Dispersion of Single-Walled Carbon Nanotubes with Oligo(<i>p</i>-phenylene ethynylene)s: A DFT Study
Bibliographic record
Abstract
Pure carbon nanotubes (CNT) and CNT–polymer composites have many useful properties, ranging from electrical conductivity to superior mechanical strength. However, the full potential of using CNTs as reinforcements (in a polymer matrix, for example) has been severely limited because of complications associated with the dispersion of CNTs. CNTs tend to entangle with each other, forming materials with properties that fall short of expectations. One of the effective ways of dispersing CNTs is the use of short π-conjugated oligomers like oligo( p -phenylene ethynylene)s (OPEs) as dispersants. In this study, we provide a comprehensive investigation of the interactions between single-walled CNTs (SWCNTs) and OPEs with two different end groups; aldehyde (ALD) and dithiafulvene (DTF). The hybrid B3LYP and the dispersion (D)- and/or the long-range (LR)-corrected density functional theory (DFT) methods such as B97D, wB97XD, and CAM-B3LYP with the 6-31G(d) basis set are employed in obtaining electronic structure information (dipole moments and energy levels) for the gas-phase (single) oligomers and the (6,5) SWCNT and their combinations. In addition, the D- and/or LR-corrected DFT methods are used in determining binding energies and intermolecular distances for the OPE/SWCNT combinations. We focus on understanding the roles of oligomer’s end groups and side chains in the dispersion of SWCNTs. In agreement with the experimental observations, the electronic structure and the binding energy results show that OPE-DTF interacts more strongly with the SWCNT than OPE-ALD. This work also provides insight into why OPEs end-capped with DTFs are much more effective in the dispersion of CNTs than OPEs end-capped with ALDs. Furthermore, this computational analysis can be of use in choosing an appropriate D- and/or LR-corrected DFT method when studying properties of systems containing CNTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".