Sorting of Semiconducting Single-Walled Carbon Nanotubes in Polar Solvents with an Amphiphilic Conjugated Polymer Provides General Guidelines for Enrichment
Bibliographic record
Abstract
Conjugated polymer extraction (CPE) has been shown to be a highly effective method to isolate high-purity semiconducting single-walled carbon nanotubes (sc-SWCNTs). In both literature reports and industrial manufacturing, this method has enabled enrichment of sc-SWCNTs with high purity (≥99.9%). High selectivity is typically obtained in nonpolar aromatic solvents, yet polar solvents may provide process improvements in terms of yield, purity and efficiency. Using an amphiphilic fluorene-alt-pyridine conjugated copolymer with hydrophilic side chains, we have investigated the enrichment of sc-SWCNTs in polar solvents. Various conditions such as polymer/SWCNT ratio, solvent polarity, solvent dielectric constant as well as polymer solubility and SWCNT dispersibility were explored in order to optimize the purity and yield of the enriched product. Herein, we provide insights on CPE by demonstrating that a conjugated polymer having a hydrophobic backbone and hydrophilic oligo(ethylene oxide) side chains provides near full recovery (95%) of sc-SWCNTs using a multiextraction protocol. High purity is also obtained, and differences in chiral selectivity compared to analogous hydrophobic systems were confirmed by optical absorption and Raman spectroscopy as well as photoluminescence excitation mapping. Taking into consideration the solvent dielectric constant, polarity index as well as polymer solubility and SWCNT dispersibility provides a better understanding of structure-property effects on sc-SWCNT enrichment. The resulting hydrophilic SWCNT dispersions demonstrate long-term colloidal stability, making them suitable for ink formulation and high-performance thin-film transistors fabrication.
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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 | 0.001 |
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".