Probing Structure–Nanoaggregation Relations of Polyaromatic Surfactants: A Molecular Dynamics Simulation and Dynamic Light Scattering Study
Bibliographic record
Abstract
Four synthetic perylene bisimide-based polyaromatic (PA) surfactants with a structural or functional group difference in their attached hydrophilic/hydrophobic substituent side chains were used to probe structure-nanoaggregation relations in organic media by molecular dynamics simulations and dynamic light scattering. The results from the simulated radial distribution functions and light scattering experiments indicate that variation in the structure of side chains and polarity of functional groups leads to significant variations in molecular association, dynamics of molecular nanoaggregation and structure of nanoaggregates. The aggregates of PA surfactant molecules grow to much larger sizes in heptane than in toluene. The aromatic solvent is shown to hinder molecular association by weakening π-π stacking, demonstrating the control of molecular aggregation by tuning solvent properties. In aliphatic solvent, the aggregates formed from PA surfactants of aliphatic alkyl groups and phenylalanine derivatives as a side chain usually have a higher solvent accessible surface area to accessible volume ratio (SASA:AV) than that of tryptophan derivatives in their side chains. PA surfactants with an aliphatic functional group in both side chains does not form polyaromatic π-π stacking (T-stacking) due to its strong steric hindrance in both solvents. Depending on the nature of the side chains attached, various stacking distributions, aggregation sizes, and SASA:AV ratios were obtained. In PA surfactant nanoaggregates, all of the solvent molecules were found to be excluded from the interstices of the stacked polyaromatic cores, regardless of whether the solvent molecules are aliphatic or aromatic. Although the change in the structure of side chain substituent in polyaromatic surfactants has a negligible impact on their self-diffusivity, it can strongly influence their intermolecular interactions, leading to different aggregate diffusion coefficients.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".