Polycarbazole‐Sorted Semiconducting Single‐Walled Carbon Nanotubes for Incorporation into Organic Thin Film Transistors
Bibliographic record
Abstract
Abstract The realization of organic thin film transistors (OTFTs) with performances that support low‐cost and large‐area fabrication remains an important and challenging topic of investigation. The unique electrical properties of single‐walled carbon nanotubes (SWNTs) make them promising building blocks for next generation electronic devices. Significant advances in the enrichment of semiconducting SWNTs, particularly via π‐conjugated polymers for purification and dispersal, have allowed the preparation of high‐performance OTFTs on a small scale. The intimate interaction of the conjugated polymer with both SWNTs and the dielectric necessitates the investigation of a variety of conjugated polymer derivatives for device optimization. Here, the preparation of polymer–SWNT composites containing carbazole moieties, a monomer unit that has remained relatively overlooked for the dispersal of large‐diameter semiconducting SWNTs, is reported. This polymer selectively discriminates semiconducting SWNTs using a facile procedure. OTFTs prepared from these supramolecular complexes are ambipolar, and possess superior mobilities and on/off ratios compared to homo poly(fluorene) dispersions, with hole mobilities from random‐network devices reaching 21 cm2 V−1 s−1. Atomic force microscopy measurements suggest the poly(carbazole)–SWNT composites form more uniform thin films compared to the poly(fluorene) dispersion. Additionally, treating the silicon dioxide dielectric with octyltrichlorosilane is a simple and effective way to reduce operational hysteresis in SWNT OTFTs.
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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".