Measurement of CO<sub>2</sub> Diffusivity for Carbon Sequestration: A Microfluidic Approach for Reservoir-Specific Analysis
Bibliographic record
Abstract
Predicting carbon dioxide (CO(2)) security and capacity in sequestration requires knowledge of CO(2) diffusion into reservoir fluids. In this paper we demonstrate a microfluidic based approach to measuring the mutual diffusion coefficient of carbon dioxide in water and brine. The approach enables formation of fresh CO(2)-liquid interfaces; the resulting diffusion is quantified by imaging fluorescence quenching of a pH-dependent dye, and subsequent analyses. This method was applied to study the effects of site-specific variables--CO(2) pressure and salinity levels--on the diffusion coefficient. In contrast to established, macro-scale pressure-volume-temperature cell methods that require large sample volumes and testing periods of hours/days, this approach requires only microliters of sample, provides results within minutes, and isolates diffusive mass transport from convective effects. The measured diffusion coefficient of CO(2) in water was constant (1.86 [± 0.26] × 10(-9) m(2)/s) over the range of pressures (5-50 bar) tested at 26 °C, in agreement with existing models. The effects of salinity were measured with solutions of 0-5 M NaCl, where the diffusion coefficient varied up to 3 times. These experimental data support existing theory and demonstrate the applicability of this method for reservoir-specific testing.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".