Dynamic optoelectric trapping and deposition of multiwalled carbon nanotubes
Why this work is in the frame
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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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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 | 0.000 |
| 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 it