Decomposable <i>s</i>‐Tetrazine Copolymer Enables Single‐Walled Carbon Nanotube Thin Film Transistors and Sensors with Improved Sensitivity
Bibliographic record
Abstract
Abstract Semiconducting single‐walled carbon nanotubes (sc‐SWCNTs) enriched by a conjugated polymer extraction process have been actively studied for various applications in both electronics and optoelectronics. Although the resulting tube samples usually have high sc‐purity and concentration, SWCNT networks from such dispersions typically contain residual conjugated polymer that may degrade device performance and its removal remains a challenge while maintaining uniform, dense SWCNT thin film networks. In this study, a novel polymer–SWCNT combination based on an alternating bisfuran‐s‐tetrazine and benzo[1,2‐b:4,5‐b′]dithiophene copolymer abbreviated as PBDTFTz is proposed. This polymer decomposes at >250 °C or under UV irradiation. In situ transistor characterization under laser irradiation confirms the polymer decomposition. The study of the tube network in the transistor channel at various channel lengths reveals significantly reduced contact resistance attributed to removal of the wrapping PBDTFTz polymer. In ammonia sensing experiments, sc‐SWCNT networks demonstrate rapid and reversible responses, while the unwrapped nanotube networks prove superior in terms of signal to noise ratio and a detection limit of 2.5 ppb is calculated, almost four times better than polymer wrapped nanotubes.
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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".