Experimental investigation and assessment of a new direct urea-hydrogen peroxide fuel cell stack
Bibliographic record
Abstract
This study addresses the existing technology gaps in fuel cell development by investigating the design and performance assessment of a Direct Urea-Hydrogen Peroxide Fuel Cell (DUHPFC) stack. A significant focus was placed on the preparation of electrodes, where nickel zinc iron oxide was successfully deposited on stainless steel foil via electrodeposition, resulting in high-activity, stable anodes. The 16-cell fuel cell stack was tested under various conditions, with optimal performance observed at 65°C, achieving a power output of 0.307 kW and an open circuit voltage (OCV) of 8.8 V. The energy and exergy efficiencies at 65°C were 48.88% and 41.27%, respectively, highlighting the crucial role of temperature optimization. The electrochemical impedance spectroscopy (EIS) measurements showed a reduction in impedance from 30 Ωcm 2 at 25°C to 15 Ωcm 2 at 65°C, suggesting improved charge transfer characteristics and reduced internal resistance, which contribute to enhanced fuel cell performance. These findings not only demonstrate the efficiency and scalability of the DUHPFC stack for large-scale energy applications but also address the need for more efficient and scalable fuel cell technologies by offering a viable solution to harness urea as a sustainable fuel source.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".