Experimental determination of gas diffusivity in liquids—A review
Bibliographic record
Abstract
Abstract Unit operations and processes abound with gas diffusion in liquids, which is a sophisticated phenomenon in which mass transfer is characterized by diffusion coefficient or diffusivity. Compared to diffusion in gas phase, the closely packed liquid molecules strongly influence diffusive mass transfer to the extent that it is impossible to have a general theory for a reasonably accurate estimation of diffusivity in liquids. This situation is further compounded by the fact that diffusivity cannot be measured directly but can only be estimated indirectly with the help of a number of observable properties (eg, mass, volume, pressure, etc). This fact gives rise to a myriad of experimental methods for the determination of gas diffusivity in liquids. These methods report gas diffusivities over widely varying ranges of temperature, pressure, and liquid composition. To provide a state‐of‐the‐art knowledge base for such methods is the objective of this work. The focus is on gas diffusion in binary gas‐liquid systems. Starting with necessary theoretical foundations, we provide a systematic categorization of these methods based on the property change utilized for diffusivity determination. The methods are then concisely described, and the diffusivity data are summarized for over 160 gas‐liquid systems at different temperature and pressure conditions. Empirical correlations are provided for different gas‐liquid systems, which could be used for interpolating gas diffusivity as a function of temperature, pressure, and composition.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".