Piezo-photocatalytic reduction of nitrates to N2 over silver dispersed on BaTiO3@TiO2.

This communication first achieved piezo-photocatalytic reduction of nitrates to N2 through designing an Ag2O/BaTiO3@TiO2 core-shell catalyst. The built-in electric field induced by piezoelectric polarization suppresses photoexcited carrier recombination, and simultaneously causes energy band tilting, leading to the generation of electrons with higher reducibility to directly trigger the NO3- reduction to ˙NO32-, even without hole scavengers.

Low-Cytotoxic Core-Sheath ZnO NWs@TiO2-xNy Triggered Piezo-photocatalytic Antibacterial Activity.

Sonophotodynamic antimicrobial therapy (SPDAT) is recognized as a highly efficient biomedical treatment option, known for its versatility and remarkable healing outcomes. Nevertheless, there is a scarcity of sonophotosensitizers that demonstrate both low cytotoxicity and exceptional antibacterial effectiveness in clinical applications. In this paper, a novel ZnO nanowires (NWs)@TiO2-xNy core-sheath composite was developed, which integrates the piezoelectric effect and heterojunction to build dual built-in electric fields. Remarkably, it showed superb antibacterial effectiveness (achieving 95% within 60 min against S. aureus and ∼100% within 40 min against E. coli, respectively) when exposed to visible light and ultrasound. Due to the continuous interference caused by light and ultrasound, the material's electrostatic equilibrium gets disrupted. The modification in electrical properties facilitates the composite's ability to attract bacterial cells through electrostatic forces. Moreover, Zn-O-Ti and Zn-N-Ti bonds formed at the interface of ZnO NWs@TiO2-xNy, further enhancing the dual internal electric fields to accelerate the excited carrier separation to generate more reactive oxygen species (ROS), and thereby boosting the antimicrobial performance. In addition, the TiO2 layer limited Zn2+ dissolution into solution, leading to good biocompatibility and low cytotoxicity. Lastly, we suggest a mechanistic model to offer practical direction for the future development of antibacterial agents that are both low in toxicity and high in efficacy. In comparison to the traditional photodynamic therapy systems, ZnO NWs@TiO2-xNy composites exhibit super piezo-photocatalytic antibacterial activity with low toxicity, which shows great potential for clinical application as an antibacterial nanomaterial.

A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML) algorithms, this study presents an innovative approach for classifying the quality grade of fresh tea leaves. This approach leverages an integration of image recognition and deep learning (DL) algorithm to accurately classify tea leaves' grades by identifying distinct bud and leaf combinations. The method begins by acquiring separate images of orderly scattered and randomly stacked fresh tea leaves. These images undergo data augmentation techniques, such as rotation, flipping, and contrast adjustment, to form the scattered and stacked tea leaves datasets. Subsequently, the YOLOv8x model was enhanced by Space pyramid pooling improvements (SPPCSPC) and the concentration-based attention module (CBAM). The established YOLOv8x-SPPCSPC-CBAM model is evaluated by comparing it with popular DL models, including Faster R-CNN, YOLOv5x, and YOLOv8x. The experimental findings reveal that the YOLOv8x-SPPCSPC-CBAM model delivers the most impressive results. For the scattered tea leaves, the mean average precision, precision, recall, and number of images processed per second rates of 98.2%, 95.8%, 96.7%, and 2.77, respectively, while for stacked tea leaves, they are 99.1%, 99.1%, 97.7% and 2.35, respectively. This study provides a robust framework for accurately classifying the quality grade of fresh tea leaves.

Point cloud registration method for maize plants based on conical surface fitting-ICP.

Reconstructing three-dimensional (3D) point cloud model of maize plants can provide reliable data for its growth observation and agricultural machinery research. The existing data collection systems and registration methods have low collection efficiency and poor registration accuracy. A point cloud registration method for maize plants based on conical surface fitting-iterative closest point (ICP) with automatic point cloud collection platform was proposed in this paper. Firstly, a Kinect V2 was selected to cooperate with an automatic point cloud collection platform to collect multi-angle point clouds. Then, the conical surface fitting algorithm was employed to fit the point clouds of the flowerpot wall to acquire the fitted rotation axis for coarse registration. Finally, the interval ICP registration algorithm was used for precise registration, and the Delaunay triangle meshing algorithm was chosen to triangulate the point clouds of maize plants. The maize plant at the flowering and kernel stage was selected for reconstruction experiments, the results show that: the full-angle registration takes 57.32 s, and the registration mean distance error is 1.98 mm. The measured value's relative errors between the reconstructed model and the material object of maize plant are controlled within 5%, the reconstructed model can replace maize plants for research.