Transferable Thin Films of Pristine Carbon Nanotubes
Bibliographic record
Abstract
We describe here a simple and low-cost method to prepare ultra-thin, homogeneous, and transferable films of pristine carbon nanotubes (CNTs). The highly efficient chemical vapor deposition (CVD) growth method involves silica supported catalysts and alcohol vapor as gaseous carbon source. By varying the amount of catalysts, the thickness of synthesized films can be easily tuned from 20 nm (sub-monolayer) to 150 nm in a controlled fashion. High-resolution transmission electron microscopy (HRTEM) revealed that the films are composed primarily of single-walled and a small fraction of double-walled CNTs. A nonlinear relationship between film conductivity and thickness was observed. Our sub-monolayer ( 20 nm) film, which is noticeably thinner than conductive CNT films synthesized using other methods (typically > 50 nm and up to 100 microm), shows the highest conductivity of 400 mho x cm(-1) with 90% transparency in the visible range and close to 100% transparency in the infrared range. This ultra-thin film can also be transferred carrier-film free to a wide range of substrates including low-cost plastics for flexible electronics. Compared to CNT films prepared by filtration techniques, our films demonstrated superior stability against mechanical bending.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".