Engineering manganese oxide/nanocarbon hybrid materials for oxygen reduction electrocatalysis
Bibliographic record
Abstract
Manganese oxides are cost-effective and green materials with rich electrochemical properties. Continuous research efforts have been undertaken to obtain MnO x materials with improved activity and stability for catalyzing the oxygen reduction reaction (ORR). Here, we have developed a novel ORR catalyst by nucleation and growth of Mn3O4 nanoparticles on graphene oxide (GO) sheets interconnected by electrically conducting multi-walled carbon nanotubes (MWCNTs). X-ray near edge absorption structure (XANES) spectroscopy revealed the partially reduced nature of GO and strong chemical coupling between the nanoparticles and the GO sheets. Incorporation of MWCNTs was found to improve the activity and stability of the hybrid by imparting higher conductivity to the hybrid material. Furthermore, surface oxidation of the manganese oxide nanoparticles through a calcination step was found to increase the density of ORR active sites. The strongly coupled and electrically interconnected Mn3O4/nanocarbon (Mn3O4/Nano-C) hybrid is one of the most active and stable manganese oxide-based ORR catalysts and shows promise for electrochemical energy conversion applications.
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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".