Enrichment of Metallic Carbon Nanotubes Using a Two-Polymer Extraction Method
Bibliographic record
Abstract
Abstract The large-scale enrichment of metallic carbon nanotubes is a challenging goal that has proven elusive. Selective dispersion of carbon nanotubes by specifically designed conjugated polymers is effective for isolating semiconducting species, but a comparable system does not exist for isolating metallic species. Here, we report a two-polymer system where semiconducting species are extracted from the raw HiPCO or plasma-torch nanotube starting material using an electron-rich poly(fluorene-co-carbazole) derivative, followed by isolation of the metallic species remaining in the residue using an electron-poor methylated poly(fluorene-co-pyridine) polymer. Characterization of the electronic nature of extracted samples was carried out via a combination of absorption, Raman, and fluorescence spectroscopy, as well as electrical conductivity measurements. Using this methodology, the metallic species in the sample were enriched 2-fold in comparison to the raw starting material. These results indicate that the use of electron-poor polymers is an effective strategy for the enrichment of metallic species.
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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 | 0.001 | 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".