Performance of surface‐modified poly(etherimide) hollow‐fiber membranes in a membrane gasLiquid contacting process with response surface methodology
Bibliographic record
Abstract
Abstract A surface‐modifying macromolecule (SMM) was used to enhance the surface hydrophobicity of poly(ether imide) (PEI) hollow‐fiber membranes. The membranes were used for a membrane contactor to absorb CO2 with water. The effects of three hollow‐fiber fabrication parameters, that is, the PEI concentration in the casting dope, the SMM concentration in the casting dope, and the air gap, on the liquid entry pressure of water (LEPw) and the absorption rate (AR) of CO2 were investigated with response surface methodology. The model developed for LEPw satisfied the criterion for regression but had a low goodness of fit. The model predicted that LEPw would increase with PEI (weight percentage) but decrease with air gap. Furthermore, it showed a minimum value with a change in SMM (weight percentage). The model developed for the AR of CO2 had meaningful statistical parameters and was accurate; this indicated that interactions existed between the fabrication parameters on the AR of CO2. The performance of one of the fabricated membranes was compared with in‐house and commercially made hydrophobic membranes in terms of the AR of CO2 with distilled water as an absorbent on the lumen side and pure CO2 on the shell side. The comparison showed a superior CO2 flux in the surface‐modified membrane; for example, at a liquid velocity of 0.4 m/s, the surface‐modified membrane exhibited a 416% higher AR than the commercial membrane contactor (Celgard MiniModule 0.75X5) made of polypropylene. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".