Morphological Instabilities and the Dynamics of Carbon Nanotube Forest Growth
Bibliographic record
Abstract
Vertically aligned carbon nanotube forests show various morphologies on both macro- and micro-scales. These morphologies are a result of growth mechanisms and interactions between nanotubes. By investigating these morphologies, we study both the growth mechanisms and the interactions. We examine forest morphologies in situ, dynamically during chemical vapor deposition growth and ex situ, post growth. In situ observations allow the separate characterization of nucleation, growth and termination phases, and the exploration of connections between morphology and growth. Forests systematically show different morphologies, ranging from uniform to cracked, delaminated, and periodically rippled. These are discussed in terms of the balance of forces within the forests including cohesion, adhesion, and stiffness. We propose a simplified model that predicts termination as a result of an imbalance in the forces present. We show that growth rate differences drive many morphological effects, and these differences originate in the nucleation phase due to gas diffusion.
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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 | 0.000 | 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 | 0.001 |
| 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 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".