Probing the interactions between pour point depressants (PPDs), viscosity index improvers (VIIs), and wax in octane using fluorescently labeled PPDs
Bibliographic record
Abstract
A poly(octadecyl methacrylate) sample fluorescently labeled with 6.7 mol% of pyrene (Py(6.7)-PC18MA) was used as a mimic of a pour point depressant (PPD) to investigate how Py(6.7)-PC18MA interacts with wax found in engine oils and ethylene-propylene (EP) copolymers used as mimics of viscosity index improvers (VIIs). The fluorescence spectra of Py(6.7)-PC18MA solutions in octane were acquired in octane at low and high concentrations of Py(6.7)-PC18MA and analysed to obtain the molar fraction ( finter) of pyrene labels, which formed excimer intermolecularly, a measure of the level of intermolecular interactions between Py(6.7)-PC18MA molecules in the solution. The finter-versus- T profile obtained for Py(6.7)-PC18MA alone in octane confirmed that Py(6.7)-PC18MA formed microcrystals at solution temperatures below 0 °C. The effect induced by the addition of wax and an amorphous (EP(AM)) and semicrystalline (EP(SM)) EP copolymer on the interactions experienced by Py(6.7)-PC18MA were characterized by monitoring finter as a function of temperature and comparing the different finter-versus- T plots obtained after the addition of the different components with the finter-versus- T plot obtained for Py(6.7)-PC18MA alone. These studies demonstrated that wax and EP(AM) increased the level of intermolecular interactions between the Py(6.7)-PC18MA molecules at all temperatures in octane. EP(SM) increased the interactions between Py(6.7)-PC18MA molecules at high temperature, where it was soluble in octane, but finter reverted to its value in the absence of EP(SM) at low temperatures, where EP(SM) had crystallized. These experiments illustrate how pyrene excimer fluorescence can be applied to probe the complex interactions taking place between the different components found in engine oils.
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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 | 0.001 |
| 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".