Restructuring of Electrocatalysts and our Electron Microscopy Approach
Nejc Hodnik a, Ana Rebeka Kamšek a, Francisco Ruiz-Zepeda a, Andraž Pavlišič a, Armin Hrnjić a
a Department of Materials Chemistry, National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
Proceedings of International Conference on Frontiers in Electrocatalytic Transformations (INTERECT)
València, Spain, 2021 November 22nd - 23rd
Organizers: Elena Mas Marzá and Ward van der Stam
Invited Speaker, Nejc Hodnik, presentation 006
Publication date: 10th November 2021

Obtaining transmission electron microscopy (TEM) micrographs of nanostructured electrocatalysts is a well-established characterization practice known for decades. It provides local nanoscale morphological, structural and recently also compositional information about the studied materials. Atomically resolved images provide even more insights like strain maps, locations of twin boundaries, surface facets, etc. These are all governing electrocatalyst performance via structure-function relationships.

In my talk, I will introduce a method and a concept of identical location transmission electron microscopy (IL-TEM) [1] and how it can help us gain insights into structure-stability relationships of the studied electrocatalysts. I will argue that IL-TEM provides us with an objective evaluation and certainty of observed events. Compared to random ex-situ TEM there is at least one crucial and obvious limitation, namely no information about the exact history of the observed location before the electrochemical treatment. Thus, in ex-situ microscopy, only general statistical descriptive insights are possible by evaluating numerous locations, which are always subjectively chosen by the operator.

Secondly, based on precise atomically resolved TEM data, Kinetic Monte Carlo (KMC) simulations can provide further feedback into the physical parameters governing electrochemically induced structural dynamics. [2] A few examples of the IL-TEM approach will be given on the topic of low-temperature fuel cell and electrolyzer catalysts. [3, 4]

Furthermore, advancements in the development of the modern TEM detectors (e.g., 4D STEM) even more information can be gained providing large amounts of data sets for the processing and simulations. In order to access relevant information in an objective and accurate manner, advanced data processing algorithms to analyze TEM images need to be used. With this, also the opportunity to use machine learning algorithms becomes viable. In perspective, much is still left to be explored in the field of nanoparticulate metallic electrocatalysts’ structure-stability understanding via high-resolution IL-TEM and data processing.

The author thanks the Slovenian research agency (ARRS) programs P2–0393 and P2-0152, project N2-0106 and European Research Council (ERC) Starting Grant 123STABLE (Grant agreement ID: 852208) for funding.

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