Pt Nanoparticles Supported on Iron and Nitrogen-Doped Holey Graphene for Boosting ORR Performance
Bibliographic record
Abstract
Developing low-platinum catalysts is considered a promising strategy to facilitate the commercialization of fuel cells. However, the electrochemical performance of such materials is often hindered by mass-transfer issues. In this study, platinum nanoparticles supported on iron and nitrogen-doped holey graphene (Pt/Fe, N-HG) were synthesized by a simple method and used as an oxygen reduction reaction (ORR) catalyst. The unique holey structure and the co-doping of Fe and N atoms are proved beneficial for not only the formation of Pt nanoparticles but also enhancing the electrochemical performance of the catalyst. Density functional theory calculations indicate that the co-doping of Fe and N atoms increases the ability to adsorb Pt, as well as enhances the Pt adsorption of O 2 and oxygen-containing intermediates in the ORR. This study presents a novel approach for the controllable synthesis of multidoped holey graphene-based electrocatalysts, with optimized surface holey structures and electrochemical performances. These findings offer significant insights into the development of efficient catalysts for fuel cell 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 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".