Evaluation of niobium-based bipolar plates with ultra-low precious metal coatings for high-performance and durable PEM water electrolyzers
Bibliographic record
Abstract
The performance of bipolar plates (BPPs) significantly impacts the durability and efficiency of proton exchange membrane water electrolyzers (PEMWEs). This study explores the potential of niobium (Nb) as a BPP substrate, assessing its performance against commercial pure titanium (CP-Ti) and 316L-stainless steel (SS316L). To further improve Nb's performance, 120 nm thin layer of platinum coating (Nb-Pt120) is applied using magnetron sputtering. Electrochemical tests are performed alongside interfacial contact resistance (ICR), wettability and surface characterization. Nb demonstrates superior corrosion resistance, hydrophobicity, lower ICR, and enhanced durability, establishing it as a promising candidate for BPPs. With Pt coating, the results reveal that Nb-Pt120 exhibits a significantly lower corrosion current density ( i corr ) of approximately 0.8 μA.cm −2 meeting the DOE technical targets. Additionally, Nb-Pt120 demonstrates reduced ICR (<10.0 mΩ.cm 2 ), greater hydrophobicity, and enhanced electrochemical stability compared to both uncoated and other Pt-coated substrates. The Nb-Pt120 BPP maintains durability for over 41 hours (approximately 20 and 40 times longer than Ti-Pt120 and SS316L-Pt120, respectively) under accelerated stress test at a constant current density of +2.0 A.cm −2 . This study establishes Nb as a superior substrate for BPPs in PEMWEs, with additional performance benefits realized through Pt coating, providing a path toward more durable water electrolyzer systems.
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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) | 0.001 | 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 | 0.001 | 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".