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In the context of an increased interest in the abatement of CO2 emissions generated by industrial activities, CO2 hydrogenation processes show an important potential to be used for the production of valuable compounds (methane, methanol, formic acid, light olefins, aromatics, syngas and/or synthetic fuels), with important benefits for the decarbonization of the energy sector. However, in order to increase the efficiency of the CO2 hydrogenation processes, the selection of active and selective catalysts is of utmost importance. In this context, the interest in graphene-based materials as catalysts for CO2 hydrogenation has significantly increased in the last years. The aim of the present paper is to review and discuss the results published until now on graphene-based materials (graphene oxide, reduced graphene oxide, or N-dopped graphenes) used as metal-free catalysts or as catalytic support for the thermocatalytic hydrogenation of CO2. The reactions discussed in this paper are CO2 methanation, CO2 hydrogenation to methanol, CO2 transformation into formic acid, CO2 hydrogenation to high hydrocarbons, and syngas production from CO2. The discussions will focus on the effect of the support on the catalytic process, the involvement of the graphene-based support in the reaction mechanism, or the explanation of the graphene intervention in the hydrogenation process. Most of the papers emphasized the graphene’s role in dispersing and stabilizing the metal and/or oxide nanoparticles or in preventing the metal oxidation, but further investigations are needed to elucidate the actual role of graphenes and to propose reaction mechanisms.

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.