Revealing the natUre and ideNtity of actiVe sites through structure-depEndent mIcrokinetic modeLing for CO2 electroreduction reaction

Informazioni relative al progetto

UNVEIL

ID dell’accordo di sovvenzione: 101106487

DOI

10.3030/101106487

Progetto chiuso

Data della firma CE 23 Marzo 2023

Data di avvio 16 Dicembre 2023

Data di completamento 15 Dicembre 2025

Finanziato da

• Marie Skłodowska-Curie Actions (MSCA)

Costo totale

Nessun dato

Contributo UE

€ 188 590,08

Coordinato da

POLITECNICO DI MILANO
Italy

Periodic Reporting for period 1 - UNVEIL (Revealing the natUre and ideNtity of actiVe sites through structure-depEndent mIcrokinetic modeLing for CO2 electroreduction reaction)

Periodo di rendicontazione: 2023-12-16 al 2025-12-15

Sintesi del contesto e degli obiettivi generali del progetto

Reducing carbon dioxide (CO2) emissions is one of the central challenges of the European Green Deal and global climate policy. Heavy industries such as chemicals, fuels and materials production still depend strongly on fossil resources. Transforming captured CO2 using catalysts into useful chemicals using renewable electricity offers a promising pathway to reduce dependence on fossil feedstocks. However, despite intense research efforts worldwide, this technology has not yet reached large-scale industrial deployment. Main scientific barriers in this direction are poor catalyst activity or selectivity and their degradation under certain reaction conditions. This makes catalyst design iterative and costly and may limit economic feasibility of the overall process.
The project addressed this fundamental knowledge gap by developing a new digital modelling framework capable of linking catalyst structure to performance under realistic operating conditions. Instead of assuming an ideal and static catalyst surface, the project introduced a structure-dependent multiscale modelling approach that captures how different surface orientations and atomic arrangements influence reaction pathways. By combining first-principles simulations with thermodynamic analysis and reactor-scale kinetic modelling, the project created a predictive toolchain that connects atomic-level properties to observable product formation rates. This allows researchers and industrial developers to understand why certain catalyst shapes favour specific products and how to design improved materials with higher selectivity and stability. A predictive modelling approach can accelerate catalyst development cycles, lower research costs and guide targeted experimental synthesis. This contributes to strengthening Europe’s scientific leadership in sustainable chemistry and digital modelling technologies. In the longer term, improved catalyst design supports more efficient CO2 utilisation processes, helping to reduce greenhouse gas emissions and advance industrial decarbonization.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

The project developed a new modelling framework to understand how the atomic structure of copper catalysts influences the conversion of CO2 into useful chemicals. The work progressed in four main scientific stages, moving from atomic-scale simulations to reactor-scale predictions. First, realistic digital models of copper nanoparticles were created using quantum mechanical calculations. These simulations determined which crystal surfaces are stable and how different surface orientations appear under reaction conditions. Second, the models were extended to account for the presence of chemical intermediates that attach to the catalyst surface during operation. By incorporating thermodynamic corrections and temperature effects, the project determined how these adsorbed species modify surface stability. This analysis revealed that surface structures that are rare under equilibrium conditions can become highly relevant during reaction because they stabilise key reaction intermediates. Third, the activation energy barriers of the elementary reaction steps were calculated for competing reaction pathways leading to different products. Transition states were identified and temperature-dependent reaction rates were determined. The results showed that different reaction pathways share common intermediate species but diverge at a critical structure-dependent branching point. This provided a mechanistic explanation for experimentally observed structure–selectivity relationships. Fourth, the atomic-scale kinetic information was translated into simplified reactor-scale kinetic models. Apparent activation energies and product selectivity trends were derived and compared with published experimental measurements. The model successfully reproduced the parallel formation of carbon monoxide and methanol and captured the qualitative trends observed in experiments. Overall, the project delivered a structure-dependent multiscale modelling toolchain that connects nanoparticle morphology, surface chemistry and observable reaction rates within a unified computational approach. This represents a significant advance beyond traditional structure-agnostic kinetic models and provides a predictive basis for rational catalyst design in CO2 conversion technologies.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

The project advances the state of the art by introducing a structure-dependent multiscale modelling approach to electrochemical carbon dioxide conversion. Conventional kinetic models typically assume simplified, static catalyst surfaces and treat active sites as identical and unchanging. This limits their ability to explain why catalysts with different shapes or surface orientations produce different chemical products. The key scientific breakthrough of the project is the development of a modelling framework that explicitly links nanoparticle morphology, surface thermodynamics, reaction energetics and macroscopic product formation within a single, coherent toolchain. Instead of relying on averaged or idealised surface descriptions, the approach captures how realistic catalyst structures evolve under reaction conditions and how specific surface sites influence competing reaction pathways.
The results demonstrate that minority surface structures, which may be present only in small amounts under equilibrium conditions, can dominate catalytic selectivity because of their ability to stabilise critical reaction intermediates. This finding provides a quantitative explanation for experimentally observed structure–selectivity relationships that were previously interpreted only qualitatively. The framework therefore moves the field from empirical correlation toward predictive understanding. Beyond the scientific advance, the project delivers a reusable digital modelling capability that can be adapted to other catalytic systems. By enabling the translation of atomic-scale reaction energetics into reactor-scale kinetic behaviour, the methodology provides a foundation for predictive catalyst design in sustainable chemical manufacturing. It provides both a scientific advance and a practical digital foundation for future research, industrial innovation and low-carbon process development.