Magnetic chitosan/ZrO2 composites for vanadium(V) adsorption while concurrently being transformed to a dual functional catalyst.

Spent adsorbents for recycling as catalysts have drawn considerable attention due to their environmentally benign chemistry properties. However, traditional thermocatalytic strategies limit their applications. Here, we developed an enhanced photocatalytic strategy to expand the range of their applications. A magnetic chitosan/ZrO2 composites (MZT) for V(V) adsorption, which were prepared using chitosan, ZrO2 and Fe3O4 by one-pot synthesis. The spent MZT as a catalyst was used to synthesize 2-phenylbenzimidazole, yielding up to 89.7%. It also was implemented to photocatalysis reactions for recycle. The discolored rates of rhodamine B (RhB) were 72.3% and 97.4% by new and spent MZT, respectively. The new and spent MZT showed the forbidden bands were 251 nm and 561 nm, respectively. The result displayed spent MZT red shifted to the cyan light region. The mechanism of catalysis also has been studied in detail.

HRegNet: A Hierarchical Network for Efficient and Accurate Outdoor LiDAR Point Cloud Registration.

Point cloud registration is a fundamental problem in 3D computer vision. Outdoor LiDAR point clouds are typically large-scale and complexly distributed, which makes the registration challenging. In this paper, we propose an efficient hierarchical network named HRegNet for large-scale outdoor LiDAR point cloud registration. Instead of using all points in the point clouds, HRegNet performs registration on hierarchically extracted keypoints and descriptors. The overall framework combines the reliable features in deeper layer and the precise position information in shallower layers to achieve robust and precise registration. We present a correspondence network to generate correct and accurate keypoints correspondences. Moreover, bilateral consensus and neighborhood consensus are introduced for keypoints matching, and novel similarity features are designed to incorporate them into the correspondence network, which significantly improves the registration performance. In addition, we design a consistency propagation strategy to effectively incorporate spatial consistency into the registration pipeline. The whole network is also highly efficient since only a small number of keypoints are used for registration. Extensive experiments are conducted on three large-scale outdoor LiDAR point cloud datasets to demonstrate the high accuracy and efficiency of the proposed HRegNet. The source code of the proposed HRegNet is available at https://github.com/ispc-lab/HRegNet2.