A 3D rGO/g-C₃N₄ nanocomposite loaded with Ag NPs was fabricated and this obtained nanocomposite with excellent performance was used as an adsorbent and catalyst to efficiently remove MB under visible light irradiation.

Cavitation, characterized by formation of vapor bubbles in a low-pressure or high-temperature region of a liquid, is often destructive, but it can be harnessed for actuators and robots. We exploit cavitation to accumulate substantial energy in superheated liquids by suppressing its immediate release until reaching a stability limit. The energetic, unstable bubbles collapse violently, producing a burst of high power and force that initiates motion. Notably, a millimeter-scale device launched by cavitation can jump to a height of 1.5 meters—reaching a 12 meters per second (m/s) peak velocity, a 7.14 × 10 ⁴ m/s ² acceleration, and a 0.64% energy efficiency—and can also swim on water at 12 centimeters per second. Cavitation-based launching works with a broad range of device materials, liquid media, stimuli, and operational environments.

Accurately classifying the quality of apples is crucial for maximizing their commercial value. Deep learning techniques are being widely adopted for apple quality classification tasks, achieving impressive results. While existing research excels at classifying apple variety, size, shape, and defects, color and deformity analysis remain an under-explored area. Therefore, this study investigates the feasibility of utilizing convolutional neural networks (CNN) to classify the color and deformity of apples based on machine vision technology. Firstly, a custom-assembled machine vision system was constructed for collecting apple images. Then, image processing was performed to extract the largest fruit diameter from the 45 images taken for each apple, establishing an image dataset. Three classic CNN models (AlexNet, GoogLeNet, and VGG16) were employed with parameter optimization for a three-category classification task (non-deformed slice–red apple, non-deformed stripe–red apple, and deformed apple) based on apple features. VGG16 achieved the best results with an accuracy of 92.29%. AlexNet and GoogLeNet achieved 91.66% and 88.96% accuracy, respectively. Ablation experiments were performed on the VGG16 model, which found that each convolutional block contributed to the classification task. Finally, prediction using VGG16 was conducted with 150 apples and the prediction accuracy was 90.50%, which was comparable to or better than other existing models. This study provides insights into apple classification based on color and deformity using deep learning methods.