Porous Transport Layer Related Mass Transport Losses in Polymer Electrolyte Membrane Electrolysis: A Review
Bibliographic record
Abstract
The unintended accumulation of oxygen gas in polymer electrolyte membrane (PEM) electrolyzers has been recently identified as one of the main hurdles to achieving high cell efficiencies. Oxygen is a by-product of the electrochemical reaction used to produce hydrogen, and this oxygen must be removed in order to reduce mass transport losses. The porous transport layer (PTL) is a key component of the PEM electrolyzer which facilitates mass transport and electrical conductance. However, oxygen bubble accumulation potentially dominates the total mass transport losses during operation. Many experimental and computational studies have been performed in an attempt to understand the relationship between the morphology of the PTL and the voltage loss of the electrolyzer, but this relationship has yet to be fully defined. In this work, efforts towards identifying and understanding mass transport losses are discussed. PTL structural parameters that were shown to affect performance, such as bulk porosity, particle size, pore size, thickness, and permeability are reviewed. Visualization techniques that have been employed to investigate the behavior of oxygen bubbles are also discussed. This work presents a summary of the studies which have been performed to investigate the key parameters of the PTL that should be tailored for improved PEM electrolyzer performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".