Controlled Growth of Platinum Nanowire Arrays on Sulfur Doped Graphene as High Performance Electrocatalyst
Bibliographic record
Abstract
Graphene supported Pt nanostructures have great potential to be used as catalysts in electrochemical energy conversion and storage technologies; however the simultaneous control of Pt morphology and dispersion, along with ideally tailoring the physical properties of the catalyst support properties has proven very challenging. Using sulfur doped graphene (SG) as a support material, the heterogeneous dopant atoms could serve as nucleation sites allowing for the preparation of SG supported Pt nanowire arrays with ultra-thin diameters (2-5 nm) and dense surface coverage. Detailed investigation of the preparation technique reveals that the structure of the resulting composite could be readily controlled by fine tuning the Pt nanowire nucleation and growth reaction kinetics and the Pt-support interactions, whereby a mechanistic platinum nanowire array growth model is proposed. Electrochemical characterization demonstrates that the composite materials have 2-3 times higher catalytic activities toward the oxygen reduction and methanol oxidation reaction compared with commercial Pt/C catalyst.
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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".