Preparation of Multipurpose Polyvinylidene Fluoride Membranes via a Spray-Coating Strategy Using Waterborne Polymers
Bibliographic record
Abstract
As reported herein, the waterborne polymers poly(glycidyl methacrylate- co -poly(ethylene glycol) methyl ether methacrylate) P(GMA- co -mPEGMA) and polyethyleneimine (PEI) were used to prepare multipurpose polyvinylidene fluoride (PVDF) membranes via a direct spray-coating method. P(GMA- co -mPEGMA) and PEI were alternately sprayed onto the PVDF membrane to yield stable cross-linked copolymer coatings. The successful coating of polymers onto the membrane surface was verified by scanning electron microscopy, attenuated total reflectance-Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy characterization. The coated membrane exhibited oil rejection rates that exceeded 99.0% for oil water mixture separation and 98.0% for oil/water emulsion separation. The flux recovery ratio reached 96.7% after bovine serum albumin filtration and washing with water. The removal efficiencies of the coated membrane M3 for Congo red, methyl orange, methylene blue, and crystal violet, Pb(II), Cu(II), and Cd(II) were 82.4, 83.9, 6.3, 26.8, 90.6, 91.3, and 86.2%, respectively. Thus, it can be used for the removal of dyes and heavy metal ions from wastewater. The antibacterial activities of the coated membranes were also confirmed by the inhibition zone tests and confocal laser scanning microscopy analysis. In addition, the cross-linking strategy provides the coated membranes with excellent durability and repeatability. More importantly, the use of water as the solvent can ensure that the application of these membrane coatings proceeds via a very safe and environmentally friendly coating process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".