Effects of Polymer Ratio and Film-Penetration Time on the Properties and Performance of Nanocomposite PVDF Membranes in Membrane Distillation
Bibliographic record
Abstract
Nanocomposite membranes were prepared for vacuum membrane distillation (VMD) by casting the dope suspension on top of a nonwoven polyester backing material. The dope consisted of 7.0 wt % hydrophilic SiO 2 nanoparticles and a polyvinylidene fluoride (PVDF) blend of high molecular weight (H) and low molecular weight (L). The effects of the blend ratio (H:L) and the penetration time (τ), defined as the period between the completion of membrane casting and the immersion in the coagulation bath, on the membrane properties and performance were studied. It was found that the VMD flux is governed by the pore size and thickness of the top layer (defined as the layer formed above the backing material), both of which are affected by the H:L ratio and the penetration time. Results indicate that the VMD flux increased as the portion of L in the casting dope increased at constant τ, while a maximum flux was observed at τ = 2 min when the penetration time was changed at constant H:L ratio. It was also observed that the liquid entry pressure of water (LEP w ) of membranes changed with the PVDF blend ratio and penetration time due to alteration of the maximum pore size. Considering all the data collected, the combination of H:L = 2:8 and penetration time of 3 min was identified as the best condition for the preparation of nanocomposite membranes, achieving one of the highest VMD fluxes, 12.1 kg/m 2 h at 27.5 °C and 1.2 kPa, an improved LEP w of 27.0 psig, and a near complete NaCl rejection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".