The effect of catalyst particle size and temperature on CNT growth on supported Fe catalysts during methane pyrolysis
Bibliographic record
Abstract
Iron catalysts supported on magnesium aluminate at different loadings were used in methane pyrolysis between 700 and 850 °C to evaluate the effect of particle size on the amount and properties of carbon nanotubes (CNT). All particles associated with CNTs were detached from the support, exhibiting a tip-growth mechanism. The lowest-loading catalysts with the average particle size of 6 nm produced the most carbon products with the lowest defect-to-graphite intensity ratios in Raman spectroscopy (0.13) when the reactor was at the lowest temperature. Higher temperatures led to iron particle sintering and lower carbon accumulation; at 850 °C, the thermal contribution to the total carbon mass was significant, catalyst particle encapsulation with graphite occurred and there was no CNT formation. There was no difference in the diameter of CNTs produced at different temperatures when the tubes were associated with the same Fe particle size, while reactions at the same temperature but different particle sizes produced CNTs of various diameters. The same correlation of CNT diameter with Fe particle size, rather than temperature, was observed in the characteristics of Raman spectra. This work provides evidence of the importance of particle size control and lower methane pyrolysis temperatures to enable enhanced production of CNT with higher quality. • Fe/MgAl 2 O 4 catalysts with various Fe loadings were tested in CH 4 pyrolysis. • Lower temperatures and lowest Fe loading favoured higher carbon yield. • Smaller particles associated with lower temperatures resulted in high-quality CNT.
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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) | 0.001 | 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".