Functionalization of polyfluorene‐wrapped carbon nanotubes using thermally cleavable side‐chains
Bibliographic record
Abstract
Abstract The length and nature of side‐chains in conjugated polymer‐wrapped carbon nanotubes can impact their conductivity. We investigate functionalization of polyfluorene‐single‐walled carbon nanotubes (SWNT's) using cleavable side‐chains that are removable post‐processing. The triethylene glycol side‐chains contain a thermally cleavable carbonate linker. Upon heating the films to 170 °C, the conductivity increased, reaching a plateau of (2.0 ± 0.1) × 10−2 S/m after 16 h, compared to (1.0 ± 0.2) × 10−3 S/m for the control sample. UV–Vis–near‐infrared (NIR) and Raman spectroscopy show well‐dispersed SWNT samples and confirm that the heating treatment did not damage the nanotubes. Functionalization using longer polyethylene glycol side‐chains was also investigated. After heating, cleavage of the longer chains resulted in conductivity of (8.2 ± 1.6) × 10−4 S/m compared to (8.1 ± 1.4) × 10−5 S/m for the control. UV–Vis–NIR and Raman spectroscopy showed well‐dispersed SWNT samples and confirmed that the nanotubes were not damaged. Finally, we investigate dispersions in triethylene glycol monomethyl ether and tetraethylene glycol dimethyl ether, generally deemed “green” solvents. Polymer‐SWNT complexes functionalized with shorter side‐chains did not form stable dispersions, resulting in precipitation of the nanotubes upon standing for a few minutes after the removal of tetrahydrofuran, while complexes functionalized with longer side‐chains formed stable dispersions.
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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".