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

Project Information

UNVEIL

Grant agreement ID: 101106487

DOI

10.3030/101106487

Project closed

EC signature date 23 March 2023

Start date 16 December 2023

End date 15 December 2025

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 188 590,08

Coordinated by

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)

Reporting period: 2023-12-16 to 2025-12-15

Summary of the context and overall objectives of the project

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.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

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.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

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.