Selective hydrogenation over earth-abundant metal catalysts is challenging but particularly valuable for practical applications in heterogeneous catalysis.Herein,we demonstrate that the catalytic selectivity of the co...Selective hydrogenation over earth-abundant metal catalysts is challenging but particularly valuable for practical applications in heterogeneous catalysis.Herein,we demonstrate that the catalytic selectivity of the commercial Raney nickel catalyst can be greatly tuned by modulation of the nickel surface by silica.Using quinoline hydrogenation as a model,we show that the silica-modified Raney nickel catalysts exhibit good activity,excellent selectivity,and long stability,whereas the undesired over-hydrogenation reactions are effectively hindered.In contrast,the pristine Raney nickel catalyst shows inferior selectivity for the targeted product.Mechanistic studies confirm a positive role of silica to facilitate the desorption of 1,2,3,4-tetrahydroquinoline from the catalyst surface,thus enhancing the product selectivity.展开更多
In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complem...In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet.展开更多
基金the National Key Research and Development Program of China(2022YFA1503502)National Natural Science Foundation of China(U21B20101,21932006,and 22202175)China Postdoctoral Science Foundation(2021M700119).
文摘Selective hydrogenation over earth-abundant metal catalysts is challenging but particularly valuable for practical applications in heterogeneous catalysis.Herein,we demonstrate that the catalytic selectivity of the commercial Raney nickel catalyst can be greatly tuned by modulation of the nickel surface by silica.Using quinoline hydrogenation as a model,we show that the silica-modified Raney nickel catalysts exhibit good activity,excellent selectivity,and long stability,whereas the undesired over-hydrogenation reactions are effectively hindered.In contrast,the pristine Raney nickel catalyst shows inferior selectivity for the targeted product.Mechanistic studies confirm a positive role of silica to facilitate the desorption of 1,2,3,4-tetrahydroquinoline from the catalyst surface,thus enhancing the product selectivity.
基金supported by the National Natural Science Foundation of China(No.61976023)。
文摘In this paper,we propose a Structure-Aware Fusion Network(SAFNet)for 3D scene understanding.As 2D images present more detailed information while 3D point clouds convey more geometric information,fusing the two complementary data can improve the discriminative ability of the model.Fusion is a very challenging task since 2D and 3D data are essentially different and show different formats.The existing methods first extract 2D multi-view image features and then aggregate them into sparse 3D point clouds and achieve superior performance.However,the existing methods ignore the structural relations between pixels and point clouds and directly fuse the two modals of data without adaptation.To address this,we propose a structural deep metric learning method on pixels and points to explore the relations and further utilize them to adaptively map the images and point clouds into a common canonical space for prediction.Extensive experiments on the widely used ScanNetV2 and S3DIS datasets verify the performance of the proposed SAFNet.
