Decoration of Polyfluorene-Wrapped Carbon Nanotubes with Photocleavable Side-Chains
Bibliographic record
Abstract
Functionalizing polyfluorene-wrapped carbon nanotubes without damaging their properties is effective via Copper-Catalyzed Azide–Alkyne Cycloaddition (CuAAC). However, the length and nature of polymer side-chains can impact the conductivity of polyfluorene-SWNT films by preventing close contact between the nanotubes. Here, we investigate the functionalization of a polyfluorene-SWNT complex using photocleavable side-chains that can be removed post-processing. The cleavage of the side-chains containing an ortho-nitrobenzyl ether derivative is efficient when exposed to a UV lamp at 365 nm. The photoisomerization of the o-nitrobenzyl ether linker into the corresponding o-nitrosobenzaldehyde was first monitored via UV-Vis absorption spectroscopy and 1H-NMR spectroscopy on the polymer, which showed efficient cleavage after 2 h. We next investigated the cleavage on the polyfluorene-SWNT complex via UV-Vis-NIR absorption spectroscopy. The precipitation of the nanotube dispersion and the broad absorption peaks after overnight irradiation also indicated effective cleavage. In addition, Raman spectroscopy post-irradiation showed that the nanotubes were not damaged upon irradiation. This paper reports a proof of concept that may find applications for SWNT-based materials in which side-chain removal could lead to higher device performance.
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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".