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.

Approaches to altering particle distributions in cryo-electron microscopy sample preparation.

Cryo-electron microscopy (cryo-EM) can now be used to determine high-resolution structural information on a diverse range of biological specimens. Recent advances have been driven primarily by developments in microscopes and detectors, and through advances in image-processing software. However, for many single-particle cryo-EM projects, major bottlenecks currently remain at the sample-preparation stage; obtaining cryo-EM grids of sufficient quality for high-resolution single-particle analysis can require the careful optimization of many variables. Common hurdles to overcome include problems associated with the sample itself (buffer components, labile complexes), sample distribution (obtaining the correct concentration, affinity for the support film), preferred orientation, and poor reproducibility of the grid-making process within and between batches. This review outlines a number of methodologies used within the electron-microscopy community to address these challenges, providing a range of approaches which may aid in obtaining optimal grids for high-resolution data collection.