Addressing the degradation of persistent organic pollutants like bisphenol A (BPA) and rhodamine B (RhB) with a photocatalyst that is both cost-effective and environmentally friendly is a notable challenge. This research presents the synthesis of an optimized g-C₃N₄/Bi₄O₅Br₂ composite featuring a Z-scheme heterojunction structure. The precise band alignment of this composite significantly enhances the separation of photogenerated charges and the production of dominant reactive species. The composite demonstrated exceptional photocatalytic performance, with BPA degradation efficiency nearing 98% and RhB achieving complete degradation within 80 and 35 min under visible light, respectively. These results are approximately 1.3 times greater than the individual performance of CN and BOB, surpassing recent literature benchmarks. Through EPR and free radical capture experiments, the role of h⁺ and ·O₂⁻ as the primary active free radicals in the degradation process have been confirmed. First-principles calculations validated the experimental results, indicating that the Z-type heterojunction is instrumental in generating active species, thus improving degradation efficiency. This study offers a promising strategy for the design of photocatalysts targeting emerging organic pollutants.

Curb detection and mapping are of great importance to ensure the safety and efficiency of intelligent vehicles. However, it remains challenging because shape estimation under noise and outliers is not well addressed in real traffic scenarios. In this paper, an efficient curb detection and mapping algorithm is proposed to achieve an accurate representation of curb shape. More specifically, an iterative Gaussian process regression (iGPR) is introduced, where each candidate point is verified multiple times. Then iGPR is employed in shape estimation of both road profile and curb, which serves as the backbone unit in curb candidate detection. During this process, the input 3D point cloud is segmented into road and obstacles, and potential curb points are selected by evaluating physically interpretable curb features. Finally, the proposed iGPR is validated and tested on two large-scale, complex urban datasets under real traffic scenarios. Experimental results show that the proposed iGPR achieves better performance than several state-of-the-art algorithms.