Automated microfluidic platform for studies of carbon dioxide dissolution and solubility in physical solvents
Bibliographic record
Abstract
We present an automated microfluidic (MF) approach for the systematic and rapid investigation of carbon dioxide (CO(2)) mass transfer and solubility in physical solvents. Uniformly sized bubbles of CO(2) with lengths exceeding the width of the microchannel (plugs) were isothermally generated in a co-flowing physical solvent within a gas-impermeable, silicon-based MF platform that is compatible with a wide range of solvents, temperatures and pressures. We dynamically determined the volume reduction of the plugs from images that were accommodated within a single field of view, six different downstream locations of the microchannel at any given flow condition. Evaluating plug sizes in real time allowed our automated strategy to suitably select inlet pressures and solvent flow rates such that otherwise dynamically self-selecting parameters (e.g., the plug size, the solvent segment size, and the plug velocity) could be either kept constant or systematically altered. Specifically, if a constant slug length was imposed, the volumetric dissolution rate of CO(2) could be deduced from the measured rate of plug shrinkage. The solubility of CO(2) in the physical solvent was obtained from a comparison between the terminal and the initial plug sizes. Solubility data were acquired every 5 min and were within 2-5% accuracy as compared to literature data. A parameter space consisting of the plug length, solvent slug length and plug velocity at the microchannel inlet was established for different CO(2)-solvent pairs with high and low gas solubilities. In a case study, we selected the gas-liquid pair CO(2)-dimethyl carbonate (DMC) and volumetric mass transfer coefficients 4-30 s(-1) (translating into mass transfer times between 0.25 s and 0.03 s), and Henry's constants, within the range of 6-12 MPa.
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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 | 0.000 |
| 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 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".