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.

Luminescent Cd(II)–organic frameworks with chelating NH2 sites for selective detection of Fe(III) and antibiotics

Abstract: Excess and deficiency of iron(III) and antibiotics from normal permissible limits will induce serious disorders, so their detection is important but challenging. In this work, by introducing a new amino triazole ligand N1-(4-(1H-1,2,4-triazole-1-yl)benzyl)-N1-(2-aminoethyl)ethane-1,2-diamine (L), a series of Cd(II)-based metal–organic frameworks (MOFs) [Cd3(BDC)3(DMF)2] (1), [Cd(L)(BDC)]2·2DMF·H2O (2), [NaCd2(L)(BDC)2.5]·9H2O (3), [Cd2(L)(2,6-NDC)2]·DMF·5H2O (4) and [Cd2(L)(BPDC)2]·DMF·9H2O (5) were synthesized. MOFs 1, 2 and 3 obtained under the same conditions with the same auxiliary ligand (H2BDC) but different amounts of alkali (NaOH) show distinct 3D, 1D and 3D framework structures, respectively, in which L and BDC2− exhibit varied coordination modes. 4 and 5 with 3D structures were isolated by using longer auxiliary ligands of 2,6-H2NDC and H2BPDC. The porosity and excellent fluorescence performance of 3, 4 and 5 make them potential luminescent sensors for Fe(III) and antibiotics. The results show that 3, 4 and 5 represent high sensitivity for the detection of Fe(III) ions with detection limits of 155 ppb for 3, 209 ppb for 4 and 297 ppb for 5 due to the existence of open channels and chelating NH2 sites. In addition, the strong emissions of 3, 4 and 5 can be quenched efficiently by trace amounts of NFs (nitrofurazone, NZF; nitrofurantoin, NFT; furazolidone, FZD) antibiotics even in the presence of other competing antibiotics such as β-lactams (penicillin, PCL). They are responsive to NZF with detection limits of 162 ppb for 3, 75 ppb for 4 and 60 ppb for 5.

A brief introduction to the fabrication and synthesis of graphene based composites for the realization of electromagnetic absorbing materials

Abstract: Owing to the fast development of wireless information technologies at the high-frequency range, the electromagnetic interference problem has been of increasing significance and attracting global attention. One key solution for this problem is to develop materials that are able to attenuate the unwanted electromagnetic waves. The desired properties of these materials include low reflection loss value, wide attenuation band, light weight, and low cost. This review gives a brief introduction to graphene-based composites and their electromagnetic absorption properties. The ultimate goal of these graphene absorbers is to achieve a broader effective absorption frequency bandwidth (fE) at a thin coating thickness (d). Representative and popular composite designs, synthesis methods, and electromagnetic energy attenuation mechanisms are summarized in detail. The two key factors, impedance matching behavior and attenuation ability, that determine the electromagnetic behavior of graphene-based materials are given particular attention in this article.

Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation

Abstract: Fruit category identification is important in factories, supermarkets, and other fields Current computer vision systems used handcrafted features, and did not get good results In this study, our team designed a 13-layer convolutional neural network (CNN) Three types of data augmentation method was used: image rotation, Gamma correction, and noise injection We also compared max pooling with average pooling The stochastic gradient descent with momentum was used to train the CNN with minibatch size of 128 The overall accuracy of our method is 9494%, at least 5 percentage points higher than state-of-the-art approaches We validated this 13-layer is the optimal structure The GPU can achieve a 177? acceleration on training data, and a 175? acceleration on test data We observed using data augmentation can increase the overall accuracy Our method is effective in image-based fruit classification