Separation of Highly Pure Semiconducting Single-Wall Carbon Nanotubes in Alkane Solvents via Double Liquid-Phase Extraction
Bibliographic record
Abstract
This study delves into the distinctive selective property exhibited by a non-conjugated cholesterol-based polymer, poly(CEM11-b-EHA7), in sorting semiconducting single-walled carbon nanotubes (s-SWCNTs) within isooctane. Comprised of 11 repeating units of cholesteryloxycarbonyl-2-hydroxy methacrylate (CEM) and 7 repeating units of 2-ethylhexyl acrylate (EHA), this non-conjugated polymer demonstrates robust supramolecular interactions across the sp2 surface structure of carbon nanotubes and graphene. When coupled with the Double Liquid-Phase Extraction (DLPE) technology, the polymer effectively segregates s-SWCNTs into the isooctane phase (nonpolar) while excluding metallic SWCNTs (m-SWCNTs) in the water phase (polar). DLPE proves particularly efficient in partitioning larger-diameter s-SWCNTs (0.85–1.0 nm) compared to those dispersed directly in isooctane by poly(CEM11-b-EHA7) using direct liquid-phase exfoliation (LPE) techniques for diameters ranging from 0.75 to 0.95 nm. The DLPE method, bolstered by poly(CEM11-b-EHA7), successfully eliminates impurities from s-SWCNT extraction, including residual metallic catalysts and carbonaceous substances, which constitute up to 20% of raw HiPCO SWCNTs. DLPE emerges as a scalable and straightforward approach for selectively extracting s-SWCNTs in nonpolar, low-boiling-point solvents like alkanes. These dispersions hold promise for fabricating fast-drying s-SWCNT inks, which are ideal for printed and flexible thin-film transistors.
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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 | 0.001 |
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".