Enhanced catalytic performance of Pt by coupling with carbon defects
Bibliographic record
Abstract
Defect engineering is a promising strategy for supported catalysts to improve the catalytic activity and durability. Here, we selected the carbon (C) matrix enriched with topological defects to serve as the substrate material, in which the topological defects can act as anchoring centers to trap Pt nanoparticles for driving the O 2 reduction reactions (ORRs). Both experimental characterizations and theoretical simulations revealed the strong Pt-defect interaction with enhanced charge transfer on the interface. Despite a low Pt loading, the supported catalyst can still achieve a remarkable 55 mV positive shift of half-wave potential toward ORR in O 2 -saturated 0.1 M HClO 4 electrolyte compared with the commercial Pt catalyst on graphitized C. Moreover, the degeneration after 5,000 voltage cycles was negligible. This finding indicates that the presence of strong interaction between Pt and topological C defects can not only stabilize Pt nanoparticles but also optimize the electronic structures of Pt/C catalysts toward ORR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".