The role of the solvent and the size of the nanotube in the non-covalent dispersion of carbon nanotubes with short organic oligomers – a DFT study
Bibliographic record
Abstract
Among different dispersants of single-walled carbon nanotubes (SWCNTs), conjugated organic oligomers have the ability to interact strongly with SWCNTs and allow for effective dispersion in several organic solvents. Recently, we have carried out two computational investigations on the intermolecular interactions between conjugated organic oligomers and SWCNTs in order to gain insight into an important process of the non-covalent dispersion of carbon nanotubes with short oligomers. These studies highlighted the fact that two additional factors, namely, the effects of the solvent and the carbon nanotube's size on these interactions need further investigation. In this work, with the help of model compounds (which are molecular fragments of the short oligomers used in our previous investigations), we analyze the significance of these two factors. We employ three dispersion corrected density functional theory (D-DFT) approximations (B97D, wB97XD, and B3LYP-D3) to assess the effect of the DFT method, and two basis sets (6-31G(d) and 6-31++G(d,p)) to assess the importance of using a higher basis set in our computations. The main focus of this work is to assess the effect of solvation and nanotube's size on the structure, electronic properties, and binding energies of the respective pairs of model compounds and segments of carbon nanotubes. No significant differences are found between the results of (6,5) and (8,7) SWCNTs in either the geometrical parameters of interacting oligomers or the general tendency of wrapping of their long side chains (SCs) around the nanotubes. However, we find that the binding energies per atom between nanotubes and model compounds are larger for nanotubes with the smaller diameter. The results of electronic properties also show that all model compounds interact more strongly with the (6,5) SWCNT than with the (8,7) SWCNT. Polar solvents such as chloroform show lower binding energies relative to those obtained without a solvent or with non-polar solvents such as hexane. It appears that the presence of a solvent weakens the oligomer/nanotube interactions and, presumably, strengthens the oligomer/solvent and nanotube/solvent interactions to facilitate dispersion of SWCNTs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".