Project title: Reaction Mechanisms for Material Systems with Complex Potential Energy Surfaces

Supervisor: Professor R. Peter Lindstedt, Dr. Daniel Dini & Dr. Konstantinos Gkagkas (TME)

Project description:

The continuous quest for improvements of vehicle efficiency leads to a requirement of careful control of processes for reliable and stable operation. High temperature chemistry impacts emissions and material dependent surface degradation, which impacts friction and hence efficiency. The interactions between surfaces and adjoining fluid phases are poorly understood and detailed chemistry modelling across scales, starting from the fluid-material surface boundary, becomes essential for predicting the associated phenomena. The current project is focused on the development of advanced computational tools for the determination of the chemical species distribution on surfaces as will result from advanced engine operation modes. The step requires the use of comprehensive detailed chemical kinetics for the material surface. The microkinetic analysis of heterogeneous systems leading to surface degradation and/or deposit formation currently requires either experimentally derived sticking coefficients or major computational expense (e.g. ab initio, density functional or plane wave methods). The present work focuses on developing a more complete theoretical, reaction class based, framework for estimating Arrhenius parameters in such systems. The framework is initially leveraging the unity bond index-quadratic exponential potential (UBIQEP) method for estimating barrier heights. As part of the work, key reaction pathway parameters will be replaced using transition state theory (TST) based estimates obtained via the M06 family of density functionals and the Stuttgart-Dresden effective core potential for surface atoms. In conclusion, the current research project will provide a new basis from which it will be possible to evaluate the chemically induced degradation of surfaces not only of automotive engines, but also applications will be performed on graphene and carbon based nanotubes.