Periodic Reporting for period 1 - DESCRIPTOR (Advanced simulations in electrocatalysis for efficient production of C3+ by carbon dioxide reduction)
Reporting period: 2022-05-01 to 2024-04-30
The increasing atmospheric CO2 levels from fossil fuel use have led to global warming. The IPCC report warns that global warming will exceed 1.5°C in the 21st century if greenhouse gas emissions, mainly CO2, are not drastically reduced. To combat this, Europe aims to achieve net-zero emissions by 2050 through the European Green Deal. A critical step is converting CO2 and renewable electricity into fuels and chemicals. One major challenge of this process is the lack of an electrocatalyst that can reduce CO2 to high-energy, high-value long-chain hydrocarbons, like propanol. This project aims to use Machine Learning and simulations to help developing the effective CO2 electroreduction catalysts.
In this reaction, oxide-derived copper catalysts have attracted widespread attention due to their excellent ability to promote carbon-carbon coupling. The outstanding performance of oxide derived Cu is typically attributed to their unique surface structures. However, the intense dynamic behaviour of such catalysts under reaction conditions leads to surface restructuring. Due to the catalyst's active nature, it is highly susceptible to oxidation during characterization processes using ex-situ experimental techniques, making it difficult to reflect its true structure during the reaction. Consequently, there remains significant controversy regarding the active sites, particularly the existence forms and distributions of oxygen atoms in the material under reaction conditions. Furthermore, traditional simulation methods such as first-principles calculations and classical molecular dynamics simulations struggle to balance accuracy and speed, thus failing to capture this complex dynamic process. Limited understanding of this fundamental process hinders further optimization of catalysts and reaction conditions. To address this challenge, the objective of the project is to provide atomic-level insights with the help of advanced machine learning techniques, thus providing design principles for better catalyst development.
In this reaction, oxide-derived copper catalysts have attracted widespread attention due to their excellent ability to promote carbon-carbon coupling. The outstanding performance of oxide derived Cu is typically attributed to their unique surface structures. However, the intense dynamic behaviour of such catalysts under reaction conditions leads to surface restructuring. Due to the catalyst's active nature, it is highly susceptible to oxidation during characterization processes using ex-situ experimental techniques, making it difficult to reflect its true structure during the reaction. Consequently, there remains significant controversy regarding the active sites, particularly the existence forms and distributions of oxygen atoms in the material under reaction conditions. Furthermore, traditional simulation methods such as first-principles calculations and classical molecular dynamics simulations struggle to balance accuracy and speed, thus failing to capture this complex dynamic process. Limited understanding of this fundamental process hinders further optimization of catalysts and reaction conditions. To address this challenge, the objective of the project is to provide atomic-level insights with the help of advanced machine learning techniques, thus providing design principles for better catalyst development.
First, the researcher proposed a machine learning based computational framework for systematically analyzing material changes from bulk to surface during operando conditions, considering both thermodynamic and kinetic perspectives. The simulations revealed that the oxygen content in OD-Cu at zero electric potential versus SHE varies significantly with pH. Under strongly acidic conditions, Cu2O reduces to Cu, while it remains stable under strongly basic conditions. The size of particles influences their reduction degree; larger results in higher reduction. Size distribution affects the time for the simultaneous presence of oxidic and metallic surfaces, with strong reduction potentials potentially leading to complete reduction of Cu2O. However, the slow oxygen diffusion from inside to the surface extends the process time from seconds to hours. This agrees with experimental observations.
The researcher used large-scale MD simulations with first-principles accuracy (r.m.s.e. of 4.58 meV per atom with respect to PBE-D2) to study OD-Cu structures during the reduction process. This implied to calculate at least 50,000 structures at the PBE-D2 level and setting up a complete new active learning project develop the potential which was the first of its kind in the literature. Results showed that oxygen concentration in OD-Cu materials depends on sample history and reaction conditions; higher pH/potential/SSA leads to higher oxygen concentration. Oxygen atoms aggregate to form Cu2O on the surface and inside the bulk to lower formation and surface energy. Long electrochemical experiments indicated that OD-Cu materials eventually reduce to Cu, but complete removal of trapped oxygen takes considerable time. The highly reconstructed Cu surface exhibits widely distributed oxygen adsorption energy values, with residual oxygen reducing under common experimental conditions. Part of the result has been published in Nature Catalysis.
The researcher used large-scale MD simulations with first-principles accuracy (r.m.s.e. of 4.58 meV per atom with respect to PBE-D2) to study OD-Cu structures during the reduction process. This implied to calculate at least 50,000 structures at the PBE-D2 level and setting up a complete new active learning project develop the potential which was the first of its kind in the literature. Results showed that oxygen concentration in OD-Cu materials depends on sample history and reaction conditions; higher pH/potential/SSA leads to higher oxygen concentration. Oxygen atoms aggregate to form Cu2O on the surface and inside the bulk to lower formation and surface energy. Long electrochemical experiments indicated that OD-Cu materials eventually reduce to Cu, but complete removal of trapped oxygen takes considerable time. The highly reconstructed Cu surface exhibits widely distributed oxygen adsorption energy values, with residual oxygen reducing under common experimental conditions. Part of the result has been published in Nature Catalysis.
These results advance the state of the art by providing detailed mechanistic insights into the reduction dynamics of OD-Cu materials under various conditions. Using large-scale molecular dynamics simulations with first-principles accuracy, the researcher modeled the sluggish kinetics of oxygen diffusion, explaining the time-dependent reduction process. The aggregation of oxygen atoms to form Cu2O rather than uniform distribution and the highly reconstructed Cu surface with varied oxygen adsorption energy values provide new insights into material behavior. These results offer a guildline for optimizing catalyst design and experimental conditions, advancing CO2 reduction catalyst development. Advanced simulation on disorder OD-Cu will deepen the understanding of such a complex system on molecular level and provide an approach to simulate the complex system. These results will have a direct scientific impact, by increasing the basic knowledge on catalysis to achieve the storage of renewable energies into synthetic fuels. The work helps to guide experimental researchers and companies to more efficiently develop catalysts for the production of specified products, thus reduce the time and economic costs.