Ultra-small metal clusters have attracted great attention owing to their superior catalytic performance and extensive application in heterogeneous catalysis. However, the synthesis of high-density metal clusters is very challenging due to their facile aggregation. Herein, one-step pyrolysis was used to synthesize ultra-small clusters and single-atom Fe sites embedded in graphitic carbon nitride with high density (iron loading up to 18.2 wt %), evidenced by high-angle annular dark field-scanning transmission electron microscopy, X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and Fe Mössbauer spectroscopy. The catalysts exhibit enhanced activity and stability in degrading various organic samples in advanced oxidation processes. The drastically increased metal site density and stability provide useful insights into the design and synthesis of cluster catalysts for practical application in catalytic oxidation reactions.

BackgroundMammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well.MethodsIn this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass.ResultsThe proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier.ConclusionThe performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.Electronic supplementary materialThe online version of this article (doi:10.1186/s12938-017-0332-0) contains supplementary material, which is available to authorized users.

Antimicrobial polypropylene (PP) has been widely used. Its highly effective antimicrobial activities and nonleaching characteristics remain concerns. In this study, polypropylene wax (PPW) grafted with maleic anhydride was first prepared; this was followed by a melting reaction with polyhexamethylene guanidine hydrochloride (PHMG) to obtain antimicrobial PPW [polypropylene wax grafted with polyhexamethylene guanidine hydrochloride (PPW‐g‐PHMG)]. PPW‐g‐PHMG was then melt‐blended with PP to prepare antimicrobial PP. Fourier transform infrared spectra confirmed that PHMG was covalently bonded on the PPW chains, and transmission electron microscopy images showed a uniform distribution of PHMG in the PPW matrix. The resulting antimicrobial PP exhibited excellent antimicrobial activity against Escherichia coli. The ring‐diffusion test further disclosed its nonleaching characteristics. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 44190.

No abstract

We report room temperature spin transport in an InAs nanowire device. A large spin signal of 35 kΩ and long spin diffusion length of 1.9 μm are achieved. We believe that these results open a practical way to design InAs NW based spintronic devices.

No abstract

No abstract

No abstract