Publications

The working principle of the triboelectric nanogenerator (TENG), contact electrification and electrostatic induction, has been used to harvest raindrop energy in recent years. However, the existing research is mainly concentrated on solid-liquid electrification, and adopts traditional electrostatic induction (TEI) for output. As a result, the efficiency of droplet electricity generators (DEGs) is severely constrained. Therefore, previous studies deem that the DEG output is limited by interfacial effects. This study reveals that this view is inappropriate and, in reality, the output strategy is the key bottleneck restricting the DEG performance. Here, a switch effect based on an electric-double-layer capacitor (EDLC) is introduced, and an equivalent circuit model is established to understand its working mechanism. Without pre-charging, a single droplet can generate high voltage over 100 V and the output is directly improved by two-orders of magnitude compared with TEI, which is precisely utilizing the interfacial effect. This work provides insightful perspective and lays solid foundation for DEG applications in large scale.

• An new method for assessing forest carbon potential based on annual biomass growth. • Larix gmelinii and Populus forests have significant promotion space of carbon sink. • Northeastern Yellow Rivers' Picea asperata has greater growth potential. • R package: Forestat can evaluate the forest carbon sequestration potential of whole cycle. • Forestat can be used for forest suitability assessment and site quality assessment. 1. Forest ecosystem is a basic component of terrestrial ecosystem that mitigates the impact of climate change through absorbing significant amount of carbon dioxide. Forest is crucial in maintaining ecological equilibrium, reducing greenhouse gas emission, and preserving biodiversity. Thus, standardised and precise methodologies for measuring forest carbon sequestration and assessing its potential are of paramount importance in evaluating the impacts of climate change. 2. We presented the "forestat", an approach for assessing forest carbon sequestration and its potential based on annual growth of biomass, considering site class and forest age conditions, wrapped in an R package, which is applicable to all forest types. By using a comprehensive system of forest stand models, "forestat" contributes to a refined comprehension of relationships between forest biomass and carbon sequestration and offers an evaluation of the carbon sequestration potential in forest ecosystem, unifying the methods of carbon measurement and its potential estimation in both natural and plantation forests. 3. Based on the two applications, this study (a) introduces and demonstrates the methods in R package "forestat"; (b) uses the 6th, 7th, 8th, and 9th National Forest Inventory data to investigate carbon sequestration and potential productivity of degraded forests in China; and (c) conducts suitability analysis for tree species in the Yellow River Basin. 4. "forestat" provides a unified, efficient, and standardized analytical tool for forest carbon sequestration and its potential. This study also highlights the primary forest functionalities.

Abstract In the process of particle transport in pipes, particles get charged, and the discharge process can cause fire and explosion, which causes great harm to the safe production. In this paper, a particle transport triboelectric nanogenerator (PT‐TENG) based on particle transport in pipes is developed. The PT‐TENG can be utilized to monitor the mass‐flow of the particles and make explosion early warning without supplying the power. Under the condition of particles passing one by one, the moving speed of the particles can be obtained. When the particles transport continuously, the PT‐TENG can be used as a self‐powered detector to detect the mass flow of the particles in time. The mass flow detector can be installed both inside and outside the transport pipe. The PT‐TENG can be used as a power source to charge capacitors and light up light‐emitting diodes (LEDs). Moreover, a self‐powered explosion early warning device has been developed based on the PT‐TENG, which will have widespread application in the safety production.

Having sufficient training images with fully annotated object locations is undoubtedly critical for modern learning-based image annotation, retrieval, and object detection methods. Typically, collecting such annotations for large-scale datasets is notoriously tedious because the process involves amount of manual cropping and hand labeling operations. In this work, following the principle of games with a purpose (GWAP), we design a so-called purposive hidden-object-game (P-HOG), which imperceptibly embeds localizing objects into enjoyable playing game process and thus attracts many people to make voluntary contribution to annotating images. In particular, besides preserving the interestingness as popular HOG games, P-HOG is able to automatically generate satisfactory game images (i.e., “hide” certain items into target images) by integrating several semantic and visual processing techniques. P-HOG is also built in an effective mechanism to prevent the players from cheating. The mechanism inherits the merit of Recaptcha and identifies potential cheating behavior based on the annotation accuracy of some known items. Moreover, P-HOG will filter noisy annotations effectively based on a weighted majority method and improve the accuracy of the raw annotations from the players. Most importantly, players only play P-HOG for entertainment purpose and they are unaware of the background data collection procedure. The collected data are used towards constructing a large database, which may benefit general learning-based algorithms for multimedia tasks. To the best of our knowledge, this is the first work dedicated to such a specific and important task under the GWAP framework. We conduct a pilot study of the game prototype and the comprehensive experiments show that the P-HOG appeals to general players, and is effective for collecting massive object locations with satisfactory accuracy, which further boosts the algorithmic performances for both tag refinement and image annotation tasks.

A talented endophytic

Abstract Utilizing hydrogen energy produced by water electrolysis is conducive to carbon neutrality from a technological point of view. However, this perspective is restrained by the high electricity cost in commercial development. Powering electrochemical reactions by renewable electricity converted from distributed mechanical energy can lead to commercial electric energy saving and cost reduction. In this work, a self‐powered electrochemical system (SPECS) is proposed for the conversion from blue energy of ocean waves to green energy via hydrogen fuel. This system consists of triboelectric nanogenerator (TENG) devices and an electrolyzer, where the TENG devices serve as ocean wave energy harvesters for electricity generation, while the seawater‐containing electrolyzer is employed for hydrogen generation. After power management on the output of the TENGs and electrochemical reaction optimization, the system can generate hydrogen at a rate of 814.8 µ L m ‐2 d ‐1 and a Faraday efficiency of 69.1% under ideal operation conditions, demonstrating the practical potential of the constructed system toward carbon neutralization.

Abstract Inspired by biological neural networks, the fabrication of artificial neuromorphic systems with multimodal perception capacity shows promises in overcoming the “von Neumann bottleneck” and takes advantage of the efficient perception and computation of diverse types of signals. Here, we combine a triboelectric nanogenerator with an α ‐phase indium selenide ( α ‐In 2 Se 3 ) optoelectronic synaptic transistor to construct a tribo‐ferro‐optoelectronic artificial neuromorphic device with multimodal plasticity. Based on the excellent ferroelectric and optoelectronic characteristics of the α ‐In 2 Se 3 channel, typical synaptic behaviors (e.g., pair‐pulse facilitation and short‐term/long‐term plasticity) are successfully simulated in response to the synergistic effect of mechanical and optical stimuli. The interaction of mechanical displacement and light illumination enables heterosynaptic plasticity and spatiotemporal dynamic logic. Furthermore, multiple Boolean logical functions and associative learning behaviors are successfully implemented using the paired stimuli of displacement pulses and light pulses. The proposed tribo‐ferro‐optoelectronic artificial neuromorphic devices have great potential for application in interactive neural networks and next‐generation artificial intelligence.

'Piezoresistive Effect' published in 'Encyclopedia of Nanotechnology'

Gradient asymmetry is employed to design metal-free polymer electrocatalysts. The superior activity of the highly asymmetric PPA-BN is attributed to the abundant active sites and adequate oxygen intermediate adsorption by local charge distribution.

Electrochromic (EC) devices are capable of reversibly changing optical properties upon charge injection and extraction driven by an externally applied voltage. A nanogenerator (NG) is a device that converts mechanical energy into electricity. This paper demonstrates a self-powered EC device driven by an NG. An average visible optical modulation (ΔTvis) as large as 15.3% was obtained, with electrochromic response time (ERT) and coloration efficiency (CE) of 10 seconds and 58.7 cm2 C−1, respectively. Such a performance is comparable to an EC when powered by a hardwired DC power. This study indicates that the self-powered electrochromic system can be a candidate for monochrome displays or electronic billboards.

A nanogenerator (NG) is a device that converts vibration energy into electricity. Here, a flexible, small size and lightweight NG is successfully demonstrated as an active sensor for detecting the vibration spectrum of a drum membrane without the use of an external power source. The output current/voltage signal of the NG is a direct measure of the strain of the local vibrating drum membrane that contains rich informational content, such as, notably, the vibration frequency, vibration speed and vibration amplitude. In comparison to the laser vibrometer, which is excessively complex and expensive, this kind of small and low cost sensor based on an NG is also capable of detecting the local vibration frequency of a drum membrane accurately. A spatial arrangement of the NGs on the membrane can provide position-dependent vibration information on the surface. The measured frequency spectrum can be understood on the basis of the theoretically calculated vibration modes. This work expands the application of NGs and reveals the potential for developing sound wave detection, environmental/infrastructure monitoring and many more applications.

The tactile peripheral nervous system innervating human hands, which is essential for sensitive haptic exploration and dexterous object manipulation, features overlapped receptive fields in the skin, arborization of peripheral neurons and many-to-many synaptic connections. Inspired by the structural features of the natural system, we report a supersensitive artificial slowly adapting tactile afferent nervous system based on the triboelectric nanogenerator technology. Using tribotronic transistors in the design of mechanoreceptors, the artificial afferent nervous system exhibits the typical adapting behaviours of the biological counterpart in response to mechanical stimulations. The artificial afferent nervous system is self-powered in the transduction and event-driven in the operation. Moreover, it has inherent proficiency of neuromorphic signal processing, delivering a minimum resolvable dimension two times smaller than the inter-receptor distance which is the lower limit of the dimension that existing electronic skins can resolve. These results open up a route to scalable neuromorphic skins aiming at the level of human's exceptional perception for neurorobotic and neuroprosthetic applications.