Direct determination of permeability, diffusivity and solubility of gases in membrane based on simultaneous monitoring of pressure rise and decay in a single time-lag experiment
Bibliographic record
Abstract
The time-lag method is the most common approach for evaluating the membrane gas permeability, diffusion and solubility coefficients in a single gas permeation experiment. However, only permeability and diffusivity are determined directly, while the solubility is evaluated from the ratio of permeability and diffusivity. Herein, we present how the conventional time-lag experiment can be used to determine all three transport coefficients directly when, in addition to monitoring the pressure rise in the constant volume downstream from the membrane, the corresponding pressure decay is also monitored in the constant volume upstream of the membrane. The new design of the upstream part of the constant volume (CV) system used in this study allowed pressure decay monitoring. We provide the mathematical fundamentals of the new methods enabled by the new CV system and demonstrate them experimentally using a commercial poly (dimethylsiloxane) PDMS membrane. • Novel design of the upstream part of a constant volume system. • Simultaneous monitoring of pressure decay and rise in the time lag experiments. • Monitoring of gas accumulation in membrane during the permeation tests. • Direct determination of the solubility coefficient in membrane. • Four independent S and D and three independent P from a single permeation test.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".