Sonication-Enhanced Alignment Relay Technique for the Orientation of Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
The high demand for thin, lightweight yet fast and efficient devices is a driving force behind the miniaturization trend in the electronics industry. Specifically, the advancement of semiconducting single-walled carbon nanotubes (SWNTs) can continue to revolutionize transistors, although there are still many challenges ahead. We have previously reported an alignment relay technique (ART) that is capable of simultaneously controlling the alignment, length, and diameter of surface deposited SWNTs. However, the current technique yields inconsistencies in orientation, lengths of tubes, and their density. Here, we present a reviewed ART protocol that includes sonication for improved selectivity. We show that the SWNTs average alignment increased from 40% to 77% within a 10° range in orientation with sonication times as low as 5 min. Sonication generated larger diameter nanotubes on the surface, with a preference for semiconducting chiral tubes in the range of 1.44–1.61 nm in diameter. Consequently, simple alterations to the standard alignment relay technique can prove to be prosperous in improving selectivity and orientational control of single-walled carbon nanotubes. This work has direct impact for the simultaneous control of nanotube alignment and nanotube chiralities.
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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) | 0.001 | 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".