Effects of polymer solvents on the performance of cellulose acetate membranes in methanol/methyl tertiary butyl ether separation
Bibliographic record
Abstract
Abstract The performances of cellulose acetate membranes prepared with casting solutions, with acetone, dimethylformamide (DMF), and N‐methylpyrrolidone (NMP) as solvents, were studied in a series of methanol/methyl tertiary butyl ether separation experiments. The flux and selectivity of the membrane samples were affected by the type of solvent used to prepare the casting solution. The sample with DMF consistently gave the highest selectivity and lowest flux, followed by the samples with NMP and acetone. The differences in the performances were attributed to the effects of the volatility and evaporation rates of the solvents. Scanning electron microscopy and atomic force microscopy techniques were used for comparing the morphologies of the membranes. In addition, we used Raman spectroscopy as a novel technique to study the sorption selectivities of the membrane samples prepared with the three different solvents. In a parallel study, the relation between the polymer concentration in the casting solution and the morphology and performance of the membrane samples was studied. Under similar preparation conditions, the morphology of the membrane changed from being porous to being dense when the membrane was prepared with casting solutions with increasing polymer concentration. Also, the selectivity increased and the permeability decreased with increasing polymer concentration in the casting solution. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 82: 2882–2895, 2001
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".