Speaker
Descrizione
The transition towards a circular carbon economy requires catalytic technologies capable of converting captured CO₂ into fuels, energy carriers and valuable chemicals using renewable hydrogen. Achieving this goal calls for efficient and selective processes that integrate carbon recovery, energy storage and the production of industrially relevant molecules. Despite considerable progress, the rational design of catalysts combining high activity, controlled selectivity, long-term stability and efficient resource use remains an open challenge. Within this framework, CO₂ methanation and selective hydrogenation are promising routes for producing synthetic methane, CO, methanol, light olefins and other valuable building blocks ⦋1⦌.
Addressing this challenge requires a deeper understanding of the relationships among catalyst properties, reaction conditions and process performance. Current research therefore focuses on heterogeneous gas-phase CO₂ conversion, with particular emphasis on methanation and selective hydrogenation. Catalytic behaviour is governed by the relationship of metal dispersion, metal-support interactions, redox properties, surface basicity, oxygen mobility, defect concentration and the evolution of active phases under operating conditions. These features also affect competing and deactivation pathways, including reverse water–gas shift, carbon formation, sintering and catalyst restructuring. Catalyst composition alone is therefore insufficient to establish reliable structure-activity relationships or predict selectivity and long-term stability .
Future developments could arise from integrating experimental catalysis with advanced characterization, multiscale modelling and data-driven methods. In situ and operando techniques could provide direct information on oxidation states, surface intermediates, active interfaces and structural changes under realistic reaction environments. Combined with electronic-structure calculations, these observations could clarify CO₂ and H₂ activation, oxygen-vacancy stability and the energetics of intermediates involved in methanation and selective hydrogenation.
Machine-learning and multivariate approaches could further support catalyst development by correlating synthesis parameters, physicochemical descriptors, operating conditions and catalytic performance. These tools could identify the variables controlling conversion, selectivity, stability and regenerability, guide the selection of new formulations and make catalyst development more efficient and knowledge-driven. Multi-objective optimization would be particularly valuable, since conditions maximizing CO₂ conversion may not coincide with those favouring selectivity, energy efficiency or catalyst lifetime.
Within a circular perspective, this integrated approach could connect renewable H₂ production, CO₂ capture and catalytic conversion into synthetic fuels or higher-value products. Low-impact preparation methods and waste-derived or locally available materials could further reduce the environmental footprint. Achieving this vision requires a shared effort combining scientific, technical and organizational competences across the Institute. Closer interaction among catalysis, spectroscopy, materials science, computational modelling, data analysis and process development could promote the exchange of methods, facilities and expertise. This integrated approach could therefore help identify complementary skills and lay the foundations for shared experiments, interdisciplinary activities and joint project proposals, while strengthening a more connected research community around common environmental and energy challenges.
References
⦋1⦌ Consentino L, González-Castaño M, et al. Insights into the reactivity of Ni-La catalysts for CO₂ methanation. Journal of CO₂ Utilization. 2025; 95: 103076. DOI: 10.1016/j.jcou.2025.103076.
⦋2⦌ Consentino L, Deganello F, et al. Hydrogen production from chemical looping reforming of methane: A screening of Ni-based oxygen carriers. Applied Catalysis B: Environment and Energy. 2026; 381: 125819. DOI: 10.1016/j.apcatb.2025.125819.
| Giovane Ricercatore (under 40) | Yes |
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