Dynamic Interfacial Tension Method for Measuring Gas Diffusion Coefficient and Interface Mass Transfer Coefficient in a Liquid
Bibliographic record
Abstract
This paper presents a new dynamic interfacial tension method for measuring the gas diffusion coefficient and the interface mass transfer coefficient in a liquid at a high pressure and a constant temperature. In the experiment, a see-through windowed high-pressure cell is filled with a test gas at a prespecified pressure and a constant temperature. Then a liquid sample is introduced by using a syringe delivery system to form a pendant liquid drop inside the pressure cell. With the dissolution of the gas into the pendant liquid drop, the dynamic interfacial tension between the test gas and the liquid keeps reducing and eventually reaches its equilibrium value when the saturation state is achieved. The sequential digital images of the pendant liquid drop are acquired and analyzed by applying computer-aided digital image acquisition and processing techniques to measure the dynamic interfacial tensions. Theoretically, a mass transfer model is developed to study the diffusion process of the gas inside the pendant liquid drop. This model is solved numerically by applying the semidiscrete Galerkin finite element method to obtain the transient gas concentration distribution inside the pendant liquid drop at any time. With a predetermined calibration curve of the equilibrium interfacial tension versus the equilibrium gas concentration for the gas−liquid system, the corresponding dynamic interfacial tension is calculated. The gas diffusion coefficient and the interface mass transfer coefficient are, thus, determined by finding the best fit of the theoretically calculated dynamic interfacial tensions to the experimentally measured data. This newly developed dynamic interfacial tension method is applied to measure the diffusion coefficient and the interface mass transfer coefficient of CO 2 in a reservoir brine sample at P = 0.1−6.0 MPa and T = 27 °C.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".