Covalent Hybrid of Spinel Manganese–Cobalt Oxide and Graphene as Advanced Oxygen Reduction Electrocatalysts
Bibliographic record
Abstract
Through direct nanoparticle nucleation and growth on nitrogen doped, reduced graphene oxide sheets and cation substitution of spinel Co(3)O(4) nanoparticles, a manganese-cobalt spinel MnCo(2)O(4)/graphene hybrid was developed as a highly efficient electrocatalyst for oxygen reduction reaction (ORR) in alkaline conditions. Electrochemical and X-ray near-edge structure (XANES) investigations revealed that the nucleation and growth method for forming inorganic-nanocarbon hybrids results in covalent coupling between spinel oxide nanoparticles and N-doped reduced graphene oxide (N-rmGO) sheets. Carbon K-edge and nitrogen K-edge XANES showed strongly perturbed C-O and C-N bonding in the N-rmGO sheet, suggesting the formation of C-O-metal and C-N-metal bonds between N-doped graphene oxide and spinel oxide nanoparticles. Co L-edge and Mn L-edge XANES suggested substitution of Co(3+) sites by Mn(3+), which increased the activity of the catalytic sites in the hybrid materials, further boosting the ORR activity compared with the pure cobalt oxide hybrid. The covalently bonded hybrid afforded much greater activity and durability than the physical mixture of nanoparticles and carbon materials including N-rmGO. At the same mass loading, the MnCo(2)O(4)/N-graphene hybrid can outperform Pt/C in ORR current density at medium overpotentials with stability superior to Pt/C in alkaline solutions.
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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".