Computational analyses of co2 electroreduction and interactions with transition metals

  1. Piqué Caufapé, Oriol
Supervised by:
  1. Federico Calle Vallejo Director
  2. Francesc Viñes Solana Director

Defence university: Universitat de Barcelona

Fecha de defensa: 03 May 2022

  1. Santiago Builes Toro Chair
  2. Elvira Gómez Valentín Secretary
  3. Mónica Calatayud Antonino Committee member

Type: Thesis

Teseo: 712173 DIALNET


Thereby saving considerable amounts of energy. Most catalytic processes in industry are heterogeneous in nature, typically involving a solid catalyst and a gas or liquid phase containing the reactants and products. Although numerous types of materials are used, most heterogeneous catalysts are made of metals and/or metal oxides. Research towards improving the activity and selectivity of catalytic systems along with reducing the cost of the catalysts is crucial. In this thesis, different aspects regarding two relevant processes are investigated: (i) the improvement of CO2 electroreduction catalytic systems and (ii) atomic carbon interactions with transition metals (TMs), often present in many heterogeneous catalysts. First, a brief introduction to catalysis, heterogeneous catalysis, and electrocatalysis is tackled. Note that applications based on catalysis can be traced back to thousands of years ago with the discovery of fermentation to produce wine and beer through processes that are today described as enzymatic catalysis. Nowadays, catalysts are used in more than 90% of all chemical industrial processes, contributing directly or indirectly to around 35% of the world’s gross domestic product (GDP). Catalysis is key to numerous industrial applications, including the production of commodity, fine, and specialty chemicals as well as the manufacturing of pharmaceuticals, cosmetics, food, and polymers. Moreover, catalysis is central in the design of processes for clean energy provision and in environmental protection and remediation, both by eliminating environmental pollutants and providing alternative cleaner chemical synthetic procedures. Next, a description of the theoretical methods used for the computational analysis of the systems of interest is included. Several topics are discussed such as density functional theory, modelling of periodic solids, machine learning algorithms, phase diagrams assembly, and computational hydrogen electrode, among others. Following the theoretical methods chapter, a discussion of the obtained results is presented. Results are divided in two parts. In the first part, the electroreduction of CO2 (CO2RR) to valuable fuels for energy storage and environmental mitigation is studied through DFT calculations on suitable models. Some findings are the result of collaborations with the experimental research groups of Prof. Boon Siang Yeo (NUS, Singapore) and Dr. Katsounaros (HI-ERN, Germany). Four-atom square islands on top of Cu(100) are identified as the active sites in oxide-derived Cu surfaces responsible for ethanol evolution. It is observed that the C2 product selectivity at the late stages of the CO2RR is dictated by the coordination of the different active sites. Moreover, adding Ag to Cu is shown to enhance the production of ethanol via the opening of an alternative pathway. Finally, it is shown how formic acid, usually considered a deadend product of CO2RR, can be electroreduced to methanol at the oxygen vacancies of anodized titanium catalysts. In the second part, a broad and detailed atomistic view of the interaction of C with TM surfaces is given by determining its most stable sites, bond strength, possible subsurface stability, and diffusion kinetics. Hundreds of simulations are performed within the density functional theory (DFT) framework and the resulting data are analyzed to find correlations and draw conclusions. Initially, it is observed that atomic C features preferential subsurface stability in the (111) facets of Cu, Ag, and Au surfaces and nanoparticles, which has important implications when using those metals as catalysts. A dynamic simulation provided estimates of the lifetimes of surface and subsurface C species. Moreover, data analysis using different descriptors together with machine learning tools shows that both the thermodynamic and kinetic data are better described when using a combination of several descriptors.