Photocatalytic Removal of Organics over BiVO4-Based Photocatalysts

Organic compounds, such as organic dyes and phenols, are the main pollutants in wastewater. In the past years, a large number of studies on the fabrication and photocatalytic organics degradation of BiVO4 and its related materials have been reported in the literature. In this chapter, we shall focus on the advancements in the synthesis and photocatalytic applications of several kinds of BiVO4-based photocatalysts: (i) well-defined morphological BiVO4 photocatalysts, (ii) porous BiVO4 photocatalysts, (iii) heteroatom-doped BiVO4 photocatalysts, (iv) BiVO4-based heterojunction photocatalysts, and (v) supported BiVO4 photocatalysts. We shall discuss the structure–photocatalytic performance relationship of the materials and the involved photocatalytic degradation mechanisms. In addition, we also propose the research trends and technologies for practical applications of the BiVO4-based photocatalytic materials

Diaqua­bis­(4-hy­droxy-5-nitro­pyridine-2-carboxyl­ato-κ2 N 1,O 2)copper(II)

In the title compound, [Cu(C6H3N2O5)2(H2O)2], the CuII ion, lying on an inversion center, is coordinated by two pyridine N atoms and two carboxyl­ate O atoms from symmetry-related two 4-hy­droxy-5-nitro­pyridine-2-carboxyl­ate ligands, and two water mol­ecules, forming a distorted octa­hedral geometry. In the crystal, O—H⋯O hydrogen bonds link the complex mol­ecules. One of the H atoms of the water mol­ecule is disordered over two sites of equal occupancy

Poly[[dodeca­aqua­bis­(μ3-pyridine-2,6-dicarboxyl­ato)tetra­kis­(μ2-pyridine-2,6-dicarboxyl­ato)tri­calciumdieuropium(III)] 10.5-hydrate]

In the title compound, {[Ca3Eu2(C7H3NO4)6(H2O)12]·10.5H2O}n, the EuIII ion is nine-coordinated by three tridentate pyridine-2,6-dicarboxyl­ate (PDA) ligands, forming a [Eu(PDA)3]3− building block. The Ca2+ ions adopt two types of coordination geometries. One Ca2+ ion, lying on a twofold rotation axis, is eight-coordinated by four carboxyl­ate O atoms from four PDA ligands and four water mol­ecules, and the other two Ca2+ ions, each lying on an inversion center, are six-coordinated by two carboxyl­ate O atoms from two PDA ligands and four water mol­ecules. The carboxyl­ate groups bridge the EuIII and Ca2+ ions into a three-dimensional porous framework, with channels extending along [010] and [001] in which lattice water mol­ecules are located. Two of the lattice water mol­ecules are disordered over two sets of sites with equal occupancy and one water mol­ecule is 0.25-occupied. Numerous O—H⋯O hydrogen bonds involving the water mol­ecules and carboxyl­ate O atoms are present

Mode control and loss compensation of propagating surface plasmons

ABSTRACT Plasmonic devices can be used to construct nanophotonic circuits and are very promising candidates for next-generation information technology. The functions of plasmonic circuits rely on the rigorous control of plasmon modes. Two different methods were proposed to control the propagation of surface plasmons (SPs) supported by Ag nanowires (NWs). The first one is modulating the beat period of the near-field distribution pattern, which can be realized by depositing Al 2 O 3 layer or changing the refractive index of surrounding medium. The beat period increasing by 90 nm per nanometer of Al 2 O 3 coating or by 16 μm per refractive index unit was obtained in experiments. The second one is introducing local structural symmetry breaking to realize mode conversion of SPs. Three typical structures including NW-nanoparticle (NP) structure, branched NW and bent NW were used to investigate the mode conversion. It's revealed that the mode conversion is a scattering induced process. The lossy characteristic of SPs at optical frequencies typically limits the propagation length and hinders the further development of integrated plasmonic circuits. CdSe nanobelt/Al 2 O 3 /Ag film hybrid plasmonic waveguide was proposed to compensate the loss of SPs by using an optical pump-probe technique. Compared to the measured internal gain, the propagation loss was almost fully compensated for the TM mode. These results for mode control and loss compensation of propagating SPs are important for constructing functional nanophotonic circuits

Crop pest image classification based on improved densely connected convolutional network

IntroductionCrop pests have a great impact on the quality and yield of crops. The use of deep learning for the identification of crop pests is important for crop precise management.MethodsTo address the lack of data set and poor classification accuracy in current pest research, a large-scale pest data set named HQIP102 is built and the pest identification model named MADN is proposed. There are some problems with the IP102 large crop pest dataset, such as some pest categories are wrong and pest subjects are missing from the images. In this study, the IP102 data set was carefully filtered to obtain the HQIP102 data set, which contains 47,393 images of 102 pest classes on eight crops. The MADN model improves the representation capability of DenseNet in three aspects. Firstly, the Selective Kernel unit is introduced into the DenseNet model, which can adaptively adjust the size of the receptive field according to the input and capture target objects of different sizes more effectively. Secondly, in order to make the features obey a stable distribution, the Representative Batch Normalization module is used in the DenseNet model. In addition, adaptive selection of whether to activate neurons can improve the performance of the network, for which the ACON activation function is used in the DenseNet model. Finally, the MADN model is constituted by ensemble learning.ResultsExperimental results show that MADN achieved an accuracy and F1Score of 75.28% and 65.46% on the HQIP102 data set, an improvement of 5.17 percentage points and 5.20 percentage points compared to the pre-improvement DenseNet-121. Compared with ResNet-101, the accuracy and F1Score of MADN model improved by 10.48 percentage points and 10.56 percentage points, while the parameters size decreased by 35.37%. Deploying models to cloud servers with mobile application provides help in securing crop yield and quality