Bitumen–Toluene Mutual Diffusion Coefficients Using Microfluidics
Bibliographic record
Abstract
In this paper, we present a microfluidic approach to measure liquid solvent diffusivity in Athabasca bitumen. The method has three distinguishing features: (a) a sharp initial condition enabled by the high wettability of the solvent; (b) one-dimensional diffusive transport (in the absence of convection) ensured by microscale confinement; and (c) visible-light-based measurement enabled by the partial transparency of the bitumen at small scales. The method is applied to measure the diffusion of toluene into bitumen by imaging transmitted light profiles over time, and relating intensities to the mass fractions. Plotting toluene mass fraction versus distance/sqrt(time), results in a tight superposition of all curves (time-dependent mass fractions) demonstrating the diffusion dominated nature of the system and the robustness of the method. The diffusion transport equations were solved and fit to a constant diffusion coefficient as well as a variety of concentration-dependent diffusion coefficient relations found in the literature. For intermediate toluene mass fractions (0.2–0.8), a constant diffusion coefficient of 2.0 × 10 –10 m 2 /s provides an appropriate representation. However, at low toluene mass fractions (<0.2), significantly reduced diffusive transport is observed, and endpoint analysis indicates diffusion coefficients trending toward 4.3 × 10 –11 m 2 /s. At high toluene mass fractions (>0.8), the values trend toward 1.5 × 10 –10 m 2 /s. This microfluidic method provides an inexpensive and rapid mutual diffusion coefficient evaluation, with significantly improved spatial/composition resolution vis-à-vis competing measurement methods.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".