Porous transport layers with low Pt loading having Nb–Ta alloy as interlayer for proton exchange membrane water electrolyzers
Bibliographic record
Abstract
Titanium (Ti), commercially used as substrate for porous transport layers (PTLs) in proton exchange membrane water electrolyzers (PEMWEs), tends to form passivating oxide layer, increasing interfacial contact resistance (ICR) and reducing performance and durability; practice of using precious metal coatings for mitigation significantly increases costs. This study investigates niobium-tantalum (Nb–Ta) alloys as cost-effective interlayer coatings on Ti-felt to reduce precious metal loading. Nb–Ta coated samples significantly increase corrosion potential, lower current densities by 3–4 orders of magnitude, reduce ICR by 3.5 times, and improve durability. The best performance sample with an ultra-low amount of platinum, shows 8 times greater durability, 12.5% reduction in ohmic resistance and 28% increase in current density at +2.0 V than the commercial PTL in a single cell stack. Improved contact angle, electrical, and thermal conductivity highlight Nb–Ta interlayer coatings for PTLs, offering a cost-effective strategy to enhance PEMWE performance and durability for green hydrogen production. • Nb–Ta alloy-coated Ti-felt improved corrosion potential, reduced corrosion current. • Nb–Ta alloy-coated Ti-felt lowered interfacial resistance, enhanced durability. • Pt layer on alloy interlayer increased durability eightfold over commercial samples. • Pt/Nb–Ta alloy reduced ohmic resistance by 12.5%, increased current density by 28%. • Pt/Nb–Ta alloy sample enhanced corrosion resistance, durability, and conductivity.
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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 | 0.000 |
| 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".