Dr. Zan Lian

Prof. Nuria López

Advanced simulations in electrocatalysis for efficient production of C3+ by carbon dioxide reduction

Dr. Zan is a Marie Skłodowska-Curie Individual Fellow (ref. MSCA-PF-2021 101064867-DESCRIPTOR) since May 1st, 2022. He is carrying out simulations of electrochemical reactions in the conversion of renewable energy into chemical products and fuels through computational modeling with Density Functional Theory and advanced computational techniques as well as with “Machine Learning” concepts.

From 01/05/2022 to

30/04/2024

DESCRIPTOR

The climate change has raised concerns about closing the carbon cycle by converting CO2 and renewable electricity to chemically stored energy in the form of fuels and commodity chemicals. Among these, long-chain hydrocarbons and alcohols are more attractive because of their high energy density and value. Recently, reconstructed oxide-derived Cu (OD-Cu) catalysts have shown the potential to produce multicarbon species at lower overpotentials. However, few simulations have addressed the formation mechanism of these compounds due to the complex dynamics of this system under high currents, and the exact site for the excellent OD-Cu catalytic performance remains to be discovered. The DESCRIPTOR project aims to obtain the first generation of CO2 electroreduction catalyst with useful faradaic efficiencies towards C3+ products by employing computational simulations based on Density Functional Theory (DFT) and augmented by Machine Learning techniques. Firstly, suitable structures for the OD-Cu materials will be obtained through large scale Molecular Dynamics simulations based on Machine Learning potentials, by screening the most common ensembles identified via graph theory. Secondly, the mechanism towards C3+ will be identified via jDFTx scheme, and descriptors of activity and selectivity will be found through dimensionality reduction techniques. Finally, to assess and compare to experimental work from our collaborators, the contribution of the solvent/electrolyte and effect of experimental parameters will be investigated via ab initio Molecular Dynamics and microkinetic modelling. The structures will be characterized via simulations of X-ray Photoelectron Spectroscopy, and Raman spectra, etc. In summary, the outcome of DESCRIPTOR will have a direct scientific and social impact, by increasing the basic knowledge on catalysis of achieving renewable fuel sources and improving EU’s industrial competitiveness within new technologies for CO2 reduction.

This project has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement 101064867

Complementary probes for the electrochemical interface
Ernest Pastor, Zan Lian, Lu Xia, David Ecija, José Ramón Galán-Mascarós, Sara Barja, Sixto Giménez, Jordi Arbiol, Núria López, F. Pelayo García de Arquer
Nature Reviews Chemistry 2024, 8, 159-178
DOI: https://doi.org/10.1038/s41570-024-00575-5

Diffusion trapped oxygen in oxide derived Copper electrocatalyst in CO2 reduction
Zan Lian, Federico Dattila, Núria López
Theoretical and Computational Chemistry - ChemRxiv (Preprint) 2023, 1
DOI: https://doi.org/10.26434/chemrxiv-2023-v73sf

Poster Dynamic Behavior Study of Oxide-Derived Copper Augmented by Machine Learning Zan Lian*, Núria López

Poster from Dr. Zan Lian at the PSI-K CONFERENCE

Dr. Lian, ICIQ MSCA fellow, showcased his pioneering research at the 6th general conference for the worldwide Psi-k community (August 22–25, 2022 in Lausanne). This prestigious event, known as the