Development and experimental investigation of a new direct urea fuel cell
Bibliographic record
Abstract
This study concerns the development and experimental investigation of Direct Urea-Hydrogen Peroxide Fuel Cells (DUHPFC), with a particular emphasis on electrode preparation using nickel zinc iron oxide coated on stainless steel foil via the electrochemical deposition method, and the performance evaluation of single cells under varying operational conditions is also performed. This electrochemical deposition helps achieve a uniform and stable anode coating, which exhibits the high catalytic activity and stability, resulted in significantly enhanced urea oxidation reaction. The research further identifies an optimal performance for a single cell at 25 °C with 9 M KOH and 0.5 M urea, achieving a peak power density of 46.38 mW/cm 2 . The single cell demonstrates an open circuit voltage (OCV) of 0.72 V. Both energy and exergy efficiencies are further investigated for the cell performance and found to be 58% and 24%, respectively, at 5 M KOH. The electrochemical impedance spectroscopy (EIS) results reveal a significant reduction in impedance, from 30-Ωcm 2 at 25 °C to 15-Ωcm 2 at 65 °C, indicating an enhanced ionic conductivity and a reduced resistance. The present study results suggest that optimizing the electrode composition and operational parameters significantly improves the DUHPFC's performance, offering valuable insights for future fuel cell development. • Urea and H₂O₂ enhance electrochemical reactions in DUHPFC systems. • Nickel zinc iron oxide catalyst boosts urea oxidation and cell efficiency. • Optimizing electrode composition and conditions improves DUHPFC performance. • Temperature and KOH concentration affect urea oxidation and cell efficiency. • Testing confirms the stability and durability of the developed anode.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".