A Ternary composite UiO-66/CdS/1% reduced graphene oxide (RGO) was successfully prepared, with a photocatalytic hydrogen evolution rate 13.8 times as high as that of pure commercial CdS. It shows great advantages over the perfect composite photocatalyst-P25/CdS/1%RGO.

The cadmium sulfide (CdS) microsphere decorated graphene (GR) nanocomposite (GR-CdS) was prepared by a facile hydrothermal approach in which CdS ingredients were closely enwrapped by the GR scaffold.The GR-CdS nanocomposite was subjected to a number of characterizations including X-ray diffraction (XRD), UV-vis diffuse reflectance spectroscopy (DRS), field emission scanning electron microscopy (FESEM), transmission scanning electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS).It was found that integration of CdS microspheres with two-dimensional GR scaffolds exerts a profound influence on the properties of hybrid nanocomposites, such as optical and electronic nature along with morphology. Photocatalytic performances of the GR-CdS nanocomposites were evaluated by selective organic transformation under mild conditions. The results demonstrate that the GR-CdS nanocomposite can serve as an efficient visible-light-driven photocatalyst for the selective oxidation of benzyl alcohol to benzaldehyde under ambient conditions. The significantly enhanced photocatalytic performance of GR-CdS nanocomposites can be attributed to the synergistic effect of enhanced light absorption intensity and high electron conductivity of GR, which facilitates charge separation and lengthens the lifetime of photogenerated electron-hole pairs. Moreover, photocatalytic performances of various GR-CdS nanocomposites featuring different degrees of interfacial contact between GR and CdS were also systematically explored. It is anticipated that our work could enrich the information on the preparation of narrow bandgap semiconductor/GR hybrid nanocomposites for a wide range of photocatalytic applications.

Colon cancer identification is of great significance in medical diagnosis. Real-time, objective and accurate inspection results will facilitate medical professionals to explore symptomatic treatment promptly. However, the existing methods depend on hand-crafted features which require extensive professional expertise and long inspection period. Therefore, we propose a multi-scale feature fusion convolutional neural network (MFF-CNN) based on shearlet transform to identify histopathological image of colon cancer. The characteristic of the framework is the shearlet coefficients of histopathological image in multiple decomposition scales were extracted as supplementary feature which were also fed to the network with the original pathological image. After feature learning and feature fusion, the MFF-CNN based on shearlet transform can achieve the identification accuracy of 96% and average F-1 score of 0.9594 for colorectal adenocarcinoma epithelium (TUM) and normal colon mucosa (NORM). The false negative rate and false positive rate can be reduced to 5.5% and 2.5%, respectively. The superior performance of the network opens a new perspectives for real-time, objective and accurate diagnosis of cancer.

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