Acetylated Cellulose/Polyethylene Glycol Composite Membranes for Removal of Polycyclic Aromatic Hydrocarbons from Marine Sediments
Bibliographic record
Abstract
Cellulose acetate (CA)/polyethylene glycol (PEG) composite membranes are promising advanced materials for the removal of polycyclic aromatic hydrocarbons (PAHs) from wastewater.Therefore, this study aimed at synthesizing CA/PEG composite membranes using the phase inversion method with CA/PEG composition ratios (w/w) of 12:6, 13:5, 14:4, and 15:3.The synthesis process began with the acetylation of cellulose to produce CA, and the obtained products were characterized using ATR-FTIR, SEM, and TGA.The water contact angle (WCA) and swelling index (SI) were evaluated to determine the hydrophobicity.The results showed that the CA/PEG (15:3) membrane had the highest water contact angle and the lowest swelling index, indicating its superior hydrophobic characteristics.FTIR analysis confirmed the successful synthesis by identifying characteristic peaks of CA and PEG, particularly at 3300 cm⁻¹ (associated with -OH groups), 1049 cm -1 , and 617 cm⁻¹ (corresponding to C-O and -OH torsional vibrations).In addition, SEM images revealed a less porous surface with non-uniform pore sizes across the products.TGA analysis indicated that the CA/PEG (14:4) membrane had the best thermal stability, evidenced by the lowest weight loss percentage of 70.64%.The evaluation of water flux indicated the CA/PEG (12:6) membrane achieved the highest flux value of 54.081 L/m² • h.Conversely, the CA/PEG (14:4) membrane exhibited superior anti-fouling performance, with the highest FRR value of 84.62, demonstrating its consistency.Furthermore, the CA/PEG (14:4) membrane testing on real samples of marine sediment extract confirmed the maximum rejection rate of pyrene compound of 87.67%.These results demonstrate the high effectiveness and efficiency of cellulose acetate-based membranes in removing PAHs from wastewater.Moreover, this membrane is renewable and environmentally friendly.
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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) | 0.000 | 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".