Constructing a Multifunctional Interface between Membrane and Porous Transport Layer for Water Electrolyzers
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The cell performance and durability of polymer electrolyte membrane (PEM) water electrolyzers are limited by the surface passivation of titanium-based porous transport layers (PTLs). In order to ensure stable performance profiles over time, large amounts (≥1 mg·cm –2 ) of noble metals (Au, Pt, Ir) are most widely used to coat titanium-based PTLs. However, their high cost is still a major obstacle toward commercialization and widespread application. In this paper, we assess different loadings of iridium, ranging from 0.005 to 0.05 mg·cm –2 in titanium PTLs, that consequently affect the investment costs of PEM water electrolyzers. Concerning a reduction in the precious metal costs, we found that Ir as a protective layer with a loading of 0.025 mg·cm –2 on the PTLs would be sufficient to achieve the same cell performance as PTLs with a higher Ir loading. This Ir loading is a 40-fold reduction over the Au or Pt loading typically used for protective layers in current commercial PEM water electrolyzers. We show that the Ir protective layer here not only decreases the Ohmic resistance significantly, which is the largest part of the gain in performance, but moreover, the oxygen evolution reaction activity of the iridium layer makes it promising as a cost-effective catalyst layer. Our work also confirms that the proper construction of a multifunctional interface between a membrane and a PTL indeed plays a crucial role in guaranteeing the superior performance and efficiency of electrochemical devices.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".