Preparation and characterization of <scp>PVA‐g‐GO</scp> / <scp>PVA</scp> / <scp>PAN</scp> composite membrane for pervaporation desalination
Bibliographic record
Abstract
Abstract Graphene oxide (GO) has extensive applications in membrane‐based separations, but its dispersion in the membrane has always been a problem due to the presence of π–π interactions in GO nanosheets. In this study, a grafting reaction was designed by using poly (vinyl alcohol) (PVA) for GO grafting modification and poly (vinyl alcohol)‐g‐graphene oxide (PVA‐g‐GO) nanocomposites were synthesized. The grafting material to GO was the same as the basic separation polymer material. PVA‐g‐GO showed better dispersibility and hydrophilicity than GO, and a series of composite membranes were prepared using a polyacrylonitrile (PAN) ultrafiltration (UF) membrane as a substrate. PVA‐g‐GO nanocomposites and membranes were characterized by using X‐ray diffraction (XRD), thermogravimetric analysis (TGA), Fourier‐transform infrared spectroscopy (FTIR), water contact angle, scanning electron microscopy (SEM), etc. The addition of PVA‐g‐GO improved both the separation performance and anti‐swelling property of the composite membrane, and the PVA‐g‐GO/PVA/PAN composite membrane loaded with 2 wt.% PVA‐g‐GO obtained a high flux of 4.46 kg/m 2 · h and a high rejection of 99.99% when dehydrating 3.5 wt.% NaCl solution at 30°C by pervaporation.
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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) | 0.001 | 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".