Water vapor sorption and transport in dense polyimide membranes
Bibliographic record
Abstract
Abstract The sorption and transport of water vapor in five dense polyimide membranes were studied by thermogravimetry. The sorption isotherms of water vapor in the polyimides could be successfully interpreted by both the dual‐mode sorption model and the Guggenheim–Anderson–de Boer equation. The water vapor diffusion behavior was found to be nearly Fickian at higher water vapor activities, whereas non‐Fickian diffusion was observed at lower water activities. The phenomena could be well described by the mechanism of combined Fickian and time‐dependent diffusion. The diffusion coefficient and water vapor uptake in the polyimides were strongly dependent on the polymer molecular structure. Except for the polyimide prepared from 3,3′,4,4′‐diphenylsulfone tetracarboxylic dianhydride and 1,3‐bis(4‐aminophenoxy) benzene, the permeability of water vapor in the dense polyimide membranes predicted from the sorption measurement at 30°C corresponded well with the water vapor permeability measured at 85°C. Among the polyimides studied, pyromellitic dianhydride–4,4′‐diaminophenylsulfone (50 mol%)/4,4′‐oxydianiline (50 mol%) showed both high water sorption and diffusion and, therefore, high water vapor permeability, which for vapor permeation membranes is necessary for the separation of water vapor from gas streams. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 87: 2306–2317, 2003
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".