Frictionless multiphasic interface for near-ideal aero-elastic pressure sensing.

Conventional pressure sensors rely on solid sensing elements. Instead, inspired by the air entrapment phenomenon on the surfaces of submerged lotus leaves, we designed a pressure sensor that uses the solid-liquid-liquid-gas multiphasic interfaces and the trapped elastic air layer to modulate capacitance changes with pressure at the interfaces. By creating an ultraslippery interface and structuring the electrodes at the nanoscale and microscale, we achieve near-friction-free contact line motion and thus near-ideal pressure-sensing performance. Using a closed-cell pillar array structure in synergy with the ultraslippery electrode surface, our sensor achieved outstanding linearity (R2 = 0.99944 ± 0.00015; nonlinearity, 1.49 ± 0.17%) while simultaneously possessing ultralow hysteresis (1.34 ± 0.20%) and very high sensitivity (79.1 ± 4.3 pF kPa-1). The sensor can operate under turbulent flow, in in vivo biological environments and during laparoscopic procedures. We anticipate that such a strategy will enable ultrasensitive and ultraprecise pressure monitoring in complex fluid environments with performance beyond the reach of the current state-of-the-art.

Implant-to-implant wireless networking with metamaterial textiles.

Implanted bioelectronic devices can form distributed networks capable of sensing health conditions and delivering therapy throughout the body. Current clinically-used approaches for wireless communication, however, do not support direct networking between implants because of signal losses from absorption and reflection by the body. As a result, existing examples of such networks rely on an external relay device that needs to be periodically recharged and constitutes a single point of failure. Here, we demonstrate direct implant-to-implant wireless networking at the scale of the human body using metamaterial textiles. The textiles facilitate non-radiative propagation of radio-frequency signals along the surface of the body, passively amplifying the received signal strength by more than three orders of magnitude (>30 dB) compared to without the textile. Using a porcine model, we demonstrate closed-loop control of the heart rate by wirelessly networking a loop recorder and a vagus nerve stimulator at more than 40 cm distance. Our work establishes a wireless technology to directly network body-integrated devices for precise and adaptive bioelectronic therapies.

Battery-free and AI-enabled multiplexed sensor patches for wound monitoring.

Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We report a paper-like battery-free in situ AI-enabled multiplexed (PETAL) sensor for holistic wound assessment by leveraging deep learning algorithms. This sensor consists of a wax-printed paper panel with five colorimetric sensors for temperature, pH, trimethylamine, uric acid, and moisture. Sensor images captured by a mobile phone were analyzed by neural network-based machine learning algorithms to determine healing status. For ex situ detection via exudates collected from rat perturbed wounds and burn wounds, the PETAL sensor can classify healing versus nonhealing status with an accuracy as high as 97%. With the sensor patches attached on rat burn wound models, in situ monitoring of wound progression or severity is demonstrated. This PETAL sensor allows early warning of adverse events, which could trigger immediate clinical intervention to facilitate wound care management.

Dynamic Modeling of Intrinsic Self-Healing Polymers Using Deep Learning.

The properties of self-healing polymers are traditionally identified through destructive testing. This means that the mechanics are explored in hindsight with either theoretical derivations and/or simulations. Here, a self-healing property evolution using energy functional dynamical (SPEED) model is proposed to predict and understand the mechanics of self-healing of polymers using images of cuts dynamically healing over time. Using machine learning, an energy functional minimization (EFM) model extracted an effective underlying dynamical system from a time series of two-dimensional cut images on a self-healing polymer of constant thickness. This model can be used to capture the physics behind the self-healing dynamics in terms of potential and interface energies. When combined with a static property prediction model, the SPEED model can predict the macroscopic evolution of material properties after training only on a small set of experimental measurements. Such temporal evolutions are usually inaccessible from pure experiments or computational modeling due to the need for destructive testing. As an example, we validate this approach on toughness measurements of an intrinsic self-healing conductive polymer by capturing over 100 000 image frames of cuts to build the machine learning (ML) model. The results show that the SPEED model can be applied to predict the temporal evolution of macroscopic properties using few measurements as training data.

Macromolecule conformational shaping for extreme mechanical programming of polymorphic hydrogel fibers.

Mechanical properties of hydrogels are crucial to emerging devices and machines for wearables, robotics and energy harvesters. Various polymer network architectures and interactions have been explored for achieving specific mechanical characteristics, however, extreme mechanical property tuning of single-composition hydrogel material and deployment in integrated devices remain challenging. Here, we introduce a macromolecule conformational shaping strategy that enables mechanical programming of polymorphic hydrogel fiber based devices. Conformation of the single-composition polyelectrolyte macromolecule is controlled to evolve from coiling to extending states via a pH-dependent antisolvent phase separation process. The resulting structured hydrogel microfibers reveal extreme mechanical integrity, including modulus spanning four orders of magnitude, brittleness to ultrastretchability, and plasticity to anelasticity and elasticity. Our approach yields hydrogel microfibers of varied macromolecule conformations that can be built-in layered formats, enabling the translation of extraordinary, realistic hydrogel electronic applications, i.e., large strain (1000%) and ultrafast responsive (~30 ms) fiber sensors in a robotic bird, large deformations (6000%) and antifreezing helical electronic conductors, and large strain (700%) capable Janus springs energy harvesters in wearables.

A wireless and battery-free wound infection sensor based on DNA hydrogel.

The confluence of wireless technology and biosensors offers the possibility to detect and manage medical conditions outside of clinical settings. Wound infections represent a major clinical challenge in which timely detection is critical for effective interventions, but this is currently hindered by the lack of a monitoring technology that can interface with wounds, detect pathogenic bacteria, and wirelessly transmit data. Here, we report a flexible, wireless, and battery-free sensor that provides smartphone-based detection of wound infection using a bacteria-responsive DNA hydrogel. The engineered DNA hydrogels respond selectively to deoxyribonucleases associated with pathogenic bacteria through tunable dielectric changes, which can be wirelessly detected using near-field communication. In a mouse acute wound model, we demonstrate that the wireless sensor can detect physiologically relevant amounts of Staphylococcus aureus even before visible manifestation of infection. These results demonstrate strategies for continuous infection monitoring, which may facilitate improved management of surgical or chronic wounds.

Wirelessly operated bioelectronic sutures for the monitoring of deep surgical wounds.

Monitoring surgical wounds post-operatively is necessary to prevent infection, dehiscence and other complications. However, the monitoring of deep surgical sites is typically limited to indirect observations or to costly radiological investigations that often fail to detect complications before they become severe. Bioelectronic sensors could provide accurate and continuous monitoring from within the body, but the form factors of existing devices are not amenable to integration with sensitive wound tissues and to wireless data transmission. Here we show that multifilament surgical sutures functionalized with a conductive polymer and incorporating pledgets with capacitive sensors operated via radiofrequency identification can be used to monitor physicochemical states of deep surgical sites. We show in live pigs that the sutures can monitor wound integrity, gastric leakage and tissue micromotions, and in rodents that the healing outcomes are equivalent to those of medical-grade sutures. Battery-free wirelessly operated bioelectronic sutures may facilitate post-surgical monitoring in a wide range of interventions.

Scaling Metal-Elastomer Composites toward Stretchable Multi-Helical Conductive Paths for Robust Responsive Wearable Health Devices.

Stretchable electronics have advanced rapidly and many applications require high repeatability and robustness under various mechanical deformations. It has been described here that how a highly stretchable and reliable conductor composite made from helical copper wires and a soft elastomer, named eHelix, can provide mechanically robust and strain-insensitive electronic conductivity for wearable devices. The reversibility of the mechanical behavior of the metal-elastomer system has been studied using finite element modeling methods. Optimal design parameters of such helical metal-elastomer structures are found. The scaling of multiple copper wires into such helical shapes to form a Multi-eHelix system is further shown. With the same elastomer volume, Multi-eHelix has more conductive paths and a higher current density than the single-eHelix. Integrations of these eHelix stretchable conductors with fabrics showed wearable displays that can survive machine-washes and hundreds of mechanical loading cycles. The integration of the eHelix developed by us with a wearable optical heart rate sensor enabled a wearable health monitoring system that can display measured heart rates on clothing. Furthermore, Multi-eHelix conductors are used to connect flexible printed circuit boards and piezoresistive sensors on a tactile sensing glove for the emerging sensorized prosthetics.

Progress and Roadmap for Intelligent Self-Healing Materials in Autonomous Robotics.

Robots are increasingly assisting humans in performing various tasks. Like special agents with elite skills, they can venture to distant locations and adverse environments, such as the deep sea and outer space. Micro/nanobots can also act as intrabody agents for healthcare applications. Self-healing materials that can autonomously perform repair functions are useful to address the unpredictability of the environment and the increasing drive toward the autonomous operation. Having self-healable robotic materials can potentially reduce costs, electronic wastes, and improve a robot endowed with such materials longevity. This review aims to serve as a roadmap driven by past advances and inspire future cross-disciplinary research in robotic materials and electronics. By first charting the history of self-healing materials, new avenues are provided to classify the various self-healing materials proposed over several decades. The materials and strategies for self-healing in robotics and stretchable electronics are also reviewed and discussed. It is believed that this article encourages further innovation in this exciting and emerging branch in robotics interfacing with material science and electronics.

Artificially innervated self-healing foams as synthetic piezo-impedance sensor skins.

Human skin is a self-healing mechanosensory system that detects various mechanical contact forces efficiently through three-dimensional innervations. Here, we propose a biomimetic artificially innervated foam by embedding three-dimensional electrodes within a new low-modulus self-healing foam material. The foam material is synthesized from a one-step self-foaming process. By tuning the concentration of conductive metal particles in the foam at near-percolation, we demonstrate that it can operate as a piezo-impedance sensor in both piezoresistive and piezocapacitive sensing modes without the need for an encapsulation layer. The sensor is sensitive to an object's contact force directions as well as to human proximity. Moreover, the foam material self-heals autonomously with immediate function restoration despite mechanical damage. It further recovers from mechanical bifurcations with gentle heating (70 °C). We anticipate that this material will be useful as damage robust human-machine interfaces.

Near-hysteresis-free soft tactile electronic skins for wearables and reliable machine learning.

Electronic skins are essential for real-time health monitoring and tactile perception in robots. Although the use of soft elastomers and microstructures have improved the sensitivity and pressure-sensing range of tactile sensors, the intrinsic viscoelasticity of soft polymeric materials remains a long-standing challenge resulting in cyclic hysteresis. This causes sensor data variations between contact events that negatively impact the accuracy and reliability. Here, we introduce the Tactile Resistive Annularly Cracked E-Skin (TRACE) sensor to address the inherent trade-off between sensitivity and hysteresis in tactile sensors when using soft materials. We discovered that piezoresistive sensors made using an array of three-dimensional (3D) metallic annular cracks on polymeric microstructures possess high sensitivities (> 107 Ω â‹… kPa-1), low hysteresis (2.99 ± 1.37%) over a wide pressure range (0-20 kPa), and fast response (400 Hz). We demonstrate that TRACE sensors can accurately detect and measure the pulse wave velocity (PWV) when skin mounted. Moreover, we show that these tactile sensors when arrayed enabled fast reliable one-touch surface texture classification with neuromorphic encoding and deep learning algorithms.

Super Tough and Self-Healable Poly(dimethylsiloxane) Elastomer via Hydrogen Bonding Association and Its Applications as Triboelectric Nanogenerators.

Poly(dimethylsiloxane) (PDMS) as one of the electron-drawing materials has been widely used in triboelectric nanogenerators (TENG), which is expected to generate electron through friction and required to endure dynamic loads. However, the nature of the siloxane bond and the low interchain interaction between the methyl side groups result in low fracture energy in PDMS elastomers. Here, a strategy that combined the advantages of the dynamic of hierarchical hydrogen bonding and phase-separation-like structure was adopted to improve the toughness of PDMS elastomers. By varying both stronger and weaker hydrogen bonding within the PDMS network, a series of super tough (up to 24,000 J/m2), notch-insensitive, transparent, and autonomous self-healable elastomers were achieved. In addition, a hydrophilic polymeric material (PDMAS-U10) was synthesized as the conductive layer. A transparent TENG was fabricated by sandwiching the PDMAS-U10 between two pieces of the PDMS elastomer. Despite its hydrophilic nature, PDMAS-U10 exhibit strong adhesion interaction with hydrophobic PDMS elastomers. As such, a tough (16,500 J/m2), self-healable (efficiency ∼97%), and transparent triboelectric nanogenerator was constructed. A self-powered system employing the TENG is also demonstrated in this work.

Osmosis-Powered Hydrogel Microneedles for Microliters of Skin Interstitial Fluid Extraction within Minutes.

Hydrogel microneedle patch enables the extraction of skin interstitial fluid (ISF) through in situ swelling in a minimally invasive manner without assistance of mechano-chemical peripherals. However, existing hydrogel microneedles require tens of minutes with multistep process to collect sufficient volume (1 mL) for effective analysis. This study introduces an osmolyte-powered hydrogel microneedle patch that can extract ISF three times faster than the existing platforms and provide in situ analysis of extracted biomarkers. The microneedle patch is composed of osmolytes (i.e., maltose) and hydrogel (i.e., methacrylated hyaluronic acid). During the extraction process, the osmolytes dissolve in the matrix and provide the osmotic pressure that increases the diffusion of ISF from skin to the hydrogel matrix. A patch with 100 microneedles can extract 7.90 µL of ISF from pig skin ex vivo and 3.82 µL of ISF from mouse skin in vivo within 3 min, whereas the control (i.e., hydrogel microneedle without osmolytes) requires >10 min to achieve similar results. The extracted ISF allows the quantification of biomarkers such as glucose and/or drugs such as insulin in vivo. Through the integration with the electronic glucose sensors, the whole system permits the direct and rapid analysis of the extracted glucose.

Wireless battery-free body sensor networks using near-field-enabled clothing.

Networks of sensors placed on the skin can provide continuous measurement of human physiological signals for applications in clinical diagnostics, athletics and human-machine interfaces. Wireless and battery-free sensors are particularly desirable for reliable long-term monitoring, but current approaches for achieving this mode of operation rely on near-field technologies that require close proximity (at most a few centimetres) between each sensor and a wireless readout device. Here, we report near-field-enabled clothing capable of establishing wireless power and data connectivity between multiple distant points around the body to create a network of battery-free sensors interconnected by proximity to functional textile patterns. Using computer-controlled embroidery of conductive threads, we integrate clothing with near-field-responsive patterns that are completely fabric-based and free of fragile silicon components. We demonstrate the utility of the networked system for real-time, multi-node measurement of spinal posture as well as continuous sensing of temperature and gait during exercise.

A transparent, self-healing and high-κ dielectric for low-field-emission stretchable optoelectronics.

Stretchable optoelectronic materials are essential for applications in wearable electronics, human-machine interfaces and soft robots. However, intrinsically stretchable optoelectronic devices such as light-emitting capacitors usually require high driving alternating voltages and excitation frequencies to achieve sufficient luminance in ambient lighting conditions. Here, we present a healable, low-field illuminating optoelectronic stretchable (HELIOS) device by introducing a transparent, high permittivity polymeric dielectric material. The HELIOS device turns on at an alternating voltage of 23 V and a frequency below 1 kHz, safe operating conditions for human-machine interactions. We achieved a brightness of 1,460 cd m-2 at 2.5 V µm-1 with stable illumination demonstrated up to a maximum of 800% strain. The materials also self-healed mechanically and electronically from punctures or when severed. We further demonstrate various HELIOS light-emitting capacitor devices in environment sensing using optical feedback. Moreover, our devices can be powered wirelessly, potentially enabling applications for untethered damage-resilient soft robots.

Design and applications of stretchable and self-healable conductors for soft electronics.

Soft and conformable electronics are emerging rapidly and is envisioned as the future of next-generation electronic devices where devices can be readily deployed in various environments, such as on-body, on-skin or as a biomedical implant. Modern day electronics require electrical conductors as the fundamental building block for stretchable electronic devices and systems. In this review, we will study the various strategies and methods of designing and fabricating materials which are conductive, stretchable and self-healable, and explore relevant applications such as flexible and stretchable sensors, electrodes and energy harvesters.

A neuro-inspired artificial peripheral nervous system for scalable electronic skins.

The human sense of touch is essential for dexterous tool usage, spatial awareness, and social communication. Equipping intelligent human-like androids and prosthetics with electronic skins-a large array of sensors spatially distributed and capable of rapid somatosensory perception-will enable them to work collaboratively and naturally with humans to manipulate objects in unstructured living environments. Previously reported tactile-sensitive electronic skins largely transmit the tactile information from sensors serially, resulting in readout latency bottlenecks and complex wiring as the number of sensors increases. Here, we introduce the Asynchronously Coded Electronic Skin (ACES)-a neuromimetic architecture that enables simultaneous transmission of thermotactile information while maintaining exceptionally low readout latencies, even with array sizes beyond 10,000 sensors. We demonstrate prototype arrays of up to 240 artificial mechanoreceptors that transmitted events asynchronously at a constant latency of 1 ms while maintaining an ultra-high temporal precision of <60 ns, thus resolving fine spatiotemporal features necessary for rapid tactile perception. Our platform requires only a single electrical conductor for signal propagation, realizing sensor arrays that are dynamically reconfigurable and robust to damage. We anticipate that the ACES platform can be integrated with a wide range of skin-like sensors for artificial intelligence (AI)-enhanced autonomous robots, neuroprosthetics, and neuromorphic computing hardware for dexterous object manipulation and somatosensory perception.

Soft Electronically Functional Polymeric Composite Materials for a Flexible and Stretchable Digital Future.

Flexible/stretchable electronic devices and systems are attracting great attention because they can have important applications in many areas, such as artificial intelligent (AI) robotics, brain-machine interfaces, medical devices, structural and environmental monitoring, and healthcare. In addition to the electronic performance, the electronic devices and systems should be mechanically flexible or even stretchable. Traditional electronic materials including metals and semiconductors usually have poor mechanical flexibility and very limited elasticity. Three main strategies are adopted for the development of flexible/stretchable electronic materials. One is to use organic or polymeric materials. These materials are flexible, and their elasticity can be improved through chemical modification or composition formation with plasticizers or elastomers. Another strategy is to exploit nanometer-scale materials. Many inorganic materials in nanometer sizes can have high flexibility. They can be stretchable through the composition formation with elastomers. Ionogels can be considered as the third type of materials because they can be stretchable and ionically conductive. This article provides the recent progress of soft functional materials development including intrinsically conductive polymers for flexible/stretchable electrodes, and thermoelectric conversion and polymer composites for large area, flexible stretchable electrodes, and tactile sensors.

Self-Healing Electronic Materials for a Smart and Sustainable Future.

The survivability of living organisms relies critically on their ability to self-heal from damage in unpredictable situations and environmental variability. Such abilities are most important in external facing organs such as the mammalian skin. However, the properties of bulk elemental materials are typically unable to perform self-repair. Consequently, most conventional smart electronic devices today are not designed to repair themselves when damaged. Thus, inspired by the remarkable capability of self-healing in natural systems, smart self-healing materials are being intensively researched to mimic natural systems to have the ability to partially or completely self-repair damages inflicted on them. This exciting area of research could potentially power a sustainable and smart future.