Revealing the Impact of Cell Conditioning on PEMWE Catalyst Layer Morphology
Bibliographic record
Abstract
Polymer electrolyte membrane (PEM) water electrolysis is a promising green hydrogen generation technology to mitigate the effects of anthropogenic climate change. To achieve stable cell voltages, a conditioning procedure is typically required, where current or voltage cycling is applied until stable performance is achieved. Previous studies have demonstrated that a decrease in voltage occurs within the first few hours of operation [1], but this conditioning period has not been extensively studied for PEM electrolyzers, despite a variety of standard conditioning protocols available for PEM fuel cells in the literature. Previous works additionally suggest that changes in performance may be induced due to structural changes in the anode catalyst layer (CL) caused by large gas bubble evolution during the first few hours of operation [2]. However, to establish and optimize a conditioning procedure for PEMWEs, the specific structural changes caused by conditioning on the anode CL must be elucidated and correlated with the electrochemical performance parameters. In this work, we applied scanning transmission X-ray microscopy (STXM) to PEMWE catalyst layers to reveal pore and ionomer distributions in pristine and conditioned commercial CL samples. Prior to imaging, CL samples were embedded in epoxy and cut to 50 nm thick slices using an ultramicrotome. Using near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, Carbon 1s and Fluorine 1s absorption edges were probed to reveal pore and ionomer distributions, respectively. After image acquisition, the image stacks were processed to obtain the ionomer and epoxy spectra to enable spectral fitting and thresholding. Through this analysis, we quantified various morphological properties of catalyst layers (including CL thickness, porosity, pore size distribution, agglomerate distribution, and ionomer content) and compared these properties to those of pristine CLs to explain changes in cell performance. The insights gained from this work will aid in the development of efficient conditioning protocols and identify optimal post-conditioned anode CL morphology to inform the design of next-generation catalyst materials. A. Weiß et al., J Electrochem Soc, 166, F487–F497 (2019). O. Panchenko et al., Mater Today Energy, 16, 100394 (2020).
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".