Selective Excitation of Exciton-Polariton Condensate Modes in an Annular Perovskite Microcavity.

Exciton-polariton systems composed of a light-matter quasi-particle with a light effective mass easily realize Bose-Einstein condensation. In this work, we constructed an annular trap in a halide perovskite semiconductor microcavity and observed the spontaneous formation of symmetrical petal-shaped exciton-polariton condensation in the annular trap at room temperature. In our study, we found that the number of petals of the petal-shaped exciton-polariton condensates, which is decided by the orbital angular momentum, is dependent on the light intensity distribution. Therefore, the selective excitation of perovskite microcavity exciton-polariton condensates under all-optical control can be realized by adjusting the light intensity distribution. This could pave the way to room-temperature topological devices, optical cryptographical devices, and new quantum gyroscopes in the exciton-polariton system.

An intelligent identification and classification system of decoration waste based on deep learning model.

Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional method of manual sorting for decoration waste is inefficient and poses health risks to sorting workers. It is therefore imperative to develop an accurate and efficient intelligent classification method to address these issues. To meet the demand for intelligent identification and classification of decoration waste, this paper applied the deep learning method You Only Look Once X (YOLOX) to the task and proposed an identification and classification framework of decoration waste (YOLOX-DW framework). The proposed framework was validated and compared using a multi-label image dataset of decoration waste, and a robot automatic sorting system was constructed for practical sorting experiments. The research results show that the proposed framework achieved a mean average precision (mAP) of 99.16 % for different components of decoration waste, with a detection speed of 39.23 FPS. Its classification efficiency on the robot sorting experimental platform reached 95.06 %, indicating a high potential for application and promotion. This provides a strategy for the intelligent detection, identification, and classification of decoration waste.