Effect of Preparation Conditions of Sol–Gel-Derived Co–N–C-Based Catalysts on ORR Activity in Acidic Solutions
Bibliographic record
Abstract
Using a Co oxide ethanol-based sol as a precursor solution, nitrogen (N)- and carbon (C)-containing ligands [1,2 phenylene diamine (phen) and ethylene diamine (en)] were added to produce an oxygen reduction reaction (ORR) catalyst precursor. After adsorption on carbon powder, heat-treatment in an inert atmosphere at 500 – 900 ° C , and the addition of Nafion as a binder, the powdered mixture was coated on a glassy carbon electrode and evaluated for its ORR activity in 0.5 M H 2 SO 4 . The catalyst–carbon powder ratio, heat-treatment temperature, ligand type, and ligand:Co ratio were optimized, and the effect of increased catalyst layer thickness, particle size, and C support was also determined. It was concluded that a phen-based catalyst with a metal to ligand ratio of 1:2, a heat-treatment at 900 ° C , and a concentration of 4 wt % Co was the most active, selective, and stable ORR catalyst material of the materials developed in the present work. X-ray photoelectron spectroscopy and X-ray diffraction analysis showed the formation of both Co metal and CoN 4 units upon heat-treatment, with the most active catalyst having a 5.3:1 overall N–Co ratio.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".