Dispersion of single‐walled carbon nanotubes using nucleobase‐containing poly(acrylamide) polymers
Bibliographic record
Abstract
ABSTRACT Single‐walled carbon nanotubes (SWNTs) possess extraordinary properties, but suffer from poor solubility and a lack of purity. Of the possible routes available to solubilize and purify nanotube samples, the use of noncovalent functionalization is ideal as carbon nanotube properties are not deleteriously affected. A multitude of different dispersants have been investigated thus far, but of particular interest is deoxyribonucleic acid (DNA), which has previously been demonstrated to effectively separate metallic and semiconducting carbon nanotubes. Here, we investigate the ability of synthetic nucleobase‐containing poly(acrylamide) polymers to produce stable nanotube dispersions in organic solvents. Polymers bearing different nucleobase and backbone structures, as well as block copolymers with different block sequences were investigated. Polymer:SWNT mass ratios and solvent compositions were optimized for the nucleobase‐functionalized polymers, and semiconducting and metallic SWNT populations were identified by a combination of UV‐Vis‐NIR absorption, Raman, and fluorescence spectroscopy. These results demonstrate the capacity for synthetic DNA analogues to disperse SWNTs in organic media. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2017, 55, 2611–2617
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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) | 0.000 | 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 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".