Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

Band Structure Engineering of Carbon Nitride: In Search of a Polymer Photocatalyst with High Photooxidation Property

Abstract: The electronic band structure of a semiconductor photocatalyst intrinsically controls its level of conduction band (CB) and valence band (VB) and, thus, influences its activity for different photocatalytic reactions. Here, we report a simple bottom-up strategy to rationally tune the band structure of graphitic carbon nitride (g-C3N4). By incorporating electron-deficient pyromellitic dianhydride (PMDA) monomer into the network of g-C3N4, the VB position can be largely decreased and, thus, gives a strong photooxidation capability. Consequently, the modified photocatalyst shows preferential activity for water oxidation over water reduction in comparison with g-C3N4. More strikingly, the active species involved in the photodegradation of methyl orange switches from photogenerated electrons to holes after band structure engineering. This work may provide guidance on designing efficient polymer photocatalysts with the desirable electronic structure for specific photoreactions.

Amorphous FeOOH Quantum Dots Assembled Mesoporous Film Anchored on Graphene Nanosheets with Superior Electrochemical Performance for Supercapacitors

Abstract: Previous research on iron oxides/hydroxides has focused on the crystalline rather than the amorphous phase, despite that the latter could have superior electrochemical activity due to the disordered structure. In this work, a simple and scalable synthesis route is developed to prepare amorphous FeOOH quantum dots (QDs) and FeOOH QDs/graphene hybrid nanosheets. The hybrid nanosheets possess a unique heterostructure, comprising a continuous mesoporous FeOOH nanofilm tightly anchored on the graphene surface. The amorphous FeOOH/graphene hybrid nanosheets exhibit superior pseudocapacitive performance, which largely outperforms the crystalline iron oxides/hydroxides-based materials. In the voltage range between −0.8 and 0 V versus Ag/AgCl, the amorphous FeOOH/graphene composite electrode exhibits a large specific capacitance of about 365 F g−1, outstanding cycle performance (89.7% capacitance retention after 20 000 cycles), and excellent rate capability (189 F g−1 at a current density of 128 A g−1). When the lower cutoff voltage is extended to −1.0 and −1.25 V, the specific capacitance of the amorphous FeOOH/graphene composite electrode can be increased to 403 and 1243 F g−1, respectively, which, however, compromises the rate capability and cycle performance. This work brings new opportunities to design high-performance electrode materials for supercapacitors, especially for amorphous oxides/hydroxides-based materials.

Feature Learning based Deep Supervised Hashing with Pairwise Labels

Abstract: Recent years have witnessed wide application of hashing for large-scale image retrieval. However, most existing hashing methods are based on hand-crafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hash-code learning with deep neural networks, which have shown better performance than traditional hashing methods with hand-crafted features. Most of these deep hashing methods are supervised whose supervised information is given with triplet labels. For another common application scenario with pairwise labels, there have not existed methods for simultaneous feature learning and hash-code learning. In this paper, we propose a novel deep hashing method, called deep pairwise-supervised hashing(DPSH), to perform simultaneous feature learning and hash-code learning for applications with pairwise labels. Experiments on real datasets show that our DPSH method can outperform other methods to achieve the state-of-the-art performance in image retrieval applications.