Репозиторий Университета

© 2020 The Author(s). Published by IOP Publishing Ltd Printed in the UK Graphene-based nano-porous materials (GNM) are potentially useful for all those applications needing a large specific surface area (SSA), typical of the bidimensional graphene, yet realized in the bulk dimensionality. Such applications include for instance gas storage and sorting, catalysis and electrochemical energy storage. While a reasonable control of the structure is achieved in micro-porous materials by using nano-micro particles as templates, the controlled production or even characterization of GNMs with porosity strictly at the nano-scale still raises issues. These are usually produced using dispersion of nano-flakes as precursors resulting in little control on the final structure, which in turn reflects in problems in the structural model building for computer simulations. In this work, we describe a strategy to build models for these materials with predetermined structural properties (SSA, density, porosity), which exploits molecular dynamics simulations, Monte Carlo methods and machine learning algorithms. Our strategy is inspired by the real synthesis process: starting from randomly distributed flakes, we include defects, perforation, structure deformation and edge saturation on the fly, and, after structural refinement, we obtain realistic models, with given structural features. We find relationships between the structural characteristics and size distributions of the starting flake suspension and the final structure, which can give indications for more efficient synthesis routes. We subsequently give a full characterization of the models versus H2 adsorption, from which we extract quantitative relationship between the structural parameters and the gravimetric density. Our results quantitatively clarify the role of surfaces and edges relative amount in determining the H2 adsorption, and suggest strategies to overcome the inherent physical limitations of these materials as adsorbers. We implemented the model building and analysis procedures in software tools, freely available upon request.

© 2019 by the authors. Developing bone scaffolds can greatly improve the patient's quality of life by accelerating the rehabilitation process. In this paper, we studied the process of composite polycaprolactone supercritical foaming for tissue engineering. The influence of graphene oxide and reduced graphene oxide on the foaming parameters was studied. The structural and mechanical properties were studied. The scaffolds demonstrated mechanical flexibility and endurance. The co-culturing and live/dead tests demonstrated that the obtained scaffolds are biocompatible. Different composite scaffolds induced various surface cell behaviors. The experimental data demonstrate that composite foams are promising candidates for in vivo medical trials.

© 2018, Springer Science+Business Media, LLC, part of Springer Nature.  New composite materials of collagen with electrical conductivity up to 6.1•10 –4 S cm –1 were obtained using colloidal dispersions of polyvinylpyrrolidone-stabilized graphene.