Developing computational strategies for platinum nanocatalyst based fuel cell applications

Abstract

Proton exchange membrane fuel cells (PEMFCs) have been identified as integral tools to address the excessive energy demand of the 21st century owing to their efficient, non-polluting conversion of chemical energy to electrical energy. The so-far unachieved ideal PEMFC efficiency is attributed to the sluggish kinetics of oxygen reduction reaction (ORR) on the cathode. Therefore, developing platinum nanocatalysts that can enhance the ORR activity is of profound research interest. In this regard, computational studies focused on platinum-based nanocatalysis reap attention as the wise designing and thorough analysis of the catalytic activity can provide valuable thumbnails which can serve as guidelines for the experimental synthesis of efficient catalysts. With the recent development of various quantum chemistry/solid-state computational packages, a comprehensive screening of size, facet, shape, and composition-dependent ORR activity of platinum nanocatalysts can be obtained. This thesis focuses on developing computational strategies for understanding and enhancing the ORR activity of Pt-based nanocatalysts with a strong emphasis on nanoclusters. Atomistic modelling of nanocatalysts is systematically employed for understanding the reaction mechanism, energetics, and kinetics of the catalysis process. Computational techniques for modulating the activity of such catalysts by addressing various factors are investigated. Furthermore, refining the theoretical frameworks adapted in computational simulations to obtain a better realistic picture compatible with the experimental scenario is another objective of the thesis.