Recent Advances in Laser Synthesis of Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
Very shortly following the discovery of single-walled carbon nanotubes laser-based methods emerged as reliable ways to produce moderate quantities with very consistent composition and properties. They have also proven to be good platforms for the systematic investigation of various synthesis parameters in the hope of better understanding the growth process. Several variations exist with differences such as laser wavelength, number of lasers, background temperature, and target composition. A number of common elements have emerged for the production of both high quality and high yields of SWNT, such as the effectiveness of bimetal catalysts like CoNi and NiY, and the need for high process temperatures and controlling the rate of cooling. Combined with the growing amount of in situ diagnostic and computational modelling data we are slowly making progress towards understanding the growth process. This article will present an overview of recent advancements in laser-based synthesis methods and what information can be extracted about the growth process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".