Triboelectric Sensor Research Articles

Multimodal Pressure Sensor: Advancing Continuous Cardiovascular Monitoring through Synergistic Capacitive and Piezoresistive Sensing

Recent advancements in flexible and skin-mountable health monitoring devices have sparked interest in non-invasive methods for continuously capturing subtle human physiological signals. Among these, pressure sensors play a pivotal role, particularly in applications involving pulse detection and electronic skin. While various mechanisms like piezoresistive, capacitive, triboelectric, and piezoelectric sensors have shown promise, their reliance on flexible substrates presents an opportunity to merge different sensing mechanisms into a single, multifaceted sensor, thereby maximizing benefits and mitigating limitations. This study introduces a novel approach by proposing and fabricating a multimodal sensor that seamlessly combines the piezoresistive and capacitive modes of operation. Leveraging finite element analysis, we established design parameters tailored to create a highly sensitive piezoresistive sensor capable of discerning weak arterial pulse waveforms. Simultaneously, for the capacitive sensor aspect, we optimized the performance of a supercapacitive sensor utilizing paper as the sensing layer. This innovative approach in capacitive sensing offers high sensitivity and reliability. The fabrication of both sensors culminated in their integration into a compact, multi-mechanism sensing platform. The resultant multimodal sensor holds promise as a sophisticated, wearable technology for continuous monitoring of cardiovascular pulse waveforms at wrist arterial sites. By synergizing the advantages of capacitive and piezoresistive mechanisms, this innovation paves the way for enhanced accuracy and precision in capturing and analyzing subtle physiological signals.

Self-Powered Wearable Pressure Sensor System for Smart Healthcare Applications

Wearable sensors mark a groundbreaking advancement in the realm of health monitoring technology. However, traditional wearable devices lack comprehensive solutions for tracking sports exercise behaviors and providing feedback on rehabilitation for those undergoing home-based recovery. To bridge this gap, a self-powered triboelectric nanogenerator (TENG)-based wearable sensing system has been developed with in-house monitoring capabilities. The wearable sensor system is composed of triboelectric pressure sensors with an electro-conductive silicone sheet and patterned Ecoflex film as the contact layers. Highly sensitive TENG sensors have been successfully embedded into a bicycle saddle and human shoe insole for real-time pressure monitoring. Experimental results demonstrate that biomechanical energy, originating from both the pelvic-saddle interactions on bicycle and human gait movements, has been successfully converted into electrical energy. The generated sensor voltage remains stable under frequency variations and external environmental conditions, such as temperature and humidity variations. Utilizing distinctive information contained in individual body mass imbalances and gait characteristics, a smart healthcare monitoring system is envisioned to extract essential biomechanical insights via artificial intelligence, facilitating a pivotal role in in-house monitoring and tracking of sports rehabilitation exercises. The as-designed low-cost, highly scalable self-powered wearable sensor system offers an accessible, user-friendly point-of-care solution for gait detection and rehabilitation monitoring for individuals with various physical impairments, with considerable potential for commercialization. Figure 1

(Invited) Triboelectric Nanogenerators for Self-Powered Sensing and Human-Machine-Interfaces

Wearable and portable electronics are garnering substantial interest for applications ranging from personalized healthcare monitoring to implantable devices and prosthetics. The demand for long-term operation poses significant challenges to their power supply. To address this, the development of novel self-powered systems holds considerable promise. In this talk, I will discuss our recent advancements in crafting triboelectric nanogenerators (TENGs) for self-powered sensing and human-machine interfaces (HMIs). I will first introduce our multifunctional tactile sensors inspired by human skin. These sensors combine a triboelectric nanogenerator layer with a piezoresistive sensing layer, emulating the ability of human skin to detect a variety of static and dynamic stimuli. This sensor can successfully recognize voice patterns, monitor physiological signals, and track human motion in real-time. Then, I will unveil our self-powered smart packaging system, which is driven by a novel desiccant-based triboelectric nanogenerator (D-TENG). This system offers a viable method for continuous monitoring of product conditions and quality control throughout the supply chain, thus minimizing food waste and maintaining a comprehensive log of logistics, especially critical in vaccine transport. Lastly, I will present our novel breath-driven triboelectric sensor in respiratory monitoring. This sensor can precisely detect breath variations and convert different breathing patterns into electrical signals, all without impeding normal breathing. It enables severely disabled individuals to control household appliances through a breath-driven HMI and provides an intelligent monitoring system for emergency situations. This innovative work paves the way for future development in self-powered sensors and advanced HMIs, enhancing assistive technology for individuals with disabilities.

LDIPRS: A novel longitudinal driving intention prior recognition technique empowered by TENG and deep learning

For autonomous vehicles, real-time and accurate longitudinal driving intention recognition is crucial as it effectively enhances driving safety and improves the driving experience. This study proposes a novel data and model hybrid-driven fine-grained longitudinal driving intention prior recognition system (LDIPRS). Firstly, the system integrates a human-pedal interaction sensor (HPIS) based on triboelectric nanogenerators for fine-grained longitudinal driving maneuver monitoring and the channel attention (CA)-enhanced convolutional neural network (CBRCNet). The HPIS, integrated into the vehicle's acceleration and brake pedals, is capable of monitoring driver foot movement information in the form of electrical signals before the vehicle responds, achieving data level advance. The collected electrical signals are fed into the CBRCNet network, which models and learns the mapping relationship between these signals and fine-grained longitudinal driving intentions, leading to model level advances. The HPIS completes the capture of longitudinal maneuver information 541 ms before the driving simulator starts to respond at the data level. At the model level, CBRCNet can achieve a recognition accuracy of 96.1 % based on partial response data (50 ms after starting response) rather than complete response data of the HPIS. Finally, our proposed LDIPRS realizes the recognition of emergency braking, rapid acceleration, normal braking, and normal acceleration in advance by 732 ms, 1035 ms, 1757 ms, and 2227 ms, respectively. This study introduces self-powered, low-cost, highly sensitive triboelectric sensors into the field of intention recognition, and combines the triboelectric sensors with deep learning algorithms to offer a promising solution to improve the safety of autonomous vehicles and the efficiency of intelligent transportation systems.

Mechanical-computing metastructure for self-powered vibration sensing

Triboelectric nanogenerator-based vibration sensors (TVSs) present a promising approach for online vibration monitoring, offering early detection of excessive vibrations that pose risks to the functionality of devices and infrastructure. However, the structural design of most current TVSs lacks consideration for information processing capabilities, resulting in limited detection bandwidth and displacement signal fidelity. This study first introduces a quasi-zero-stiffness (QZS) mechanical-computing metastructure aimed at enhancing the structural intelligence of TVSs and extending their vibration signal detection bandwidth. The optimization of the QZS metastructure involved adjusting the length ratio and inclination angle of the zero-order polynomial beam to achieve ultra-low resonant frequencies. The results indicate that the TVS based on the mechanical-computing metastructure (MCM-TVS) is capable of efficiently detecting complex random signals, achieving an average measurement accuracy above 85 % for the spectral components of random vibration signals. Furthermore, the sensor exhibited remarkable linearity (99 %) in measuring vibration amplitude and maintained consistent sensitivity (with less than 25.8 % fluctuation), facilitating the lossless conversion of displacement to electrical signals. Consequently, this novel approach of incorporating an MCM into TVS design offers substantial insights for the advancement of high-performance vibration signal monitoring.

A fully encapsulated flexible triboelectric sensor for swimming posture monitoring

A fully encapsulated flexible triboelectric sensor for swimming posture monitoring

Mica/nylon composite nanofiber film based wearable triboelectric sensor for object recognition

The wearable sensors have essential impacts on the progress of intelligent technology, while the traditional sensors cannot meet the sensitivity requirements. Here, we propose a high performance intelligent triboelectric wearable sensor (HITWS) by Mica/Nylon composite nanofiber film (MCNF), which is fabricated by the electrospinning and dielectric particle doping process. The addition of Mica particles effectively improves the dielectric constant, surface charge density and functional group properties of MCNF and further optimizes its positive triboelectric properties. Therefore, the HITWS demonstrates significantly higher signal-to-noise ratio, sensitivity and power density (11.82 W/m2) compared to previous similar studies. Furthermore, based on HITWS, 4-channel STM32 signal acquisition and deep learning algorithm, an intelligent tactile sensing system is achieved. Combined with the structurally optimized VGG16 network model, the recognition of 7 kinds of objects can reach an average accuracy of 96.57 %. This work provides new idea and method for the development of applications based on object recognition, such as remote interaction, medical prosthetics and smart robots.

Self-powered and speed-adjustable sensor for abyssal ocean current measurements based on triboelectric nanogenerators

The monitoring of currents in the abyssal ocean is an essential foundation of deep-sea research. The state-of-the-art current meter has limitations such as the requirement of a power supply for signal transduction, low pressure resistance, and a narrow measurement range. Here, we report a fully integrated, self-powered, highly sensitive deep-sea current measurement system in which the ultra-sensitive triboelectric nanogenerator harvests ocean current energy for the self-powered sensing of tiny current motions down to 0.02 m/s. Through an unconventional magnetic coupling structure, the system withstands immense hydrostatic pressure exceeding 45 MPa. A variable-spacing structure broadens the measuring range to 0.02–6.69 m/s, which is 67% wider than that of commercial alternatives. The system successfully operates at a depth of 4531 m in the South China Sea, demonstrating the record-deep operations of triboelectric nanogenerator-based sensors in deep-sea environments. Our results show promise for sustainable ocean current monitoring with higher spatiotemporal resolution.

Shear Thickening Fluid and Sponge-Hybrid Triboelectric Nanogenerator for a Motion Sensor Array-Based Lying State Detection System.

Issues of size and power consumption in IoT devices can be addressed through triboelectricity-driven energy harvesting technology, which generates electrical signals without external power sources or batteries. This technology significantly reduces the complexity of devices, enhances installation flexibility, and minimizes power consumption. By utilizing shear thickening fluid (STF), which exhibits variable viscosity upon external impact, the sensitivity of triboelectric nanogenerator (TENG)-based sensors can be adjusted. For this study, the highest electrical outputs of STF and sponge-hybrid TENG (SSH-TENG) devices under various input forces and frequencies were generated with an open-circuit voltage (VOC) of 98 V and a short-circuit current (ISC) of 4.5 µA. The maximum power density was confirmed to be 0.853 mW/m2 at a load resistance of 30 MΩ. Additionally, a lying state detection system for use in medical settings was implemented using SSH-TENG as a hybrid triboelectric motion sensor (HTMS). Each unit of a 3 × 2 HTMS array, connected to a half-wave rectifier and 1 MΩ parallel resistor, was interfaced with an MCU. Real-time detection of the patient's condition through the HTMS array could enable the early identification of hazardous situations and alerts. The proposed HTMS continuously monitors the patient's movements, promptly identifying areas prone to pressure ulcers, thus effectively contributing to pressure ulcer prevention.

Deep-learning-assisted triboelectric whisker for near field perception and online state estimation of underwater vehicle

Agile near field perception remains a challenge for underwater vehicles, which could significantly enhance their capability of online state estimation. Harbor seals have evolved a whisker array that can accurately measure and identify environmental information in their surroundings. Inspired by the "smart" whiskers of harbor seals, the study has designed a deep learning-assisted bionic underwater triboelectric whisker sensor (UTWS) to passively perceive diverse hydrodynamic flow fields, including omni-directional steady flow and wakes induced by bluff body upstream. The device mainly comprised an elliptical whisker shaft with a high-aspect-ratio of 0.403, four flexible triboelectric sensing units mimicking the nerve in the follicular sinus complex, and a flexible corrugated joint imitating the surface skin of marine organisms' cheeks. The UTWS demonstrated impressive advantages of rapid response times of 21 ms, high sensitivity of 1.16 V/m.s−1, high signal-to-noise-ratio of 61.66 dB. By implementing deep learning analytics to process the multi-channel signals, the underwater vehicle equipped with the UTWS can accomplish online velocity estimation proficiently, with an approximate root mean square error of approximately 0.093 in the verification case. Thus, this UTWS-based, deep-learning-assisted perception could become a promising tool for integration with underwater vehicles in the local navigation tasks.

Cobalt ferrite-embedded polyvinylidene fluoride electrospun nanocomposites as flexible triboelectric sensors for healthcare and polysomnographic monitoring applications

Human respiration is a vital physiological function of the body and a key metric for assessing overall health, particularly in conditions related to sleep deprivation. However, developing a real-time system for detecting sleeping position, heartbeat, and respiration is challenging yet crucial. Such a system should be easy to fabricate, comfortable to wear, and highly sensitive. In this study, we fabricated a flexible electrospun cobalt ferrite (CoFe2O4, CF) embedded polyvinylidene fluoride (PVDF) nanocomposite (NC) using an electrospinning technique. Additionally, we investigated the influence of varying CF content (0, 1, 3, and 5 wt%) on the crystalline β-phase in PVDF. The triboelectric nanogenerator (TENG) device was fabricated using PVDF-CF (P-CF) NC as the tribo-negative layer and non-woven fabric of thermoplastic polyurethane (TPU) as the tribo-positive layer. Among the four sample combinations used in this study, P-CF-3 (3 wt% CF in PVDF)/TPU TENG exhibited a significantly higher triboelectric open circuit voltage (Voc) of 5.8 V, nearly three times higher as compared to P-CF-0/TPU TENG (1.7 V). This work demonstrates an efficient method for enhancing the output efficacy of flexible TENG devices by varying the nanofiller concentration. Moreover, the fabricated TENG device was efficiently tested for real-time healthcare monitoring (HCM) and polysomnographic (PSG) related studies. This study aspires to provide a novel and pragmatic way of identifying real-time sleeping disorders and respiratory monitoring.

Flexible self-powered triboelectric nanogenerator sensor for wind speed measurement driven by moving trains

Flexible self-powered sensors have been extensively applied to the Internet of Things, structural health monitoring (SHM), and intelligent transportation. It would be more demanding for the power supply to these sensors during the long-term maintenance of the rail transit system. The wind pressure/velocity generated by high-speed trains poses a substantial threat to safety of human, and new sensors without an external power supply should be developed to monitor wind pressure/velocity in the trackside. Flexible self-powered wind triboelectric nanogenerator (W-TENG) sensor with a single-electrode mode based on conductive hydrogel is designed to wind pressure/velocity monitoring without power supply by harvesting wind energy. It is devoted the relationship between the output voltage of the sensors and the wind pressure/velocity driven by high-speed trains. Material selection and structural design methods are adopted to enhance the energy harvesting efficiency and sensing accuracy of self-powered W-TENG sensors. Open-circuit current of 2.8 μA and open-circuit voltage of 12 V are achieved, and the output voltage signal has the linear relationship with trackside wind pressure/velocity. Field tests are implemented to evaluate the performance of self-powered W-TENG sensors in wind pressure/velocity measurement caused by moving trains, providing an idea to SHM application in intelligent transmit systems.

High-Performance Self-Cleaning Triboelectric Mini Humidity Sensor Enabled by Facet Engineered Titania Nanocrystals Adorned Nylon-6,6 Electrospun Nanofibers

High-Performance Self-Cleaning Triboelectric Mini Humidity Sensor Enabled by Facet Engineered Titania Nanocrystals Adorned Nylon-6,6 Electrospun Nanofibers

Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition.

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

Flexible arch-shaped triboelectric sensor based on 3D printing for badminton movement monitoring and intelligent recognition of technical movements

Efficient monitoring and recognition of movement are crucial in enhancing athletic performance. Traditional methods have limitations in terms of high site requirements and power consumption, making them unsuitable for long-term tracking and monitoring. A potential solution to low-power monitoring of body area networks is triboelectric sensors. However, the current analysis method for badminton triboelectric sensing data is relatively simple, while flexible, triboelectric sensors based on 3D printing face issues such as discomfort when joints are bent or twisted in a large range. In light of this, a flexible arch-shaped triboelectric sensor based on 3D printing (FA-Sensor) is proposed. By combining neural network algorithms with the signal acquisition module and the master computer, an intelligent multi-sensor node system for badminton monitoring is established. The FA-Sensor exhibits high sensitivity to bending and twisting motions due to its elastic TPE shell and arched shape design. It minimizes interference with human motion during bending (10°–150°) or twisting (20°–100°) over a wide range. The peak output voltage of the FA-Sensor demonstrates a clear functional relationship with the bending angle, exhibiting piecewise sensitivities of 7.98 and 29.28 mV/°, respectively. For seven different parts of the human body, it can be quickly customized to different sizes, with stable and repeatable response outputs. In application, the badminton sports monitoring system enables real-time feedback and recognition of four typical technical movements, achieving a recognition accuracy rate of 97.2%. The system enables athletes to analyze and enhance badminton technology while also exhibiting promising potential for application in other intelligent sports domains.

A self-powered spring-based triboelectric vibration sensor

Abstract Self-powered vibration sensors have gained attention due to their versatility. However, a limitation of many existing self-powered sensors is their single-direction functionality, which hinders their effectiveness in capturing multidirectional human movement’s swinging motions. To address this, this study introduces an innovative self-powered vibration sensor based on the triboelectrification effect of an inverted pendulum metal ball. This novel sensor excels at detecting micro-vibrations through the freestanding sliding electrification of a metal ball using Kapton tape. The generated charge is transferred through interdigital electrodes arranged in a spiral pattern. To ensure adaptability to various motion types, the metal ball is affixed to a spring and configured as an inverted pendulum. This setup allows the sensor to detect both linear and rotary motions across a range of acceleration levels. The fabricated sensor exhibits remarkable sensitivity, measuring 0.203 V/mm. It was affixed to the human body to detect low-frequency vibrations, particularly those below 20 Hz. Impressively, it can detect millimeter-scale vibrations, even up to 3 mm, at different rotational angles (0°, 30°, 60°, and 90°). This outcome highlights the promising performance of our vibration sensor in the field of human motion monitoring, making it a significant advancement in the realm of self-powered vibration sensors.

A bioinspired triboelectric wireless anemometer with low cut-in wind speed for meteorological UAVs

Currently, unmanned aerial vehicles (UAVs) play a pivotal role in meteorological wind speed measurements. Nevertheless, existing anemometers exhibit notable issues, including excessive weight, high cost and high cut-in wind speed. This investigation introduces a biomimetic owl wing airfoil wind speed sensor utilizing the principles of the triboelectric nanogenerator (BOW-TENG) designed for UAV applications. Inspired by owl wings, an airfoil configuration with superior aerodynamic characteristics is ascertained, subsequently fabricating it into an impeller, and a wind speed sensor with low weight, low cost and low cut-in wind speed is developed. Experimental results affirm the BOW-TENG’s precision in depicting wind speed across the range of 1.6–10.7 m/s, featuring a sensitivity of 21.893 Hz/(m·s−1), a goodness of fit R2 of 0.9995, and a remarkable resolution of 0.057 m/s. The sensor weighs only 30 g. Furthermore, a cost-effective triboelectric signal processing system is devised for integrating the BOW-TENG into the UAV, as well as the optimal positioning for BOW-TENG on the UAV is identified through simulations. The relative wind direction can be calculated from signals of the BOW-TENGs on the four arms of the UAV. Ultimately, application experiments demonstrate the feasibility of employing BOW-TENG for wireless wind speed transmission from UAVs. This work incorporates bionics and proposes a practical application of triboelectric sensors for UAVs, as well as a solution for developing wind speed sensors in the field of meteorological UAV detection.

A novel tiny triboelectric acoustic sensor design based on nanocomposite enhancement for highly-sensitive, broadband, and self-powered multi-functional applications

This work proposes a novel acoustic nanocomposite fibrous membrane-based triboelectric nanogenerator (NFM-TENG) with excellent acoustical-to-electrical conversion performance. The optimal combination for the triboelectric pairs of NFM-TENG is identified: a polyvinylidene fluoride-multiwalled carbon nanotubes (PVDF-MWCNTs (1 wt%)) nanofibrous membrane as the tribo-positive layer, and a corona-charged fluorinated ethylene-propylene (FEP) solid membrane as the tribo-negative layer, resulting in higher electric output than single-friction-layered NFM-TENG (32 times). Under the acoustic excitation of 116 dB at 200 Hz, the acoustic NFM-TENG can generate a maximum areal power density of 3.78 W/m2. The NFM-TENG can be used not only as a acoustic energy harvester, but also as a self-powered triboelectric acoustic sensor (TAS) for real-time voice recording and control. For convenience, for the first time, an advanced tiny TAS (TTAS, diameter: only 9.7 mm) based on the NFM-TENG is developed and its sensitivity is calibrated as -50 dB according to the International standard IEC61094. Experimental results also verified that the TTAS with broadband response ability (20–20,000 Hz) is fully competent for commercial applications such as real-time voice control, HMI, and other multi-scenarios. The TTAS is smart, reliable and customizable, potentially leading to a new revolution in intelligent interaction.

Magnetic arthropod soft robot with triboelectric bionic antennae for obstacle identifying and avoidance

Magnetic soft robots exhibit attractive advantages such as nontethered constraints and remote control capabilities, with broad prospects in environmental exploration, minimally invasive surgery and other fields. However, constrained by the magnetic control mode and the high flexibility of the soft materials, achieving rapid movement and perception-feedback of magnetic soft robots are still challenging. Therefore, this paper aims to draw inspiration from biomimetic joint structures to design and fabricate a magnetic arthropod soft robot capable of rapid cyclic extension-contraction deformation under an alternating magnetic field. First, a type A magnetic arthropod soft robot (MASR-A) is developed with a speed up to 1.4 body length per second (BL/s) with three-joint-unit. Next, we designed a type B magnetic arthropod soft robot (MASR-B) that offers higher flexibility and deformation adaptability with bodies owing five-joints structure. Then, inspired by snail’s tentacles, we developed bionic antennae based on triboelectric tactile sensors (TTSs), which can convert external mechanical collisions into electrical signals output, with a sensitivity of up to 0.13 V KPa−1. By mounting the bionic antennae on the head of MASR-B to realize the magnetic soft robot’s sensing function, enabling MASR-B to detect collisions with obstacles of various materials (PMMA, Resin, PLA and etc.). Particularly, the bionic antennae consists of two completely independent triboelectric sensing units (LTTS and RTTS). The signals from these two TTSs are received and identified by a microcontroller to determine the direction of obstacle relative to the robot. The relay is then controlled to alter the magnetic field generated by the electromagnetic coils to enable MASR-B to avoid obstacle. This paper provides a biomimetic design approach for the rapid movement and flexible sensing of magnetic soft robots.

Neuromorphic Computing-Assisted Triboelectric Capacitive-Coupled Tactile Sensor Array for Wireless Mixed Reality Interaction.

Flexible tactile sensors show promise for artificial intelligence applications due to their biological adaptability and rapid signal perception. Triboelectric sensors enable active dynamic tactile sensing, while integrating static pressure sensing and real-time multichannel signal transmission is key for further development. Here, we propose an integrated structure combining a capacitive sensor for static spatiotemporal mapping and a triboelectric sensor for dynamic tactile recognition. A liquid metal-based flexible dual-mode triboelectric-capacitive-coupled tactile sensor (TCTS) array of 4 × 4 pixels achieves a spatial resolution of 7 mm, exhibiting a pressure detection limit of 0.8 Pa and a fast response of 6 ms. Furthermore, neuromorphic computing using the MXene-based synaptic transistor achieves 100% recognition accuracy of handwritten numbers/letters within 90 epochs based on dynamic triboelectric signals collected by the TCTS array, and cross-spatial information communication from the perceived multichannel tactile data is realized in the mixed reality space. The results illuminate considerable application possibilities of dual-mode tactile sensing technology in human-machine interfaces and advanced robotics.