Polycardanol's potential to deasphalt crude oil: Influence of polymer conversion degree, molar mass, and structure
Bibliographic record
Abstract
Abstract Cardanol is a natural material that acts to stabilize asphaltenes in crude oil. However, its derivative, polycardanol, obtained via cationic polymerization, has divergent behaviour, with reports of its acting both as asphaltenes stabilizer and flocculant. Recently, it was demonstrated that the reaction conditions influence the conversion rates, structures, and molar masses of the products from synthesizing polycardanol initiated with BF 3 · O(C 2 H 5 ) 2 . Seeking to elucidate the influence of these variables on the phase behaviour of asphaltenes, in this work six products, previously synthesized and characterized, had their performance evaluated by asphaltenes precipitation onset, using n ‐heptane titration and monitoring by near‐infrared spectroscopy, using asphaltenes model systems (C5I and C7I) at 1.00 wt./vol.% in toluene. All the products had the ability to flocculate asphaltenes and have potential for deasphalting process. Flocculation efficiency increased with rising molar mass, higher reaction conversion degree, and presence of lateral hydrocarbon chains. The most efficient structures were not affected by the presence of unreacted cardanol. This indicated that the reaction does not require a purification step. Aged cardanol was also able to produce flocculant polymer, indicating that the distillation of cardanol is not required. The cost to obtain a product without needing reagent distillation becomes lower.
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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".