A Flexible, Adaptive, and Self-Powered Triboelectric Vibration Sensor with Conductive Sponge-Silicone for Machinery Condition Monitoring.

Vibration sensors for continuous and reliable condition monitoring of mechanical equipment, especially detection points of curved surfaces, remain a great challenge and are highly desired. Herein, a highly flexible and adaptive triboelectric vibration sensor for high-fidelity and continuous monitoring of mechanical vibration conditions is proposed. The sensor is entirely composed of flexible materials. It consists of a conductive sponge-silicone layer and a fluorinated ethylene propylene film. It can detect vibration acceleration of 5 to 50 m s-2 and vibration frequency of 10 to 100 Hz. It has strong robustness and stability, and the output performance barely changes after the durability test of 168 000 working cycles. Additionally, the flexible sensor can work even when the detection point of the mechanical equipment is curved, and the linear fit of the output voltage and acceleration is very close to that when the detection point is flat. Finally, it can be applied to monitoring the working condition of blower and vehicle engine, and can transmit vibration signal to mobile phone application through Wi-Fi module for real-time monitoring. The flexible triboelectric vibration sensor is expected to provide a practical paradigm for smart, green, and sustainable wireless sensor system in the era of Internet of Things.

Recent Advances in Mechanical Vibration Energy Harvesters Based on Triboelectric Nanogenerators.

With the development of autonomous/smart technologies and the Internet of Things (IoT), tremendous wireless sensor nodes (WSNs) are of great importance to realize intelligent mechanical engineering, which is significant in the industrial and social fields. However, current power supply methods, cable and battery for instance, face challenges such as layout difficulties, high cost, short life, and environmental pollution. Meanwhile, vibration is ubiquitous in machinery, vehicles, structures, etc., but has been regarded as an unwanted by-product and wasted in most cases. Therefore, it is crucial to harvest mechanical vibration energy to achieve in situ power supply for these WSNs. As a recent energy conversion technology, triboelectric nanogenerator (TENG) is particularly good at harvesting such broadband, weak, and irregular mechanical energy, which provides a feasible scheme for the power supply of WSNs. In this review, recent achievements of mechanical vibration energy harvesting (VEH) related to mechanical engineering based on TENG are systematically reviewed from the perspective of contact-separation (C-S) and freestanding modes. Finally, existing challenges and forthcoming development orientation of the VEH based on TENG are discussed in depth, which will be conducive to the future development of intelligent mechanical engineering in the era of IoT.

A High-Performance Flag-Type Triboelectric Nanogenerator for Scavenging Wind Energy toward Self-Powered IoTs.

Pervasive and continuous energy solutions are highly desired in the era of the Internet of Things for powering wide-range distributed devices/sensors. Wind energy has been widely regarded as an ideal energy source for distributed devices/sensors due to the advantages of being sustainable and renewable. Herein, we propose a high-performance flag-type triboelectric nanogenerator (HF-TENG) to efficiently harvest widely distributed and highly available wind energy. The HF-TENG is composed of one piece of polytetrafluoroethylene (PTFE) membrane and two carbon-coated polyethylene terephthalate (PET) membranes with their edges sealed up. Two ingenious internal-structure designs significantly improve the output performance. One is to place the supporting sponge strips between the PTFE and the carbon electrodes, and the other is to divide the PTFE into multiple pieces to obtain a multi-degree of freedom. Both methods can improve the degree of contact and separation between the two triboelectric materials while working. When the pair number of supporting sponge strips is two and the degree of freedom is five, the maximum voltage and current of HF-TENG can reach 78 V and 7.5 µA, respectively, which are both four times that of the untreated flag-type TENG. Additionally, the HF-TENG was demonstrated to power the LEDs, capacitors, and temperature sensors. The reported HF-TENG significantly promotes the utilization of the ambient wind energy and sheds some light on providing a pervasive and sustainable energy solution to the distributed devices/sensors in the era of the Internet of Things.

A Robust Silicone Rubber Strip-Based Triboelectric Nanogenerator for Vibration Energy Harvesting and Multi-Functional Self-Powered Sensing.

Vibration is a common phenomenon in various fields which can not only indicate the working condition of the installation, but also serve as an energy source if it is efficiently harvested. In this work, a robust silicone rubber strip-based triboelectric nanogenerator (SRS-TENG) for vibration energy harvesting and multi-functional self-powered sensing is proposed and systematically investigated. The SRS-TENG consists of a silicone rubber strip and two aluminum electrode layers supported by polylactic acid (PLA), and acts as a sustainable power source and vibration frequency, amplitude and acceleration sensor as well. The soft contact between the aluminum electrode and silicone rubber strip makes it robust and stable even after 14 days. It can be applied in ranges of vibration frequencies from 5 to 90 Hz, and amplitudes from 0.5 to 9 mm, which shows it has advantages in broadband vibration. Additionally, it can achieve lower startup limits due to its soft structure and being able to work in multi-mode. The output power density of the SRS-TENG can reach 94.95 W/m3, matching a resistance of 250 MΩ, and it can light up more than 100 LEDs and power a commercial temperature sensor after charging capacitors. In addition, the vibration amplitude can be successfully detected and displayed on a human-machine interface. Moreover, the frequency beyond a specific limit can be distinguished by the SRS-TENG as well. Therefore, the SRS-TENG can be utilized as an in situ power source for distributed sensor nodes and a multifunctional self-powered vibration sensor in many scenarios.

A Deep-Learning-Assisted On-Mask Sensor Network for Adaptive Respiratory Monitoring.

Wearable respiratory monitoring is a fast, non-invasive, and convenient approach to provide early recognition of human health abnormalities like restrictive and obstructive lung diseases. Here, a computational fluid dynamics assisted on-mask sensor network is reported, which can overcome different user facial contours and environmental interferences to collect highly accurate respiratory signals. Inspired by cribellate silk, Rayleigh-instability-induced spindle-knot fibers are knitted for the fabrication of permeable and moisture-proof textile triboelectric sensors that hold a decent signal-to-noise ratio of 51.2 dB, a response time of 0.28 s, and a sensitivity of 0.46 V kPa-1 . With the assistance of deep learning, the on-mask sensor network can realize the respiration pattern recognition with a classification accuracy up to 100%, showing great improvement over a single respiratory sensor. Additionally, a customized user-friendly cellphone application is developed to connect the processed respiratory signals for real-time data-driven diagnosis and one-click health data sharing with the clinicians. The deep-learning-assisted on-mask sensor network opens a new avenue for personalized respiration management in the era of the Internet of Things.

An Array of Flag-Type Triboelectric Nanogenerators for Harvesting Wind Energy.

Harvesting wind energy from the ambient environment is a feasible method for powering wireless sensors and wireless transmission equipment. Triboelectric nanogenerators (TENGs) have proven to be a stable and promising technology for harvesting ambient wind energy. This study explores a new method for the performance enhancement and practical application of TENGs. An array of flag-type triboelectric nanogenerators (F-TENGs) for harvesting wind energy is proposed. An F-TENG consists of one piece of polytetrafluoroethylene (PTFE) membrane, which has two carbon-coated polyethylene terephthalate (PET) membranes on either side with their edges sealed. The PTFE was pre-ground to increase the initial charge on the surface and to enhance the effective contact area by improving the surface roughness, thus achieving a significant improvement in the output performance. The vertical and horizontal arrays of F-TENGs significantly improved the power output performance. The optimal power output performance was achieved when the vertical parallel distance was approximately 4D/15 (see the main text for the meaning of D), and the horizontal parallel distance was approximately 2D. We found that the peak output voltage and current of a single flag-type TENG of constant size were increased by 255% and 344%, respectively, reaching values of 64 V and 8 µA, respectively.

Ambulatory Cardiovascular Monitoring Via a Machine-Learning-Assisted Textile Triboelectric Sensor.

Wearable bioelectronics for continuous and reliable pulse wave monitoring against body motion and perspiration remains a great challenge and highly desired. Here, a low-cost, lightweight, and mechanically durable textile triboelectric sensor that can convert subtle skin deformation caused by arterial pulsatility into electricity for high-fidelity and continuous pulse waveform monitoring in an ambulatory and sweaty setting is developed. The sensor holds a signal-to-noise ratio of 23.3 dB, a response time of 40 ms, and a sensitivity of 0.21 µA kPa-1 . With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital. Additionally, a customized cellphone application (APP) based on built-in algorithm is developed for one-click health data sharing and data-driven cardiovascular diagnosis. The textile triboelectric sensor enabled wireless biomonitoring system is expected to offer a practical paradigm for continuous and personalized cardiovascular system characterization in the era of the Internet of Things.

A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring.

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.

Leverage Surface Chemistry for High-Performance Triboelectric Nanogenerators.

Triboelectric Nanogenerators (TENGs) are a highly efficient approach for mechanical-to-electrical energy conversion based on the coupling effects of contact electrification and electrostatic induction. TENGs have been intensively applied as both sustainable power sources and self-powered active sensors with a collection of compelling features, including lightweight, low cost, flexible structures, extensive material selections, and high performances at low operating frequencies. The output performance of TENGs is largely determined by the surface triboelectric charges density. Thus, manipulating the surface chemical properties via appropriate modification methods is one of the most fundamental strategies to improve the output performances of TENGs. This article systematically reviews the recently reported chemical modification methods for building up high-performance TENGs from four aspects: functional groups modification, ion implantation and decoration, dielectric property engineering, and functional sublayers insertion. This review will highlight the contribution of surface chemistry to the field of triboelectric nanogenerators by assessing the problems that are in desperate need of solving and discussing the field's future directions.

Ternary Electrification Layered Architecture for High-Performance Triboelectric Nanogenerators.

The triboelectric nanogenerator (TENG) has been proved to be a green and efficient energy harnessing technology for electricity generation from ambient mechanical motions based on its ability to leverage the triboelectrification process. Enhancing TENG output performance through rational structural design still triggers increasing research interest. Here, we report a ternary electrification layered architecture beyond the current binary TENG systems, with improved performance for mechanical energy harvesting. Introducing a ternary Kapton layer into the traditional binary electrification layered architecture of TENGs consisting of copper and fluorinated ethylene propylene, yields a 2.5 times enhancement of peak power output, representing a 6.29-fold increase compared to the TENG composed of copper and Kapton. A wide-range of material configurations were systematically tested using this ternary electrification layered architecture to prove its practical effectiveness. The ternary electrification layered architecture invented in this work provides an alternative strategy to enhance TENG output performance, which represents a solid step for TENGs application in high-performance mechanical energy harvesting.

Triboelectric Nanogenerator Enabled Smart Shoes for Wearable Electricity Generation.

The parallel evolution of wearable electronics, artificial intelligence, and fifth-generation wireless technology has created a technological paradigm with the potential to change our lives profoundly. Despite this, addressing limitations linked to continuous, sustainable, and pervasive powering of wearable electronics remains a bottleneck to overcome in order to maximize the exponential benefit that these technologies can bring once synergized. A recent groundbreaking discovery has demonstrated that by using the coupling effect of contact electrification and electrostatic induction, triboelectric nanogenerators (TENGs) can efficiently convert irregular and low-frequency passive biomechanical energy from body movements into electrical energy, providing an infinite and sustainable power source for wearable electronics. A number of human motions have been exploited to properly and efficiently harness this energy potential, including human ambulation. Shoes are an indispensable component of daily wearing and can be leveraged as an excellent platform to exploit such kinetic energy. In this article, the latest representative achievements of TENG-based smart electricity-generating shoes are comprehensively reviewed. We summarize ways in which not only can biomechanical energy be scavenged via ambulatory motion, but also biomonitoring of health parameters via tracking of rhythm and strength of pace can be implemented to aid in theranostic fields. This work provides a systematical review of the rational structural design, practical applications, scenario analysis, and performance evaluation of TENG-based smart shoes for wearable electricity generation. In addition, the perspective for future development of smart electricity-generation shoes as a sustainable and pervasive energy solution towards the upcoming era of the Internet of Things is discussed.