Interface charge transfer process in ZnO:Mn/ZnS nanocomposites

Journal of Nanoparticle Research - TiO2 photocatalysts co-doped with F and Fe were synthesized by a sol–gel method. Synergistic effects of F and Fe in the co-doped TiO2 were verified by NH3...     

Comparative study of core–shell iron/iron oxide gold covered magnetic nanoparticles obtained in different conditions

In this study, we report the synthesis and characterization of the core–shell Fe covered with Au shells nanoparticles with mean diameters between 5 and 8 nm. The inverse micelles method was utilized to produce the samples. X-ray diffraction studies show that both core–shell systems have the expected crystalline structure. High resolution transmission electron microscopy and atomic emission spectroscopy techniques give additional information concerning the structure and composition of nanoparticles. An intermediate shell of amorphous oxidized iron was found between the magnetic Fe core and the external gold shell. The magnetic behavior of different core–shell samples shows no hysteresis loop indicating the superparamagnetic behavior of Fe@Au systems. The superparamagnetic behavior is also evidenced from FC and ZFC dependences of the magnetization versus temperature. By using the temperature dependence of the thermoremanent magnetization combined with magnetization versus applied magnetic field, the effective anisotropy constant was determined. The Fe/Au interface contribution to the effective anisotropy constant was calculated and discussed in relation with the combined shape and stress anisotropies.     

Eco-Friendly Nitrogen-Doped Graphene Preparation and Design for the Oxygen Reduction Reaction

Four N-doped graphene materials with a nitrogen content ranging from 8.34 to 13.1 wt.% are prepared by the ball milling method. This method represents an eco-friendly mechanochemical process that can be easily adapted for industrial-scale productivity and allows both the exfoliation of graphite and the synthesis of large quantities of functionalized graphene. These materials are characterized by transmission and scanning electron microscopy, thermogravimetry measurements, X-ray powder diffraction, X-ray photoelectron and Raman spectroscopy, and then, are tested towards the oxygen reduction reaction by cyclic voltammetry and rotating disk electrode methods. Their responses towards ORR are analysed in correlation with their properties and use for the best ORR catalyst identification. However, even though the mechanochemical procedure and the characterization techniques are clean and green methods (i.e., water is the only solvent used for these syntheses and investigations), they are time consuming and, generally, a low number of materials can be prepared, characterized and tested. In order to eliminate some of these limitations, the use of regression learner and reverse engineering methods are proposed for facilitating the optimization of the synthesis conditions and the materials’ design. Thus, the machine learning algorithms are applied to data containing the synthesis parameters, the results obtained from different characterization techniques and the materials response towards ORR to quickly provide predictions that allow the best synthesis conditions or the best electrocatalysts’ identification.