Investigation of hydrogen peroxide reduction reaction by rotating disk electrode for fuel cells application
Bibliographic record
Abstract
In this dissertation, HPRR is studied on some graphene based materials and LaNiO3 using the Rotating Disk Electrode (RDE) technique. The Koutecky-Levich equation is typically used to determine the kinetic parameters of a simple electrochemical reaction. However, this equation is not an ideal equation for complex electrochemical reactions such as HPRR. Using the Koutecky-Levich equation, for instance, no information is provided about the heterogeneous reaction rate constant of H₂O₂ decomposition. Hence, as the first step to study HPRR, the mass transfer equation of the electroactive species (H₂O₂) inside the solution and the reaction equations on the electrode surface (H₂O₂ reduction, H₂O₂ decomposition, O2 reduction and O2 desorption) are solved simultaneously under steady state conditions to derive a specific equation for HPRR. This new equation is used to determine the apparent reaction rate constants, k' and k", and develop a methodology to predict the general mechanism of HPRR. As a case study, HPRR is studied on nitrogen doped graphene nanoflakes with 32 at% of N (N-GNF₂32) in 0.1M Na2SO4 solution by both the Koutecky-Levich equation and the equation derived in this work. While the Koutecky-Levich equation only determines the reaction rate constant of H₂O₂ reduction at different electrode potentials, the new equation determines as well the reaction rate constant of H₂O₂ decomposition. Moreover, the equation developed here suggests that the decomposition of H₂O₂ occurs as a side reaction on this electrode and the produced O2 is desorbed into the solution before it can be reduced on the electrode surface.
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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 | 0.001 | 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".