Full Characterization of CO<sub>2</sub>–Oil Properties On-Chip: Solubility, Diffusivity, Extraction Pressure, Miscibility, and Contact Angle
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it