Nanoscale in-Situ Characterization of Polymer Electrolyte Membrane Fuel Cell Catalyst Layers for Improved Water Management
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cells (PEMFCs) facilitate a sustainable energy infrastructure by offering emission-free electricity generation using renewably sourced hydrogen gas. The electrochemical reactions in a fuel cell occur at platinum-loaded catalyst layers (CLs), which are susceptible to degradation. Obstructing catalyst sites (triple phase boundaries1) with reaction by-products, such as water2,3, is the primary degradation mechanism in PEMFCs. Although various CL designs have been tested for improved electrochemical performance, in situ nanoscale visualization of platinum degradation and water accumulation in the CL at controlled temperature and relative humidity (RH) values are required to understand fundamental water mechanics through platinum and carbon nanostructures. In this work, we employed scanning transmission X-ray microscopy (STXM) and X-ray absorption fine structure spectroscopy (XANES) to evaluate the effect of temperature on pristine and broken-in fuel cell CLs. We developed a novel CL in-situ sample cell to enable nanostructured characterization of catalyst layers using STXM in a controlled temperature environment. Carbon 1s, Fluorine 1s, and Oxygen 1s spectral edges were probed utilizing near-edge X-ray absorption fine structure spectroscopy (NEXAFS) to reveal chemical degradation mechanisms and differentiate material structure. Through our custom in-situ cell, we demonstrate that the chemical composition and water accumulation throughout the PEMFC CLs at low, intermediate, and high operating temperatures (25°C, 40°C, and 60°C, respectively) can be quantified, along with thermal expansion analyses of the CL and ionomer membrane. Moreover, we reveal realistic structural and chemical characteristics of nanoscale catalysts by distinguishing regions of Nafion® Ionomer, catalyst carbon support, and platinum throughout the PEMFC’s membrane electrode assembly at industrially relevant fuel cell temperatures. The insights from this work will inform strategies to mitigate water flooding from a nanoscale perspective in addition to the novel in-situ spectromicroscopy characterization of current CL features. 1. Fouzaï, I., Gentil, S., Bassetto, V. C., Silva, W. O., Maher, R., & Girault, H. H. (2021). 9(18), 11096-11123. 2. Li, H., Tang, Y., Wang, Z., Shi, Z., Wu, S., Song, D., ... & Mazza, A. (2008). 178(1), 103-117. 3. Kumsa, D. W., Bhadra, N., Hudak, E. M., Kelley, S. C., Untereker, D. F., & Mortimer, J. T. (2016). 13(5), 052001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".