Fabrication and VMD Performance of TiO2 Nanocomposite PVDF Membranes and PVDF-PTFE Composite Membranes
Bibliographic record
Abstract
In this study, two different strategies were carried out to modify the polyvinylidene fluoride (PVDF) distillation membrane for desalination. The first strategy was the addition of TiO2 nanoparticles into the target membranes and a synergistic effect of hydrophilic and hydrophobic nanoparticles was found for the first time in this work. And the other strategy was the introduction of another polymer material, polytetrafluoroethylene (PTFE), to the PVDF membranes to fabricate a flat sheet PVDF-PTFE composite membrane and this is the first attempt that such a membrane to be made. Two types of membranes were characterized by scanning electron microscopy (SEM) detection, porosity measurement, energy dispersive X-ray spectroscopy (EDX), Attenuated total reflectance (ATR)-Fourier transformed infrared spectroscopy (FTIR), contact angle (CA) measurement, atomic force spectroscopy (AFM) detection and liquid entry pressure of water (LEPw) measurement. Their performance was evaluated by vacuum membrane distillation (VMD) experiments. And the best VMD pure water permeate flux of the membranes fabricated under these two modify strategies could achieve 4.26 kg/m2h (M-L5-B2) and 5.61 kg/m2h (M-40), respectively, when that of pure PVDF membrane is only 0.71 kg/m2h. The salt rejection of the prepared composite membranes are all stably higher than 99.5% which demonstrate their capacity for desalination.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".