Determination of time lag by accurate monitoring of pressure decay in a new generation constant volume system
Bibliographic record
Abstract
The time-lag method is the standard approach for evaluating membrane permeability, diffusivity and solubility in a single gas permeation experiment. The conventional time-lag method relies on accurately monitoring the pressure rise in a constant volume downstream from the membrane following a change in pressure upstream from the membrane. The same information could be extracted from the upstream pressure decay in the same time-lag experiment. However, accurately monitoring the pressure decay presents a challenge due to the resolution limitations of absolute pressure transducers. If the membrane was characterized based on pressure decay, a mass spectrometer could be used to simultaneously monitor the composition of the gas permeating from the membrane, opening the time-lag method to gas mixtures. Also, the simultaneous monitoring of pressure rise and decay could provide additional information about gas transport in the membrane, which is critical for more complex membrane materials.•The resolution challenge was overcome by splitting the upstream volume into the working and reference volumes and monitoring pressure decay using a differential pressure transducer between the two volumes.•The validity of the measured pressure decay was confirmed by the unique relation between the upstream and downstream time lags for a commercial PDMS membrane.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".