Modeling the Phase Behavior of Asphaltene + Toluene + Polystyrene Mixtures—A Depletion Flocculation Approach
Bibliographic record
Abstract
Mixtures of polystyrene + Maya and Athabasca pentane asphaltene + toluene split into two stable phases, one toluene + polymer rich and one toluene + asphaltene rich. This phase behavior was attributed to depletion flocculation, previously, and a Fleer-Tuinier based model was used to simulate the phase diagrams including one-phase to two-phase boundaries, tie lines, critical points, and relative phase volumes in the two-phase region. The distribution of asphaltenes between molecular and aggregated species and the variation of the mean size and size distribution of aggregated asphaltene species with global composition are not known a priori. This knowledge gap presents a key conceptual challenge. Consequently, the variation of the fraction of asphaltenes participating in the depletion flocculation mechanism, γ(η), and variation of asphaltene mean size R s (η) with asphaltene volume fraction, η, are fitted parameters introduced in the phase behavior model. In this work, the phase behavior modeling approach is presented and illustrated, and the joint conformance of R s (η) and γ(η) with trends anticipated by the well-known Ostwald–Freundlich equation for solute solubility with solute particle size is demonstrated for both Maya and Athabasca pentane asphaltenes. With this conformance, the behavior of asphaltenes in toluene is shown to be consistent with the behavior of a temperature invariant distribution of solid, spherical, colloids where the mean size varies with asphaltene volume fraction, η. Furthermore, the correlations γ(η) and R s (η) are validated with measured differential enthalpies of solution for asphaltenes in toluene. These outcomes provide additional theoretical underpinnings for a phase behavior modeling approach that may lead to the development of predictive models for asphaltene + polystyrene + toluene and analogous mixtures as additional phase behavior data become available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".