An assessment of newly developed urea fuel cell electrodes
Bibliographic record
Abstract
This research developed more efficient Direct Urea-Hydrogen Peroxide Fuel Cell (DUHPFC) utilizing an H-type electrochemical cell methodology, offering a regulated setting to assess a unique nickel zinc iron oxide coated stainless-steel anode, and examines their electrochemical performance. The cell underwent systematic testing at several KOH concentrations (1 M, 5 M, and 9 M) at 25 °C, as well as at various temperatures (25 °C, 35 °C, 45 °C, 55 °C, and 65 °C) using 9 M KOH, with a catholyte of 2 M H 2 SO 4 and 2 M H 2 O 2 . The electrochemical methods, such as open circuit potential (OCP), linear sweep voltammetry (LSV), and electrochemical impedance spectroscopy (EIS), were utilized to examine cell voltage, current density, power density, and impedance properties. The highest open circuit voltage (OCV) of 0.86 V was recorded at 65 °C using 9 M KOH, reflecting improved electrochemical potential at elevated temperature. The research findings indicated that KOH concentration markedly influences performance, with 9 M KOH at 25 °C producing the peak power density of 63.50 mW/cm 2 at 24.23 mA/cm 2 , whereas diminished concentrations displayed decreased electrochemical activity. Likewise, an increase in temperature enhanced reaction kinetics, with 65 °C yielding the peak power density of 88.2 mW/cm 2 at 27.4 mA/cm 2 . Electrochemical impedance spectroscopy (EIS) revealed a low transfer resistance of 8 Ω, indicating efficient electron transfer at the anode-electrolyte interface. The efficiency studies performed here revealed that energy and exergy efficiencies improved with temperature, attaining 30.7 % and 29.5 %, respectively, at 65 °C. The method proves effective for electrode screening and performance assessment, offering key insights for optimizing catalysts and advancing urea-based fuel cells for sustainable energy applications.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".