Interactions of Polyaromatic Compounds. Part 1: Nanoaggregation Probed by Electrospray Ionization Mass Spectrometry and Molecular Dynamics Simulation
Bibliographic record
Abstract
Nanoaggregation of three synthetic polyaromatic compounds, N -(1-hexylhepyl)- N ′-(5-carboxylicpentyl)-perylene-3,4,9,10-tetracarboxylicbisimide (C5Pe), N -(1-undecyldodecyl)- N ′-(5-carboxylicpentyl)-perylene-3,4,9,10-tetracarboxylbisimide (C5PeC11), and N, N ′-bis(1-undecyldodecyl)perylene-3,4,9,10-tetracarboxylbisimide (BisAC11), individually or in their binary mixtures was studied under various solution conditions using electrospray ionization mass spectrometry (ESI–MS) and molecular dynamics (MD) simulation. The results from ESI–MS showed a significant enhancement in nanoaggregation of each individual component by increasing their concentration or heptane addition to toluene. Mixing a polyaromatic compound of longer aliphatic chain with a shorter chain polyaromatic compound in a given solvent was found to significantly reduce the apparent average nanoaggregation number. Replacing the −COOH group with an aliphatic group induced further steric hindrance to nanoaggregation of polyaromatic cores in the mixture. The results from MD simulations showed a similar trend of reducing nanoaggregation by mixing of two different polyaromatic compounds. The results of MD simulation further revealed that π–π stacking between polyaromatic cores is the major driving force for nanoaggregation, while steric repulsion and strong solvation of longer aliphatic chains connected to the polyaromatic core hinder nanoaggregation of polyaromatic compounds studied. The results from this study provide a scientific basis for controlling nanoaggregation of polyaromatic compounds and shed light on understanding the observed aggregation of asphaltenes in crude oil.
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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) | 0.001 | 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".