Nanoaggregation of Polyaromatic Compounds Probed by Electrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
This paper reports the results of the first detailed experimental study on probing nanoaggregation of a polyaromatic compound. Electrospray ionization mass spectrometry (ESI–MS) was used to monitor the self-association of a well-defined polyaromatic compound, N -(1-hexylhepyl)- N ′-(5-carboxylicpentyl)-perylene-3,4,9,10-tetracarboxylicbisimide (C5Pe), under various solution conditions. Gaseous ions corresponding to nanoaggregates of C5Pe molecules were directly observed on ESI mass spectra. The dominant aggregation number ( n ) was found to be less than 10, although larger nanoaggregates with an aggregation number larger than 10 were also observed. The aggregation number of C5Pe decreased by replacing toluene with xylene, while it increased with the C5Pe concentration or upon the addition of heptane to toluene as the solvent. The consecutive aggregation number was found only for small C5Pe nanoaggregates (2 ≤ n ≤ 11), which suggests a stepwise self-association at n ≤ 11. The larger nanoaggregates ( n > 11) were formed by interactions between small nanoaggregates. The presence of naphthenic acids (NAs) was observed to hinder C5Pe self-association. The dispersive effect of NAs was found to be in the order of 1-methyl-1-cyclohexanecarboxylic acid ∼ cyclohexanebutyric acid < stearic acid < 5β-cholanic acid < 1-naphthalene pentanoic acid. The nanoaggregation behavior of C5Pe was compared to that of two other polyaromatic compounds.
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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".