Computational design of sustainable 2D catalysts

Catalysis is ubiquitous in chemistry and biology. Catalysts allows chemical transformations to proceed at a faster rate by lowering the overall energy demand for several energy-intensive chemical mechanism, such as those involving covalent bond breaking. Decreasing the energy demand and therefore the temperature dependence of a reaction can allow enormous energy savings and therefore reduction of CO2 emissions in the atmosphere. Several catalytic processes are also involved in emission control (NOx reduction) and promising green technologies, for instance: electrochemical production of hydrogen, carbon capture and photocatalytic reduction of CO2.

Unfortunately, some of the most effective catalytic materials currently employed in the chemical industry and in air pollution control employ costly metals such as platinum and palladium or rare earth elements, for which long-term availability has been questioned. Furthermore, the increasing demand for these impoverished natural resources raises concern for maintaining access to supplies in developing countries as well as fear of exploitation in resource-rich regions. For the these reasons, there is increasing interest for designing and developing more sustainable catalytic materials. Among those, graphene and other 2D materials have attracted considerable attention in the catalysis and surface science communities.

The research objective is to employ highly accurate computational methods based on density functional theory to design novel catalysts based on 2D materials such as graphene as boron nitride, in which the nature of the active sites is tuned according to the desired chemical activity. In particular, we will investigate the role of chemical doping and functionalisation by substitutional insertion of heteroatoms, oxidation, amination and by creating specific defects (e.g., single vacancies, edges). The activity of the catalysts will be tested for key environmental and industrial reactions such as the reduction of NOx (deNOx process) and the catalytic partial oxidation (CPOX) which is an attractive option to produce H2 and CO from hydrocarbon fuels for Green Energy (e.g., fuel cell) applications.

Applicants should hold a good Master Degree (2:1) or First Class BSc in Chemistry, Physics, Material Science or related discipline. The ideal candidate will have a passion for computational chemistry and material simulations and possess excellent written and oral communication skills. Experience with computational software (e.g. CASTEP, VASP, Quantum ESPRESSO) and programming languages (e.g. Fortran and Python) is highly desirable, but not essential.

This studentship is only open to UK students.

English language requirements

If English is not your first language, you will be required to have an IELTS Academic of 6.5 or above (or equivalent), with no sub-test score below 6.

Marco Sacchi

https://doi.org/10.1016/j.cartre.2021.100111

https://doi.org/10.1016/j.jcis.2022.03.128