Effects of Resin I on Asphaltene Adsorption onto Nanoparticles: A Novel Method for Obtaining Asphaltenes/Resin Isotherms
Bibliographic record
Abstract
The main objective of this study is to investigate the effect of resin I on the adsorption behavior of n -C 7 asphaltenes onto silica and hematite nanoparticles. It is worthwhile to mention, for the first time, that competitive adsorption of n -C 7 asphaltene and resin I over nanoparticles is reported. Indeed, a novel method based on thermogravimetric analysis (TGA) and softening point (SP) measurements was used for the simultaneously construction of adsorption isotherms of n -C 7 asphaltenes and resins. The adsorption experiments were conducted in the batch mode at different n -C 7 asphaltene to resin I (A:R) ratios of 7:3, 1:1, and 3:7 and different concentrations of the asphaltene–resin mixture from 500 mg/L to 5000 mg/L. The adsorption isotherms were described by the solid–liquid equilibrium (SLE) model. The results showed different shapes of the adsorption isotherms according to the A:R ratio. However, the nanoparticles become more selective for asphaltene at a high asphaltene/resin ratio. In addition, the amount of n -C 7 asphaltenes adsorbed at any of the A:R ratios evaluated was successfully predicted from a known amount adsorbed at a determined A:R ratio, following a simple rule of three. Results indicated that resin I does not have significant influence on the adsorbed amount of asphaltenes, showing that resin I has a solvent-like behavior, such as toluene, mainly at low concentrations (<3000 mg/L).
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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) | 0.000 | 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 | 0.001 |
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".