Electrocatalytic activity of CoPy/C catalyst for the Oxygen reduction reaction in Alkaline Electrolyte
Bibliographic record
Abstract
In this communication, we report a novel CoPy/C catalyst for the oxygen reduction reaction(ORR) in alkaline electrolyte using cobalt sulfate heptahydrate (CoSO4·7H2O) and pyridine (Py) as the Coand N precursors supported on Vulcan XC-72R, followed by heat treatment in an inert atmosphere.Electrochemical performances were evaluated using cyclic voltammograms (CVs) and rotating diskelectrode (RDE) technique in terms of its ORR activity as a function of Co content in the catalyst synthesis.Results show that the presence of Co in the CoPy/C catalyst greatly affects the formation of ORR catalyticactive sites and that the best performing catalyst is 10%Co%30Py/C, which was synthesized at 800 °C. In 3.0mol·L-1 KOH, 10%Co30%Py/C (in O2) produces an obvious ORR current with an on-set potential at 0.014 V.Compared with the 40% Py/C the on-set potential of the 10% Co30% Py/C for oxygen reduction shiftedpositively by 71 mV (versus RHE (reversible hydrogen electrode)) and a well-defined limiting current plateauwas achieved. Therefore, a maximum current density of 1.0 mA·cm-2 was obtained at -0.16 V with ahalf-wave potential of -0.07 V. Transmission electron microscopy (TEM) measurements show that thenanoparticles with a diameter of 20 nm are uniformly dispersed on Vulcan carbon (Vulcan XC-72R). © Editorial office of Acta Physico-Chimica Sinica.
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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 | 0.000 | 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".