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.

Noise is considered severe environmental pollutant that affects human health. Using sound absorption materials to reduce noise is a way to decrease the hazards of noise pollution. Micro/nanofibers have advantages in sound absorption due to their properties such as small diameter, large specific surface area, and high porosity. Electrospinning is a technology for producing micro/nanofibers, and this technology has attracted interest in the field of sound absorption. To broaden the applications of electrospun micro/nanofibers in acoustics, the present study of electrospun micro/nano fibrous materials for sound absorption is summarized. First, the factors affecting the micro/nanofibers’ sound absorption properties in the process of electrospinning are presented. Through changing the materials, process parameters, and duration of electrospinning, the properties, morphologies, and thicknesses of electrospun micro/nanofibers can be controlled. Hence, the sound absorption characteristics of electrospun micro/nanofibers will be affected. Second, the studies on porous sound absorbers, combined with electrospun micro/nanofibers, are introduced. Then, the studies of electrospun micro/nanofibers in resonant sound absorption are concluded. Finally, the shortcomings of electrospun micro/nano fibrous sound absorption materials are discussed, and the future research is forecasted.

This review summarizes the important properties required for applying soft grippers to agricultural harvesting, focusing on their actuation methods and structural types. The purpose of the review is to address the challenges of limited load capacity and stiffness, which significantly hinder the broader application of soft grippers in agriculture. This paper examines the research progress on variable stiffness methods for soft grippers over the past five years. We categorize various variable stiffness techniques and analyze their advantages and disadvantages in enhancing load capacity, stiffness, dexterity, degree of integration, responsiveness, and energy consumption of soft grippers. The applicability and limitations of these techniques in the context of agricultural harvesting are also discussed. This paper concludes that combined material variable stiffness technology with a motor actuation claw structure in soft grippers is better suited for agricultural harvesting operations of woody crops (e.g., apples, citrus) and herbaceous crops (e.g., tomatoes, cucumbers) in unstructured environments.

Inchworms are a widely adopted bio-inspired model for soft crawling robots. Taking advantage of the good controllability of Shape Memory Alloy (SMA), this paper designs and manufactures an inchworm-inspired soft robot driven by SMA. Firstly, in the structural design, the paper compares the heat dissipation performance and driving efficiency of SMA actuators with two assembly forms: embedded and external to the silicone body. The external structure assembly design with superior performance is chosen. Secondly, in the analysis of the motion characteristics of the soft robot, a kinematic model is developed. Addressing the issue of inaccurate representation in traditional constitutive models due to difficult-to-measure parameters, such as martensite volume fraction, this paper derives an exclusive new constitutive model starting from traditional models using methods like the Taylor series and thermodynamic laws. The kinematic model is simulated using the Simulink platform to obtain its open-loop step response and sinusoidal signal response. Finally, an experimental platform is set up to conduct crawling tests on the soft robot in different planes. The experimental results show that the inchworm-inspired soft robot can perform continuous crawling motion, with a crawling speed of 0.041 mm/s on sandpaper under a constant current of 4A.

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.