Impact of Measuring Devices and Data Analysis on the Determination of Gas Membrane Properties
Bibliographic record
Abstract
The time-lag method, using a gas permeation experiment, is currently the most popular method for determining the membrane properties: diffusivity coefcient and permeability coefcient, and from which the solubility coefcient can be calculated. In this investigation, the impact of systematic, random (noise), resolution and extrapolation errors associated with gas permeation experiments on the determination of the membrane properties using the time-lag method is investigated. A comprehensive error analysis for each type of errors and their combination is presented. Random and resolution errors have a greater impact on the determination of the time lag for low rates of downstream pressure accumulation which can be alleviated by increasing the capacity parameter. Increasing the feed pressure lowers the resolution errors, but has no effect on random errors. Extrapolation errors associated with the time-lag method, which increase with time, can be reduced by increasing the number of evaluation points and the length of the evaluation window. Because of their strong correlation, it is difcult to decouple solubility and diffusivity coefcients accurately without using the time-lag. A judicious balance between data precision, the drop in the driving force and the duration of an experiment must be considered in the design of a constant-volume membrane system and in the selection of experimental operating conditions to minimize the impact of pressure variability. The necessity of a small capacity parameter for the accurate determination of membrane properties needs to be reconsidered in the presence of experimental noise.
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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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".