Dispersion‐Driven Cooperativity in Alkyl Perylene Diimide Oligomers: Insights from Density Functional Theory
Bibliographic record
Abstract
The cooperative mechanism is of paramount importance in the synthesis of supramolecular polymers with desired characteristics, including molecular mass, polydispersity, and morphology. It is primarily driven by the presence of intermolecular interactions, which encompass strong hydrogen bonding, metal-ligand interactions, and dipole-dipole interactions. In this study, we utilize density functional theory and energy decomposition analysis to investigate the cooperative behavior of perylene diimide (PDI) oligomers with alkyl chains at their imide positions, which lack the previously mentioned interactions. Our systematic examination reveals that dispersion interactions originating from the alkyl side-chain substituents play an important role in promoting cooperativity within these PDIs. This influence becomes even more pronounced for alkyl chain lengths beyond hexyl groups. The energy decomposition analysis reveals that the delicate balance between dispersion energy and Pauli repulsion energy is the key driver of cooperative behavior in PDIs. Additionally, we have developed a mathematical model capable of predicting the saturated binding energies for PDI oligomers of varying sizes and alkyl chain lengths. Overall, our findings emphasize the previously undervalued significance of dispersion forces in cooperative supramolecular polymerization, enhancing our overall understanding of the cooperative mechanism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".