Dynamic optoelectric trapping and deposition of multiwalled carbon nanotubes
Bibliographic record
Abstract
In the path toward the realization of carbon nanotube (CNT)-driven electronics and sensors, the ability to precisely position CNTs at well-defined locations remains a significant roadblock. Highly complex CNT-based bottom-up structures can be synthesized if there is a method to accurately trap and place these nanotubes. In this study, we demonstrate that the rapid electrokinetic patterning (REP) technique can accomplish these tasks. By using laser-induced alternating current (AC) electrothermal flow and particle-electrode forces, REP can collect and maneuver a wide range of vertically aligned multiwalled CNTs (from a single nanotube to over 100 nanotubes) on an electrode surface. In addition, these trapped nanotubes can be electrophoretically deposited at any desired location onto the electrode surface. Apart from active control of the position of these deposited nanotubes, the number of CNTs in a REP trap can also be dynamically tuned by changing the AC frequency or by adjusting the concentration of the dispersed nanotubes. On the basis of a calculation of the stiffness of the REP trap, we found an upper limit of the manipulation speed, beyond which CNTs fall out of the REP trap. This peak manipulation speed is found to be dependent on the electrothermal flow velocity, which can be varied by changing the strength of the AC electric field.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".