The accumulation of plastic waste resulting from the increasing demand for non-degradable plastics has led to a global environmental crisis. The severe environmental and economic drawbacks of inefficient, expensive, and impractical traditional waste disposal methods, such as landfills, incineration, plastic recycling, and energy production, limit the expansion of their applications to solving the plastic waste problem. Finding novel ways to manage the large amount of disposed plastic waste is urgent. Until now, one of the most valuable strategies for the handling of plastic waste has been to reutilize the waste as raw material for the preparation of functional and high-value products. Electrospun micro/nanofibers have drawn much attention in recent years due to their advantages of small diameter, large specific area, and excellent physicochemical features. Thus, electrospinning recycled plastic waste into micro/nanofibers creates diverse opportunities to deal with the environmental issue caused by the growing accumulation of plastic waste. This paper presents a review of recycling and reutilizing polymer waste via electrospinning. Firstly, the advantages of the electrospinning approach to recycling plastic waste are summarized. Then, the studies of electrospun recycled plastic waste are concluded. Finally, the challenges and future perspectives of electrospun recycled plastic waste are provided. In conclusion, this paper aims to provide a comprehensive overview of electrospun recycled plastic waste for researchers to develop further studies.

Polyethylene oxide (PEO)-based solid-state electrolytes for lithium-ion batteries have garnered significant interest due to their enhanced potential window, high energy density, and improved safety features. However, the issues such as low ionic conductivity at ambient temperature, substantial ionic conductivity fluctuations with temperature changes, and inadequate electrolyte interfacial compatibility hinder their widespread applications. Electrospinning is a popular approach for fabricating solid-state electrolytes owing to its superior advantages of adjustable component constitution and the unique internal fiber structure of the resultant electrolytes. Thus, this technique has been extensively adopted in related studies. This review provides an overview of recent advancements in optimizing the performance of PEO solid-state electrolytes via electrospinning technology. Initially, the impacts of different lithium salts and their concentrations on the performance of electrospun PEO-based solid-state electrolytes were compared. Subsequently, research pertaining to the effects of various additives on these electrolytes was reviewed. Furthermore, investigations concerning the enhancement of electrospun solid-state electrolytes via modifications of PEO molecular chains are herein detailed, and lastly, the prevalent challenges and future directions of PEO-based solid-state electrolytes for lithium-ion batteries are summarized.

Laser technology can be used to control weeds by irradiating the apical meristematic tissue (AMT) of weeds when they are still seedlings. Two factors are necessary for the successful large-scale implementation of this technique: the ability to accurately identify the apical meristematic tissue and the effectiveness of the localization algorithm used in the process. Based on this, this study proposes a lightweight weed AMT localization algorithm based on YOLO (look only once) instance segmentation. The YOLOv8n-seg network undergoes a lightweight design enhancement by integrating the FasterNet lightweight network as its backbone, resulting in the F-YOLOv8n-seg model. This modification effectively reduces the number of parameters and computational demands during the convolution process, thereby achieving a more efficient model. Subsequently, F-YOLOv8n-seg is combined with the connected domain analysis algorithm (CDA), yielding the F-YOLOv8n-seg-CDA model. This integration enables the precise localization of the AMT of weeds by calculating the center-of-mass coordinates of the connected domains. The experimental results indicate that the optimized model significantly outperforms the original model; the optimized model reduces floating-point computations by 26.7% and the model size by 38.2%. In particular, the floating-point calculation is decreased to 8.9 GFLOPs, and the model size is lowered to 4.2 MB. Comparing this improved model against YOLOv5s-seg and YOLOv10n-seg, it is lighter. Furthermore, it exhibits exceptional segmentation accuracy, with a 97.2% accuracy rate. Experimental tests conducted on five different weed species demonstrated that F-YOLOv8n-seg-CDA exhibits strong generalization capabilities. The combined accuracy of the algorithm for detecting these weeds was 81%. Notably, dicotyledonous weeds were detected with up to 94%. Additionally, the algorithm achieved an average inference speed of 82.9 frames per second. These results indicate that the algorithm is suitable for the real-time detection of apical meristematic tissues across multiple weed species. Furthermore, the experimental results demonstrated the impact of distinctive variations in weed morphology on identifying the location of the AMT of weeds. It was discovered that dicotyledonous and monocotyledonous weeds differed significantly in terms of the detection effect, with dicotyledonous weeds having significantly higher detection accuracy than monocotyledonous weeds. This discovery can offer novel insights and avenues for future investigation into the identification and location of the AMT of weeds.

In response to the issues of high-intensity labor, low efficiency, and potential damage to crayfish associated with traditional manual sorting methods, an automated and non-contact sorting approach based on an improved YOLOv5 algorithm is proposed for the rapid sorting of crayfish maturity and size. To address the difficulty in focusing on small crayfish, the Backbone is augmented with Coordinate Attention to boost its capability to extract features. Additionally, to address the difficulty in achieving high overall algorithm efficiency and reducing feature redundancy, the Bottleneck Transformer is integrated into both the Backbone and Neck, which improves the accuracy, generalization performance, and the model’s computational proficiency. The dataset of 3464 images of crayfish collected from a crayfish breeding farm is used for the experiments. The dataset is partitioned randomly, with 80% of the data used for training and the remaining 20% used for testing. The results indicate that the proposed algorithm achieves an mAP of 98.8%. Finally, the model is deployed using TensorRT, and the processing time for an image is reduced to just 2 ms, which greatly improves the processing speed of the model. In conclusion, this approach provides an accurate, efficient, fast, and automated solution for crayfish sorting.

Hydrogen is regarded as an ideal energy carrier to cope with the energy crisis and environmental problems due to its high energy density, cleanliness, and renewability. Although there are several primary methods of industrial hydrogen production, hydrogen evolution reaction (HER) is an efficient, eco-friendly, and sustainably green method for the preparation of hydrogen which has attracted considerable attention. However, this technique is characterized by slow reaction kinetics and high energy potential owing to lack of electrocatalysts with cost-effective and high performance which impedes its scale-up. To address this issue, various studies have focused on electrospun micro/nanofiber-based electrocatalysts for HER due to their excellent electron and mass transport, high specific surface area, as well as high porosity and flexibility. To further advance their development, recent progress of highly efficient HER electrospun electrocatalysts is reviewed. Initially, the characteristics of potential high-performance electrocatalysts for HER are elucidated. Subsequently, the advantages of utilizing electrospinning technology for the preparation of electrocatalysts are summarized. Then, the classification of electrospun micro/nanofiber-based electrocatalysts for HER are analyzed, including metal-based electrospun electrocatalyst (noble metals and alloys, transition metals, and alloys), metal–non-metal electrocatalysts (metal sulfide-based electrocatalysts, metal oxide-based electrocatalysts, metal phosphide-based electrocatalysts, metal nitride-based electrocatalysts, and metal carbide-based electrocatalysts), metal-free electrospun micro/nanofiber-based electrocatalysts, and hybrid electrospun micro/nanofiber-based electrocatalysts. Following this, enhancement strategies for electrospun micro/nanofiber-based electrocatalysts are discussed. Finally, current challenges and the future research directions of electrospun micro/nanofiber-based electrocatalysts for HER are concluded.

Defects within chip solder joints are usually inspected visually for defects using X-ray imaging to obtain images. The phenomenon of voids inside solder joints is one of the most likely types of defects in the soldering process, and accurate detection of voids becomes difficult due to their irregular shapes, varying sizes, and defocused edges. To address this problem, an X-ray void image segmentation algorithm based on improved PCB-DeepLabV3 is proposed. Firstly, to meet the demand for lightweight and easy deployment in industrial scenarios, mobilenetv2 is used as the feature extraction backbone network of the PCB-DeepLabV3 model; then, Attentional multi-scale two-space pyramid pooling network (AMTPNet) is designed to optimize the shallow feature edges and to improve the ability to capture detailed information; finally, image cropping and cleaning methods are designed to enhance the training dataset, and the improved PCB-DeepLabV3 is applied to the training dataset. The improved PCB-DeepLabV3 model is used to segment the void regions within the solder joints and compared with the classical semantic segmentation models such as Unet, SegNet, PSPNet, and DeeplabV3. The proposed new method enables the solder joint void inspection to get rid of the traditional way of visual inspection, realize intelligent upgrading, and effectively improve the problem of difficult segmentation of the target virtual edges, to obtain the inspection results with higher accuracy.