Ultrathin Carbon-Coated Pt/Carbon Nanotubes: A Highly Durable Electrocatalyst for Oxygen Reduction
Bibliographic record
Abstract
Nanostructures constituted of Pt nanoparticles (NPs) supported on carbon materials are considered to be among the most active oxygen reduction reaction (ORR) catalysts for fuel cells. However, in practice, the usage of such ORR catalysts is limited by their insufficient durability caused by the low physical and chemical stability of Pt NPs during the reaction. We herein present a strategy to synthesize highly durable and active electrocatalysts composed of Pt NPs supported on carbon nanotubes (CNTs) and covered with an ultrathin layer of graphitic carbon. Such hybrid ORR catalysts were obtained by an interfacial in situ polymer encapsulation–graphitization method, where a glucose-containing polymer was grown directly on the surface of Pt/CNTs. The thickness of the carbon-coating layer can be precisely tuned between 0.5 nm and several nanometers by simply programming the polymer growth on Pt/CNTs. The resulting Pt/CNTs@C with a carbon layer thickness of ∼0.8 nm (corresponding to ∼2–3 graphene layers) showed high activity, and excellent durability, with no noticeable activity loss, even after 20 000 cycles of accelerated durability tests. These ultrathin carbon coatings not only act as a protective layer to prevent aggregation of Pt NPs but they also lead to better sample dispersion in solvent which are devoid of aggregates, resulting in a better utilization of Pt. We envision that this polymeric nanoencapsulation strategy is a promising technique for the production of highly active and stable ORR catalysts for fuel cells and metal–air batteries.
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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 | 0.001 | 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".