Effect of surface modifying macromolecules stoichiometric ratio on composite hydrophobic/hydrophilic membranes characteristics and performance in direct contact membrane distillation
Bibliographic record
Abstract
Abstract The stoichiometric ratio for the synthesis components of hydrophobic new surface modifying macromolecules (nSMM) was altered systematically to produce three different types of nSMMs, which are called hereafter nSMM1, nSMM2, and nSMM3. The newly synthesized SMMs were characterized for fluorine content, average molecular weight, and glass transition temperature. The results showed that fluorine content decreased with increasing the ratio of α,ω‐aminopropyl poly(dimethyl siloxane) to 4,4′‐methylene bis(phenyl isocyanate). The synthesized nSMMs were blended into hydrophilic polyetherimide (PEI) host polymer to form porous hydrophobic/hydrophilic composite membranes by the phase inversion method. The prepared membranes were characterized by the contact angle measurement, X‐ray photoelectron spectroscopy, gas permeation test, measurement of liquid entry pressure of water, and scanning electron microscopy. Finally, these membranes were tested for desalination by direct contact membrane distillation and the results were compared with those of commercial polytetraflouroethylene membrane. The effects of the nSMM type on the membrane morphology were identified, which enabled us to link the membrane morphology to the membrane performance. It was found that the nSMM2/PEI membrane yielded the best performance among the tested membranes. In particular, it should be emphasized that the above membrane was superior to the commercial one. © 2009 American Institute of Chemical Engineers AIChE J, 2009
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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) | 0.000 | 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) | 0.001 | 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".