Semi‐IPN asymmetric membranes based on polyether imide (ULTEM) and polyethylene glycol diacrylate for gaseous separation
Bibliographic record
Abstract
Abstract Semi‐interpenetrating polymer networks (semi‐IPN) formed with commercial polyether imide (ULTEM®, PEI) and poly (ethylene glycol) diacrylate (PEGDA) were used to make asymmetric membranes. The effect of increasing amount of PEGDA on the bulk and the gas separation properties of semi‐IPN membranes were studied. The formation of IPNs was confirmed by Fourier Transform Infra Red (FT‐IR) spectroscopy. The 5% weight loss temperature decreased and the percent weight loss of the first step increased with increase in the PEGDA content, which indicated the incorporation of more poly (ethylene glycol) (PEG) segments to the semi‐IPNs. The microscopic experiments revealed the change in morphology with change in PEGDA content. The Scanning electron micrographs exhibited typical finger‐like voids in the sub layer, which is characteristic morphology of asymmetric membranes. The increase in PEGDA content up to 5.7 wt % increased the CO2/N2 selectivity of the semi‐IPN after which the selectivity decreased and permeance increased. Although, the increase in the polar poly (ethylene glycol) molecules is expected to render better CO2 selectivity, the performance of the membrane was found to decrease as PEGDA content exceeded 5.7% for the given ratio. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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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".