Application of taylor dispersion technique to measure mutual diffusion coefficient in hexane + bitumen system
Bibliographic record
Abstract
A novel approach with fewer technical and analytic limitations in liquid solvent‐bitumen diffusion studies is used in this article. The Taylor dispersion technique was selected for its convenient short run time experiments and reliable data analysis to find mutual diffusion coefficients in a hexane + bitumen mixture. For the first time, the infinite‐dilution molecular diffusion coefficients of bitumen in hexane were measured in both the presence and relative absence of asphaltene particles in the solution at atmospheric pressure and temperatures of 303.15, 310.15, and 317.15 K. The polydisperse nature of bitumen was clearly revealed. Results were compared with common predictive tools. Also, the asphaltene surface charge in the hexane precipitating solvent was demonstrated. Through concentration dependency investigations at atmospheric pressure and 303.15 K, it was determined that the mutual diffusion coefficients monotonically decrease as the viscosity of mixture increases within the studied 0–34% volumetric concentration of bitumen. The Taylor dispersion technique shows great potential for diffusion studies of liquid solvent‐bitumen systems. © 2014 American Institute of Chemical Engineers AIChE J , 60: 2670–2682, 2014
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".