Investigating Anode Structural Characteristics with Multistage Data Quantification: Influence of Extrinsic Properties of Anodes for Oxidation Evolution Reaction in Alkaline Water Electrolysis
Authors: Jain Adarsh , Vinayakumar Vineetha , Olean-Oliveira André , Marcks Christian , Chatwani Mohit , Mechler Anna K. , Andronescu Corina , Chatwani Mohit 
Published: December 2024

Tackling climate change demands the deployment of sustainable technologies, among which alkaline water electrolysis (AWE) emerges as one of the pivotal solutions. In AWE, anode morphology plays a critical role in facilitating the oxygen evolution reaction (OER), which is a key step in splitting water molecules into oxygen and hydrogen gas. OER performance can be enhanced by fine-tuning the morphology of the anodes [1, 2]. Therefore, there is a critical need to quantitatively comprehend surface morphology or coating quality quantitatively and define the relationships between these surface features and the activity of anodes.

We have observed that conducting a quantitative analysis of the nanoscale structure of anodes produced via ultrasonic spray coating provides a reliable metric, serving as a descriptor to evaluate the quality of particle-based catalyst coatings [3, 4]. For this quantitative analysis, it is crucial to characterize the structure with high resolution. However, high-resolution techniques commonly employed such as atomic force microscopy (AFM) have a significant limitation. They cover only a small measurement area that may not represent the entire surface area. In response to this need, we have introduced multistage data quantification (MSDQ) technique as a full-scale surface extrapolation method. The MSDQ technique is a statistical approach that facilitates an impartial characterization of surfaces by optimizing the process of region selection (ROS) and feature extraction, as exemplified in Figure 1.

We validated MSDQ through the characterization of anodes produced utilizing different spray coating processing parameters. AFM was employed to capture surface morphology. Through MSDQ, the following surface characteristics are extracted: the surface roughness indicates the variations of surface texture, measuring the peaks and valleys on the surface, the roughness factor refers to the total area covered by the electrode surface to the projected area, and the homogeneity score quantifies the uniformity of the surface structure, essential for assessing the consistency of catalyst coatings were extracted.

For validation, in a first proof of principle, extrinsic surface features were correlated to electrocatalytic activity for the oxygen evolution reaction (OER), as quantified through scanning droplet cell (SDC) measurements. The anode that showed a consistent surface topography yielded uniform electrochemical activity, while the anode that displayed irregular surface characteristics also lead to a more wide-spread electrochemical activity. To validate the reliability of the MSDQ technique, we conducted independent ROS measurements and found that the measured features were consistent with the ranges previously predicted by MSDQ. This consistency underscores the effectiveness of MSDQ in accurately characterizing surface features of anode, further establishing its credibility as a robust tool for surface analysis.

Taken together, the developed strategy exhibits high potential for adaptability across diverse electrochemical systems and their components. Moreover, it acts as a powerful approach for linking statistically backed-up surface characteristics, also beyond AFM, with electrochemical activity. This capability enhances our understanding of how surface nuances influence the overall electrochemical performance, offering valuable insights into optimizing material compositions and processing techniques for improved functionality.

References

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[4] V. Vinayakumar et al., "Scalable Production of Nickel-Cobalt Based Anode Materials for Alkaline Electrolysis: A Multi-Technique Approach from Micro Powder Analysis to Electrochemical Performance," Meet. Abstr., MA2023-02, no. 58, p. 2846, 2023, doi: 10.1149/MA2023-02582846mtgabs.

[5] A. Jain et al., "From Small‐Area Observations to Insight: Surface‐Feature‐Extrapolation of Anodes for Alkaline Oxygen Evolution Reaction," ChemCatChem, 2024, doi: 10.1002/cctc.202301461.

Figure 1