Evaluation of Hydrogen-Oxygen Recombiner Catalysts Under Various Conditions for Nuclear and Non-Nuclear Hydrogen Safety
Bibliographic record
Abstract
Hydrogen plays an important role in nuclear and non-nuclear safety, as the unsafe manipulation of hydrogen can lead to dangerous accidents. This concern has led the Hydrogen Isotopes Technology Branch at the Canadian Nuclear Laboratories to develop catalysts in order to aid in the recombination of hydrogen and oxygen. This research project evaluates catalysts with different compositions under various conditions (i.e. dry, humid) in a spinning basket reactor (SBR). Results suggest that catalysts with lower wetproof coating loading have higher activity levels after being exposed to water vapour or immersed in liquid water compared to catalysts with higher amounts of wetproof coating. The specific activity levels were 1.91 cm 3 H 2 /s·gCAT and 1.57 cm 3 H 2 /s·gCAT, normalized to the benchmark catalyst’s activity in humid conditions, for catalysts with low and high amounts of wetproof coating, respectively. Normalization was performed so that the activity levels of the benchmark catalyst in humid conditions was equal to one, thus assigning the other activity level values based on their relation to the benchmark catalyst’s activity levels in humid conditions. For the benchmark catalyst, activity levels in water vapour and immersion conditions were about the same, whereas activity levels for the newly developed catalysts (with low and high wetproofing agent loadings) varied depending on the water exposure levels of the test. Thus, despite the wetproofing, the amount of water exposure had an effect on catalyst activity levels. This trend demonstrates that the benchmark catalyst was well wetproofed and suggests that further improvement is needed in the wetproofing method used for the new catalyst.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".