Impact of the growth environment in inductively coupled plasma on the synthesis and morphologies of carbon nanohorns
Bibliographic record
Abstract
The fabrication of carbon nanohorns (CNHs) from a methane precursor with argon in an inductively coupled plasma was recently demonstrated with a high production rate of ∼20 g/h by Casteignau et al. [Plasma Chem. Plasma Process. 42, 465 (2022)]. The presence of a promotor gas such as hydrogen was found to be important for the growth of CNHs, but the mechanisms at play remain unclear. Here, we study the impact of different promotor gases by replacing hydrogen with nitrogen and helium at different promotor:precursor (Pm:Pr) ratios, X:CH4 = 0.3–0.7 (X = H2 or N2, Ar, and He), and global flow rates FX+FCH4=1.7 and 3.4 slpm. The nature of the promotor gas is shown to directly influence the morphology and the relative occurrence of CNHs, graphitic nanocapsules (GNCs), and graphene nanoflakes. Using quantitative transmission electron microscopy, we show that CNHs are favored by an X:CH4 = 0.5, preferably with X = He or N2. With a lower total flow rate (1.7 slpm) of N2, even larger production rates and higher selectivity toward CNHs are achieved. Optical emission spectroscopy was used to probe the plasma and to demonstrate that the nature promotor gas strongly modulates the C2 density and temperature profile of the plasma torch. It is shown that CNHs nucleation is favored by high C2 density at temperatures exceeding 3500 K localized at the exit-end of the nozzle, creating a reaction zone with extended isotherms. H2 favors CH4 dissociation and creates a high C2 density but cools the nucleation zone, which leads to structures with a strong graphitic character such as GNCs.
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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) | 0.000 | 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) | 0.000 | 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 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".