Influence of novel surface modifying macromolecules and coagulation media on the gas permeation properties of different polymeric gas separation membranes
Bibliographic record
Abstract
Abstract Integrally skinned asymmetric membranes for the separation of O2 and N2 were fabricated by the phase inversion technique from polysulfone, polyetherimide, and polyimide. Two types of surface modifying macromolecules (SMMs) including hydrophilic SMM (LSMM) and charged SMM (cSMM) were synthesized and blended with the casting solution to modify the membrane surface. The cast film was then immersed in the first coagulant alcohol (methanol, ethanol, or isopropanol) for a predetermined period, before being immersed in the second coagulant (water). The SMMs used in these experiments were laboratory synthesized by the two‐step process of polyurethane prepolymer synthesis and end capping, before being characterized by differential scanning calorimetry. Their molecular structure was determined from the molecular weight obtained by gel permeation chromatography. The membranes were characterized by contact angle measurement and O2 and N2 gas permeation performance. Attempts were made to interpret the gas permeation data by delayed demixing affected by solubility parameters of polymer, solvent, and nonsolvent. Furthermore, the permeation performance of cSMM membranes was interpreted by the solvation of the charged sulfonate groups present in cSMM. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 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".