“Click” generation of a conjugated polymer library for SWNT dispersion
Bibliographic record
Abstract
ABSTRACT Noncovalent functionalization of single‐walled carbon nanotubes (SWNTs) with conjugated polymers enhances SWNT processability and allows for selective dispersion of various SWNT species. Selective dispersions can be obtained by tuning the nature of the polymer, which can involve using various polymer backbones or side‐chains. However, a clear understanding of selectivity determinants is elusive, as the degree of polymerization (DP) has a large effect on SWNT selectivity. Additionally, preparing libraries of conjugated polymers with varying functionality while keeping DP consistent is difficult. Here, we report the utilization of a strained cyclooctyne‐containing conjugated polymer that serves as a versatile scaffold, enabling systematic preparation of a small library of conjugated polymers with different side‐chain functionality, while maintaining a consistent DP. The resulting polymers were used as dispersants for SWNTs, forming supramolecular polymer‐SWNT complexes that were characterized by UV‐Vis‐NIR absorption and Raman spectroscopy. In the series of polymers, we were able to probe the effect of small changes within the side chains, such as the incorporation of a carbonyl group or an aromatic unit, on the quality of the polymer‐SWNT dispersion. The results of these studies provide new insight into the factors that dictate the ability of a polymer to form strong interactions with SWNTs. © 2018 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2018, 56, 2053–2058
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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 | 0.001 |
| 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".