The Preparation of Polyvinyl Chloride Nanofiber Membrane by Melt Electrospinning for Ester Plasticizer Adsorption
Bibliographic record
Abstract
ABSTRACT Ester plasticizers are widely used in plastic products and can cause irreversible reproductive health damage and carcinogenic risk with prolonged exposure or ingress into the body, especially in drinking water. Therefore, it is urgent to study hydrophobic and lipophilic ester plasticizer filtration membranes. Due to the molecular polarity characteristics of polyvinyl chloride (PVC), it is more likely to attract ester plasticizers compared to other materials. Nanofiber membranes prepared from PVC may have good filtration effects on ester plasticizers. In this study, a green preparation method of PVC nanofiber membrane for adsorption of ester plasticizers is proposed, and the process includes gel preparation, melt electrospinning, and extraction. Extracted melt electrospun membrane shows excellent mechanical properties, with a tensile strength of 35.011 MPa, an elongation at break of 60%, and an average fiber diameter of 952 nm. The average adsorption multiplicity of the extracted fiber membrane for ester plasticizers was 9.32 g/g. The adsorption efficiency was 64.9% after 5 times reuse. The static adsorption multiplicity data of this study is 31 times higher than that of activated carbon materials and 23.3 times higher than that of reported resin materials.
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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".