Preparation of stimulus‐responsive, polyfluorene‐wrapped carbon nanotubes via palladium cross coupling
Bibliographic record
Abstract
ABSTRACT Decoration of carbon nanotube surfaces without damaging nanotube optoelectronic properties is an ongoing challenge. Here, we utilize Sonogashira coupling chemistry to decorate the nanotube surface without perturbing optoelectronic properties. Reactive, noncovalently functionalized polymer–nanotube complexes were prepared using a polyfluorene with aryl iodide groups in its side chains. The aryl iodides enable Pd cross coupling between polymer–nanotube complexes and small molecules or polymers derivatized with an alkyne. Modestly efficient coupling was found to occur under dilute conditions at elevated temperatures. Successful coupling between aryl iodide and alkyne partners was observed using infrared spectroscopy via the appearance of carbonyl stretches that originate from covalently linked, carbonyl‐containing alkynes, and thermogravimetric analysis was used to measure reaction conversion under various conditions. Grafting of the hydrophobic polymer–nanotube complex with poly(ethylene glycol) enabled the dispersion to be transferred from organic to aqueous solution. This chemistry resulted in no damage to the nanotube sidewall, as evidenced by Raman spectroscopy. The aryl iodide‐containing polyfluorene–nanotube complex was also coupled to a photoswitchable alkyne‐containing spiropyran moiety and it was found that the photoswitch retained its functionality after coupling to the polymer–nanotube complex. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2018, 56, 2723–2729
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".