Zirconia on Reduced Graphene Oxide Sheets: Synergistic Catalyst with High Selectivity for H<sub>2</sub>O<sub>2</sub> Electrogeneration
Bibliographic record
Abstract
Abstract In situ electrogeneration of hydrogen peroxide through the oxygen reduction reaction (ORR) represents a potentially greener route for wastewater treatment. However, the development of adequate catalysts following a two‐electron pathway with high selectivity and low overpotential is still of great importance. To address this issue, nanoparticles of zirconium oxide supported on reduced graphene oxide (rGO) sheets were prepared through a hydrothermal reaction, and the composite catalysts were tested for the ORR in both acid and alkaline media. The presence of zirconium oxides (ZrO2‐x and ZrO2) significantly improved the activity of rGO for the ORR and its selectivity toward H2O2 electrogeneration. Indeed, an increase from 73.7 to 89.5% was obtained in acid solution, and from 72.9 to 83.1% in alkaline medium. The high activity of the composite catalysts is assigned to the synergistic effect between ZrO2‐x and rGO. The highest selectivity for H2O2 electrogeneration was correlated to the presence of ZrO2 phase. In addition, the zirconia‐rGO catalysts are stable and reusable. Therefore, these composites are very promising catalysts to be used in gas diffusion electrodes for advanced oxidation processes.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".