Full Characterization of CO<sub>2</sub>–Oil Properties On-Chip: Solubility, Diffusivity, Extraction Pressure, Miscibility, and Contact Angle
Bibliographic record
Abstract
Carbon capture, storage, and utilization technologies target a reduction in net CO 2 emissions to mitigate greenhouse gas effects. The largest such projects worldwide involve storing CO 2 through enhanced oil recovery—a technologically and economically feasible approach that combines both storage and oil recovery. Successful implementation relies on detailed measurements of CO 2 –oil properties at relevant reservoir conditions ( P = 2.0–13.0 MPa and T = 23 and 50 °C). In this paper, we demonstrate a microfluidic method to quantify the comprehensive suite of mutual properties of a CO 2 and crude oil mixture including solubility, diffusivity, extraction pressure, minimum miscibility pressure (MMP), and contact angle. The time-lapse oil swelling/extraction in response to CO 2 exposure under stepwise increasing pressure was quantified via fluorescence microscopy, using the inherent fluorescence property of the oil. The CO 2 solubilities and diffusion coefficients were determined from the swelling process with measurements in strong agreement with previous results. The CO 2 –oil MMP was determined from the subsequent oil extraction process with measurements within 5% of previous values. In addition, the oil–CO 2 –silicon contact angle was measured throughout the process, with contact angle increasing with pressure. In contrast with conventional methods, which require days and ∼500 mL of fluid sample, the approach here provides a comprehensive suite of measurements, 100-fold faster with less than 1 μL of sample, and an opportunity to better inform large-scale CO 2 projects.
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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 | 0.001 |
| 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".