Recent developments in the selective dispersion of single-walled carbon nanotubes using conjugated polymers
Bibliographic record
Abstract
A significant barrier that impedes the commercialization of single-walled carbon nanotube-related applications is that all known synthetic methods produce a complicated mixture of semiconducting and metallic species. For device applications, pure semiconducting or pure metallic samples are desirable. Thus far, the purification methods that have been identified are capable of separating individual carbon nanotube species on a microgram scale, but purification on a large scale has remained elusive. The use of conjugated polymers to selectively disperse specific nanotube species is a promising approach to resolve the scalability issue, but a comprehensive understanding of the selectivity mechanism has not yet been achieved. Here, several of the trends reported in the literature are outlined to further the rational design of conjugated polymers for nanotube sorting. Numerous variables influence dispersion selectivity, including polymer structure and molecular weight, nanotube type used, sonication temperature, amount of polymer relative to nanotube, and solvent. We have organized these seemingly disparate parameters into two simple categories: conjugated polymer structure, and dispersion preparation conditions. Most importantly, we consider the mechanistic arguments that have been proposed, and provide additional insights based on the observations in the literature.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".