Search

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade.

Wind sensors have always played an irreplaceable role in environmental information monitoring and are expected to operate with lower power consumption to extend service lifetime. Here, we propose a breeze wake-up anemometer (B-WA) based on a rolling-bearing triboelectric nanogenerator (RB-TENG) with extremely low static power. The B-WA consists of two RB-TENGs, a self-waking-up module (SWM), a signal processing module (SPM), and a wireless transmission unit. The two RB-TENGs are employed for system activation and wind-speed sensing. Once the ambient wind-speed exceeds 2 m/s, the wake TENG (W-TENG) and the SWM can wake up the system within 0.96 s. At the same time, the SPM starts to calculate the signal frequency from the measured TENG (M-TENG) to monitor the wind speed with a sensitivity of 9.45 Hz/(m/s). After the wind stops, the SWM can switch off the B-WA within 0.52 s to decrease the system energy loss. In quiescent on-duty mode, the operating power of the B-WA is less than 30 nW, which can greatly extend the service lifetime of the B-WA. By integrating triboelectric devices and rolling bearings, this work has realized an ultralow quiescent power and self-waked-up wireless wind-speed monitoring system, which has foreseeable applications in remote weather monitoring, IoT nodes, and so on. [ABSTRACT FROM AUTHOR]

Simple Summary: With the rapid development of aquaculture, searching for alternative lipid sources to fish oil has become an important task for the aqua-feed industry. Lard is regarded as a valuable alternative lipid source. Basa fish (Pangasius bocourti) offal oil also has a large annual production and high potential to be used in fish feeds. Evaluation of the efficacy of lard and Basa fish offal oil in fish feeds will provide useful information for their application in aquaculture. Both lard and Basa fish offal oil have high levels of saturated fatty acids and monounsaturated fatty acids. The "n-3 LC-PUFA sparing effects" of saturated fatty acids and monounsaturated fatty acids in fish feeds have been observed in many fish species. The present study was also aimed at validating the "n-3 LC-PUFA sparing effects" of saturated fatty acids and monounsaturated fatty acids in lard and Basa fish offal oil. Moreover, the effects of dietary lard and Basa fish offal oil on fish body composition and muscle quality were also evaluated. Lard (LD) and Basa fish offal oil (BFO) have similar fatty acid profiles, both containing high contents of saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA). The present study aimed to investigate the efficacy of partial or complete replacement of marine fish oil (MFO, herring oil) by LD or BFO in the diets of tiger puffer. The control diet contained 49.1% crude protein and 9.28% crude lipid content including 6% added MFO. In other diets, 1/3, 2/3, and 3/3 of the added MFO was replaced by LD or BFO, respectively. Each diet was fed to triplicate tanks of juvenile fish (initial body weight, 13.88 g). A 46-day feeding trial was conducted in a flow-through seawater system. Each diet was fed to triplicate 200-L rectangular polyethylene tanks, each of which was stocked with 30 fish. Fish were fed to satiation three times a day. The complete replacement of added MFO (replacing 65% of the total crude lipid) had no adverse effects on fish growth performance in terms of survival (>94%), weight gain (360–398%), feed intake (2.37–3.04%), feed conversion ratio (0.84–1.02), and somatic indices. The dietary LD or BFO supplementation also had marginal effects on fish body proximate composition, biochemical parameters, muscle texture, and water-holding ability, as well as the hepatic expression of lipid metabolism-related genes. Partial (2/3) replacement of added MFO by LD or BFO did not significantly reduce the muscle n-3 LC-PUFA content, indicating the n-3 LC-PUFA sparing effects of SFA and MUFA in LD and BFO. In general, dietary LD or BFO reduced the peroxidation level and led to significant changes in the muscle volatile flavor compound profile, which were probably attributed to the change in fatty acid composition. The results of this study evidenced that LD and BFO are good potential lipid sources for tiger puffer feeds. [ABSTRACT FROM AUTHOR]

As an emerging technology to convert environmental high‐entropy energy into electrical energy, triboelectric nanogenerator (TENG) has great demands for further enhancing the service lifetime and output performance in practical applications. Here, an ultra‐robust and high‐performance rotational triboelectric nanogenerator (R‐TENG) by bearing charge pumping is proposed. The R‐TENG composes of a pumping TENG (P‐TENG), an output TENG (O‐TENG), a voltage‐multiplying circuit (VMC), and a buffer capacitor. The P‐TENG is designed with freestanding mode based on a rolling ball bearing, which can also act as the rotating mechanical energy harvester. The output low charge from the P‐TENG is accumulated and pumped to the non‐contact O‐TENG, which can simultaneously realize ultralow mechanical wear and high output performance. The matched instantaneous power of R‐TENG is increased by 32 times under 300 r/min. Furthermore, the transferring charge of R‐TENG can remain 95% during 15 days (6.4 × 106 cycles) continuous operation. This work presents a realizable method to further enhance the durability of TENG, which would facilitate the practical applications of high‐performance TENG in harvesting distributed ambient micro mechanical energy. [ABSTRACT FROM AUTHOR]

Body area network has attracted extensive attention for its applications in athletics, medical, diagnosis, and rehabilitation training in the next generation personalized health care solutions. Here, a contact‐separation direct current triboelectric nanogenerators (CSDC‐TENGs) based self‐powered wireless body area network (SWBAN) is reported that enables multi‐joint movements monitoring for human motion. The CSDC‐TENG is designed as a flexible active sensor with an internal contact switch, and the flexible substrate makes the TENG‐sensor stick onto skin easily. Due to the internal switch, the CSDC‐TENG could generate a DC current, a large instantaneous output voltage exceeds 700 V, and an instantaneous power can reach 1.076 W, which is more than 23 000 times higher than that of the traditional contact‐separation mode TENG in same size and materials without the switch. By coupling with flexible coil, the fixed high‐frequency radio signals can be modulated and emitted clearly ranging from 6 to 16 MHz, which can be wirelessly received and demodulated through a reader. Moreover, the SWBAN is demonstrated in a real time monitoring system for joints motion. This work has realized the wearable TENG for self‐powered wireless real‐time monitoring of body movements driven by low‐frequency human daily activities, which may promote a tremendous development of intelligent healthcare, wireless sensing system and body area network. [ABSTRACT FROM AUTHOR]

Highlights: A flexible tribotronic artificial synapse with bioinspired neurosensory behaviour was demonstrated, which establishes an active interaction mechanism with the environment. The device can well exhibit tuneable synaptic behaviours by changing the mechanical input modes, including excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memorial process. The device has excellent mechanical flexibility that can exhibit stable synaptic functions even under strain conditions with a bending radius of 20 mm after 1000 bending cycles. As key components of artificial afferent nervous systems, synaptic devices can mimic the physiological synaptic behaviors, which have attracted extensive attentions. Here, a flexible tribotronic artificial synapse (TAS) with bioinspired neurosensory behavior is developed. The triboelectric potential generated by the external contact electrification is used as the ion-gel-gate voltage of the organic thin film transistor, which can tune the carriers transport through the migration/accumulation of ions. The TAS successfully demonstrates a series of synaptic behaviors by external stimuli, such as excitatory postsynaptic current, paired-pulse facilitation, and the hierarchical memory process from sensory memory to short-term memory and long-term memory. Moreover, the synaptic behaviors remained stable under the strain condition with a bending radius of 20 mm, and the TAS still exhibits excellent durability after 1000 bending cycles. Finally, Pavlovian conditioning has been successfully mimicked by applying force and vibration as food and bell, respectively. This work demonstrates a bioinspired flexible artificial synapse that will help to facilitate the development of artificial afferent nervous systems, which is great significance to the practical application of artificial limbs, robotics, and bionics in future. [ABSTRACT FROM AUTHOR]

In the era of the Internet of Things, various electronics play an important role in information interaction, in which the power supply is an urgent problem to be solved. Triboelectric nanogenerator (TENG) is an emerging mechanical energy harvesting technology that can serve as a power source for electronics, which is developing towards high performance, miniaturization and integration. Herein, the advanced micro-nano manufacturing technologies are systematically reviewed for TENGs. First, film preparation such as physical vapor deposition, chemical vapor deposition, electrochemical deposition, electrospinning and screen printing for triboelectric layers are introduced and discussed. Then, surface processing, such as soft lithography, laser ablation, inductively coupled plasma and nanoimprint for micro-nano structures on the surface of triboelectric layers are also introduced and discussed. In addition, micro-electromechanical system fabrication for TENG devices such as acoustic and vibration sensors, is introduced, and their current challenges are analyzed. Finally, the challenges of the advanced micro-nano manufacturing technologies for the TENGs are systematically summarized, and further development is prospected. [ABSTRACT FROM AUTHOR]

With the continuous development of smart ports, the construction of port information systems has become the construction focus of the future development of such ports. However, the existing performance evaluation system for port information systems represents a research gap. It cannot effectively achieve specific improvement in the construction of information systems for port services and capabilities. In this paper, an evaluation index system was forged from the three perspectives of the operating level, management capability, and economic efficiency. As a first step, starting from the current situation of Beibu Gulf Port (Port B) construction, the three dimensions of the evaluation index system were determined using the scheme of literature review, questionnaire research, and fieldwork. Additionally, through adversarial interpretive structural modeling (AISM), it is concluded that the port loading and unloading operation capability and service level are the fundamental factors for measuring the construction performance of the container terminal operation system (CTOS). The results are used as input to construct an analytic network process (ANP) model to obtain the index weights. Finally, the gray clustering method (GCM) is introduced to construct a quantitative evaluation model to quantitatively evaluate the construction performance of the Port B CTOS. The quantitative benefits brought by the construction of the CTOS are finally verified. The scores of the corresponding dimensions of the port before and after the construction of the CTOS system in Beibu Gulf Port are obtained experimentally. The conclusion shows that the construction of CTOS makes the port operating level module improve the most, from 40.023 to 70.733 points; at the same time, it is found that two aspects, i.e., green security and economic benefits, in current construction are directions requiring further work in future port construction. Finally, a visualized quantitative analysis and evaluation method for the performance of smart port construction is proposed. [ABSTRACT FROM AUTHOR]

With the extensive application of wireless sensing nodes, the demand for sustainable energy in unattended environments is increasing. Here, we report a self-powered and autonomous vibrational wake-up system (SAVWS) based on triboelectric nanogenerators and micro-electromechanical system (MEMS) switches. The energy triboelectric nanogenerator (E-TENG) harvests vibration energy to power the wireless transmitter through a MEMS switch. The signal triboelectric nanogenerator (S-TENG) controls the state of the MEMS switch as a self-powered accelerometer and shows good linearity in the acceleration range of 1–4.5 m/s2 at 30 Hz with a sensitivity of about 14.6 V/(m/s2). When the acceleration increases, the S-TENG turns on the MEMS switch, and the wireless transmitter transmits an alarm signal with the energy from E-TENG, using only 0.64 mJ. Using TENGs simultaneously as an energy source and a sensor, the SAVWS provides a self-powered vibration monitoring solution for unattended environments and shows extensive applications and great promise in smart factories, autonomous driving, and the Internet of Things. [ABSTRACT FROM AUTHOR]

Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise. Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model. Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment. [ABSTRACT FROM AUTHOR]

Based on the coupling effect of contact electrification and electrostatic induction, the triboelectric nanogenerator (TENG) as an emerging energy technology can effectively harvest mechanical energy from the ambient environment. However, due to its inherent property of large impedance, the TENG shows high voltage, low current and limited output power, which cannot satisfy the stable power supply requirements of conventional electronics. As the interface unit between the TENG and load devices, the power management circuit can perform significant functions of voltage and impedance conversion for efficient energy supply and storage. Here, a review of the recent progress of switching power management for TENGs is introduced. Firstly, the fundamentals of the TENG are briefly introduced. Secondly, according to the switch types, the existing power management methods are summarized and divided into four categories: travel switch, voltage trigger switch, transistor switch of discrete components and integrated circuit switch. The switch structure and power management principle of each type are reviewed in detail. Finally, the advantages and drawbacks of various switching power management circuits for TENGs are systematically summarized, and the challenges and development of further research are prospected. [ABSTRACT FROM AUTHOR]

The wearable tactile sensors have attracted great attention in the fields of intelligent robots, healthcare monitors and human-machine interactions. To create active tactile sensors that can directly generate electrical signals in response to stimuli from the surrounding environment is of great significance. Triboelectric nanogenerators (TENGs) have the advantages of high sensitivity, fast response speed and low cost that can convert any type of mechanical motion in the surrounding environment into electrical signals, which provides an effective strategy to design the self-powered active tactile sensors. Here, an overview of the development in TENGs as tactile stimulators for multifunctional sensing and artificial synapses is systematically introduced. Firstly, the applications of TENGs as tactile stimulators in pressure, temperature, proximity sensing, and object recognition are introduced in detail. Then, the research progress of TENGs as tactile stimulators for artificial synapses is emphatically introduced, which is mainly reflected in the electrolyte-gate synaptic transistors, optoelectronic synaptic transistors, floating-gate synaptic transistors, reduced graphene oxides-based artificial synapse, and integrated circuit-based artificial synapse and nervous systems. Finally, the challenges of TENGs as tactile stimulators for multifunctional sensing and artificial synapses in practical applications are summarized, and the future development prospects are expected. [ABSTRACT FROM AUTHOR]

It is an increasingly mature application solution that triboelectric nanogenerator (TENG) supplies power to electronic devices through its power management system (PMS). However, the previous PMS is able to manage a limited voltage magnitude and the energy storage elements are limited to capacitors. This work proposes an ultrahigh voltage PMS (UV-PMS) to realize the charging of commercial lithium cells (LCs) by TENG. The design of UV-PMS enables energy management of TENGs with ultrahigh open-circuit voltages up to 3500 V and boosts the peak charging current from 30.9 µA to 2.77 mA, an increase of 89.64 times. With the introduction of UV-PMS, the effective charging capacity of LC charged by a TENG at a working frequency of 1.5 Hz for 1 h comes to 429.7 µAh, making a 75.3 times enhancement compared to charging by TENG directly. The maximum charging power comes to 1.56 mW. The energy storage efficiency is above 97% and the overall charge efficiency can be maintained at 81.2%. This work provides a reliable strategy for TENG to store energy in LC, and has promising applications in energy storage, LC's life, and self-powered systems., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Uneven illumination, obstruction of leaves or branches, and the overlapping of fruit significantly affect the accuracy of tomato detection by automated harvesting robots in natural environments. In this study, a proficient and accurate algorithm for tomato detection, called SBCS-YOLOv5s, is proposed to address this practical challenge. SBCS-YOLOv5s integrates the SE, BiFPN, CARAFE and Soft-NMS modules into YOLOv5s to enhance the feature expression ability of the model. First, the SE attention module and the C3 module were combined to form the C3SE module, replacing the original C3 module within the YOLOv5s backbone architecture. The SE attention module relies on modeling channel-wise relationships and adaptive re-calibration of feature maps to capture important information, which helps improve feature extraction of the model. Moreover, the SE module's ability to adaptively re-calibrate features can improve the model's robustness to variations in environmental conditions. Next, the conventional PANet multi-scale feature fusion network was replaced with an efficient, weighted Bi-directional Feature Pyramid Network (BiFPN). This adaptation aids the model in determining useful weights for the comprehensive fusion of high-level and bottom-level features. Third, the regular up-sampling operator is replaced by the Content Aware Reassembly of Features (CARAFE) within the neck network. This implementation produces a better feature map that encompasses greater semantic information. In addition, CARAFE's ability to enhance spatial detail helps the model discriminate between closely spaced fruits, especially for tomatoes that overlap heavily, potentially reducing the number of merging detections. Finally, for heightened identification of occluded and overlapped fruits, the conventional Non-Maximum-Suppression (NMS) algorithm was substituted with the Soft-NMS algorithm. Since Soft-NMS adopts a continuous weighting scheme, it is more adaptable to varying object sizes, improving the handling of small or large fruits in the image. Remarkably, this is carried out without introducing changes to the computational complexity. The outcome of the experiments showed that SBCS-YOLOv5s achieved a mean average precision (mAP (0.5:0.95)) of 87.7%, which is 3.5% superior to the original YOLOv5s model. Moreover, SBCS-YOLOv5s has a detection speed of 2.6 ms per image. Compared to other state-of-the-art detection algorithms, SBCS-YOLOv5s performed the best, showing tremendous promise for tomato detection in natural environments., Competing Interests: Author GL was employed by the company Univalsoft Joint Stock Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Touko Mbouembe, Liu, Park and Kim.)

Automatic and accurate detection of fruit in greenhouse is challenging due to complicated environment conditions. Leaves or branches occlusion, illumination variation, overlap and cluster between fruits make the fruit detection accuracy to decrease. To address this issue, an accurate and robust fruit-detection algorithm was proposed for tomato detection based on an improved YOLOv4-tiny model. First, an improved backbone network was used to enhance feature extraction and reduce overall computational complexity. To obtain the improved backbone network, the BottleneckCSP modules of the original YOLOv4-tiny backbone were replaced by a Bottleneck module and a reduced version of BottleneckCSP module. Then, a tiny version of CSP-Spatial Pyramid Pooling (CSP-SPP) was attached to the new backbone network to improve the receptive field. Finally, a Content Aware Reassembly of Features (CARAFE) module was used in the neck instead of the traditional up-sampling operator to obtain a better feature map with high resolution. These modifications improved the original YOLOv4-tiny and helped the new model to be more efficient and accurate. The experimental results showed that the precision, recall, F 1 score, and the mean average precision (mAP) with Intersection over Union (IoU) of 0.5 to 0.95 were 96.3%, 95%, 95.6%, and 82.8% for the improved YOLOv4-tiny model, respectively. The detection time was 1.9 ms per image. The overall detection performance of the improved YOLOv4-tiny was better than that of state-of-the-art detection methods and met the requirements of tomato detection in real time., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mbouembe, Liu, Sikati, Kim and Kim.)

The accurate and robust detection of fruits in the greenhouse is a critical step of automatic robot harvesting. However, the complicated environmental conditions such as uneven illumination, leaves or branches occlusion, and overlap between fruits make it difficult to develop a robust fruit detection system and hinders the step of commercial application of harvesting robots. In this study, we propose an improved anchor-free detector called TomatoDet to deal with the above challenges. First, an attention mechanism is incorporated into the CenterNet backbone to improve the feature expression ability. Then, a circle representation is introduced to optimize the detector to make it more suitable for our specific detection task. This new representation can not only reduce the degree of freedom for shape fitting, but also simplifies the regression process from detected keypoints. The experimental results showed that the proposed TomatoDet outperformed other state-of-the-art detectors in respect of tomato detection. The F 1 score and average precision of TomatoDet reaches 95.03 and 98.16%. In addition, the proposed detector performs robustly under the condition of illumination variation and occlusion, which shows great promise in tomato detection in the greenhouse., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Hou, Liu, Liu, Zhao and Li.)

Tomato plants are infected by diseases and insect pests in the growth process, which will lead to a reduction in tomato production and economic benefits for growers. At present, tomato pests are detected mainly through manual collection and classification of field samples by professionals. This manual classification method is expensive and time-consuming. The existing automatic pest detection methods based on a computer require a simple background environment of the pests and cannot locate pests. To solve these problems, based on the idea of deep learning, a tomato pest identification algorithm based on an improved YOLOv4 fusing triplet attention mechanism (YOLOv4-TAM) was proposed, and the problem of imbalances in the number of positive and negative samples in the image was addressed by introducing a focal loss function. The K-means + + clustering algorithm is used to obtain a set of anchor boxes that correspond to the pest dataset. At the same time, a labeled dataset of tomato pests was established. The proposed algorithm was tested on the established dataset, and the average recognition accuracy reached 95.2%. The experimental results show that the proposed method can effectively improve the accuracy of tomato pests, which is superior to the previous methods. Algorithmic performance on practical images of healthy and unhealthy objects shows that the proposed method is feasible for the detection of tomato pests., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Wang, Miao and Liu.)