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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".