Venus Flytrap-Inspired Data-Center-Free Fast-Responsive Soft Robots Enabled by 2D Ni3(HITP)2 MOF and Graphite.

The rapid and responsive capabilities of soft robots in perceiving, assessing, and reacting to environmental stimuli are highly valuable. However, many existing soft robots, designed to mimic humans and other higher animals, often rely on data centers for the modulation of mechanoelectrical transduction and electromechanical actuation. This reliance significantly increases system complexity and time delays. Herein, drawing inspiration from Venus flytraps, a soft robot employing a power modulation strategy is presented for active stimulus reaction, eliminating the need for a data center. This robot achieves mechanoelectrical transduction through Ni3(2,3,6,7,10,11-hexaiminotriphenylene)2 (Ni3(HITP)2) metal-organic framework (MOF) with an ultralow time delay (256 ns) and electromechanical actuation via graphite. The Joule heating effect in graphite is effectively modulated by Ni3(HITP)2 before and after the presence of pressure, thus enabling the stimulus reaction of soft robots. As demonstrated, three soft robots are created: low-level edge tongue robots, Venus flytrap robots, and high-level nerve-center-controlled dragonfly robots. This power modulation strategy inspires designs of edge soft robots and high-level robots with a human-like effective fusion of conditioned and unconditioned reflexes.

A double-crack structure for bionic wearable strain sensors with ultra-high sensitivity and a wide sensing range.

Flexible strain sensors are crucial in fully monitoring human motion, and they should have a wide sensing range and ultra-high sensitivity. Herein, inspired by lyriform organs, a flexible strain sensor based on the double-crack structure is designed. An MXene layer and an Au layer with cracks are constructed on both sides of the insulated polydimethylsiloxane (PDMS) film, forming an equivalent parallel circuit that guarantees the integrity of the conductive path under a large strain. The rapid disconnection of the crack junctions causes a significant change in the resistance value. Due to the effect of cracks on the conductive path, the sensitivity of the sensor is largely improved. Benefiting from the double-crack structure, the as-obtained sensor shows ultra-high sensitivity (maximum gauge factor of up to 14 373.6), a wide working range (up to 21%), a fast response time (183 ms) and excellent dynamical stability (almost no performance loss after 1000 stretching cycles and different frequency cycles). In practical applications, the sensor is applied to different parts of the human body to sense the deformation of the skin, demonstrating its great potential application value in human physiological detection and the human-machine interaction. This study can provide new ideas for preparing high-performance flexible strain sensors.

Advances in Energy Harvesters/Nanogenerators and Self-Powered Sensors II.

The Internet of Things (IoT) has become a focal point in the realm of information technology and has facilitated the interconnectedness and communication of various objects, such as devices and sensors in smart cities, intelligent transportation, industrial automation, agriculture, healthcare, etc [...].

Energy Harvesters and Self-Powered Sensors for Smart Electronics, 2nd Edition.

With the worldwide rollout of the 5G communication network and 6G around the corner, we have witnessed the rapid development of the Internet of Things (IoT) technology, enabling big data and digital transformation in various fields [...].

Bioinspired Young's Modulus-Hierarchical E-Skin with Decoupling Multimodality and Neuromorphic Encoding Outputs to Biosystems.

As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature-pressure electronic skin with decoupling capability (TPD e-skin), inspired by the high-low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual-state amplifying microstructure and contact resistance modulation, the MXene TPD e-skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high-low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature-pressure signals into three types of nerve-acceptable frequency signals, corresponding to thermoreceptors, slow-adapting receptors, and fast-adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain-like machine learning-based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality-decoupling sensing and deep neural integration.

Bioinspired Stretchable MXene Deformation-Insensitive Hydrogel Temperature Sensors for Plant and Skin Electronics.

Temperature sensing is of high value in the wearable healthcare, robotics/prosthesis, and noncontact physiological monitoring. However, the common mechanic deformation, including pressing, bending, and stretching, usually causes undesirable feature size changes to the inner conductive network distribution of temperature sensors, which seriously influences the accuracy. Here, inspired by the transient receptor potential mechanism of biological thermoreceptors that could work precisely under various skin contortions, we propose an MXene/Clay/poly(N-isopropylacrylamide) (PNIPAM) (MCP) hydrogel with high stretchability, spike response, and deformation insensitivity. The dynamic spike response is triggered by the inner conductive network transformation from the 3-dimensional structure to the 2-dimensional surface after water being discharged at the threshold temperature. The water discharge is solely determined by the thermosensitivity of PNIPAM, which is free from mechanical deformation, so the MCP hydrogels can perform precise threshold temperature (32 °C) sensing under various deformation conditions, i.e., pressing and 15% stretching. As a proof of concept, we demonstrated the applications in plant electronics for the real-time surface temperature monitoring and skin electronics for communicating between human and machines. Our research opens venues for the accurate temperature-threshold sensation on the complicated surface and mechanical conditions.

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

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.

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

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.

Special Issue: Advance in Energy Harvesters/Nanogenerators and Self-Powered Sensors.

Internet of things (IoT) technologies are greatly promoted by the rapidly developed 5G-and-beyond networks, which have spawned diversified applications in the new era including smart homes, digital health, sports training, robotics, human-machine interaction, metaverse, smart manufacturing and industry 4 [...].

Noncontact Human-Machine Interface Using Complementary Information Fusion Based on MEMS and Triboelectric Sensors.

Current noncontact human-machine interfaces (HMIs) either suffer from high power consumption, complex signal processing circuits, and algorithms, or cannot support multidimensional interaction. Here, a minimalist, low-power, and multimodal noncontact interaction interface is realized by fusing the complementary information obtained from a microelectromechanical system (MEMS) humidity sensor and a triboelectric sensor. The humidity sensor composed of a two-port aluminum nitride (AlN) bulk wave resonator operating in its length extensional mode and a layer of graphene oxide (GO) film with uniform and controllable thickness, possesses an ultra-tiny form factor (200 × 400 µm2 ), high signal strength (Q = 1729.5), and low signal noise level (±0.31%RH), and is able to continuously and steadily interact with an approaching finger. Meanwhile, the facile triboelectric sensor made of two annular aluminum electrodes enables the interaction interface to rapidly recognize the multidirectional finger movements. By leveraging the resonant frequency changes of the humidity sensor and output voltage waveforms of the triboelectric sensor, the proposed interaction interface is successfully demonstrated as a game control interface to manipulate a car in virtual reality (VR) space and a password input interface to enter high-security 3D passwords, indicating its great potential in diversified applications in the future Metaverse.

Deep Learning-Assisted Triboelectric Smart Mats for Personnel Comprehensive Monitoring toward Maritime Safety.

Monitoring the crew of a ship can be performed by combining sensors and artificial intelligence methods to process sensing data. In this study, we developed a deep learning (DL)-assisted minimalist structure triboelectric smart mat system for obtaining abundant crew information without the privacy concerns of taking video. The smart mat system is fabricated using a conductive sponge with different filling rates and a fluorinated ethylene propylene membrane. The proposed dual-channel measurement method improves the stability of the generated signal. Comprehensive crew and cargo monitoring, including personnel and status identification, as well as positioning and counting functions are realized by the DL-assisted triboelectric smart mat system according to the analysis of instant sensory data. Real-time monitoring of crews through fixed and mobile devices improves the ability and efficiency of handling emergencies. The smart mat system provides privacy concerns and an effective way to build ship Internet of Things and ensure personnel safety.

Advanced Implantable Biomedical Devices Enabled by Triboelectric Nanogenerators.

Implantable biomedical devices (IMDs) play essential roles in healthcare. Subject to the limited battery life, IMDs cannot achieve long-term in situ monitoring, diagnosis, and treatment. The proposal and rapid development of triboelectric nanogenerators free IMDs from the shackles of batteries and spawn a self-powered healthcare system. This review aims to overview the development of IMDs based on triboelectric nanogenerators, divided into self-powered biosensors, in vivo energy harvesting devices, and direct electrical stimulation therapy devices. Meanwhile, future challenges and opportunities are discussed according to the development requirements of current-level self-powered IMDs to enhance output performance, develop advanced triboelectric nanogenerators with multifunctional materials, and self-driven close-looped diagnosis and treatment systems.

A Non-Resonant Piezoelectric-Electromagnetic-Triboelectric Hybrid Energy Harvester for Low-Frequency Human Motions.

With the rapid development of wireless communication and micro-power technologies, smart wearable devices with various functionalities appear more and more in our daily lives. Nevertheless, they normally possess short battery life and need to be recharged with external power sources with a long charging time, which seriously affects the user experience. To help extend the battery life or even replace it, a non-resonant piezoelectric-electromagnetic-triboelectric hybrid energy harvester is presented to effectively harvest energy from low-frequency human motions. In the designed structure, a moving magnet is used to simultaneously excite the three integrated energy collection units (i.e., piezoelectric, electromagnetic, and triboelectric) with a synergistic effect, such that the overall output power and energy-harvesting efficiency of the hybrid device can be greatly improved under various excitations. The experimental results show that with a vibration frequency of 4 Hz and a displacement of 200 mm, the hybrid energy harvester obtains a maximum output power of 26.17 mW at 70 kΩ for one piezoelectric generator (PEG) unit, 87.1 mW at 500 Ω for one electromagnetic generator (EMG) unit, and 63 µW at 140 MΩ for one triboelectric nanogenerator (TENG) unit, respectively. Then, the generated outputs are adopted for capacitor charging, which reveals that the performance of the three-unit integration is remarkably stronger than that of individual units. Finally, the practical energy-harvesting experiments conducted on various body parts such as wrist, calf, hand, and waist indicate that the proposed hybrid energy harvester has promising application potential in constructing a self-powered wearable system as the sustainable power source.

Biometrics-protected optical communication enabled by deep learning-enhanced triboelectric/photonic synergistic interface.

Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables the loading of biometric information into the optical domain and the multiplexing of digital and biometric information at zero power consumption. The multiplexing process seals digital signals with a biometric envelope to avoid disrupting the original high-speed digital information and enhance the complexity of transmitted information. The system can perform demultiplexing, recover high-speed digital information, and implement deep learning to identify 15 users with around 95% accuracy, irrespective of biometric information data types (electrical, optical, or demultiplexed optical). Secure communication between users and the cloud is established after user identification for document exchange and smart home control. Through integrating triboelectric and photonics technology, our system provides a low-cost, easy-to-access, and ubiquitous solution for secure communication.

Progress of Advanced Devices and Internet of Things Systems as Enabling Technologies for Smart Homes and Health Care.

In the Internet of Things (IoT) era, various devices (e.g., sensors, actuators, energy harvesters, etc.) and systems have been developed toward the realization of smart homes/buildings and personal health care. These advanced devices can be categorized into ambient devices and wearable devices based on their usage scenarios, to enable motion tracking, health monitoring, daily care, home automation, fall detection, intelligent interaction, assistance, living convenience, and security in smart homes. With the rapidly increasing number of such advanced devices and IoT systems, achieving fully self-sustained and multimodal intelligent systems is becoming more and more important to realize a sustainable and all-in-one smart home platform. Hence, in this Review, we systematically present the recent progress of the development of advanced materials, fabrication techniques, devices, and systems for enabling smart home and health care applications. First, advanced polymer, fiber, and fabric materials as well as their respective fabrication techniques for large-scale manufacturing are discussed. After that, functional devices classified into ambient devices (at home ambiance such as door, floor, table, chair, bed, toilet, window, wall, etc.) and wearable devices (on body parts such as finger, wrist, arm, throat, face, back, etc.) are presented for diverse monitoring and auxiliary applications. Next, the current developments of self-sustained systems and intelligent systems are reviewed in detail, indicating two promising research directions in this field. Last, conclusions and outlook pinpointed on the existing challenges and opportunities are provided for the research community to consider.

Special Issue on Energy Harvesters and Self-Powered Sensors for Smart Electronics.

In recent years, we have witnessed the revolutionary innovation and flourishing advancement of the Internet of things (IoT), which will maintain a strong momentum even more with the gradual rollout of the fifth generation (5G) wireless network and the rapid development of personal healthcare electronics [...].

Artificial Intelligence of Things (AIoT) Enabled Floor Monitoring System for Smart Home Applications.

To enable smart homes and relative applications, the floor monitoring system with embedded triboelectric sensors has been proven as an effective paradigm to capture the ample sensory information from our daily activities, without the camera-associated privacy concerns. Yet the inherent limitations of triboelectric sensors such as high susceptibility to humidity and long-term stability remain a great challenge to develop a reliable floor monitoring system. Here we develop a robust and smart floor monitoring system through the synergistic integration of highly reliable triboelectric coding mats and deep-learning-assisted data analytics. Two quaternary coding electrodes are configured, and their outputs are normalized with respect to a reference electrode, leading to highly stable detection that is not affected by the ambient parameters and operation manners. Besides, due to the universal electrode pattern design, all the floor mats can be screen-printed with only one mask, rendering higher facileness and cost-effectiveness. Then a distinctive coding can be implemented to each floor mat through external wiring, which permits the parallel-array connection to minimize the output terminals and system complexity. Further integrating with deep-learning-assisted data analytics, a smart floor monitoring system is realized for various smart home monitoring and interactions, including position/trajectory tracking, identity recognition, and automatic controls. Hence, the developed low-cost, large-area, reliable, and smart floor monitoring system shows a promising advancement of floor sensing technology in smart home applications.

All in One, Self-Powered Bionic Artificial Nerve Based on a Triboelectric Nanogenerator.

Sensory and nerve systems play important role in mediating the interactions with the world. The pursuit of neuromorphic computing has inspired innovations in artificial sensory and nervous systems. Here, an all-in-one, tailorable artificial perception, and transmission nerve (APTN) was developed for mimicking the biological sensory and nervous ability to detect and transmit the location information of mechanical stimulation. The APTN shows excellent reliability with a single triboelectric electrode for the detection of multiple pixels, by employing a gradient thickness dielectric layer and a grid surface structure. The sliding mode is used on the APTN to eliminate the amplitude influence of output signal, such as force, interlayer distance. By tailoring the geometry, an L-shaped APTN is demonstrated for the application of single-electrode bionic artificial nerve for 2D detection. In addition, an APTN based prosthetic arm is also fabricated to biomimetically identify and transmit the stimuli location signal to pattern the feedback. With features of low-cost, easy installation, and good flexibility, the APTN renders as a promising artificial sensory and nervous system for artificial intelligence, human-machine interface, and robotics applications.

Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self-Powered Sensor Enhanced Soft Robotic Manipulator.

Rapid advancements of artificial intelligence of things (AIoT) technology pave the way for developing a digital-twin-based remote interactive system for advanced robotic-enabled industrial automation and virtual shopping. The embedded multifunctional perception system is urged for better interaction and user experience. To realize such a system, a smart soft robotic manipulator is presented that consists of a triboelectric nanogenerator tactile (T-TENG) and length (L-TENG) sensor, as well as a poly(vinylidene fluoride) (PVDF) pyroelectric temperature sensor. With the aid of machine learning (ML) for data processing, the fusion of the T-TENG and L-TENG sensors can realize the automatic recognition of the grasped objects with the accuracy of 97.143% for 28 different shapes of objects, while the temperature distribution can also be obtained through the pyroelectric sensor. By leveraging the IoT and artificial intelligence (AI) analytics, a digital-twin-based virtual shop is successfully implemented to provide the users with real-time feedback about the details of the product. In general, by offering a more immersive experience in human-machine interactions, the proposed remote interactive system shows the great potential of being the advanced human-machine interface for the applications of the unmanned working space.

Triboelectric Nanogenerators and Hybridized Systems for Enabling Next-Generation IoT Applications.

In the past few years, triboelectric nanogenerator-based (TENG-based) hybrid generators and systems have experienced a widespread and flourishing development, ranging among almost every aspect of our lives, e.g., from industry to consumer, outdoor to indoor, and wearable to implantable applications. Although TENG technology has been extensively investigated for mechanical energy harvesting, most developed TENGs still have limitations of small output current, unstable power generation, and low energy utilization rate of multisource energies. To harvest the ubiquitous/coexisted energy forms including mechanical, thermal, and solar energy simultaneously, a promising direction is to integrate TENG with other transducing mechanisms, e.g., electromagnetic generator, piezoelectric nanogenerator, pyroelectric nanogenerator, thermoelectric generator, and solar cell, forming the hybrid generator for synergetic single-source and multisource energy harvesting. The resultant TENG-based hybrid generators utilizing integrated transducing mechanisms are able to compensate for the shortcomings of each mechanism and overcome the above limitations, toward achieving a maximum, reliable, and stable output generation. Hence, in this review, we systematically introduce the key technologies of the TENG-based hybrid generators and hybridized systems, in the aspects of operation principles, structure designs, optimization strategies, power management, and system integration. The recent progress of TENG-based hybrid generators and hybridized systems for the outdoor, indoor, wearable, and implantable applications is also provided. Lastly, we discuss our perspectives on the future development trend of hybrid generators and hybridized systems in environmental monitoring, human activity sensation, human-machine interaction, smart home, healthcare, wearables, implants, robotics, Internet of things (IoT), and many other fields.