Ionic Liquid Modified Electrocatalysts: a STEM‐EDX Approach for Identification of Local Distributions within Ionomer Containing Catalysts Layers

A sophisticated data processing routine based on machine learning is presented to overcome the difficulties associated with low energy STEM-EDS mapping of catalysts. The methodology allows to map the distribution of ionic liquid on PtNiCo on carbon catalyst in presence of Nafion.

Abstract

Driven by the transition to a CO₂-neutral energy economy, research on polymer electrolyte fuel cells gained much interest during the last decade, with researchers trying to overcome the sluggish kinetics of the oxygen reduction reaction (ORR) limiting their performance. Modification of existing ORR catalysts with small amounts of ionic liquids (IL) represents an innovative approach to altering the catalytic activity and stability. ILs are supposed to take effect by modifying the local microenvironment at electrochemical interfaces. Nevertheless, a thorough understanding about the local distribution of ILs over solid catalysts is still lacking, hindering the IL modification strategy to be a generic approach to rationally modulating the catalytic performance of a catalyst. In this study we employed STEM-EDS spectral imaging to locate the IL distribution on the catalyst in presence of Nafion^TM. To overcome the difficulties associated with low energy STEM-EDS we setup a sophisticated data processing routine based on machine learning.

Zum Volltext