Structure, Properties, and Degradation of Ultrathin Ionomer Films in Fuel Cell Catalytic Layers
Bibliographic record
Abstract
The performance of polymer electrolyte fuel cells and electrolysers depends on the nanostructure of its components. Mainly the nanostructured electrodes determine cell performance and degradation. In working fuel cell electrodes, the ionomer films that encapsulate the Pt/C agglomerates have a thickness of 4-20 nm and fall within the range of ultrathin films [1]. In recent years, an oxygen transport resistance has been identified as a major hurdle in the development of low-loaded MEAs and the ionomer layer has been postulated to be the reason for additional loses [2]. The quantitative investigation of the nanostructure of fuel cell electrodes, especially the analysis of the correct dimensions of the ionomer component has proven to be exceptionally difficult. Using electron beam-based techniques that need vacuum the ionomer is drying and significantly shrinking in addition to a low contrast between ionomer and carbon components. Also cryogenic TEM analysis may alter the sample by use of alcoholic solvents that have an input on structure, despite of beam damage. Atomic force microscopy (AFM) has the advantage to work in humid environment and at elevated temperatures, close to operational conditions [3]. Using material-sensitive tapping mode, the high contrast between the ionomer- and the Pt/C phase in adhesion force mapping allows studying the distribution and thickness of the ionomer films that cover the Pt/C agglomerates (Figure 1a) [3,4]. In this contribution, the analysis of cross-sections of Nafion- and Aquivion–based fuel cell electrodes by material-sensitive AFM will be presented. Analysis of pristine electrodes delivers the distribution of the ionomer films, small ionomer clusters, and larger agglomerates. A distribution of ionomer films ranging from roughly 4 to 20 nm was retrieved (Figure 1b). After operation, significant thinning of the ionomer films depending on location within the membrane-electrode-assembly and preparation was found. Differences of the swelling behavior of the ionomer films prior and after operation were used as a measure for ionomer degradation. Part of the thinning was assigned to ionomer redistribution during operation. A dependence of macroscopic cell degradation on initial electrode ionomer film thickness was observed. For further determination of the properties of such ultrathin films model layers of Nafion®, and Aquivion® PFSA were examined. Conductive AFM allowed investigations of through- and in-plane conductivity in dependence of the film thickness. Significant differences in conductivity for films below 10 nm thickness were detected (Figure 1c). References [1] S. Holdcroft, Fuel Cell Catalyst Layers: A Polymer Science Perspective, Chem. Mater. 26, 381–393 (2014) [2] A. Kongkanand, M. F. Mathias, The Priority and Challenge of High-Power Performance of Low-Platinum Proton-Exchange Membrane Fuel Cells, J. Phys. Chem. Lett., 7, 1127−1137 (2016) [3] R. Hiesgen, T. Morawietz, M. Handl, M. Corasaniti, K.A. Friedrich, Atomic Force Microscopy on Cross Sections of Fuel Cell Membranes, Electrodes, and Membrane Electrode Assemblies, 162, Electrochimica Acta, 86–99 (2015) [4] T. Morawietz, M. Handl, C. Oldani, K.A. Friedrich, R. Hiesgen, Quantitative in Situ Analysis of Ionomer Structure in Fuel Cell Catalytic Layers, ACS Appl. Mater. Interfaces, 8, 27044–27054 (2016) Figure 1: (a) AFM adhesion force mapping of electrode cross-section with bright ionomer phase and black Pt-covered areas, (b) distribution of ionomer film thicknesses before operation on an area of 1 x 1 µm 2 , and (c) dependence of tapping-mode measured current on Aquivion film thickness at room temperature. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".