CO2 methanation with high-loading mesoporous Ni/SiO2 catalysts: toward high specific activity and new mechanistic insights

08 December 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


In the up-and-coming power-to-gas scenario (PtG), surplus of renewable electricity is stored in the form of methane, by reacting green hydrogen with waste CO2 through the Sabatier reaction (CO2 methanation). While the catalytic hydrogenation of CO2 to methane has already attracted much attention, the development of catalysts that feature a high specific activity at low temperature and a reasonable cost remains challenging but is needed in the perspective of industrial deployment. Concomitantly, the mechanism of CO2 methanation remains debated, and its elucidation would drive further progress. Herein, we disclose the preparation of a series of high-loading Ni/SiO2 catalysts via sol-gel method. Through (HR)-TEM, XRD, N2 physisorption, and H2 chemisorption, we show that small Ni particles (<5 nm, high Ni dispersion) could be obtained in a highly porous silica matrix, even at loading up to 50 wt%. The most active catalyst reached a high specific activity of 10.2 µmolCH4.g-1.s-1 at 300 °C (96% selectivity to CH4 with 79% CO2 conversion). Being based on inert silica, these catalysts are idea model materials to study the reaction mechanism. Combining XPS, CO2-TPD, in-situ CO2-DRIFTS, and TPSR on the one hand, and theoretical calculations (DFT) on the other hand, we show that CO2 methanation follows mostly the RWGS+CO-hydrogenation and the formate pathways, the former being dominant at low temperature. Upon CO2 adsorption on Ni/SiO2, the carbonyl species formed from the adsorbed bicarbonates react with H2 to form CH4 via the RWGS+CO-hydrogenation pathway, while the adsorbed monodentate carbonates are hydrogenated to CH4 via the formate pathway.


CO2 methanation
Nickel nanoparticles
mesoporous catalyst
CO2 hydrogenation mechanism

Supplementary materials

Zhao et al - ESI
Additionnal TEM, XRD, XPS, TPD, and TPSR data


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