Plasma nitriding induced growth of Pt-nanowire arrays as high performance electrocatalysts for fuel cells
Bibliographic record
Abstract
In this work, we demonstrate an innovative approach, combing a novel active screen plasma (ASP) technique with green chemical synthesis, for a direct fabrication of uniform Pt nanowire arrays on large-area supports. The ASP treatment enables in-situ N-doping and surface modification to the support surface, significantly promoting the uniform growth of tiny Pt nuclei which directs the growth of ultrathin single-crystal Pt nanowire (2.5-3 nm in diameter) arrays, forming a three-dimensional (3D) nano-architecture. Pt nanowire arrays in-situ grown on the large-area gas diffusion layer (GDL) (5 cm(2)) can be directly used as the catalyst electrode in fuel cells. The unique design brings in an extremely thin electrocatalyst layer, facilitating the charge transfer and mass transfer properties, leading to over two times higher power density than the conventional Pt nanoparticle catalyst electrode in real fuel cell environment. Due to the similar challenges faced with other nanostructures and the high availability of ASP for other material surfaces, this work will provide valuable insights and guidance towards the development of other new nano-architectures for various practical 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".