Development of Fuel Cell System Using Metal Hydride and Hydrogen Peroxide in Low-Oxygen Environments
Bibliographic record
Abstract
Hydrogen energy is attracting attention as a promising power system due to high energy storage density and environmentally friendly features. For these reasons, fuel cell system using hydrogen energy in unmanned underwater vehicles have been studied. However, in low-oxygen environment, fuel cell system requires an oxygen storage as well as a hydrogen storage. In this study, a fuel cell system using a metal hydride and hydrogen peroxide was designed. The metal hydride can charge and discharge hydrogen at a lower pressure than a high-pressure hydrogen tank and has a high hydrogen storage density. In this system, AB2 type metal hydride is used to store hydrogen. Hydrogen desorption reaction of the metal hydride is an endothermic reaction, and heat supply is required to maintain the temperature of the metal hydride in order to stably supply hydrogen. Therefore, in this system, hydrogen peroxide was used as an oxygen source. Hydrogen peroxide reacts with a metal catalyst to generate oxygen and water. The decomposition reaction of hydrogen peroxide is an exothermic reaction and can supply heat required for the metal hydride. In this system, hydrogen peroxide is decomposed into water and oxygen through a catalytic reaction, and the generated heat is supplied to the metal hydride. Finally, hydrogen and oxygen are supplied to the PEMFC stack to generate power. For system design, in this study, PCT characteristics of AB2 type metal hydride, thermal management method, and hydrogen peroxide decomposition catalyst were studied, and an operation strategy for non-humidified PEMFC operation was proposed. This system generated a power output of about 290 W for 97 hours, and the hydrogen utilization rate of the fuel cell was confirmed to be 92.3%.
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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 | 0.001 | 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".