Self-powered Triboelectric Sensor Research Articles

Self-powered triboelectric sensor based on a carbon fiber/glass fiber/epoxy structural composite for efficient traffic monitoring

Intelligent transportation systems (ITSs) are being investigated as potential solutions for traffic congestion. Triboelectric sensors (TESs) have gained prominence among ITS technologies owing to their efficiency and self-powered capabilities in detecting external changes through friction-generated electrical signals. Herein, a traffic monitoring system using a fiber-reinforced plastic-based TES (FRP-TES) is proposed, designed with high-strength and high-stiffness fiber-reinforced plastic (FRP) as the core material. FRP is a structural composite comprising high-strength fibers impregnated with engineering resin. When the surface of the epoxy, which has a charged layer, reacts with the charged tire, electrostatic induction occurs in the carbon fibers. By leveraging the principles of carbon and glass fiber/epoxy-reinforced infrastructure, along with corresponding electrical properties, we analyze electrical signals produced when a tire traverses the FRP-TES. We subject the FRP-TES to electrical durability and tensile tests to verify signal stability and mechanical properties (strength ≤ 1770.87 MPa, modulus ≤ 41.38 GPa). The analysis proposes an effective method for obtaining the tire position, movement direction, speed, and acceleration using paired FRP-TES units. The proposed approach achieves significant reduction in maximum errors (0.52 % in speed, 3.24 % in acceleration) with eight evenly spaced FRP-TES units in a 240 mm section. A larger FRP-TES is fabricated to demonstrate practicality for personal mobility. Incorporating FRP-TES units into road infrastructure can enhance structural stability while providing reliable real-time data for traffic monitoring, accident response, and prediction.

Bioinspired Robust Gas-Permeable On-Skin Electronics: Armor-Designed Nanoporous Flash Graphene Assembly Enhancing Mechanical Resilience.

Soft on-skin electrodes play an important role in wearable technologies, requiring attributes such as wearing comfort, high conductivity, and gas permeability. However, conventional fabrication methods often compromise simplicity, cost-effectiveness, or mechanical resilience. In this study, a mechanically robust and gas-permeable on-skin electrode is presented that incorporates Flash Graphene (FG) integrated with a bioinspired armor design. FG, synthesized through Flash Joule Heating process, offers a small-sized and turbostratic arrangement that is ideal for the assembly of a conductive network with nanopore structures. Screen-printing is used to embed the FG assembly into the framework of polypropylene melt-blown nonwoven fabrics (PPMF), forming a soft on-skin electrode with low sheet resistance (125.2 ± 4.7 Ω/□) and high gas permeability (≈10.08mg cm⁻2 h⁻¹). The "armor" framework ensures enduring mechanical stability through adhesion, washability, and 10,000 cycles of mechanical contact friction tests. Demonstrating capabilities in electrocardiogram (ECG) and electromyogram (EMG) monitoring, along with serving as a self-powered triboelectric sensor, the FG/PPMF electrode holds promise for scalable, high-performance flexible sensing applications, thereby enriching the landscape of integrated wearable technologies.

Environmentally Robust Triboelectric Tire Monitoring System for Self-Powered Driving Information Recognition via Hybrid Deep Learning in Time-Frequency Representation.

Developing a robust artificial intelligence of things (AIoT) system with a self-powered triboelectric sensor for harsh environment is challenging because environmental fluctuations are reflected in triboelectric signals. This study presents an environmentally robust triboelectric tire monitoring system with deep learning to capture driving information in the triboelectric signals generated from tire-road friction. The optimization of the process and structure of a laser-induced graphene (LIG) electrode layer in the triboelectric tire is conducted, enabling the tire to detect universal driving information for vehicles/robotic mobility, including rotation speeds of 200-2000 rpm and contact fractions of line. Employing a hybrid model combining short-term Fourier transform with a convolution neural network-long short-term memory, the LIG-based triboelectric tire monitoring (LTTM) system decouples the driving information, such as traffic lines and road states, from varied environmental conditions of humidity (10%-90%) and temperatures (50-70°C). The real-time line and road state recognition of the LTTM system is confirmed on a mobile platform across diverse environmental conditions, including fog, dampness, intense sunlight, and heat shimmer. This work provides an environmentally robust monitoring AIoT system by introducing a self-powered triboelectric sensor and hybrid deep learning for smart mobility.

Highly Sensitive and Stable Multifunctional Self-Powered Triboelectric Sensor Utilizing Mo2CTx/PDMS Composite Film for Pressure Sensing and Non-Contact Sensing.

Sensors based on triboelectric nanogenerators (TENGs) are increasingly gaining attention because of their self-powered capabilities and excellent sensing performance. In this work, we report a Mo2CTx-based triboelectric sensor (Mo-TES) consisting of a Mo2CTx/polydimethylsiloxane (PDMS) composite film. The impact of the mass fraction (wt%) and force of Mo2CTx particles on the output performance of Mo-TES was systematically explored. When Mo2CTx particles is 3 wt%, Mo-TES3 achieves an open-circuit voltage of 86.89 V, a short-circuit current of 578.12 nA, and a power density of 12.45 μW/cm2. It also demonstrates the ability to charge capacitors with varying capacitance values. Additionally, the Mo-TES3 demonstrates greater sensitivity than the Mo-TES0 and a faster recovery time of 78 ms. Meanwhile, the Mo-TES3 also demonstrates excellent stability in water washing and antifatigue testing. This demonstrates the effectiveness of Mo-TES as a pressure sensor. Furthermore, leveraging the principle of electrostatic induction, the triboelectric sensor has the potential to achieve non-contact sensing, making it a promising candidate for disease prevention and safety protection.

A novel self-powered triboelectric sensor for early waring of lubrication failure

In tribology, maintaining good lubrication of friction pairs is an important technical means to reduce wear and failure, which is very important but difficult to monitor the lubrication state of friction pairs. In this paper, when measuring the thickness and shape of the lubricating oil film by the ball-disk elastohydrodynamic oil film measuring instrument, a current amplifier is integrated to measure the electrical signal generated during the friction process. By monitoring the change of lubricating oil film, the change of ball-disk friction force, and the change of triboelectric signal during the friction process in real time, the relationship between the tribological behavior and the triboelectric behavior of the friction pair is explored. The experimental results show that under the conditions of limited oil supply, different rotational speeds and different lubricating oil viscosities, the time of the discharge phenomenon of the steel ball corresponds one-to-one with the time when the friction pair starts to wear under oil-scarce lubrication. The discharge occurs due to the electric charge breaking through the air in the micro-gap of the wear scar. The results demonstrate that the formation of the oil-depleted state has an important influence on the triboelectric process. Based on the relationship between the two, the state of the lubricating oil can be reflected in real time by using the triboelectric signal, which provides a reliable method for the real-time monitoring of the lubrication state.

A Durable and Self-Powered Triboelectric Sensor for Hydraulic Pressure Monitoring and Underwater Disturbance Detection

A Durable and Self-Powered Triboelectric Sensor for Hydraulic Pressure Monitoring and Underwater Disturbance Detection

A self-powered triboelectric sensor for basketball monitoring

Recently, flexible sensors have attracted attention in sports monitoring. Here, we developed a novel PDMS@MXene@BaTiO3-based triboelectric nanogenerator (PMB-TENG) by using various processing techniques, including ion treatment and dielectric regulation, for energy harvesting and basketball motion sensing. The polyethylene terephthalate (PET) film has achieved improved triboelectric properties through ion treatment. The pressed PET film and PDMS@MXene@BaTiO3 film serve as the triboelectric layers. In detail, the improvement in the PMB-TENG output in open-circuit voltage (Voc), short-circuit current (Isc), and transferred charge (Qsc) was more than 5.2 times (from 31.2 to 164.4 V), 4.4 times (from 0.96 to 4.08 µA), and 3.1 times (from 9.72 to 31.2 nC), respectively, which demonstrates the function of ion modification. When the resistance value of the load reaches 70 MΩ, the output power of the PMB-TENG reaches its maximum of 2.4 W/m2. The PMB-TENG can be installed on the foot, knee, and wrist to monitor the human posture in basketball. This research will promote the development of basketball monitoring equipment.

Self-powered triboelectric sensor with N-doped graphene quantum dots decorated polyaniline layer for non-invasive glucose monitoring in human sweat

A novel non-invasive wearable self-powered triboelectric sensor (TES) for simultaneous physiological monitoring had shed the light on its extraordinary performance, which become a promising choice for detecting glucose concentration from human sweat via non-invasive strategy. For realizing the function of wearable TES, the flexible substrate of conducting polymers commonly served as matrix for not only immobilizing the specific enzyme, but also played as the sensing layer by the coupling effect of triboelectrification/enzymatic reaction. To boost up the reliability and sensitivity of conducting polymer-based TES, in this study, a nanocomposite of N-doped graphene quantum dots decorated polyaniline (NGQDs/PANI) is designed as a triboelectric layer for non-invasive glucose detection. The surface electronegativity of PANI was changed by decorating the NGQDs with electron-rich functional groups for increasing the surface charge, which further facilitating the charge transfer between the PANI and intermedia and then enhancing triboelectric outputs. Upon the enhanced electron transfer and surface charge by NGQDs, NGQDs/PANI/GOx based TES offers greater sensitivity (23.52 mM−1) in glucose detection compared to pristine PANI/GOx one (16.44 mM−1). Notably, this study demonstrates the feasibility of wearable TES on human skin and their reliable and sensitive performance with a self-driving system without any external power source.

High-performance flexible self-powered triboelectric pressure sensor based on chemically modified micropatterned PDMS film

Flexible pressure sensors have played an increasingly important role in the Internet of Things (IoT) and human–machine interaction (HMI) systems. However, it is in a desire to fabricate the sensor with high sensitivity and low power consumption. In this paper, we have developed a self-powered triboelectric pressure sensor based on chemically modified micropatterned PDMS. Different wire meshes serve as template to fabricate microstructures on the triboelectric layer. To enhance the triboelectric output performance, introducing functional groups to the surface of the triboelectric layer to expand triboelectric polarity difference. By virtue of the cooperative effect of enhanced contact area variations from the PDMS microstructure’s gradual deformation, the reduced dielectric thickness as well as the increased charge density from the extended triboelectric polarity difference originated from chemical modification, the self-powered triboelectric sensor displays a sensitivity of 44.31 V kPa -1 in the range of 0.006-2 kPa and 7.16 V kPa -1 in the range of 2-8.5 kPa. Additionally, the self-powered triboelectric sensor is efficient in human motions measurement. This work provides a route to fabricate high-performance pressure sensor with morphological and chemical modification on the triboelectric polymers. • A self-powered triboelectric pressure sensor based on chemically modified micropatterned PDMS was developed. • Amino-containing and fluorine-containing functional groups were introduced to the mPDMS surface to expand triboelectric polarity difference to enhance the triboelectric performance. • The pressure sensor shows a high sensitivity, fast response time and high reliability.

Self-powered triboelectric sensor for cooling fan monitoring

Since the COVID-19 pandemic began, the use of computers, Internet of things devices, and artificial intelligence applications has dramatically increased. For the operation of these devices, it is important to effectively control the thermal management systems. Cooling fans play an important role in air circulation and thermal management for many types of industrial machines. In the past, a disassembly process has been inevitable for monitoring the operational status of cooling fans, making it necessary to develop devices or methods to monitor the operational status of cooling fans more conveniently. In this study, we propose a fan-shaped triboelectric sensor (F-TES) that can detect the operational status of a cooling fan via output signals of a triboelectric nanogenerator. We investigated the effects of rotational speed, as well as the working areas of the tribo-materials. Furthermore, we demonstrated those behaviors by using a light-emitting diode and capacitor charging. We designed an F-TES based on a commercial cooling fan without any structural changes, so that it could be directly utilized for various cooling fans. We anticipate that the results of this study can serve as a cornerstone for the maintenance and management of various commercial cooling fans.

Whisker-inspired and self-powered triboelectric sensor for underwater obstacle detection and collision avoidance

As key devices for underwater robot to perceive underwater environments, tactile sensors play an important role in seabed exploration. Inspired by the structure of marine mammal whiskers, we designed a bio-inspired whisker sensor (BWS) based on triboelectric nanogenerators to assist underwater robots in sensing underwater environments. The proposed device generated electrical signals through triboelectric and electrostatic induction between a fluorinated ethylene propylene film and ink. With the assistance of a structural design inspired by biology, the artificial BWS could estimate the external stimulation area. We demonstrated specific functions of the sensor, such as LED lights control and real-time external load monitoring to demonstrate its utility. Moreover, the BWS installed in underwater robots identify the direction, position, and frequency of external loads by extracting the characteristics of electrical signals, particularly in cases when optical and acoustic devices cannot be used, such as in turbid water. The results showed that the BWS can serve as an underwater whisker sensor for underwater robots for perceiving underwater environment and avoiding reactive obstacles.

Deep Learning Enabled Neck Motion Detection Using a Triboelectric Nanogenerator.

The state of neck motion reflects cervical health. To detect the motion state of the human neck is of important significance to healthcare intelligence. A practical neck motion detector should be wearable, flexible, power efficient, and low cost. Here, we report such a neck motion detector comprising a self-powered triboelectric sensor group and a deep learning block. Four flexible and stretchable silicon rubber based triboelectric sensors are integrated on a neck collar. With different neck motions, these four sensors lead-out voltage signals with different amplitudes and/or directions. Thus, the combination of these four signals can represent one motion state. Significantly, a carbon-doped silicon rubber layer is attached between the neck collar and the sensors to shield the external electric field (i.e., electrical changes at the skin surface) for a far more robust identification. Furthermore, a deep learning model based on the convolutional neural network is designed to recognize 11 classes of neck motion including eight directions of bending, two directions of twisting, and one resting state with an average recognition accuracy of 92.63%. This developed neck motion detector has promising applications in neck monitoring, rehabilitation, and control.

3D-printed endoplasmic reticulum rGO microstructure based self-powered triboelectric pressure sensor

Developing new generation of self-powered triboelectric sensors is urgent in the application of Internet of things (IoT) with low-power consumption. However, the traditional triboelectric pressure sensors demonstrate narrow detection range and are used to be less sensitive in large pressure range. In this work, a 3D-printed endoplasmic reticulum rGO microstructure based self-powered triboelectric pressure sensor (rGO-TPS) has been proposed. By employing PDMS@rGO framework as dielectric layer and spacer, a force-electric coupling model was built to investigate the electromechanical sensing mechanism. Owing to the ultra-low Young’s modulus of the 3D-printed materials and designed device structure, the rGO-TPS can reach the sensitivity of 6.28 kPa−1 and broaden high-sensitivity region from 0.65 Pa to 10 kPa. Besides, it also improves the sensitivity to 0.61 kPa−1 during the large pressure range from 10 kPa to 140 kPa. It also illustrates a fast response time of 92 ms and great stability of 3,000 cycles without fatigue. In addition, the dynamic pressure response can be monitored by detecting the change of pulse-like short-circuit current signal. Benefiting from the structural advantages and ultra-high performance, several potential applications in gauging water droplets and air flows, and vibration recognition have also been successfully demonstrated. This work provides a promising strategy to promote the progress toward the practical application of self-powered triboelectric pressure sensor.

Transparent self-powered triboelectric sensor based on PVA/PA hydrogel for promoting human-machine interaction in nursing and patient safety

Human-machine interaction (HMI) has made great progress in the intelligent medical system, realizing the interaction between patients, medical staff and medical equipment. Here, we report a triboelectric nanogenerator with the flexible-stretchable polyvinyl alcohol/phytic acid (PVA/PA) hydrogel for self-powered HMI sensing in intelligent medical system. Benefiting from effective optimization design of materials, it possesses excellent mechanical and electrical properties, such as over 20,000 cycles durability, transparency, flexibility, and power density up to 1.33 W·m−2. The device has a linearity of up to 0.949 in the tensile range of 200% and 0.992 in the bending angle range from 30° to 90°, respectively. Through processing and encoding the electrical signals collected by multiple channels in independent or synergistic mode, a self-powered medical nursing HMI system attached to patient fingers was developed to provide real-time help and assistance for both patients and medical staff, which the demand for distress calls can be successfully transmitted with just gently bending fingers in diagnosis. This work exhibits the application value of the self-powered triboelectric sensor to the medical HMI field, and can be extended to other aspects, showing a broad impact in brain computer interface.

Whisker-Inspired and Self-Powered Triboelectric Sensor for Underwater Obstacle Detection and Collision Avoidance

Whisker-Inspired and Self-Powered Triboelectric Sensor for Underwater Obstacle Detection and Collision Avoidance

Triboelectric sensor array for internet of things based smart traffic monitoring and management system

With the advanced technologies such as 5 G network and internet of things (IoT), there are incredibly increasing sensors and corresponding applications emerging to benefit our daily lives. However, the power supplies for countless sensors become a serious issue to suppress further expansion of IoT. In this work, a self-powered triboelectric sensor (CN-STS) made of electrospun composite nanofibers is developed towards smart traffic monitoring and management. To meet the requirements of fast-response and high sensitivity for the smart traffic management, transferred charge density is adopted as sensing signals compared to voltage / current outputs for it can perfectly record the subtle differences and suitable for dynamic traffic monitoring. Carbon nanotube is further doped into PVDF nanofibers to improve the electrical output performance and pressure sensitivity. A relatively high sensitivity of 0.0406 μCm−2kPa−1 at low pressure range (0–75 kPa) and a sensitivity of 0.0032 μCm−2kPa−1 at higher pressure range (75–425 kPa) can be achieved by the CN-STS, indicating that the CN-STS is more sensitive at low pressure range. Moreover, by connecting to cloud IoT services, the functions of traffic flow management, overlapping and speeding vehicle capturing, and plate number recognition are realized by the CN-STS arrays with a compensation circuit. A charge amplifier is utilized in power management to process the charge input signals for better distinguishing the signals from none-vehicles and stabilize the volage output for further reading by the A/D convertor build in Raspberry Pi. The engineered self-powered triboelectric sensors not only open a new potential area for triboelectric nanogenerators but also significantly popularize the IoT industry.

Anisotropic silver nanowire dielectric composites for self-healable triboelectric sensors with multi-directional tactile sensitivity

Self-healable wearable devices with tactile directional sensitivity have attracted significant attention due to their damage-free sustainable and precise detection of directional body motions in various applications such as hand gesture monitoring and electrooculography (EOG) sensors. In this study, a self-powered triboelectric sensor with tactile directional sensitivity and self-healing property based on the dielectric multilayers of aligned silver nanowire (AgNW) composite gel sandwiched between self-healable polymers has been developed. Its output performances are superior to those of similar devices without the AgNW gel due to the stronger interfacial polarization of the dielectric multilayers and charge trapping by AgNWs. Moreover, the aligned AgNW composite gel possesses anisotropic dielectric properties, which enhances the contact direction-sensitive triboelectric performance of the sensor during rolling or rubbing motions. The fabricated sensor can be used in wearable EOG and electrocardiography applications because of the ability to monitor extremely weak blood pulse waves in the temporal artery around the human eye, while its directional force sensitivity allows differentiating eight eye movement directions. The successful use of the multilayered structure containing the self-healable and aligned AgNW composite gel as a triboelectric dielectric material opens a new avenue for designing self-healable wearable sensors with tactile directional sensitivity.

Bamboo-inspired self-powered triboelectric sensor for touch sensing and sitting posture monitoring

Self-powered sensors, as the essential information notes in low power consumption of Internet of healthcare, exhibit a great potential to monitor the state of human health in real time. In this work, a bamboo-inspired self-powered triboelectric sensor (BSTS) using 3D-printing and freeze-drying fabrication processes is presented. The designed triboelectric layer with hollow and bamboo-joint microstructures has a low equivalent stiffness coefficient and a great deformation limit, contributing significantly to the high sensitivity and wide detection range of BSTS. An electromechanical model was proposed to better understand the influence mechanism of inner structures in the dielectric layer on the sensing performance. The sensitivity of the BSTS can reach 3.627kPa−1 in the pressure range of 0–8kPa and still keep 1.264kPa−1 in the range of 8–80kPa. Moreover, the BSTS is able to detect tiny pressure as low as 5 Pa and possesses ultra-fast response time of 40 ms. A touch-controlled lighting system and a sitting posture monitoring system based on the BSTS for healthcare application are demonstrated. This strategy paves a way to fabricate the self-powered triboelectric pressure sensor with high sensitivity in all working ranges.

A wind vector detecting system based on triboelectric and photoelectric sensors for simultaneously monitoring wind speed and direction

The wind vector sensor is a significant category of Internet of Things (IoTs) based sensors due to its irreplaceable role in weather monitoring, traffic detection, and mine safety. However, searching new power supply approaches, apart from batteries, for this type of sensor is a critical issue hindering its widespread application because of the vast number of sensor nodes. Self-powered technology based on nanogenerator offers great promise for resolving the dilemma. Herein, we propose a novel wind vector sensor system for synchronously perceiving wind speed and direction based on the coupling of self-powered triboelectric sensor and photoelectric technology. For wind speed detection, an angle-shaped triboelectric sensor (ASTS) is well designed and systemically investigated. The ASTS after structure optimization exhibits a low start-up wind speed and considerable outputs. A wide wind speed sensing range is achieved from 2.9 m/s to 24.0 m/s, within which the current frequency of ASTS shows a good proportional relationship with wind speed. Furthermore, a wind direction sensor (WDS) based on photoelectric detection is carefully fabricated to integrate with the ASTS for wind vector monitoring. Specific algorithms and programs are developed through the LabVIEW platform to process the electric signals from the system and visually display the results on the screen. The practical application demonstrates that the proposed wind vector sensing system is capable of accurately identifying wind speed from eight directions. This work would pave the way to achieve the ultra-low power consumption for IoTs-based environmental sensors. • The wind vector system is proposed by the integration of triboelectric and photoelectric technology. • The fluid dynamic and triboelectric characteristics are investigated for low start-up wind speed and considerable output. • The wind vector system realizes accurate sensing with a wide range from 2.9 m/s to 24.0 m/s for eight wind directions.

Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator.

An intelligent monitoring lubricant is essential for the development of smart machines because unexpected and fatal failures of critical dynamic components in the machines happen every day, threatening the life and health of humans. Inspired by the triboelectric nanogenerators (TENGs) work on water, we present a feasible way to prepare a self-powered triboelectric sensor for real-time monitoring of lubricating oils via the contact electrification process of oil–solid contact (O–S TENG). Typical intruding contaminants in pure base oils can be successfully monitored. The O–S TENG has very good sensitivity, which even can respectively detect at least 1 mg mL–1 debris and 0.01 wt % water contaminants. Furthermore, the real-time monitoring of formulated engine lubricating oil in a real engine oil tank is achieved. Our results show that electron transfer is possible from an oil to solid surface during contact electrification. The electrical output characteristic depends on the screen effect from such as wear debris, deposited carbons, and age-induced organic molecules in oils. Previous work only qualitatively identified that the output ability of liquid can be improved by leaving less liquid adsorbed on the TENG surface, but the adsorption mass and adsorption speed of liquid and its consequences for the output performance were not studied. We quantitatively study the internal relationship between output ability and adsorbing behavior of lubricating oils by quartz crystal microbalance with dissipation (QCM-D) for liquid–solid contact interfaces. This study provides a real-time, online, self-powered strategy for intelligent diagnosis of lubricating oils.