Dr Alberto Roldan Martinez
Senior Lecturer in Catalytic & Computational Chemistry
Overview
Dr Roldan's research is aimed at understanding the dynamism of surface processes that underlie phenomena such as catalysis and corrosion. His group employs a range of computational tools to model physical and chemical properties of these systems regarding the experimental synthetic and working conditions. The use of micro-kinetic models allows them to approach specific conditions including the optimization of the catalyst structure and working conditions improving yields, selectivity of the catalyst as well as controlling sintering effects.
www.roldan-group.com
Biography
MSc in Experimental Chemistry (2007) at University of Barcelona (Spain)
PhD (Cum laude) in Theoretical and Computational Chemistry (2010) at University Rovira i Virgili (Spain) including the Best Thesis Award in Computational Chemistry.
Research Associate (2010-2012) at University College London
Ramsay Memorial Trust Fellowship (2012-2014) at University College London
Appointed Cardiff University Research Fellow 2015.
Publications
We are currently unable to retrieve the list of publications. Visit our institutional repository.Teaching
http://www.roldan-group.com/teaching
CHT109 Applications To Materials Science
CH3406 Molecular Modelling
CH3325 Project
CH3401 Project
CH9999 Industrial Training
The main interest for our work is the optimization of catalytic processes on heterogeneous systems, extended surfaces or nanoparticles. Particularly we are interested in:
- Capture and utilization of CO2
- Renewable and clean energy
- Material design including atomic control manufacturing.
- Sintering and coalescence of nanostructures
In the quest to gain understanding of these aspects, we evaluate the balance between kinetics and thermodynamics relaying on computational technologies to simulate the reactor conditions. These have demonstrably led to reductions in development costs, shorter time-to-market, and the design and development of more efficient materials as presented by the Materials Genome Initiative. The application of computer methodologies such as ab-initio, quantum mechanics/molecular mechanics simulations or polarizable continuum models provides an easy control of the parameters affecting the processes leading to atomic level understanding of the process.