Synthesis of single-walled carbon nanotubes by induction thermal plasma
Bibliographic record
Abstract
The production of high quality single-walled carbon nanotubes (SWCNTs) on a bulk scale has been an issue of considerable interest. Recently, it has been demonstrated that high quality SWCNTs can be continuously synthesized on large scale by using induction thermal plasma technology. In this process, the high energy density of the thermal plasma is employed to generate dense vapor-phase precursors for the synthesis of SWCNTs. With the current reactor system, a carbon soot product which contains approximately 40 wt% of SWCNTs can be continuously synthesized at the high production rate of ∼100 g/h. In this article, our recent research efforts to achieve major advances in this technology are presented. Firstly, the processing parameters involved are examined systematically in order to evaluate their individual influences on the SWCNT synthesis. Based on these results, the appropriate operating conditions of the induction thermal plasma process for an effective synthesis of SWCNTs are discussed. A characterization study has also been performed on the SWCNTs produced under the optimum processing conditions. Finally, a mathematical model of the process currently under development is described. The model will help us to better understand the synthesis of SWCNTs in the induction plasma process.
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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".