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Funded Projects › HORIZON

CHAOS · Computational Studies on Heterogeneous Astrocatalysis of Space-Abundant Transition Metals

HORIZONStatus: SIGNED1 September 202431 August 2026EU funding €165,313Call HORIZON-MSCA-2022-PF-01

The formation of Solar-like planetary systems is a complex process that goes through different steps, where not only physical changes occur but also an increasing of the molecular complexity. This gives rise to a rich chemical diversity and complexity of gas-phase molecular species in different astrophysical environments. However, not all the molecules observed can be formed at this state. The presence of interstellar grains (i.e., submicron-sized solid-state particles ubiquitously present in space) is especially important for the synthesis of molecules that would not form in the gas phase in the abundance required to satisfy observations. Interstellar grains are advocated to infer catalytic effects. However, such a “catalytic role” is associated with enhancing the encountering of the reactive species on the grain surface due to adsorption and diffusion, and the capability of the grains to dissipate the energy excess of largely exothermic reactions. In fact, state-of-the-art research on the field mainly focuses on the reactivity happening on the ices covering the grains and whose capability to reduce activation barriers is rather limited. Nevertheless, other materials beyond ices are also present in interstellar environments and can indeed exhibit catalytic properties. Refractory grains containing space abundant transition metals (such as Fe and Ni) are the perfect candidates to perform as heterogeneous catalysts. By using Fischer-Tropsch (CO + H2) and Haber-Bosch (N2 + H2) processes as model reactions and, by means of quantum chemical simulations, the project CHAOS will present for the first time a complete and deep description of the heterogeneous catalytic processes that can occur in the interstellar medium. The data obtained will go beyond representing the catalytic capacity of such materials in the outer space, it will be further used for predictive purposes as inputs in machine learning models.

Consortium · 2 organisations

coordinator

UNIVERSITAT AUTONOMA DE BARCELONA

ES · €165,313

associatedPartner

SORBONNE UNIVERSITE

FR

Research fields

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