Bionic Ultra-Sensitive Self-Powered Electromechanical Sensor for Muscle-Triggered Communication Application.

We report human skin based triboelectric nanogenerators (TENG) that can either harvest biomechanical energy or be utilized as a self-powered tactile sensor system for touch pad technology. We constructed a TENG utilizing the contact/separation between an area of human skin and a polydimethylsiloxane (PDMS) film with a surface of micropyramid structures, which was attached to an ITO electrode that was grounded across a loading resistor. The fabricated TENG delivers an open-circuit voltage up to -1000 V, a short-circuit current density of 8 mA/m(2), and a power density of 500 mW/m(2) on a load of 100 MΩ, which can be used to directly drive tens of green light-emitting diodes. The working mechanism of the TENG is based on the charge transfer between the ITO electrode and ground via modulating the separation distance between the tribo-charged skin patch and PDMS film. Furthermore, the TENG has been used in designing an independently addressed matrix for tracking the location and pressure of human touch. The fabricated matrix has demonstrated its self-powered and high-resolution tactile sensing capabilities by recording the output voltage signals as a mapping figure, where the detection sensitivity of the pressure is about 0.29 ± 0.02 V/kPa and each pixel can have a size of 3 mm × 3 mm. The TENGs may have potential applications in human-machine interfacing, micro/nano-electromechanical systems, and touch pad technology.

Dealing with issues related to safety of Nuclear Power Plants (NPPs) is of high importance and priority for assuring nonstop energy production. To enhance safety, modernization and upgrade of the aging installations by incorporating automation processes is unavoidable for many reasons, among them, environmental protection and lifetime extension of currently operating NPP. In that context, Human Machine Interface (HMI) applications are a subject of thorough study. The aim of this work is to develop a mechanism to evaluate the efficiency of HMI in nuclear power plant safety. To that end, HMI applications are addressed as a single joint system and they are not seen separately as a human and machine part. The proposed evaluator was implemented by utilizing Fuzzy Logic and holistic approaches. The implementation and all the experiments were conducted in Matlab by using the fuzzy toolbox.

Since the invention of the triboelectric nanogenerator (TENG) in 2012, it has become one of the most vital innovations in energy harvesting technologies. The TENG has seen enormous progress to date, particularly in applications for energy harvesting and self-powered sensing. It starts with the simple working principles of the triboelectric effect and electrostatic induction, but can scavenge almost any kind of ambient mechanical energy in our daily life into electricity. Extraordinary output performance optimization of the TENG has been achieved, with high area power density and energy conversion efficiency. Moreover, TENGs can also be utilized as self-powered active sensors to monitor many environmental parameters. This review describes the recent progress in mainstream energy harvesting and self-powered sensing research based on TENG technology. The birth and development of the TENG are introduced, following which structural designs and performance optimizations for output performance enhancement of the TENG are discussed. The major applications of the TENG as a sustainable power source or a self-powered sensor are presented. The TENG, with rationally designed structures, can convert irregular and mostly low-frequency mechanical energies from the environment, such as human motion, mechanical vibration, moving automobiles, wind, raindrops, and ocean waves. In addition, the development of self-powered active sensors for a variety of environmental simulations based on the TENG is presented. The TENG plays a great role in promoting the development of emerging Internet of Things, which can make everyday objects connect more smartly and energy-efficiently in the coming years. Finally,the future directions and perspectives of the TENG are outlined. The TENG is not only a sustainable micro-power source for small devices, but also serves as a potential macro-scale generator of power from water waves in the future.

Self-powered flexible pressure sensors are needed in applications such as human-machine interaction. Here, we present a triboelectric nanogenerator (TENG) based self-powered flexible pressure sensor capable of operating over a wide-range of pressures (3.2 - 1176 kPa). The sensor exhibits excellent sensitivities in various regions of the pressure range, i.e., 3.16, 0.023 and 0.031 V/kPa in the low (1-10 kPa), medium-high (10-500 kPa) and ultra-high (>500 kPa) pressure regimes respectively. Stable and repeatable TENG responses are achieved for all three pressure regimes, and this is due to the way the real contact area at the TENG interface varies with contact pressure. These results highlight the potential for TENGs in a wide range of applications such as: detecting pressure in wearable devices, human-machine interface, biomedical and automotive applications. As a proof-of-concept, we have demonstrated the use of the presented device in applications such as detection of human and robot finger tapping, collection of human gait information, and detection of impact forces.

The development of the human-machine interface (HMI) is endeavored to find effective approaches to interact with machines by applying emerging technologies. Triboelectric nanogenerator (TENG) can convert mechanical stimuli to electricity, which not only shows great potential in sensing but also is widely used in various HMI applications. This paper proposed a TENGbased hexagonfractal touchpad (HTPad) system using two channels to realize 18 sliding patterns from 3 different modes and a signal recognition module. A onedimensional convolution neural network (1D CNN) model is proposed for the recognition of the sliding direction signal with 96.5% accuracy, and handwriting digit signals collected by the touchpad can be recognized with a modified model with 99% accuracy. The proposed TENGbased hexagonfractal touchpad is easy to fabricate, scalable, and with high sensitivity. Furthermore, the recognition model can serve as a unified platform for different recog.nition tasks with little computational cost, which reveals great potential in HMI applications.

The human forearm is one of the most densely distributed parts of the human body, with the most irregular spatial distribution of muscles. A number of specific forearm muscles control hand motions. Acquiring high-fidelity sEMG signals from human forearm muscles is vital for human-machine interface (HMI) applications based on gesture recognition. Currently, the most commonly used commercial electrodes for detecting sEMG or other electrophysiological signals have a rigid nature without stretchability and cannot maintain conformal contact with the human skin during deformation, and the adhesive hydrogel used in them to reduce skin-electrode impedance may shrink and cause skin inflammation after long-term use. Therefore, developing elastic electrodes with stretchability and biocompatibility for sEMG signal recording is essential for developing HMI. Here, we fabricated a nanocomposite hybrid on-skin electrode by infiltrating silver nanowires (AgNWs), a one-dimensional (1D) nano metal material with conductivity, into polydimethylsiloxane (PDMS), a silicone elastomer with a similar Young's modulus to that of the human skin. The AgNW on-skin electrode has a thickness of 300 μm and low sheet resistance of 0.481 ± 0.014 Ω/sq and can withstand the mechanical strain of up to 54% and maintain a sheet resistance lower than 1 Ω/sq after 1000 dynamic strain cycles. The AgNW on-skin electrode can record high signal-to-noise ratio (SNR) sEMG signals from forearm muscles and can reflect various force levels of muscles by sEMG signals. Besides, four typical hand gestures were recognized by the multichannel AgNW on-skin electrodes with a recognition accuracy of 92.3% using machine learning method. The AgNW on-skin electrode proposed in this study has great potential and promise in various HMI applications that employ sEMG signals as control signals.

Cation functionalized nylon composite nanofibrous mat as a highly positive friction layer for robust, high output triboelectric nanogenerators and self-powered sensors

Self-powered ultra-sensitive millijoule impact sensor using room temperature cured PDMS based triboelectric nanogenerator

In-ear acquisition of physiological signals, such as electromyography (EMG), electrooculography (EOG), electroencephalography (EEG), and electrocardiography (ECG), is a promising approach to mobile health (mHealth) due to its non-invasive and user-friendly nature. By providing a convenient and comfortable means of physiological signal monitoring, in-ear signal acquisition could potentially increase patient compliance and engagement with mHealth applications. The development of reliable and comfortable soft dry in-ear electrode systems could, therefore, have significant implications for both mHealth and human-machine interface (HMI) applications. This research evaluates the quality of the ECG signal obtained with soft dry electrodes inserted in the ear canal. An earplug with six soft dry electrodes distributed around its perimeter was designed for this study, allowing for the analysis of the signal coming from each electrode independently with respect to a common reference placed at different positions on the body of the participants. An analysis of the signals in comparison with a reference signal measured on the upper right chest (RA) and lower left chest (LL) was performed. The results show three typical behaviors for the in-ear electrodes. Some electrodes have a high correlation with the reference signal directly after inserting the earplug, other electrodes need a settling time of typically 1-3 min, and finally, others never have a high correlation. The SoftPulseTM electrodes used in this research have been proven to be perfectly capable of measuring physiological signals, paving the way for their use in mHealth or HMI applications. The use of multiple electrodes distributed in the ear canal has the advantage of allowing a more reliable acquisition by intelligently selecting the signal acquisition locations or allowing a better spatial resolution for certain applications by processing these signals independently.

Cold spray direct writing of flexible electrodes for enhanced performance triboelectric nanogenerators

As one of the few self-powered instruments and devices, triboelectric nanogenerators (TENGs) have been developed for more than 10 years since its invention in 2012. With wide material selections and diverse design structures, and without having to use an external power supply, TENG has been applied in many key technologies. By the end of 2022, more than 16,000 researchers from 83 countries and regions around the world have authored scientific papers in TENG. In this review, we start from the theoretical principles and working mechanisms of TENG, and discuss its 5 major fields of application, namely, as self-powered sensors, high-voltage energy devices, blue energy devices, micro/nano-energy devices, and solid–liquid interface probes. Next, we review the breakthrough progress made using TENG as commercial products in the following fields: medical health, intelligent security, and marine energy. Finally, we look forward to the future fields of application of TENG as advanced instruments and devices, especially in fluid dynamics sensing and aerospace fields. We firmly believe that various instruments and devices based on TENG technology will better serve the progress of human civilization.

The triboelectric nanogenerator (TENG) has great potential in the field of self-powered sensor fabrication. Recently, smart electronic devices and movement monitoring sensors have attracted the attention of scientists because of their application in the field of artificial intelligence. In this article, a TENG finger movement monitoring, self-powered sensor has been designed and analysed. Under finger movements, the TENG realizes the contact and separation to convert the mechanical energy into electrical signal. A pulse output current of 7.8 μA is generated by the bending and straightening motions of the artificial finger. The optimal output power can be realized when the external resistance is approximately 30 MΩ. The random motions of the finger are detected by the system with multiple TENG sensors in series. This type of flexible and self-powered sensor has potential applications in artificial intelligence and robot manufacturing.

Nanogenerators is the growing technology that facilitates self-powered systems, sensors, and flexible and portable electronics in the thriving era of internet of things (IoT). Since the first invention of the triboelectric nanogenerators (TENGs) in 2012, it has become one of the most important inventions in energy harvesting technologies. In this paper, a brief review on the recent progress of energy harvesting research based on TENGs technology is discussed. Basic working modes of the TENG are discussed in detail and the general procedure to synthesize, measure, and characterize a nanogenerator is presented in a direct structure. The triboelectric material choices are extremely important for TENGs since the triboelectric effects of the materials are fundamental for TENGs. The materials used as triboelectric layers are varied from polymers, metals, and inorganic materials with the commonly used materials are dielectric polymers such as PTFE, PVDF, PDMS, nylon, and Kapton. Recently, two-dimensional (2D) materials have been widely reported as candidate materials for TENGs. Graphene, the most attractive 2D materials exhibits an excellent electrical property, great flexibility, and a high surface-to-volume ratio. Owing to the very low thickness of the atomic unit, a stacking graphene structure can be also made to form a very thin and miniature TENGs device. The major applications of the graphene as active materials for TENGs as a sustainable energy harvester are presented, following which structural designs and materials optimization for output performance improvement of the graphene-based TENGs are summarized. Finally, the future directions and perspectives of the graphene-based TENGs are outlined. The graphene-based TENGs is not only a sustainable micro-power source for small devices, but also serves as a potential macro-scale generator of power from blue energy in the future.

Entering the 5G and internet of things (IoT) era, human–machine interfaces (HMIs) capable of providing humans with more intuitive interaction with the digitalized world have experienced a flourishing development in the past few years. Although the advanced sensing techniques based on complementary metal-oxide-semiconductor (CMOS) or microelectromechanical system (MEMS) solutions, e.g., camera, microphone, inertial measurement unit (IMU), etc., and flexible solutions, e.g., stretchable conductor, optical fiber, etc., have been widely utilized as sensing components for wearable/non-wearable HMIs development, the relatively high-power consumption of these sensors remains a concern, especially for wearable/portable scenarios. Recent progress on triboelectric nanogenerator (TENG) self-powered sensors provides a new possibility for realizing low-power/self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information. Leveraging the advantages of wide material choices and diversified structural design, TENGs have been successfully developed into various forms of HMIs, including glove, glasses, touchpad, exoskeleton, electronic skin, etc., for sundry applications, e.g., collaborative operation, personal healthcare, robot perception, smart home, etc. With the evolving artificial intelligence (AI) and haptic feedback technologies, more advanced HMIs could be realized towards intelligent and immersive human–machine interactions. Hence, in this review, we systematically introduce the current TENG HMIs in the aspects of different application scenarios, i.e., wearable, robot-related and smart home, and prospective future development enabled by the AI/haptic-feedback technology. Discussion on implementing self-sustainable/zero-power/passive HMIs in this 5G/IoT era and our perspectives are also provided.

Self-powered and degradable humidity sensors based on silk nanofibers and its wearable and human–machine interaction applications