Self-Powered Bio-Inspired Spider-Net-Coding Interface Using Single-Electrode Triboelectric Nanogenerator

Nanocellulose-based nanogenerators for sensor applications: A review

With the rapid development of the Internet of Things, nanogenerator as a green energy collection technology has attracted great attention in various fields. Specifically, the natural renewable nanocellulose as a raw material can significantly improve the environmental friendliness of the nanocellulose-based nanogenerators, which also makes the nanocellulose based nanogenerators expected to further develop in areas such as wearable devices and sensor networks. This paper mainly reports the application of nanocellulose in nanogenerator, focusing on the sensor. The types, sources and preparation methods of nanocellulose are briefly introduced. At the same time, the special structure of nanocellulose highlights the advantages of nanocellulose in nanogenerators. Then, the application of nanocellulose-based nanogenerators in sensors is introduced. Finally, the future development prospects and shortcomings of this nanogenerator are discussed.

Self-encapsulated ionic fibers based on stress-induced adaptive phase transition for non-contact depth-of-field camouflage sensing

Ionically conductive fibers have promising applications; however, complex processing techniques and poor stability limit their practicality. To overcome these challenges, we proposed a stress-induced adaptive phase transition strategy to conveniently fabricate self-encapsulated hydrogel-based ionically conductive fibers (se-HICFs). se-HICFs can be produced simply by directly stretching ionic hydrogels with ultra-stretchable networks (us-IHs) or by dip-drawing from molten us-IHs. During this process, stress facilitated the directional migration and evaporation of water molecules in us-IHs, causing a phase transition in the surface layer of ionic fibers to achieve self-encapsulation. The resulting sheath-core structure of se-HICFs enhanced mechanical strength and stability while endowing se-HICFs with powerful non-contact electrostatic induction capabilities. Mimicking nature, se-HICFs were woven into spider web structures and camouflaged in wild environments to achieve high spatiotemporal resolution 3D depth-of-field sensing for different moving media. This work opens up a convenient route to fabricate stable functionalized ionic fibers.

A Fully Self-Powered Wearable Leg Movement Sensing System for Human Health Monitoring

Energy-autonomous wearable human activity monitoring is imperative for daily healthcare, benefiting from long-term sustainable uses. Herein, a fully self-powered wearable system, enabling real-time monitoring and assessments of human multimodal health parameters including knee joint movement, metabolic energy, locomotion speed, and skin temperature, which are fully self-powered by highly-efficient flexible thermoelectric generators (f-TEGs) is proposed and developed. The wearable system is composed of f-TEGs, fabric strain sensors, ultra-low-power edge computing, and Bluetooth. The f-TEGs worn on the leg not only harvest energy from body heat and supply power sustainably for the whole monitoring system, but also serve as zero-power motion sensors to detect limb movement and skin temperature. The fabric strain sensor made by printing PEDOT: PSS on pre-stretched nylon fiber-wrapped rubber band enables high-fidelity and ultralow-power measurements on highly-dynamic knee movements. Edge computing is elaborately designed to estimate multimodal health parameters including time-varying metabolic energy in real-time, which are wirelessly transmitted via Bluetooth. The whole monitoring system is operated automatically and intelligently, works sustainably in both static and dynamic states, and is fully self-powered by the f-TEGs.

Advancements in Triboelectric Nanogenerators (TENGs) for Intelligent Transportation Infrastructure: Enhancing Bridges, Highways, and Tunnels

The development of triboelectric nanogenerators (TENGs) over time has resulted in considerable improvements to the efficiency, effectiveness, and sensitivity of self-powered sensing. Triboelectric nanogenerators have low restriction and high sensitivity while also having high efficiency. The vast majority of previous research has found that accidents on the road can be attributed to road conditions. For instance, extreme weather conditions, such as heavy winds or rain, can reduce the safety of the roads, while excessive temperatures might make it unpleasant to be behind the wheel. Air pollution also has a negative impact on visibility while driving. As a result, sensing road surroundings is the most important technical system that is used to evaluate a vehicle and make decisions. This paper discusses both monitoring driving behavior and self-powered sensors influenced by triboelectric nanogenerators (TENGs). It also considers energy harvesting and sustainability in smart road environments such as bridges, tunnels, and highways. Furthermore, the information gathered in this study can help readers enhance their knowledge concerning the advantages of employing these technologies for innovative uses of their powers.

Single-Electrode Triboelectric Nanogenerators Based on Ionic Conductive Hydrogel for Mechanical Energy Harvester and Smart Touch Sensor Applications

Recent advancements in wearable electronic technology demand advanced power sources to be flexible, deformable, durable, and sustainable. An ionic-solution-modified conductive hydrogel-based triboelectric nanogenerator (TENG) has advantages in wearable devices. However, fabricating a conductive hydrogel with better mechanical and electrical properties is still a challenge. Herein, a simple approach is developed to insert ion-rich pores inside the hydrogel, followed by ionic solution soaking. The suggested ionic conductive hydrogel is obtained by cross-linking the polyvinyl alcohol (PVA) hydrogel and carboxymethyl cellulose sodium salt (CMC), followed by soaking in the ionic solution. Furthermore, a flexible and shape-adaptable single-electrode TENG (S-TENG) is fabricated by combinations of ionic-solution-modified dual-cross-linked CMC/PVA hydrogel and silicone rubber. Additionally, the effects of the CMC concentration, type of ionic solution, and concentration of optimized ionic solutions on the hydrogel properties and S-TENG output performance are studied systematically. The well-dispersed CMC- and PVA-based hydrogel provides ion-rich pores with high ion migration, leading to enhanced conductivity. The fabricated S-TENG delivers maximum output performance in terms of voltage, current, and charge density of ∼584 V, 25 μA, and 120 μC/m², respectively. The rectified S-TENG-generated energy is used to charge capacitors and to power a portable electronic display. In addition to energy harvesting, the S-TENG is successfully demonstrated as a touch sensor that can automatically control the light and the speaker based on human motions. This investigation provides a deep insight into the influence of the hydrogel on the device performance and gives a guidance for designing and fabrication of highly flexible and stretchable TENGs.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

Multiplexed Complementary Signal Transmission for a Self-Regulating Artificial Nervous System

Neuromorphic engineering has emerged as a promising research field that can enable efficient and sophisticated signal transmission by mimicking the biological nervous system. This paper presents an artificial nervous system capable of facile self-regulation via multiplexed complementary signals. Based on the tunable nature of the Schottky barrier of a complementary signal integration circuit, a pair of complementary signals is successfully integrated to realize efficient signal transmission. As a proof of concept, a feedback-based blood glucose level control system is constructed by incorporating a glucose/insulin sensor, a complementary signal integration circuit, an artificial synapse, and an artificial neuron circuit. Certain amounts of glucose and insulin in the initial state are detected by each sensor and reflected as positive and negative amplitudes of the multiplexed presynaptic pulses, respectively. Subsequently, the pulses are converted to postsynaptic current, which triggered the injection of glucose or insulin in a way that confined the glucose level to a desirable range. The proposed artificial nervous system demonstrates the notable potential of practical advances in complementary control engineering.

Light-Controlled Triple-Shape-Memory, High-Permittivity Dynamic Elastomer for Wearable Multifunctional Information Encoding Devices

Self-powered information encoding devices (IEDs) have drawn considerable interest owing to their capability to process information without batteries. Next-generation IEDs should be reprogrammable, self-healing, and wearable to satisfy the emerging requirements for multifunctional IEDs; however, such devices have not been demonstrated. Herein, an integrated triboelectric nanogenerator-based IED with the aforementioned features was developed based on the designed light-responsive high-permittivity poly(sebacoyl diglyceride-co-4,4'-azodibenzoyl diglyceride) elastomer (PSeDAE) with a triple-shape-memory effect. The electrical memory feature was achieved through a microscale shape-memory property, enabling spatiotemporal information reprogramming for the IED. Macroscale shape-memory behavior afforded the IED shape-reprogramming ability, yielding wearable and detachable features. The dynamic transesterifications and light-heating groups in the PSeDAE afforded a remotely controlled rearrangement of its cross-linking network, producing the self-healing IED.

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.

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.

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.

Ultra-Sensitive, Deformable, and Transparent Triboelectric Tactile Sensor Based on Micro-Pyramid Patterned Ionic Hydrogel for Interactive Human-Machine Interfaces

Rapid advances in wearable electronics and mechno-sensational human-machine interfaces impose great challenges in developing flexible and deformable tactile sensors with high efficiency, ultra-sensitivity, environment-tolerance, and self-sustainability. Herein, a tactile hydrogel sensor (THS) based on micro-pyramid-patterned double-network (DN) ionic organohydrogels to detect subtle pressure changes by measuring the variations of triboelectric output signal without an external power supply is reported. By the first time of pyramidal-patterned hydrogel fabrication method and laminated polydimethylsiloxane (PDMS) encapsulation process, the self-powered THS shows the advantages of remarkable flexibility, good transparency (≈85%), and excellent sensing performance, including extraordinary sensitivity (45.97 mV Pa^-1 ), fast response (≈20 ms), very low limit of detection (50 Pa) as well as good stability (36 000 cycles). Moreover, with the LiBr immersion treatment method, the THS possesses excellent long-term hyper anti-freezing and anti-dehydrating properties, broad environmental tolerance (-20 to 60 °C), and instantaneous peak power density of 20 µW cm^-2 , providing reliable contact outputs with different materials and detecting very slight human motions. By integrating the signal acquisition/process circuit, the THS with excellent self-power sensing ability is utilized as a switching button to control electric appliances and robotic hands by simulating human finger gestures, offering its great potentials for wearable and multi-functional electronic applications.

A Self-Powered Triboelectric Hybrid Coder for Human-Machine Interaction

Human-machine interfaces have penetrated various academia and industry fields such as smartphones, robotic, virtual reality, and wearable electronics, due to their abundant functional sensors and information interaction methods. Nevertheless, most sensors' complex structural design, monotonous parameter detection capability, and single information coding communication hinder their rapid development. As the frontier of self-powered sensors, the triboelectric nanogenerator (TENG) has multiple working modes and high structural adaptability, which is a potential solution for multi-parameter sensing and miniaturizing of traditional interactive electronic devices. Herein, a self-powered hybrid coder (SHC) based on TENG is reported to encode two action parameters of touch and press, which can be used as a smart interface for human-machine interaction. The top-down hollow structure of the SHC, not only constructs a compositing mode to generate stable touch and press signals but also builds a hybrid coding platform for generating action codes in synergy mode. When a finger touches or presses the SHC, Morse code and Gray code can be transmitted for text information or remote control of electric devices. This self-powered coder is of reference value for designing an alternative human-machine interface and having the potential to contribute to the next generation of highly integrated portable smart electronics.

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.

Bioinspired Triboelectric Nanosensors for Self-Powered Wearable Applications

The sustainable operation of wearable sensors plays an important role in continuous and longtime health monitoring. Conventional batteries, which are bulky and rigid, do not satisfy these requirements and, rather, cause additional economic burdens and environmental problems by regular replacement of power sources. This article provides a review on an alternative solution in the form of self-powered devices that can harvest energy from the surrounding environment to support the operation of the wearable sensor. The Review starts with an introduction of the self-powered triboelectric nanosensors (TENSs) and its two independent modules: the energy harvester and the sensing module. The Review continues with the TENS-related bioinspired designs for wearable applications, while providing a bird's-eye view of their characteristics and applications. The ongoing challenges and prospects for providing personal healthcare with self-powered TENS are presented and discussed.

Recent Progress in the Energy Harvesting Technology-From Self-Powered Sensors to Self-Sustained IoT, and New Applications

With the fast development of energy harvesting technology, micro-nano or scale-up energy harvesters have been proposed to allow sensors or internet of things (IoT) applications with self-powered or self-sustained capabilities. Facilitation within smart homes, manipulators in industries and monitoring systems in natural settings are all moving toward intellectually adaptable and energy-saving advances by converting distributed energies across diverse situations. The updated developments of major applications powered by improved energy harvesters are highlighted in this review. To begin, we study the evolution of energy harvesting technologies from fundamentals to various materials. Secondly, self-powered sensors and self-sustained IoT applications are discussed regarding current strategies for energy harvesting and sensing. Third, subdivided classifications investigate typical and new applications for smart homes, gas sensing, human monitoring, robotics, transportation, blue energy, aircraft, and aerospace. Lastly, the prospects of smart cities in the 5G era are discussed and summarized, along with research and application directions that have emerged.

Gradient Architecture-Enabled Capacitive Tactile Sensor with High Sensitivity and Ultrabroad Linearity Range

The sensitivity and linearity are critical parameters that can preserve the high pressure-resolution across a wide range and simplify the signal processing process of flexible tactile sensors. Although extensive micro-structured dielectrics have been explored to improve the sensitivity of capacitive sensors, the attenuation of sensitivity with increasing pressure is yet to be fully resolved. Herein, a novel dielectric layer based on the gradient micro-dome architecture (GDA) is presented to simultaneously realize the high sensitivity and ultrabroad linearity range of capacitive sensors. The gradient micro-dome pixels with rationally collocated amount and height can effectively regulate the contact area and hence enable the linear variation in effective dielectric constant of the GDA dielectric layer under varying pressures. With systematical optimization, the sensor exhibits the high sensitivity of 0.065 kPa^-1 in an ultrabroad linearity range up to 1700 kPa, which is first reported. Based on the excellent sensitivity and linearity, the high pressure-resolution can be preserved across the full scale of pressure spectrum. Therefore, potential applications such as all-round physiological signal detection in diverse scenarios, control instruction transmission with combinatorial force inputs, and convenient Morse code communication with non-overlapping capacitance signals are successfully demonstrated through a single sensor device.

AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove

Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers' daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers.

From contact electrification to triboelectric nanogenerators

Although the contact electrification (CE) (or usually called 'triboelectrification') effect has been known for over 2600 years, its scientific mechanism still remains debated after decades. Interest in studying CE has been recently revisited due to the invention of triboelectric nanogenerators (TENGs), which are the most effective approach for converting random, low-frequency mechanical energy (called high entropy energy) into electric power for distributed energy applications. This review is composed of three parts that are coherently linked, ranging from basic physics, through classical electrodynamics, to technological advances and engineering applications. First, the mechanisms of CE are studied for general cases involving solids, liquids and gas phases. Various physics models are presented to explain the fundamentals of CE by illustrating that electron transfer is the dominant mechanism for CE for solid-solid interfaces. Electron transfer also occurs in the CE at liquid-solid and liquid-liquid interfaces. An electron-cloud overlap model is proposed to explain CE in general. This electron transfer model is extended to liquid-solid interfaces, leading to a revision of the formation mechanism of the electric double layer at liquid-solid interfaces. Second, by adding a time-dependent polarization termPscreated by the CE-induced surface electrostatic charges in the displacement fieldD, we expand Maxwell's equations to include both the medium polarizations due to electric field (P) and mechanical aggitation and medium boundary movement induced polarization term (Ps). From these, the output power, electromagnetic (EM) behaviour and current transport equation for a TENG are systematically derived from first principles. A general solution is presented for the modified Maxwell's equations, and analytical solutions for the output potential are provided for a few cases. The displacement current arising fromε∂E/∂t is responsible for EM waves, while the newly added term ∂Ps/∂t is responsible for energy and sensors. This work sets the standard theory for quantifying the performance and EM behaviour of TENGs in general. Finally, we review the applications of TENGs for harvesting all kinds of available mechanical energy that is wasted in our daily life, such as human motion, walking, vibration, mechanical triggering, rotating tires, wind, flowing water and more. A summary is provided about the applications of TENGs in energy science, environmental protection, wearable electronics, self-powered sensors, medical science, robotics and artificial intelligence.

Recent advances in nanogenerators-based flexible electronics for electromechanical biomonitoring

Electromechanical biomonitoring is essential in human health evaluation, diseases prevention and life quality improvement. Nanogenerators (NGs) have demonstrated exceptional performances and versatility in self-powered flexible electronics including piezoelectric and electrostatic sensors. Combined with artificial intelligent (AI), five generation (5G) and internet-of-thing (IoT) technologies, the NGs-based flexible electronics are paving a new way for creating intelligent electromechanical biomonitoring systems which are also capable of analyzing, transmitting, and deciding. In this review, we cover the recent remarkable developments in monitoring electromechanical physiological signals using NGs-based flexible electronics. We begin by covering the fundamentals of NGs from the perspective of mechanisms, materials, device structures, and manufacturing methods. We then give an overview of NGs-based flexible electronics in various wearable and implantable sensing applications. Finally, the present limitations and future developing trends of this field are discussed and prospected.

Bio-Inspired Hybrid Dielectric for Capacitive and Triboelectric Tactile Sensors with High Sensitivity and Ultrawide Linearity Range

The trade-off between sensitivity and linearity is critical for preserving the high pressure-resolution over a broad range and simplifying the signal processing/conversion of flexible tactile sensors. Conventional dielectrics suffer from the difficulty of quantitatively controlling the interacted mechanical and dielectric properties, thus causing the restricted sensitivity and linearity of capacitive sensors. Herein, inspired by human skin, a novel hybrid dielectric composed of a low-permittivity (low-k) micro-cilia array, a high-permittivity (high-k) rough surface, and micro-dome array is developed. The pressure-induced series-parallel conversion between the low-k and high-k components of the hybrid dielectric enables the linear effective dielectric constant and controllable initial/resultant capacitance. The gradient compressibility of the hybrid dielectric enables the linear behavior of elastic modulus with pressures, which derives the capacitance variation determined by the effective dielectric constant. Therefore, an ultrawide linearity range up to 1000 kPa and a high sensitivity of 0.314 kPa^-1 are simultaneously achieved by the optimized hybrid dielectric. The design is also applicable for triboelectric tactile sensors, which realizes the similar linear behavior of output voltage and enhanced sensitivity. With the high pressure-resolution across a broad range, potential applications such as healthcare monitoring in diverse scenarios and control command conversion via a single sensor are demonstrated.

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.

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fast-response gas-monitoring systems. However, the conventional plasma discharge system is bulky, operates at a high temperature, and inappropriate for volatile organic compounds (VOCs) concentration detection. Therefore, we report a machine learning (ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer, which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment. Based on the charge accumulation mechanism, a multi-switched manipulation triboelectric nanogenerator (SM-TENG) can provide a direct current (DC) bias at the order of a few hundred, which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs, and their mixtures, with a special tip-plate electrode configuration. Aiming to tackle the grand challenge in the detection of multiple VOCs, the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms, which significantly enhance the detection ability of the SM-TENG based VOC analyzer, showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.

Progress on Self-Powered Wearable and Implantable Systems Driven by Nanogenerators

With the rapid development of the internet of things (IoT), sustainable self-powered wireless sensory systems and diverse wearable and implantable electronic devices have surged recently. Under such an opportunity, nanogenerators, which can convert continuous mechanical energy into usable electricity, have been regarded as one of the critical technologies for self-powered systems, based on the high sensitivity, flexibility, and biocompatibility of piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs). In this review, we have thoroughly analyzed the materials and structures of wearable and implantable PENGs and TENGs, aiming to make clear how to tailor a self-power system into specific applications. The advantages in TENG and PENG are taken to effectuate wearable and implantable human-oriented applications, such as self-charging power packages, physiological and kinematic monitoring, in vivo and in vitro healing, and electrical stimulation. This review comprehensively elucidates the recent advances and future outlook regarding the human body's self-powered systems.

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.

Progress in micro/nano sensors and nanoenergy for future AIoT-based smart home applications

Self-sustainable sensing systems composed of micro/nano sensors and nano-energy harvesters contribute significantly to developing the internet of things (IoT) systems. As one of the most promising IoT applications, smart home relies on implementing wireless sensor networks with miniaturized and multi-functional sensors, and distributed, reliable, and sustainable power sources, namely energy harvesters with a variety of conversion mechanisms. To extend the capabilities of IoT in the smart home, a technology fusion of IoT and artificial intelligence (AI), called the artificial intelligence of things (AIoT), enables the detection, analysis, and decision-making functions with the aids of machine learning assisted algorithms to form a smart home based intelligent system. In this review, we introduce the conventional rigid microelectromechanical system (MEMS) based micro/nano sensors and energy harvesters, followed by presenting the advances in the wearable counterparts for better human interactions. We then discuss the viable integration approaches for micro/nano sensors and energy harvesters to form self-sustainable IoT systems. Whereafter, we emphasize the recent development of AIoT based systems and the corresponding applications enabled by the machine learning algorithms. Smart home based healthcare technology enabled by the integrated multi-functional sensing platform and bioelectronic medicine is also presented as an important future direction, as well as wearable photonics sensing system as a complement to the wearable electronics sensing system.

Biologically Plausible Artificial Synaptic Array: Replicating Ebbinghaus' Memory Curve with Selective Attention

The nature of repetitive learning and oblivion of memory enables humans to effectively manage vast amounts of memory by prioritizing information for long-term storage. Inspired by the memorization process of the human brain, an artificial synaptic array is presented, which mimics the biological memorization process by replicating Ebbinghaus' forgetting curve. To construct the artificial synaptic array, signal-transmitting access transistors and artificial synaptic memory transistors are designed using indium-gallium-zinc-oxide and poly(3-hexylthiophene), respectively. To secure the desired performance of the access transistor in regulating the input signal to the synaptic transistor, the content of gallium in the access transistor is optimized. In addition, the operation voltage of the synaptic transistor is carefully selected to achieve memory-state efficiency. Repetitive learning characterizing Ebbinghaus' oblivion curves is realized using an artificial synaptic array with optimized conditions for both transistor components. This successfully demonstrates a biologically plausible memorization process. Furthermore, selective attention for information prioritization in the human brain is mimicked by selectively applying repetitive learning to a synaptic transistor with a high memory state. The demonstrated biologically plausible artificial synaptic array provides great scope for advancement in bioinspired electronics.

Self-powered electro-tactile system for virtual tactile experiences

Tactile sensation plays important roles in virtual reality and augmented reality systems. Here, a self-powered, painless, and highly sensitive electro-tactile (ET) system for achieving virtual tactile experiences is proposed on the basis of triboelectric nanogenerator (TENG) and ET interface formed of ball-shaped electrode array. Electrostatic discharge triggered by TENG can induce notable ET stimulation, while controlled distance between the ET electrodes and human skin can regulate the induced discharge current. The ion bombardment technique has been used to enhance the electrification capability of triboelectric polymer. Accordingly, TENG with a contact area of 4 cm² is capable of triggering discharge, leading to a compact system. In this skin-integrated ET interface, touching position and motion trace on the TENG surface can be precisely reproduced on skin. This TENG-based ET system can work for many fields, including virtual tactile displays, Braille instruction, intelligent protective suits, or even nerve stimulation.

Toward Healthcare Diagnoses by Machine-Learning-Enabled Volatile Organic Compound Identification

As a natural monitor of health conditions for human beings, volatile organic compounds (VOCs) act as significant biomarkers for healthcare monitoring and early stage diagnosis of diseases. Most existing VOC sensors use semiconductors, optics, and electrochemistry, which are only capable of measuring the total concentration of VOCs with slow response, resulting in the lack of selectivity and low efficiency for VOC detection. Infrared (IR) spectroscopy technology provides an effective solution to detect chemical structures of VOC molecules by absorption fingerprints induced by the signature vibration of chemical stretches. However, traditional IR spectroscopy for VOC detection is limited by the weak light-matter interaction, resulting in large optical paths. Leveraging the ultrahigh electric field induced by plasma, the vibration of the molecules is enhanced to improve the light-matter interaction. Herein, we report a plasma-enhanced IR absorption spectroscopy with advantages of fast response, accurate quantization, and good selectivity. An order of ∼kV voltage was achieved from the multiswitched manipulation of the triboelectric nanogenerator by repeated sliding. The VOC species and their concentrations were well-quantified from the wavelength and intensity of spectra signals with the enhancement from plasma. Furthermore, machine learning has visualized the relationship of different VOCs in the mixture, which demonstrated the feasibility of the VOC identification to mimic patients.

Flourishing energy harvesters for future body sensor network: from single to multiple energy sources

Body sensor network (bodyNET) offers possibilities for future disease diagnosis, preventive health care, rehabilitation, and treatment. However, the eventual realization demands reliable and sustainable power sources. The flourishing energy harvesters (EHs) have provided prominent techniques for practically addressing the concurrent energy issue. Targeting for a specific energy source, wearable EHs with a sole conversion mechanism are well investigated. Hybrid EHs integrating different effects for a single source or multi-sources are attaining growing attention, for they provide another degree of freedom concerning a higher-level energy utility. Merging EHs with other functional electronics, diversified functional self-sustainable systems are developed, paving the way for the accomplishment of bodyNET. This review introduces the evolution of wearable EHs from a single effect to hybridized mechanisms for multiple energy sources and wearable to implantable self-sustainable systems. Last, we provide our perspectives on the future development of hybrid EHs to be more competitive with conventional batteries.

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.

The Evolution of Flexible Electronics: From Nature, Beyond Nature, and To Nature

The flourishing development of multifunctional flexible electronics cannot leave the beneficial role of nature, which provides continuous inspiration in their material, structural, and functional designs. During the evolution of flexible electronics, some originated from nature, some were even beyond nature, and others were implantable or biodegradable eventually to nature. Therefore, the relationship between flexible electronics and nature is undoubtedly vital since harmony between nature and technology evolution would promote the sustainable development. Herein, materials selection and functionality design for flexible electronics that are mostly inspired from nature are first introduced with certain functionality even beyond nature. Then, frontier advances on flexible electronics including the main individual components (i.e., energy (the power source) and the sensor (the electric load)) are presented from nature, beyond nature, and to nature with the aim of enlightening the harmonious relationship between the modern electronics technology and nature. Finally, critical issues in next-generation flexible electronics are discussed to provide possible solutions and new insights in prospective exploration directions.

A Review and Perspective for the Development of Triboelectric Nanogenerator (TENG)-Based Self-Powered Neuroprosthetics

Neuroprosthetics have become a powerful toolkit for clinical interventions of various diseases that affect the central nervous or peripheral nervous systems, such as deep brain stimulation (DBS), functional electrical stimulation (FES), and vagus nerve stimulation (VNS), by electrically stimulating different neuronal structures. To prolong the lifetime of implanted devices, researchers have developed power sources with different approaches. Among them, the triboelectric nanogenerator (TENG) is the only one to achieve direct nerve stimulations, showing great potential in the realization of a self-powered neuroprosthetic system in the future. In this review, the current development and progress of the TENG-based stimulation of various kinds of nervous systems are systematically summarized. Then, based on the requirements of the neuroprosthetic system in a real application and the development of current techniques, a perspective of a more sophisticated neuroprosthetic system is proposed, which includes components of a thin-film TENG device with a biocompatible package, an amplification circuit to enhance the output, and a self-powered high-frequency switch to generate high-frequency current pulses for nerve stimulations. Then, we review and evaluate the recent development and progress of each part.

Deep learning enabled smart mats as a scalable floor monitoring system

Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique "identity" electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security.

Wearable Triboelectric/Aluminum Nitride Nano-Energy-Nano-System with Self-Sustainable Photonic Modulation and Continuous Force Sensing

Wearable photonics offer a promising platform to complement the thriving complex wearable electronics system by providing high-speed data transmission channels and robust optical sensing paths. Regarding the realization of photonic computation and tunable (de)multiplexing functions based on system-level integration of abundant photonic modulators, it is challenging to reduce the overwhelming power consumption in traditional current-based silicon photonic modulators. This issue is addressed by integrating voltage-based aluminum nitride (AlN) modulator and textile triboelectric nanogenerator (T-TENG) on a wearable platform to form a nano-energy-nano-system (NENS). The T-TENG transduces the mechanical stimulations into electrical signals based on the coupling of triboelectrification and electrostatic induction. The self-generated high-voltage from the T-TENG is applied to the AlN modulator and boosts its modulation efficiency regardless of AlN's moderate Pockels effect. Complementarily, the AlN modulator's capacitive nature enables the open-circuit operation mode of T-TENG, providing the integrated NENS with continuous force sensing capability which is notably uninfluenced by operation speeds. Furthermore, a physical model is proposed to describe the coupled AlN modulator/T-TENG system. With the enhanced photonic modulation and the open-circuit operation mode enabled by synergies between the AlN modulator and the T-TENG, optical Morse code transmission and continuous human motion monitoring are demonstrated for practical wearable applications.

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.

Machine Learning Glove Using Self-Powered Conductive Superhydrophobic Triboelectric Textile for Gesture Recognition in VR/AR Applications

The rapid progress of Internet of things (IoT) technology raises an imperative demand on human machine interfaces (HMIs) which provide a critical linkage between human and machines. Using a glove as an intuitive and low-cost HMI can expediently track the motions of human fingers, resulting in a straightforward communication media of human-machine interactions. When combining several triboelectric textile sensors and proper machine learning technique, it has great potential to realize complex gesture recognition with the minimalist-designed glove for the comprehensive control in both real and virtual space. However, humidity or sweat may negatively affect the triboelectric output as well as the textile itself. Hence, in this work, a facile carbon nanotubes/thermoplastic elastomer (CNTs/TPE) coating approach is investigated in detail to achieve superhydrophobicity of the triboelectric textile for performance improvement. With great energy harvesting and human motion sensing capabilities, the glove using the superhydrophobic textile realizes a low-cost and self-powered interface for gesture recognition. By leveraging machine learning technology, various gesture recognition tasks are done in real time by using gestures to achieve highly accurate virtual reality/augmented reality (VR/AR) controls including gun shooting, baseball pitching, and flower arrangement, with minimized effect from sweat during operation.

Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual/augmented reality applications

Human-machine interfaces (HMIs) experience increasing requirements for intuitive and effective manipulation. Current commercialized solutions of glove-based HMI are limited by either detectable motions or the huge cost on fabrication, energy, and computing power. We propose the haptic-feedback smart glove with triboelectric-based finger bending sensors, palm sliding sensor, and piezoelectric mechanical stimulators. The detection of multidirectional bending and sliding events is demonstrated in virtual space using the self-generated triboelectric signals for various degrees of freedom on human hand. We also perform haptic mechanical stimulation via piezoelectric chips to realize the augmented HMI. The smart glove achieves object recognition using machine learning technique, with an accuracy of 96%. Through the integrated demonstration of multidimensional manipulation, haptic feedback, and AI-based object recognition, our glove reveals its potential as a promising solution for low-cost and advanced human-machine interaction, which can benefit diversified areas, including entertainment, home healthcare, sports training, and medical industry.

An epidermal sEMG tattoo-like patch as a new human-machine interface for patients with loss of voice

Throat cancer treatment involves surgical removal of the tumor, leaving patients with facial disfigurement as well as temporary or permanent loss of voice. Surface electromyography (sEMG) generated from the jaw contains lots of voice information. However, it is difficult to record because of not only the weakness of the signals but also the steep skin curvature. This paper demonstrates the design of an imperceptible, flexible epidermal sEMG tattoo-like patch with the thickness of less than 10 μm and peeling strength of larger than 1 N cm^-1 that exhibits large adhesiveness to complex biological surfaces and is thus capable of sEMG recording for silent speech recognition. When a tester speaks silently, the patch shows excellent performance in recording the sEMG signals from three muscle channels and recognizing those frequently used instructions with high accuracy by using the wavelet decomposition and pattern recognization. The average accuracy of action instructions can reach up to 89.04%, and the average accuracy of emotion instructions is as high as 92.33%. To demonstrate the functionality of tattoo-like patches as a new human-machine interface (HMI) for patients with loss of voice, the intelligent silent speech recognition, voice synthesis, and virtual interaction have been implemented, which are of great importance in helping these patients communicate with people and make life more enjoyable.

Development Trends and Perspectives of Future Sensors and MEMS/NEMS

With the fast development of the fifth-generation cellular network technology (5G), the future sensors and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS) are presenting a more and more critical role to provide information in our daily life. This review paper introduces the development trends and perspectives of the future sensors and MEMS/NEMS. Starting from the issues of the MEMS fabrication, we introduced typical MEMS sensors for their applications in the Internet of Things (IoTs), such as MEMS physical sensor, MEMS acoustic sensor, and MEMS gas sensor. Toward the trends in intelligence and less power consumption, MEMS components including MEMS/NEMS switch, piezoelectric micromachined ultrasonic transducer (PMUT), and MEMS energy harvesting were investigated to assist the future sensors, such as event-based or almost zero-power. Furthermore, MEMS rigid substrate toward NEMS flexible-based for flexibility and interface was discussed as another important development trend for next-generation wearable or multi-functional sensors. Around the issues about the big data and human-machine realization for human beings' manipulation, artificial intelligence (AI) and virtual reality (VR) technologies were finally realized using sensor nodes and its wave identification as future trends for various scenarios.