Solvent and pH resistance of surface crosslinked chitosan/poly(acrylonitrile) composite nanofiltration membranes
Bibliographic record
Abstract
The resistance of novel surface crosslinked Chitosan/poly(acrylonitrile) (PAN) composite nanofiltration (NF) membranes to pH and organic solvents was studied with respect to the effects of crosslinking parameters, namely, glutaraldehyde concentration and crosslinking time. The pH resistance was determined by permeation of aqueous acidic (pH 2.5) and basic (pH 11) solutions as well as swelling studies in the pH range of 2.5–11. The solvent resistance was determined by swelling, immersion, and permeation studies with several industrially important organic solvents, namely methanol, ethanol, iso-propanol, methyl ethyl ketone, ethyl acetate and hexane. It was observed that the crosslinked composite membranes maintain the permeate fluxes for test solvents for 2 h of continuous operation without any significant change in flux. SEM studies on membrane samples after immersion as well as permeation with the above-mentioned solvents indicated that the membrane morphology was maintained. The results are explained in terms of solvent–membrane polar and hydrophobic interactions, using solubility parameters of membrane and solvents and dielectric constants of solvents. Pure water flux and polyethylene glycol transmission data indicated that at pH 2.5 and 11, the membrane stability increased with increasing glutaraldehyde concentration and was much better at pH 11 than at pH 2.5. All surface crosslinked membranes showed reduced swelling between pH 4–10. © 2000 John Wiley & Sons, Inc. J Appl Polym Sci 77: 1782–1793, 2000
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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) | 0.000 | 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".