Dynamic Molten Cu–In Droplets Catalyze Selective Synthesis of Multi‐Walled Carbon Nanotubes During Methane Pyrolysis
Bibliographic record
Abstract
Abstract The generation of multi‐walled carbon nanotubes (MWCNTs) typically utilizes solid catalyst nanoparticles. These particles often exhibit a liquid‐like nature during synthesis and remain encapsulated inside the final MWCNTs. Molten Cu–In has been recently reported to produce high amounts of MWCNT in bubble column reactors during methane pyrolysis for clean H 2 generation. In the present work, nanodroplets are isolated and studied on supports. The droplets are observed to deform into a head and tail geometry and generate bamboo‐like MWCNTs. The compositions of 50–70 at.% Cu repeatedly generate dense bundles of MWCNTs, while higher or lower compositions yield little or no MWCNTs. The lower surface tension of the alloy at these compositions reduces the thermodynamic driving force for coalescence, stabilizing small droplets at high temperature. Graphitic structure with some defects is confirmed by transmission electron microscopy and Raman spectroscopy, showing 3.35 +/−0.08 Å interlayer spacing and an I D /I G ratio of 0.87 +/−0.13, respectively. Droplets distributed between 10 nm and 1 micron generate MWCNTs 10–400 nm in diameter, suggesting droplets above 400 nm do not generate MWCNTs at any catalyst composition. Bundles of MWCNT exceeding hundreds of microns are observed in reaction times over 1 h.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".