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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. [ABSTRACT FROM AUTHOR]

Palm oil, with its higher production, lower prices, and higher levels of palmitic acid and oleic acid, may have great potential for use in the aquafeed industry. In this study, with an 8‐week feeding experiment, the efficacy of palm oil as a substitute for fish oil in tiger puffer feeds was comprehensively evaluated. The control diets (FO group) contained 8% marine fish oil as the main lipid source, while in the treatment diets, the added marine fish oil was replaced with palm oil at 25%, 50%, 75%, and 100%, respectively, which was named 25PO, 50PO, 75PO, and 100PO, respectively. Juvenile tiger puffers with an initial weight of 15.0 ± 0.04 g were used, with three replicate tanks of 30 juvenile fish tiger puffer for each dietary group. The fish oil replacement by palm oil did not have an adverse effect on fish growth and feeding, but the weight gain decreased by 17.3% in group PO100. Palm oil had no significant effects on fish proximate composition and muscle texture. The effects of dietary palm oil on muscle fatty acid composition were not significant, with DHA and EPA significantly lowered only in the 100PO group. In contrast, the changes in liver and intestinal fatty acid compositions in response to diets were more significant than those in the muscle. In the intestine, the replacement of more than 50% fish oil by palm oil significantly downregulated the gene expression associated with peroxisomal fatty acid β‐oxidation and triglyceride hydrolysis, while upregulated the expression of cholesterol biosynthetic genes. In the liver, the replacement of more than 75% fish oil also significantly upregulated the cholesterol synthesis. In conclusion, palm oil can replace 75% of added marine fish oil in tiger puffer diets and does not adversely affect the growth performance, feed utilization, muscle composition, and muscle texture. [ABSTRACT FROM AUTHOR]

The emerging tribovoltaic nanogenerators (TVNGs) has garnered considerable attention in recent years owing to its higher power density, but it is still limited by unstable output, mismatched impedance, and lower charging speed. Herein, a power‐managed GaN‐based TVNG is first proposed and a self‐powered wireless temperature monitoring system (SWTMS) is developed. The energy backflow from transition capacitor to TVNG is effectively suppressed by applying a high‐voltage‐resistant diode. Benefiting from the combination of the maximum power point tracking (MPPT and the buck converter, an autonomous power management unit (PMU) is designed. The internal impedance of TVNG with PMU decreases by ten times and the charging speed of capacitor increases by 7.18 times compared to those without PMU. Moreover, 100 LEDs and six parallel thermohygrometers can be constantly powered. The wireless temperature monitoring in industrial environments without external power supply is demonstrated through the developed SWTMS. This work proposes an effective power management strategy for TVNG with high output voltage, which exhibits great potential to promote the development of self‐powered sensors and Industrial Internet of Things (IIoT). [ABSTRACT FROM AUTHOR]

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]

The tribovoltaic effect raises an emerging semiconductor energy technology and silicon‐based tribovoltaic nanogenerator (Si‐TVNG) has aroused great attentions. However, the electrical output of Si‐TVNG is extremely unstable due to its complicated mechanism. Here, a competitive mechanism between the built‐in electric field (BEF) and the interfacial electric field (IEF) is proposed to determine the current direction and magnitude of the Si‐TVNG. The results indicate that the natural oxide layer, load pressure, and resistivity of Si significantly affect the IEF and BEF. The natural oxide layer can store surface charges by contact electrification, resulting in a IEF dominant carriers transport process. Conversely, lower resistivity and higher load pressure can reduce the contact resistance, thereby decreasing the surface charges caused by contact electrification and leading to a BEF dominant carrier transport process. This work contributes to deeper understandings of the tribovoltaic effect and further clarifies the theoretical mechanisms of carrier excitation and directional transport. [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]

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.)