Revealing localized compression induced degradation mechanisms in polymer electrolyte membrane water electrolyzers
Bibliographic record
Abstract
We present the first multi-physics model for the polymer electrolyte membrane water electrolyzer (PEMWE), coupling mechanical compression with electrochemical reactions to predict the effect of mechanical compression on cell performance. Under compression, the catalyst layer (CL) and membrane deform more significantly than the porous transport layer (PTL). Quite alarmingly, the membrane experiences thinning, most significantly under the lands, making the land regions of the membrane particularly susceptible to fuel crossover. Compression also results in higher mass transport resistance and lower liquid water saturation in the CL due to reduced single and two-phase permeabilities of the CL (liquid water saturation decreases by 43.6 % when increasing the compression ratio (CR) from 5 % to 30 %). Despite the drawbacks of compression, for CR < 20 % cell performance is greatly improved, and we attribute this improvement to the substantial decrease in the PTL/CL interfacial contact resistance (which outweighs the trade-off with mass transport resistances). However, there are negligible benefits to increasing the CR above this 20 % threshold, beyond which local mass transport resistances in the CL dominate electrochemical performance (mass transport resistances increase 59 % at a CR of 30 %; whereas ohmic resistances decrease by only 8 %). While the bulk electrochemical performance does not change significantly with CR > 20 %, the local current density under the land decreases, which we attribute to increases in local mass transport resistances in the CL.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".