Publications

We report a triboelectric nanogenerator (TENG) that plays dual roles as a sustainable power source by harvesting wind energy and as a self-powered wind vector sensor system for wind speed and direction detection. By utilizing the wind-induced resonance vibration of a fluorinated ethylene-propylene film between two aluminum foils, the integrated TENGs with dimensions of 2.5 cm × 2.5 cm × 22 cm deliver an output voltage up to 100 V, an output current of 1.6 μA, and a corresponding output power of 0.16 mW under an external load of 100 MΩ, which can be used to directly light up tens of commercial light-emitting diodes. Furthermore, a self-powered wind vector sensor system has been developed based on the rationally designed TENGs, which is capable of detecting the wind direction and speed with a sensitivity of 0.09 μA/(m/s). This work greatly expands the applicability of TENGs as power sources for self-sustained electronics and also self-powered sensor systems for ambient wind detection.

A novel particle tracking method based on a convolutional neural network (CNN) is proposed to improve the efficiency of Lagrangian-Eulerian (L-E) approaches. Relying on the successive neighbor search (SNS) method for particle tracking, the L-E approaches face increasing computational and parallel overhead as simulations grow in scale. This issue arises primarily because the SNS method requires lengthy tracking paths, which incur intensive inter-processor communications. The proposed method, termed the CNN-SNS method, addresses this issue by approximating the spatial mapping between reference frames through the CNN. Initiating the SNS method from CNN predictions shortens the tracking paths without compromising accuracy and consequently achieves superior parallel scalability. Numerical tests demonstrate that the CNN-SNS method exhibits increasing computational advantages over the SNS method in large-scale, high-velocity flow fields. As the resolution and parallelization scale up, the CNN-SNS method achieves reductions of 95.8% in tracking path length and 97.0% in computational time.

Due to the push for carbon neutrality in various human activities, the development of methods for producing electricity without relying on chemical reaction processes or heat sources has become highly significant. Also, the challenge lies in achieving microwatt‐scale outputs due to the inherent conductivity of the materials and diverting electric currents. To address this challenge, our research has concentrated on utilizing nonconductive mediums for water‐based low‐cost microfibrous ceramic wools in conjunction with a NaCl aqueous solution for power generation. The main source of electricity originates from the directed movement of water molecules and surface ions through densely packed microfibrous ceramic wools due to the effect of dynamic electric double layer. This occurrence bears resemblance to the natural water transpiration in plants, thereby presenting a fresh and straightforward approach for producing electricity in an ecofriendly manner. The generator module demonstrated in this study, measuring 12 × 6 cm 2 , exhibited a noteworthy open‐circuit voltage of 0.35 V, coupled with a short‐circuit current of 0.51 mA. Such low‐cost ceramic wools are suitable for ubiquitous, permanent energy sources and hold potential for use as self‐powered sensors and systems, eliminating the requirement for external energy sources such as sunlight or heat.

'Patterning' published in 'Encyclopedia of Nanotechnology'

Abstract Ocean energy harvesting based on a triboelectric nanogenerator (TENG) has great application potential, while the encapsulation of triboelectric devices in water poses a critical issue. Herein, a triboelectric–electromagnetic hybrid nanogenerator (TE‐HNG) consisting of TENGs and electromagnetic generators (EMGs) is proposed to harvest water flow energy. A magnetic coupling transmission component is applied to replace traditional bearing structures, which can realize the fully enclosed packaging of the TENG devices and achieve long‐lasting energy harvesting from water flow. Under the intense water impact, magnetic coupling reduces the possibility of internal gear damage due to excessive torque, indicating superior stability and robustness compared to conventional TENG. At the waterwheel rotates speed of 75 rpm, the TE‐HNG can generate an output peak power of 114.83 mW, corresponding to a peak power density of 37.105 W m −3 . After 5 h of continuous operation, the electrical output attenuation of TENG is less than 3%, demonstrating excellent device durability. Moreover, a self‐powered temperature sensing system and a self‐powered cathodic protection system based on the TE‐HNG are developed and illustrated. This work provides a prospective strategy for improving the output stability of TENGs, which benefits the practical applications of the TENGs in large‐scale blue energy harvesting.

Abstract Flexoelectricity is an electromechanical coupling effect in which electric polarization is generated by a strain gradient. In this investigation, a potassium sodium niobite/poly(vinylidene fluoride‐trifluoroethylene) (KNN/PVDF‐TrFE)‐based nanocomposite is fabricated, and the flexoelectric effect is used to enhance the photovoltaic current ( I pv ) in the nanocomposite. It is found that both a pyroelectric current and photovoltaic current can be generated simultaneously in a light illumination process. However, the photovoltaic current ( I pv ) in this process contributes ≈85% of the total current. When assessing the effect of flexoelectricity with a curvature of 1/20, the I pv of the curved KNN/PVDF‐TrFE (20%) (K/P‐20) composite increased by ≈13.9% compared to that of the flat K/P‐20 nanocomposite. Similarly, at a curvature of 1/20, the I pv of the K/P‐20 nanocomposite is 71.6% higher than that of the PVDF‐TrFE film. However, the photovoltaic effect induced by flexoelectricity is much higher than the increased polarization from flexoelectricity, so this effect is called as the flexophotovoltaic effect. Furthermore, the calculated energy conversion efficiency of the K/P‐20 film is 0.017%, which is comparable to the previous research result. This investigation shows great promise for PVDF‐based nanocomposites in ferroelectric memory device applications.

An oscillating float-type triboelectric nanogenerator (OF-TENG) captures low-frequency ocean wave energy (0°–360°), delivering a current of 0.31 mA, a peak power of 111.56 mW, and an average volumetric power density of 21.58 W m −3 at 1 Hz.

Abstract Ammonia synthesis using low-power consumption and eco-friendly methods has attracted increasing attention. Here, based on the Tesla turbine triboelectric nanogenerator (TENG), we designed a simple and effective self-powered ammonia synthesis system by N 2 discharge. Under the driving of the simulated waste gas, the Tesla turbine TENG showed high rotation speed and high output. In addition, the performance of two Tesla turbine TENGs with different gas path connections was systematically investigated and discussed. A controllable series-parallel connection with the control of gas supply time was also proposed. Taking advantage of the intrinsic high voltage, corona discharge in a N 2 atmosphere was simply realized by a Tesla turbine TENG. With the flow of N 2 , the generated high-energy plasma can immediately react with water molecules to directly produce ammonia. The self-powered system achieved a yield of 2.14 μg h −1 (0.126 μmol h −1 ) under ambient conditions, showing great potential for large-scale synthesis.

As tactile force sensing has become increasingly significant in the field of machine haptics, achieving multidimensional force sensing remains a challenge. We propose a 3D flexible force sensor that consists of an axisymmetric hemispherical protrusion and four equally sized quarter-circle electrodes. By simulating the device using a force and electrical field model, it has been found that the magnitude and direction of the force can be expressed through the voltage relationship of the four electrodes when the magnitude of the shear force remains constant and its direction varies within 0–360°. The experimental results show that a resolution of 15° can be achieved in the range 0–90°. Additionally, we installed the sensor on a robotic hand, enabling it to perceive the magnitude and direction of touch and grasp actions. Based on this, the designed 3D flexible tactile force sensor provides valuable insights for multidimensional force detection and applications.

Abstract The ocean, as one of the most important areas for human production and living, plays a vital role in transportation, food production, and energy supply. However, it is greatly desired to develop relevant advanced technologies to achieve efficient and safe ocean exploitation and utilization. The advanced triboelectric nanogenerator (TENG), which can transform mechanical motion into electric energy to achieve the harvesting of related mechanical energy and obtain corresponding mechanical motion characteristics by the corresponding electronic signal, is a significant choice to develop the corresponding advanced technologies for marine exploitation. According to the application field, TENG based marine exploitation researches can be divided into three kinds of researching directions, which contain the in situ ocean energy harvesting, self‐powered ocean monitoring, and self‐powered marine electrochemical technologies. The latest representative research works, which are based on TENG technology for marine exploitation, are classified in detail and summarized comprehensively in this review. More importantly, the specific prospects of TENG in the marine exploitation are discussed in detail to promote future research.

Artificial haptic sensors form the basis of touch-based human-interfaced applications. However, they are unable to respond to remote events before physical contact. Some elasmobranch fishes, such as seawater sharks, use electroreception somatosensory system for remote environmental perception. Inspired by this ability, we design a soft artificial electroreceptor for sensing approaching targets. The electroreceptor, enabled by an elastomeric electret, is capable of encoding environmental precontact information into a series of voltage pulses functioning as unique precontact human interfaces. Electroceptor applications are demonstrated in a prewarning system, robotic control, game operation, and three-dimensional object recognition. These capabilities in perceiving proximal precontact events can lenrich the functionalities and applications of human-interfaced electronics.

Information security plays critical roles in modern society. However, traditional security measures like passwords, tokens and personal ID numbers only provide limited protection. Inspired by the fast training speed of extreme learning machine (ELM) and the promising feature extraction capability of extreme learning machine auto-encoder (ELM-AE), this paper proposes a keystroke dynamics identification method for intelligent keyboard (IKB) based on multi-layer extreme learning machine (ML-ELM). The IKB, as first demonstrated by Wang's group, is a self-powered, non-mechanical perforated keyboard that converts mechanical stimuli applied to the keyboard into electrical signals without the needing an external power source. ML-ELM is a multilayer neural network stacking on top of ELM-AE. Our major contribution is to develop an accurate and efficient keystroke dynamics identification method based on ML-ELM. One significant advantage of the proposed method is that it does not rely on manual feature extraction and selection. In other words, the raw current signals obtained by IKB are directly the input to the network. The network structure is determined by grid search, which minimizes the human involvement. The other one is that the whole training process of the model does not require fine tuning, which makes the training process significantly faster than that of existing deep networks. Experimental results demonstrate that the proposed method has excellent timeliness whereas obtaining high identification accuracy. The proposed method has great potential in applications to computer or network access control, online payment, and cyber security.

Abstract Rapid advancements in multifunctional triboelectric nanogenerators (TENGs) for energy harvesting and self‐powered sensing must be partnered with corresponding advances in durability and heat‐resistance, especially under harsh working conditions. A device suitable for harsh environmental applications based on the wear‐resistant triboelectric material is reported. The working modes of the harsh‐environmental TENG (heTENG) are composed of freestanding and single electrode that enable both harvesting sliding/vibration energy and self‐powered vibrational sensing. For the first time, a TENG possessing wear resistance, withstanding high temperature, and high hardness is achieved by employing micro–nanocomposite for triboelectric materials. It is demonstrated to be directly used as a key supporting part, such as automobile's brake pads. In addition, it is found that the heTENG outputs 221 V, 27.9 µA cm −2 , and 33.4 µC cm −2 . Furthermore, since heTENG is vibration‐sensitive, the automobile's self‐powered smart braking system and sensor network are developed successfully which can automatically provide a precise early‐warning signals, such as a reminder for brake replacement, and an indicator for tire overloading, and pressure. This work shows a new strategy to enhance the performance of triboelectric materials, making it applicable to harsh environments, as well as potential applications in autonomous vehicles and industrial brakes.