Covalent Functionalization of Single-Walled Carbon Nanotubes with Thermoresponsive Core Cross-Linked Polymeric Micelles
Bibliographic record
Abstract
A facile method for the covalent functionalization of single-walled carbon nanotubes (SWNTs) with thermoresponsive core cross-linked (CCL) polymeric micelles is presented. This method is based on SWNT functionalization as well as polymer self-assembly. The copolymer, poly( N,N -dimethylacrylamide)- b -poly( N -isopropylacrylamide- co - N -acryloxysuccinimide) (PDMA- b -P(NIPAM- co -NAS)), bearing an azide group at the PDMA end, was first prepared by reversible addition–fragmentation chain transfer (RAFT) polymerization. Covalent functionalization of SWNTs with well-defined, azide-derivatized PDMA- b -P(NIPAM- co -NAS) was then accomplished by a nitrene addition reaction. The copolymer-functionalized SWNTs (f-SWNTs) consisted of about 26 wt % copolymer and exhibited relatively high solubility in water. Subsequently, the coassembly of the copolymer and f-SWNT blends was carried out in aqueous solution. It was found that the copolymer chains grafted onto the surface of SWNTs were coassembled with the free chains in solution, leading to thermoresponsive polymeric micelles that adhered to the surface of nanotubes. Upon cross-linking, the copolymer aggregates were stabilized and covalently anchored to SWNTs (SWNT-micelle). The resulting assembled nanostructures were still soluble in water and were characterized by atomic force microscopy (AFM) and transmission electron microscopy (TEM).
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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) | 0.001 | 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".