Hydrogenation of CO2 on Fe-Based Catalysts: Preferred Route to Renewable Liquid Fuels

CO2 Hydrogenation to Gasoline and Aromatics: Mechanistic and Predictive Insights from DFT, DRIFTS and Machine Learning

The emission of CO2 from fossil fuels is the largest driver of global climate change. To realize the target of a carbon-neutrality by 2050, CO2 capture and utilization is crucial. The efficient conversion of CO2 to C5+ gasoline and aromatics remains elusive mainly due to CO2 thermodynamic stability and the high energy barrier of the C-C coupling step. Herein, advances in mechanistic understanding via Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), density functional theory (DFT), and microkinetic modeling are discussed. It further emphasizes the power of machine learning (ML) to accelerate the search for optimal catalysts. A significant effort has been invested into this field of research with volumes of experimental and characterization data, this study discusses how they can be used as input features for machine learning prediction in a bid to better understand catalytic properties capable of accelerating breakthroughs in the process.

The Effect of Mn, Al Doping on the CO2 Hydrogenation Performance of CaCO3 -Supported Fe-Based Catalysts

With increasingly serious environmental problems caused by the greenhouse effect, it has also become essential to reduce the concentration of CO2 in the atmosphere. In this paper, CaCO3 -supported Fe-based catalysts doped with Mn, Al, and K are prepared by a straightforward method and used for CO2 hydrogenation. The fresh and spent catalysts were characterized by SEM-EDS, BET, TG, CO2 -TPD, XRD, and XPS. The experimental results show that the highest CO2 conversion rate of Fe10Mn2Al10Ca is 35.99 %, the maximum FTY value is 293.98 μmolCO2  ⋅ g Fe - 1 ${{\rm{g}}_{{\rm{Fe}}}^{ - 1} }$  ⋅ s-1 , the maximum O/P value is 6.61, and the lowest CO selectivity is 32.21 %. At the same time, according to the characterization results, the doping of Mn and Al increased the Fe3 O4 /FeCx ratio. As the Fe3 O4 /FeCx ratio increases, the proportion of short-chain hydrocarbons (CH4 , C2-4 ) in the products increases, and the proportion of long-chain hydrocarbons (C5+ ) decrease. Therefore, the co-doping of Mn and Al promotes the conversion of CO and reduces its selectivity, and promotes the formation of light olefins. Finally, it is hoped that this study can provide a reference for further research on CaCO3 -supported Fe catalysts.

Copper and Iron Cooperation on Micro-Spherical Silica during Methanol Synthesis via CO2 Hydrogenation

A series of mono- and bi-metallic copper and iron samples were prepared by impregnation method on micro-spherical silica and used for the synthesis of methanol via CO2 hydrogenation. Compared with conventional carrier oxides, micro-spherical silica has obvious advantages in terms of absorption capacity and optimal distribution of active phases on its surface, also exhibiting excellent heat resistance properties and chemical stability. The prepared catalysts were characterized by various techniques including XRF, XRD, SEM, TEM, H2-TPR and CO2-TPD techniques, while catalytic measurements in CO2 hydrogenation reaction to methanol were performed in a fixed bed reactor at a reaction pressure of 30 bar and temperature ranging from 200 to 260 °C. The obtained results revealed that the mutual interaction of copper–iron induces promotional effects on the formation of methanol, especially on systems where Fe enrichment on the silica support favours the presence of a larger concentration of oxygen vacancies, consequently responsible for higher CO2 adsorption and selective methanol production. Surface reconstruction phenomena rather than coke or metal sintering were responsible for the slight loss of activity recorded on the catalyst samples during the initial phase of reaction; however, with no appreciable change on the product selectivity.