Revisiting the Effect of the Resistance to Gas Accumulation in Constant Volume Systems on the Membrane Time Lag
Bibliographic record
Abstract
The time-lag method is commonly used to determine membrane permeability, diffusivity and solubility in a single gas permeation experiment in a constant volume system. An unwritten assumption on which this method relies is that there is no resistance to gas accumulation in the downstream receiver of the system. However, this is not the case, even with the specially designed receiver used in this study when, in addition to tubing, the receiver utilizes an additional accumulation tank. The resistance to gas accumulation originates from a finite diffusivity (Knudsen diffusion) of gases in tubing, which are magnified by “resistance-free” accumulation tank(s). As a result of the resistance to gas accumulation, the time lag of the membrane is underestimated, which leads to an overestimation of gas diffusivity in the membrane. The experimentally predicted resistances in different configurations of the receiver, expressed by the difference in the time lag at two different receiver locations, were several times greater than the theoretically predicted values. A high molecular PPO membrane was used to demonstrate this effect. The time lags measured at different locations differed by as much as 30%. The diffusivity of nitrogen in a PPO of 4.04 × 10−12 m2/s determined at the optimum configuration of the receiver is at least 50% lower than the literature-reported values.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".