Polyfluorene-Sorted Semiconducting Single-Walled Carbon Nanotubes for Applications in Thin-Film Transistors
Bibliographic record
Abstract
Due to their superlative electrical and mechanical properties, single-walled carbon nanotubes (SWNTs) are capable of expanding the current scope of electronic device applications. Advancements in the selective isolation and purification of semiconducting SWNTs through the use of conjugated polymers has allowed for incorporation of high-quality SWNTs into solution-processed thin-film transistors (TFTs). In this study, we report an alternating copolymer based on fluorene and 2,5-dimethoxybenzene that is capable of selectively dispersing semiconducting SWNTs. The exceptional semiconductingSWNT purity (>99%) of the dispersion was confirmed by UV–vis and Raman spectroscopy, which exhibit negligible metallic SWNT features. TFTs fabricated with this polymer–SWNT complex exhibited maximum hole and electron mobilities of 19 and 7 cm 2 /V·s, respectively, with on/off ratios as high as 10 7 . Device fabrication parameters including silane surface treatment, removal of excess polymer, and SWNT dispersion concentration were investigated. Evaluation of hole and electron mobilities indicates that the presence of excess polymer in the polymer–SWNT dispersion did not adversely affect device performance. Atomic force microscopy measurements showed that our polymer–SWNT dispersions were capable of forming a complete percolation pathway between electrodes, with individual SWNTs exfoliated by the polymer.
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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) | 0.002 | 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".