Determination of Dew Point Conditions for CO<sub>2</sub> with Impurities Using Microfluidics
Bibliographic record
Abstract
Impurities can greatly modify the phase behavior of carbon dioxide (CO2), with significant implications on the safety and cost of transport in pipelines. In this paper we demonstrate a microfluidic approach to measure the dew point of such mixtures, specifically the point at which water in supercritical CO2 mixtures condenses to a liquid state. The method enables direct visualization of dew formation (∼ 1-2 μm diameter droplets) at industrially relevant concentrations, pressures, and temperatures. Dew point measurements for the well-studied case of pure CO2-water agreed well with previous theoretical and experimental data over the range of pressure (up to 13.17 MPa), temperature (up to 50 °C), and water content (down to 0.00229 mol fraction) studied. The microfluidic approach showed a nearly 3-fold reduction in error as compared to previous methods. When applied to a mixture with nitrogen (2.5%) and oxygen (5.8%) impurities--typical of flue gas from natural gas oxy-fuel combustion processes--the measured dew point pressure increased on average 17.55 ± 5.4%, indicating a more stringent minimum pressure for pipeline transport. In addition to increased precision, the microfluidic method offers a direct measurement of dew formation, requires very small volumes (∼ 10 μL), and is applicable to ultralow water contents (<0.005 mol fractions), circumventing the limits of previous 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".