Evaluation of Surface Properties and Separation Performance of NF and RO Membranes for Phthalates Removal
Bibliographic record
Abstract
Many studies indicated that phthalates, a common plasticizer, lurk silently in water bodies and can potentially harm living organisms. Therefore, removing phthalates from water sources prior to consumption is crucial. This study aims to evaluate the performance of several commercial nanofiltrations (NF) (i.e., NF3 and Duracid) and reverse osmosis (RO) membranes (i.e., SW30XLE and BW30) in removing phthalates from simulated solutions and further correlate the intrinsic properties of membranes (e.g., surface chemistry, morphology, and hydrophilicity) with the phthalates removal. Two types of phthalates, i.e., dibutyl phthalate (DBP) and butyl benzyl phthalate (BBP), were used in this work, and the effects of pH (ranging from 3 to 10) on the membrane performance were studied. The experimental findings showed that the NF3 membrane could yield the best DBP (92.5-98.8%) and BBP rejection (88.7-91.7%) regardless of pH, and these excellent results are in good agreement with the surface properties of the membrane, i.e., low water contact angle (hydrophilicity) and appropriate pore size. Moreover, the NF3 membrane with a lower polyamide cross-linking degree also exhibited significantly higher water flux compared to the RO membranes. Further investigation indicated that the surface of the NF3 membrane was severely covered by foulants after 4-h filtration of DBP solution compared to the BBP solution. This could be attributed to the high concentration of DBP presented in the feed solution owing to its high-water solubility (13 ppm) compared to BBP (2.69 ppm). Further research is still needed to study the effect of other compounds (e.g., dissolved ions and organic/inorganic matters that might be present in water) on the performance of membranes in removing phthalates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".