The Practical Aspects in the Determination of Membrane Properties for Gas Permeation
Bibliographic record
Abstract
Membrane-based pressure driven processes have been widely studied and have been used in a myriad of applications. The objective of the characterization of membranes in which gas sorption obeys Henry's law is to determine the solubility S and diffusivity D of the membrane. The time-lag method, developed by Daynes One of the important assumptions of the time-lag method is that the amount of permeate gas accumulating in the downstream receiver is small enough to have a negligible effect on the driving force. To better satisfy this assumption, it was claimed that a capacity parameter (related to the receiver volume) should be small enough Nevertheless, due to the existence of measurement noise in experimental data, the accurate determination of the time lag is difficult and a small capacity parameter will intensify the effect of noise. The accuracy of the estimated time lag is also influenced by the noise level and the data analysis procedure. An alternative to obtain the membrane properties is to fit the variation of the pressure change in the downstream reservoir as a function of time using the nonlinear regression method By minimizing the sum of squares of the differences between the experimental data and the predictions from the numerical model, the best combinations of the membrane properties can be obtained. The latter method also allows using the real rather than ideal boundary conditions at the membrane interfaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".