Modification of exterior and intraporous surfaces of polyvinylidene fluoride membranes using KOH/water/alcohol ternary: Effects of wettability, polarity, and OH− activity
Bibliographic record
Abstract
Alkaline treatment is a cost-effective and easy-to-operate method that can effectively alter the hydrophilicity of polyvinylidene fluoride (PVDF) membranes. It makes the surface of PVDF membranes chemically active by introducing hydrophilic groups, e.g., hydroxyl and carboxyl groups, and therefore could be used as an effective pretreatment for membrane surface engineering including the grafting of other target chemical molecules onto to membrane surface. This treatment enhances the hydrophilicity of the membrane surface and could therefore also improve membrane permeability and reduce fouling. In this research, we systematically studied the alkaline treatment using KOH/alcohol/water ternaries at different conditions. Results indicate that the inclusion of alcohol in the reaction system could impact the efficiency of membrane surface modification through altering the chemical reaction mechanism as well as affecting the reacting mixture's properties such as OH − activity, polarity, reactivity, and membrane wettability. The effectiveness of different alcohols was in the order of methanol > ethanol > isopropanol in terms of increasing the surface hydrophilicity, which is in accordance with the orders of their polarity and reactivity. In the meantime, the coloration of treated membranes followed a reversed order. It is evident that increasing treatment temperature, duration, OH − activity, reactivity and polarity in the tested range would lead to the increase of both the surface hydrophilicity and membrane color. On the other hand, increasing a ternary's ability to wet the PVDF membrane had none or little impact on membrane hydrophilicity but strong impact on membrane color. Results suggest that coloration reactions did not contribute to the improvement of surface hydrophilicity and should therefore be minimized. • Alkaline treatment significantly enhanced hydrophilicity of PVDF membrane surface. • Alkaline treatment using KOH/alcohol/water ternary effective in PVDF membrane surface. • Alcohol participated in modification reactions that may or may not related to hydrophilicity. • Membrane hydrophilicity influenced by OH − activity, polarity, and reactivity of alcohol.
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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 | 0.000 |
| 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".