Effect of the iron precursor on the insitu functionalization of deposited graphene nanoflakes for catalyst applications
Bibliographic record
Abstract
Graphene nanoflakes (GNF), a stack of 5 to 20 layers of graphene sheets with typically 100 nm side lengths, are the product of methane decomposition using an argon ICP thermal plasma. GNFs are good candidates to support non-noble catalytic sites for the oxygen reduction reaction. This material has a high crystallinity allowing the graphene to be acid resistant, together with a high electrical conductivity, these properties providing a good basis for a stable catalyst material in fuel cells. The GNFs are functionalized with nitrogen to support iron atoms and create catalytic sites dispersed at the atomic level on the nano-structured powders. The iron functionalization step is realized in situ as a post-processing step within the synthesis reactor through the vaporization of two different Fe precursors in the core of the plasma. The first consists of pure iron powders carried by a nitrogen flow; this method having the advantage of avoiding impurities during the functionalization step. The second precursor is an iron(II) acetate solution also carried by a nitrogen flow, with the iron already in atomic form once dissociated in the thermal plasma core. The effects of the type of precursor, the power of the plasma, and the reactor chamber pressure on the iron functionalization are studied in the present contribution. The structure, composition, and activity of the resulting catalyst are also fully characterized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".