Response and resilience of carbon nanotube micropillars to shear flow
Bibliographic record
Abstract
Interactions between carbon nanotubes (CNTs) and fluid flows are central to the operation of several emerging nanotechnologies. In this paper, we explore the fluid-structure interaction of CNT micropillars in wall-bounded shear flows, relevant to recently developed microscale wall shear stress sensors. We monitor the deformation of CNT micropillars in channel flow as the flow rate and wall shear stress are gradually varied. We quantify how the micropillars bend at low wall shear stress, and then will commonly tilt abruptly from their base above a threshold wall shear stress, which is attributed to the lower density of the micropillars in this region. Some micropillars are observed to flutter rapidly between a vertical and horizontal position around this threshold wall shear stress, before settling to a tilted position as wall shear stress increases further. Tilted micropillars are found to kink sharply near their base, similar to the observed buckling near the base of CNT micropillars in compression. Upon reducing the flow rate, micropillars are found to fully recover from a near horizontal position to a near vertical position, even with repeated on-off cycling. At sufficiently high wall shear stress, the micropillars were found to detach at the catalyst particle-substrate interface. The mechanical response of CNT micropillars in airflow revealed by this study provides a basis for future development efforts and the accurate simulation of CNT micropillar wall shear stress sensors.
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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 | 0.001 | 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".