DFT investigation of the interaction between single-walled carbon nanotubes and fluorene-based conjugated oligomers
Bibliographic record
Abstract
. The computational results disclose the effects of end groups, SCs, and DFT methods on structures, dipole moments, and energetics of isolated DPFs and DPF/SWCNT combinations. Consistent with our previous study (involving oligo(p-phenylene ethynylene)s (OPEs)) [Aljohani et al., J. Phys. Chem. C, 2017, 121, 4692-4702], our results herein demonstrate that the type of end group plays a key role in determining the strength of interactions between SWNTs and conjugated oligomers. In particular, DTF-endcapped oligomers have a stronger electrostatic interaction with SWCNT than ALD-endcapped oligomers do. As a result, DTF-endcapped conjugated oligomers become more polarized than ALD-endcapped oligomers after complexing with SWCNTs. The magnitude of binding energy, on the other hand, shows dependence on the orientation of the backbone and side chains of these oligomers relative to the SWCNT which in the case of fluorene-based oligomers is not always favourable for optimal binding. This study indicates that fluorene-based oligomers might not be as good dispersants of SWCNTs as OPEs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".