The problem of bacterial resistance has become more and more common with improvements in health care. Worryingly, the misuse of antibiotics leads to an increase in bacterial multidrug resistance and the development of new antibiotics has virtually stalled. These challenges have prompted the need to combat bacterial infections with the use of radically different approaches. Taking lessons from the exciting properties of micro-/nano-natural-patterned surfaces, which can destroy cellular integrity, the construction of artificial surfaces to mimic natural functions provides new opportunities for the innovation and development of biomedicine. Due to the diversity of natural surfaces, functional surfaces inspired by natural surfaces have a wide range of applications in healthcare. Nature-inspired surface structures have emerged as an effective and durable strategy to prevent bacterial infection, opening a new way to alleviate the problem of bacterial drug resistance. The present situation of bactericidal and antifouling surfaces with natural and biomimetic micro-/nano-structures is briefly reviewed. In addition, these innovative nature-inspired methods are used to manufacture a variety of artificial surfaces to achieve extraordinary antibacterial properties. In particular, the physical antibacterial effect of nature-inspired surfaces and the functional mechanisms of chemical groups, small molecules, and ions are discussed, as well as the wide current and future applications of artificial biomimetic micro-/nano-surfaces. Current challenges and future development directions are also discussed at the end. In the future, controlling the use of micro-/nano-structures and their subsequent functions will lead to biomimetic surfaces offering great potential applications in biomedicine.

This study discovered a “seesaw effect” in the underlying physics of the directional bouncing behavior of a droplet on superhydrophobic heterogeneous surfaces, leading to projectile motion.

Due to the irrational design of the seed discharge plate and the vacuum chamber, the high-speed seed filling effect of the air-suction maize precision seed-metering device is poor. Therefore, an air-suction maize seed-metering device with an auxiliary guide is designed to realize high-speed precision seed discharging. An auxiliary guide filling theory is put forward, and the design of the seed plate type hole charging structure is formulated. Fluent 2022 software is used to analyze nine kinds of vacuum chamber structures; the optimal vacuum chamber structure parameters were determined by polar analysis. In order to investigate the changes of negative pressure and flow speed under the dynamic flow field, a slip grid was used to analyze the dynamic flow field with three different operating speeds and negative pressures. It found that the size of negative pressure did not affect the flow field distribution, and the pressure and flow speed gradually decreased as the distance from the inlet was farther away; meanwhile, the negative pressure distribution and air speed distribution were almost unchanged when the holes at different rotational speeds were at the same position. Finally, bench tests were carried out, and three indexes, namely, the qualified index, the multiple index and the missing index, were selected, with operating speed and negative pressure as factors, two-factor five-level orthogonal test was carried out, and the optimal parameter combinations at 6.0, 7.5, 9.0, 10.5, and 12 km/h forward velocity were derived and verified by regression equations. The results showed that the designed seed-metering device was repeated five times when the pressure of the vacuum chamber was −3.5 kPa and the rotational speed of the seed-metering device was 23 r/min, the average grain spacing qualified index was 95.8%, the missing index was 1.6%, the multiple index was 2.6%, and the indexes met the requirements of precision sowing. It is of great significance for our country’s seeder to develop in the direction of high-speed and precision.

In view of the problems of high weed regeneration rate in traditional mechanical weeding and environmental risk in chemical weeding, a synergetic strategy of “mechanical damage + wound spraying mechanism” was proposed, and an intelligent weeding device combining synchronous cutting and spraying was designed to enhance the efficacy of herbicides and reduce their use. Focusing on the physical characteristics of weeds and the cutting mechanism, the analysis of the weed-cutting system and the force characteristics of the cutting tool were conducted. Key factors affecting cutting quality were identified, and their respective value ranges were determined. A targeted spraying system was developed, featuring a conical nozzle, DC diaphragm pump, and electromagnetic control valve. The Delta parallel manipulator, equipped with both the cutting tool and nozzle, was designed, and a kinematic model was established for both its forward and inverse movements. Genetic algorithms were applied to optimize structural parameters, aiming to ensure effective coverage of typical weed distribution areas within the working space. A simulated environment measurement was built to verify the motion accuracy of the manipulator. Field experiments demonstrated that the equipment achieved an 81.5% wound weeding rate on malignant weeds in the seedling stage at an operating speed of 0.6 m/s, with a seedling injury rate below 5%. These results validate the high efficiency of the integrated mechanical cutting and targeted spraying system, offering a reliable technical solution for green and intelligent weed control in agriculture. This study fills the blank of only focusing on recognition accuracy or weeding rate under a single weeding method, but lacks a cooperative weeding operation.

Effective management of malignant weeds is critical to soybean growth. This study focuses on addressing the critical challenges of targeted spraying operations for malignant weeds such as Cirsium setosum, which severely threaten soybean yield in soybean fields. Specifically, this research aims to tackle key issues in plant protection operations, including the precise identification of weeds, the lightweight deployment of segmentation models, real-time requirements for spraying operations, and the generalization ability of models in diverse field environments. To address these challenges, this study proposes an improved weed instance segmentation model based on YOLOv8s-Seg, named FCB-YOLOv8s-Seg, for targeted spraying operations in soybean fields. The FCB-YOLOv8s-Seg model incorporates a lightweight backbone network to accelerate computations and reduce model size, with optimized Squeeze-and-Excitation Networks (SENet) and Bidirectional Feature Pyramid Network (BiFPN) modules integrated into the neck network to enhance weed recognition accuracy. Data collected from real soybean field scenes were used for model training and testing. The results of ablation experiments revealed that the FCB-YOLOv8s-Seg model achieved a mean average precision of 95.18% for bounding box prediction and 96.63% for segmentation, marking an increase of 5.08% and 7.43% over the original YOLOv8s-Seg model. While maintaining a balanced model scale, the object detection and segmentation accuracy of this model surpass other existing classic models such as YOLOv5s-Seg, Mask-RCNN, and YOLACT. The detection results in different scenes show that the FCB-YOLOv8s-Seg model performs well in fine-grained feature segmentation in complex scenes. Compared with several existing classical models, the FCB-YOLOv8s-Seg model demonstrates better performance. Additionally, field tests on plots with varying weed densities and operational speeds indicated an average segmentation rate of 91.30%, which is 6.38% higher than the original model. The proposed algorithm shows higher accuracy and performance in practical field instance segmentation tasks and is expected to provide strong technical support for promoting targeted spray operations.

Optimization of hot air drying process for tiger nut and analysis of fatty acid composition of tiger nut oil