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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".