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Lauric Acid (LA) phase-change nanocapsules prepared with silver as the shell exhibit excellent energy storage capacity and high thermal conductivity. Still, their functionality could be improved by ensuring uniform morphologies, even the size and thickness of silver particles. In this study, the LA/Ag nanocapsules were prepared under different reductant drop speeds. By adjusting the droplet speed of the reducing agent, the concentration of silver in the solution can be controlled, which affects the nucleation and growth rate of silver particles, thereby influencing the deposition of silver particles on the surface of the core material. The characterization results indicate the successful preparation of high sphericity and uniform-sized LA/Ag nanocapsules. The average diameter of capsules was 117–140 nm, the latent heat was 43.69–47.78 J/g, and the encapsulation efficiency was 80.69–82.53%. As the droplet speed increased, the thickness of the silver shell increased while the encapsulation efficiency decreased. The highest encapsulation efficiency was achieved when the reducing agent dropping speed was 0.03 mL/s.

Solid–liquid organic phase-change micro/nanocapsules are potential candidates for energy storage. Recently, significant progress has been made regarding phase-change micro/nanocapsules in terms of their synthesis, properties, and applications. Extensive research has been conducted to enhance their thermal properties, such as thermal storage capacity, thermal conductivity, and thermal reliability. However, factors that influence the thermal properties of micro/nanocapsules have received little attention. This study presents a comprehensive review of phase-change micro/nanocapsules focusing on their thermal properties and their influencing factors. In addition, the thermal properties of the major solid–liquid organic pure phase-change materials are summarized. Furthermore, common micro/nanoencapsulation methods and their influence on the thermal properties were analyzed. Finally, the potential applications of these phase-change micro/nanocapsules were also investigated. This study was devoted to enhancing the thermal properties of micro/nanocapsules, which play a crucial role in their practical applications.

One of the most important food crops is rice. For this reason, the accurate identification of rice pests is a critical foundation for rice pest control. In this study, we propose an algorithm for automatic rice pest identification and classification based on fully convolutional networks (FCNs) and select 10 rice pests for experiments. First, we introduce a new encoder–decoder in the FCN and a series of sub-networks connected by jump paths that combine long jumps and shortcut connections for accurate and fine-grained insect boundary detection. Secondly, the network also integrates a conditional random field (CRF) module for insect contour refinement and boundary localization, and finally, a novel DenseNet framework that introduces an attention mechanism (ECA) is proposed to focus on extracting insect edge features for effective rice pest classification. The proposed model was tested on the data set collected in this paper, and the final recognition accuracy was 98.28%. Compared with the other four models in the paper, the proposed model in this paper is more accurate, faster, and has good robustness; meanwhile, it can be demonstrated from our results that effective segmentation of insect images before classification can improve the detection performance of deep-learning-based classification systems.

The accurate and rapid acquisition of crop and weed information is an important prerequisite for automated weeding operations. This paper proposes the application of a network model based on Faster R-CNN for weed identification in images of cropping areas. The feature pyramid network (FPN) algorithm is integrated into the Faster R-CNN network to improve recognition accuracy. The Faster R-CNN deep learning network model is used to share convolution features, and the ResNeXt network is fused with FPN for feature extractions. Tests using >3000 images for training and >1000 images for testing demonstrate a recognition accuracy of >95%. The proposed method can effectively detect weeds in images with complex backgrounds taken in the field, thereby facilitating accurate automated weed control systems.