Pyrolyzed Co-N <sub>x</sub> /C Electrocatalysts Supported on Different Carbon Materials for Oxygen Reduction Reaction in Neutral Solution
Bibliographic record
Abstract
In this work, a non-noble metal material, i.e., 5, 10, 15, 20-Tetrakis(4-methoxyphenyl)−21H, 23H-porphine cobalt(II) (CoTMPP), is chosen as the metal complex which adsorbs on three different carbon support materials, namely, black pearls 2000 (BP), ordered mesoporous carbon (MPC) and single layer graphene oxide (GO), to form CoTMPP/BP, CoTMPP/MPC, and CoTMPP/GO catalyst precursors, respectively. These precursors are heat-treated at high temperatures to form Co-N x /BP, Co-N x /MPC, and Co-N x /GO catalysts for ORR in neutral electrolyte solution, respectively. Several characterization methods including XRD, SEM/TEM and XPS are employed. Electrochemical methods such as cyclic voltammetry (CV), rotating disk electrode (RDE) and rotating ring-disk electrode (RRDE) techniques are used to quantitatively measure the catalyzed ORR kinetic parameters. Effects of both carbon supports and heat-treatment temperature on the catalyst physical properties and catalytic ORR activities were also studied. Regarding the catalytic ORR activity, the activity order was measured as: Co-N x /BP > Co-N x /MPC > Co-N x /GO. The overall electron transfer number catalyzed by Co-N x /BP is measured to be 4, while those by Co-N x /MPC and Co-N x /GO catalysts are 2-electron transfer pathways. Based on these measurements and our analysis, an ORR mechanism is also proposed for facilitating further investigation and understanding.
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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) | 0.001 | 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".