Effect of Induction on the Dispersion of Semiconducting and Metallic Single-Walled Carbon Nanotubes Using Conjugated Polymers
Bibliographic record
Abstract
Despite significant advances in single-walled carbon nanotube (SWNT) synthesis and purification strategies, the separation of metallic and semiconducting SWNTs on a large scale remains a barrier to the realization of many commercial applications. Selective extraction of specific SWNT types by wrapping and dispersion with conjugated polymers has been found effective for semiconducting SWNTs, but structural parameters that dictate selectivity are poorly understood. Here, we report nanotube dispersions with two structurally similar conjugated copolymers, both being poly(fluorene- co -phenylene) derivatives, having comparable degrees of polymerization but differing in the extent of electron donation from functional groups on the phenylene comonomers. It is found that copolymers decorated with electron donating methoxy functionalities lead to predominant dispersion of semiconducting SWNTs, while copolymers decorated with electron withdrawing nitro functionalities bias the dispersion toward metallic SWNTs. Differentiation of semiconducting and metallic SWNT populations was carried out by a combination of UV–vis–NIR absorption spectroscopy, Raman spectroscopy using multiple excitation wavelengths, and fluorescence spectroscopy. These results provide new insight into polymer design features that dictate preferential dispersion of specific SWNT types.
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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) | 0.000 | 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) | 0.000 | 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".