Enrichment of Semiconducting Single-Walled Carbon Nanotubes with Indigo-Fluorene-Based Copolymers and Their Use in Printed Thin-Film Transistors and Carbon Dioxide Gas Sensors
Bibliographic record
Abstract
High-purity semiconducting single-walled carbon nanotubes (sc-SWCNTs) are promising for portable and high-sensitivity gas sensors because of their excellent physical and electrical properties. Here, we describe the synthesis of a novel indigo-fluorene-based copolymer ( PFIDBoc ) that has been designed to selectively enrich sc-SWCNTs with excellent purity (>99.9%) yet contain a latent function in the form of a tert -butoxy ( t -BOC)-protected amine that can be later revealed and exploited for carbon dioxide (CO 2 ) gas sensing. SWCNTs wrapped with the PFIDBoc polymer can be easily converted via an on-chip thermal process to reveal a vinylogous amide moiety with a secondary amine nitrogen within the indigo building block of the copolymer which is perfectly suited for CO 2 recognition. Thin-film transistors and sensors were inkjet-printed onto rigid and flexible substrates, demonstrating the versatility of enriched PFIDBoc- derived sc-SWCNT dispersions. The printed transistors exhibited a mobility up to 9 cm 2 V –1 s –1 and on/off current ratios >10 5 . We further demonstrate herein a CO 2 sensor for indoor air quality monitoring even in low humidity environments, possessing a linear response with up to ∼5.4% sensitivity and a dynamic range between 400 and 2000 ppm in air with a relative humidity of ∼ 40%.
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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) | 0.000 | 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".