Electrical transport properties of single wall carbon nanotube/polyurethane composite based field effect transistors fabricated by UV-assisted direct-writing technology
Bibliographic record
Abstract
We report on the fabrication and transport properties of single-walled carbon nanotube (SWCNT)/polyurethane (PU) nanocomposite microfiber-based field effect transistors (FETs). UV-assisted direct-writing technology was used, and microfibers consisting of cylindrical micro-rods, having different diameters and various SWCNT loads, were fabricated directly onto SiO₂/Si substrates in a FET scheme. The room temperature dc electrical conductivities of these microfibers were shown to increase with respect to the SWCNT concentrations in the nanocomposite, and were about ten orders of magnitude higher than that of the pure polyurethane, when the SWCNT load ranged from 0.1 to 2.5 wt% only. Our results show that for SWCNT loads ≤ 1.5 wt%, all the microfibers behave as a FET with p-type transport. The resulting FET exhibited excellent performance, with an I(on)/I(off) ratio of 10⁵ and a maximum on-state current (I(on)) exceeding 70 µA. Correlations between the FET performance, SWCNTs concentration, and the microfiber diameters are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".