Challenges and important considerations when benchmarking single-cell alkaline electrolyzers
Bibliographic record
Abstract
This study outlines an approach to identifying the difficulties associated with the benchmarking of alkaline single cells under real electrolyzer conditions. A challenging task in the testing and comparison of different catalysts is obtaining reliable and meaningful benchmarks for these conditions. Negative effects on reproducibility were observed due to the reduction in conditioning time. On the anode side, a stable passivation layer of NiO can be formed by annealing of the Ni foams, which is even stable during long-term operation. Electrical contact resistance and impedance measurements showed that most of the contact resistance derived from the annealed Ni foam. Additionally, analysis of various overvoltages indicated that most of the total overvoltage comes from the anode and cathode activation overpotential. Different morphologies of the substrate material exhibited an influence on the performance of the alkaline single cell, based on an increase in the ohmic resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".