Experimental and Theoretical Determination of Equilibrium Interfacial Tension for the Solvent(s)–CO<sub>2</sub>–Heavy Oil Systems
Bibliographic record
Abstract
Equilibrium interfacial tension (IFT) between heavy oil and CO 2 with the addition of C 3 H 8 and/or n -C 4 H 10 has been experimentally and theoretically determined. Experimentally, an axisymmetric drop shape analysis (ADSA) technique is used to measure both the dynamic and equilibrium IFTs between heavy oil and three pure solvents (i.e., CO 2, C 3 H 8, and n -C 4 H 10 ) together with seven solvent mixtures. Theoretically, the Peng–Robinson equation of state (PR EOS) with a newly developed α function has been incorporated into a mechanistic parachor model to determine the equilibrium IFTs between heavy oil and pure solvents together with their corresponding mixtures. The addition of C 3 H 8 and/or n -C 4 H 10 into CO 2 stream leads to an obvious reduction of IFT between heavy oil and CO 2, although the degree of reduction depends upon the added amount of rich solvent(s). The mechanistic parachor model with the optimized parachor of the heavy oil and mass-transfer exponent provides a qualitative agreement with the measured equilibrium IFTs between solvent(s) and heavy oil in the liquid–vapor phase region.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".