Oxygen Reduction Electrocatalyst Based on Strongly Coupled Cobalt Oxide Nanocrystals and Carbon Nanotubes
Bibliographic record
Abstract
Electrocatalyst for oxygen reduction reaction (ORR) is crucial for a variety of renewable energy applications and energy-intensive industries. The design and synthesis of highly active ORR catalysts with strong durability at low cost is extremely desirable but remains challenging. Here, we used a simple two-step method to synthesize cobalt oxide/carbon nanotube (CNT) strongly coupled hybrid as efficient ORR catalyst by directly growing nanocrystals on oxidized multiwalled CNTs. The mildly oxidized CNTs provided functional groups on the outer walls to nucleate and anchor nanocrystals, while retaining intact inner walls for highly conducting network. Cobalt oxide was in the form of CoO due to a gas-phase annealing step in NH(3). The resulting CoO/nitrogen-doped CNT (NCNT) hybrid showed high ORR current density that outperformed Co(3)O(4)/graphene hybrid and commercial Pt/C catalyst at medium overpotential, mainly through a 4e reduction pathway. The metal oxide/carbon nanotube hybrid was found to be advantageous over the graphene counterpart in terms of active sites and charge transport. Last, the CoO/NCNT hybrid showed high ORR activity and stability under a highly corrosive condition of 10 M NaOH at 80 °C, demonstrating the potential of strongly coupled inorganic/nanocarbon hybrid as a novel catalyst system in oxygen depolarized cathode for chlor-alkali electrolysis.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".