Study on Long-Term Decomposition Conditions of Hydrogen Peroxide for Oxygen Supply to Pemfcs
Bibliographic record
Abstract
Hydrogen that has high energy density is one of the main energy sources for the next generation. Polymer electrolyte membrane fuel cells (PEMFCs), which can utilize hydrogen's high energy storage density, are a very useful power source. The development of oxygen supply and storage technologies is essential for PEMFCs. Hydrogen peroxide, which has a high oxygen storage density, is present in an aqueous state at room temperature, making it easier to transport and storage in sparse oxygen environments. However, decomposition of hydrogen peroxide by catalyst has some problems. In order to reserve hydrogen peroxide, it is necessary to add stabilizers, but this causes degradation of the catalyst. We identified the effect of hydrogen peroxide stabilizers on the catalyst in the long-time decomposition reaction. The activation time of the catalyst depends not only on the materials of catalyst but also on the reactor design, and on the decomposition conditions. The effect of these variables on activation time were analyzed. As a result, the possibility of using hydrogen peroxide for long-term oxygen supply to PEMFCs was verified. We observed an improved activation time of 23 hours while maintaining an 1.6L/min oxygen flow rate. Figure 1
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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 | 0.001 |
| 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".