Interactions between Asphaltenes and a Model Demulsifier in Bulk and at an Interface Studied by Small-Angle Neutron Scattering (SANS) and Neutron Reflectometry
Bibliographic record
Abstract
Abstract This article describes neutron reflectometry to probe the structure of the asphaltene layer adsorbed onto a hydrophilic silicon surface and the interactions between asphaltenes and a model demulsifier (pluronic). To start with, asphaltene nanoaggregation is studied in bulk by small-angle neutron scattering (SANS). In pure toluene, asphaltenes form nanoaggregates with sizes that are found to moderately increase when heptane is added. Then, the structure of the asphaltene layer onto a silicon surface is determined by neutron reflectometry. It is first shown that conclusive results on the structure of the adsorbed asphaltene layer can only be determined by varying the scattering length density of the solvent, i.e., by measuring reflectivity curves in various mixtures of D- and H-toluene and by simultaneously fitting all the data sets. The asphaltene layer can be successfully modeled using a single layer; a two-layer model always converges with the second layer having zero thickness. This layer has a thickness of 51 Å, with a low solvation close to the silicon surface (estimated ≈29%). The solvation increases with distance from the silicon oxide layer, reaching finally a value of 91%. Small-angle neutron scattering data indicate that the thickness is close to twice the radius of gyration of asphaltene nanoaggregates in solution. This result indicates that the extent of the asphaltene layer is linked to its self-associative properties in bulk. Finally, using the results obtained by contrast matching to constrain the fitting, the influence of a model demulsifier (pluronic PE 8100) on the asphaltene layer is investigated. In the presence of pluronic, the layer does not protrude as far into the bulk (thickness is reduced) and becomes rougher due to the partial incorporation of pluronic.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".