Effect of Single‐walled Carbon Nanotube (SWCNT) Composition on Polyfluorene‐Based SWCNT Dispersion Selectivity
Bibliographic record
Abstract
Applications of single-walled carbon nanotubes (SWCNTs) are hampered by the mixtures of metallic and semiconducting SWCNTs that are present in commercial samples. Separation of SWCNTs according to electronic type is therefore extremely important. Recently, the selective interaction between the conjugated polymer, poly(9,9-di-n-dodecyl-fluorenyl-2,7-diyl) and semiconducting SWCNTs has been reported. However, the mechanism responsible for this selectivity is poorly understood. To determine whether this polymer is only selective for semiconducting SWCNTs, we exposed it to mixtures of metallic and semiconducting SWCNTs in different ratios. We found that the polymer is indeed selective for semiconducting SWCNTs in toluene, but only when the starting ratio is below 67:33 metallic:semiconducting. When the starting ratio is increased to 67:33 or higher, the amount of metallic SWCNTs dispersed dramatically increases. If the solvent is changed to THF, the threshold ratio at which metallic SWCNTs begin to be dispersed is much lower. This indicates that the polymer exhibits a preference for interaction with semiconducting SWCNTs, but is not precluded from interaction with metallic SWCNTs if exposed to a high enough concentration.
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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 | 0.001 |
| 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) | 0.002 | 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".