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https://www.selleckchem.com/products/Adriamycin.html ave been given.Oxygen-containing functional groups tend to induce a strong interaction between solid adsorbents and iodine molecules, yet have not been systematically investigated. Herein, on the basis of a series of nitric acid-treated graphene oxide (GO) with different contents of oxygen functional groups for iodine adsorption, it was found that the iodine uptake capacity is proportionate to the oxygen content and the diversities of oxygen-containing groups. The density functional theory (DFT) calculation results also suggest that oxygen-containing groups result in strong interactions between iodine molecules and the adsorbents through a covalent bond-forming process, among which -OH groups possess a higher adsorption energy averagely. Such theoretical and experimental work deepens our understanding of the effects of oxygen functional groups on iodine adsorption and provides novel ideas for future design and synthesis of high-performance solid adsorbents for radioactive iodine.We focus on exploring the LIDAR-RGB fusion-based 3D object detection in this paper. This task is still challenging in two aspects (1) the difference of data formats and sensor positions contributes to the misalignment of reasoning between the semantic features of images and the geometric features of point clouds. (2) The optimization of traditional IoU is not equal to the regression loss of bounding boxes, resulting in biased back-propagation for non-overlapping cases. In this work, we propose a cascaded cross-modality fusion network (CCFNet), which includes a cascaded multi-scale fusion module (CMF) and a novel center 3D IoU loss to resolve these two issues. Our CMF module is developed to reinforce the discriminative representation of objects by reasoning the relation of corresponding LIDAR geometric capability and RGB semantic capability of the object from two modalities. Specifically, CMF is added in a cascaded way between the RGB and L

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