The advancement of the Internet of Things/5G infrastructure requires a low‐cost ubiquitous sensory network to realize an autonomous system for information collection and processing, aiming at diversified applications ranging from healthcare, smart home, industry 4.0 to environmental monitoring. The triboelectric nanogenerator (TENG) is considered the most promising technology due to its self‐powered, cost‐effective, and highly customizable advantages. Through the use of wearable electronic devices, advanced TENG technology is developed as a core technology enabling self‐powered sensors, power supplies, and data communications for the aforementioned applications. In this review, the advancements of TENG‐based electronics regarding materials, material/device hybridization, systems integration, technology convergence, and applications in healthcare, environment monitoring, transportation, and smart homes toward the future green earth are reported.

Triboelectric Nanogenerator Enabled Wearable Sensors and Electronics for Sustainable Internet of Things Integrated Green Earth

Electronic skin (e-skin), mimicking the physical-chemical and sensory properties of human skin, is promising to be applied as robotic skins and skin-attachable wearables with multisensory functionalities. To date, most e-skins are dedicated to sensory function development to mimic human skins in one or several aspects, yet advanced e-skin covering all the hyper-attributes (including both the sensory and physical-chemical properties) of human skins is seldom reported. Herein, a water-modulated biomimetic hyper-attribute-gel (Hygel) e-skin with reversible gel-solid transition is proposed, which exhibits all the desired skin-like physical-chemical properties (stretchability, self-healing, biocompatibility, biodegradability, weak acidity, antibacterial activities, flame retardance, and temperature adaptivity), sensory properties (pressure, temperature, humidity, strain, and contact), function reconfigurability, and evolvability. Then the Hygel e-skin is applied as an on-robot e-skin and skin-attached wearable to demonstrate its highly skin-like attributes in capturing multiple sensory information, reconfiguring desired functions, and excellent skin compatibility for real-time gesture recognition via deep learning. This Hygel e-skin may find more applications in advanced robotics and even skin-replaceable artificial skin.

Water-Modulated Biomimetic Hyper-Attribute-Gel Electronic Skin for Robotics and Skin-Attachable Wearables.

Conductive gels have received extensive attention in flexible electronics due to their diverse characteristics. The requirement for versatility and incompatibility of the conductive fillers with the hydrogel matrix remain a challenge for hydrogels. Herein, the cellulose nanofiber stabilized liquid metal droplets are utilized to initiate the polymerization and simultaneously serve as solid conductive fillers to construct the polyacrylamide/MXene/glycerol hydrogel with eligible stretchability (1000%) and high environmental adaptability (−25 to 80 °C) for multifunctional sensing. The hydrogel is utilized as a flexible electrode to construct the triboelectric nanogenerator for mechanical energy harvesting and conversion as well as self-powered sensing. Importantly, as a versatile sensor, it combines temperature sensing and deformation sensing with a wide range of stimulus responses and exceptional sensing performance. The flexible temperature and strain sensors are realized with superior thermosensitivity and strain sensitivity. In addition, the hydrogel exhibits favorable photothermal antibacterial ability. This work opens new avenues for the preparation of photothermal, conductive, stress and thermal sensitive hydrogels for multimodal sensors, sketching a promising future for them in flexible and wearable electronics. 

A stretchable, environmentally tolerant, and photoactive liquid metal/MXene hydrogel for high performance temperature monitoring, human motion detection and self-powered application

Flexible electronics such as tactile cognitive sensors have been broadly adopted in soft robotic manipulators to enable human-skin-mimetic perception. To achieve appropriate positioning for randomly distributed objects, an integrated guiding system is inevitable. Yet the conventional guiding system based on cameras or optical sensors exhibits limited environment adaptability, high data complexity, and low cost effectiveness. Herein, a soft robotic perception system with remote object positioning and a multimodal cognition capability is developed by integrating an ultrasonic sensor with flexible triboelectric sensors. The ultrasonic sensor is able to detect the object shape and distance by reflected ultrasound. Thereby the robotic manipulator can be positioned to an appropriate position to perform object grasping, during which the ultrasonic and triboelectric sensors can capture multimodal sensory information such as object top profile, size, shape, hardness, material, etc. These multimodal data are then fused for deep-learning analytics, leading to a highly enhanced accuracy in object identification (∼100%). The proposed perception system presents a facile, low-cost, and effective methodology to integrate positioning capability with multimodal cognitive intelligence in soft robotics, significantly expanding the functionalities and adaptabilities of current soft robotic systems in industrial, commercial, and consumer applications.

Soft Robotic Perception System with Ultrasonic Auto-Positioning and Multimodal Sensory Intelligence.

Stretchable on-skin touchless screen sensor enabled by ionic hydrogel

Human‐machine interfaces (HMIs) play important role in the communication between humans and robots. Touchless HMIs with high hand dexterity and hygiene hold great promise in medical applications, especially during the pandemic of coronavirus disease 2019 (COVID‐19) to reduce the spread of virus. However, current touchless HMIs are mainly restricted by limited types of gesture recognition, the requirement of wearing accessories, complex sensing platforms, light conditions, and low recognition accuracy, obstructing their practical applications. Here, an intelligent noncontact gesture‐recognition system is presented through the integration of a triboelectric touchless sensor (TTS) and deep learning technology. Combined with a deep‐learning‐based multilayer perceptron neural network, the TTS can recognize 16 different types of gestures with a high average accuracy of 96.5%. The intelligent noncontact gesture‐recognition system is further applied to control a robot for collecting throat swabs in a noncontact mode. Compared with present touchless HMIs, the proposed system can recognize diverse complex gestures by utilizing charges naturally carried on human fingers without the need of wearing accessories, complicated device structures, adequate light conditions, and achieves high recognition accuracy. This system could provide exciting opportunities to develop a new generation of touchless medical equipment, as well as touchless public facilities, smart robots, virtual reality, metaverse, etc.

Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces

As an on‐skin electronic device, artificial skin shows great potential in medical monitoring and personal electronics, which also holds promise to develop human‐machine merging interfaces. However, merging artificial skins with human bodies is largely restricted by the dissimilarity of material compositions in existing artificial skins and biological tissues. Naturally conductive protein is a potential material candidate for artificial skins, nevertheless, it suffers from the critical issue of dehydration which harms its proton conductivity. Inspired by the sebum membrane of human skin, herein, a protein‐based bioprotonic hydrogel (PBH) with reliable water retention ability is reported for artificial skins. The bovine serum albumin with natural proton conductivity is utilized in the PBH, and the glycerol that originally presents on human skin surface is used as an artificial sebum membrane to retain water. The PBH can act as a bioprotonic skin (B‐skin) for collecting electrophysiological signals and self‐powered sensing. Based on the B‐skin, intelligent robot and cellphone control systems are demonstrated. Compared with present artificial skins, this B‐skin is all made out of biological materials that are consistent with material components of human skin tissues including proteins, endogenous glycerol, and water. Such a B‐skin may enable the development of next‐generation human‐machine merging interfaces.

Sebum‐Membrane‐Inspired Protein‐Based Bioprotonic Hydrogel for Artificial Skin and Human‐Machine Merging Interface

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Deep‐Learning‐Assisted Noncontact Gesture‐Recognition System for Touchless Human‐Machine Interfaces

Sebum‐Membrane‐Inspired Protein‐Based Bioprotonic Hydrogel for Artificial Skin and Human‐Machine Merging Interface