass and sunglass modes, and accelerometers provide the capability of tracking precise human postures and behaviors. Detailed studies of electrophysiological signals including electroencephalogram and electrooculogram demonstrate the feasibility of smart electronic glasses for practical use as a platform for future HMI systems.

Matrix-Independent Highly Conductive Composites for Electrodes and Interconnects in Stretchable Electronics

Electrically conductive composites (ECCs) hold great promise in stretchable electronics because of their printability, facile preparation, elasticity, and possibility for large-area fabrication. A high conductivity at steady state and during mechanical deformation is a critical property for ECCs, and extensive efforts have been made to improve the conductivity. However, most of those approaches are exclusively functional to a specific polymer matrix, restricting their capability to meet other requirements, such as mechanical, adhesive, and thermomechanical properties. Here, we report a generic approach to prepare ECCs with conductivity close to that of bulk metals and maintain their conductivity during stretching. This approach iodizes the surfactants on the commercial silver flakes, and subsequent photo exposure converts these silver iodide nanoparticles to silver nanoparticles. The ECCs based on silver nanoparticle-covered silver flakes exhibit high conductivity because of the removal of insulating surfactants as well as the enhanced contact between flakes. The treatment of silver flakes is independent of the polymer matrix and provides the flexibility in matrix selection. In the development of stretchable interconnects, ECCs can be prepared with the same polymer as the substrate to ensure strong adhesion between interconnects and the substrate. For the fabrication of on-skin electrodes, a polymer matrix of low modulus can be selected to enhance conformal contact with the skin for reduced impedance.