Hydrophilic Antifouling Thin-Film Nanocomposite Forward Osmosis Membranes: Effect of Zwitterion-Functionalized Carbon Nanofiber Modification
Bibliographic record
Abstract
The polyamide (PA) active layer plays a key role in the permeation and antifouling properties of thin-film composite (TFC) membranes. In this study, we synthesized a nature-based zwitterionic thin-film nanocomposite (TFN) membrane with improved organic fouling resistance. Hydrophilic zwitterionic poly(sulfobetaine methacrylate) (PSBMA) was chemically introduced onto the surface of cellulose nanofibers (CNFs) to form CNF- g -PSBMA, which was subsequently incorporated into the PA selective layer. The cross-sectional transmission electron microscopy images and 3D atomic force microscopy surface topographies of synthesized TFC membranes showed that CNF- g -PSBMA led to the formation of a thinner and smoother PA layer. The performance of membranes was investigated in a forward osmosis (FO) filtration setup in two FO and pressure-retarded osmosis (PRO) modes. The water flux in FO and PRO modes showed an increase of 16.3 and 38 LMH, respectively, with a reverse salt flux similar to that of the pristine membrane. The modified TFN membranes revealed enhanced antifouling properties against sodium alginate and bovine serum albumin foulants up to four times compared with the unmodified TFC because of their improved hydrophilicity and smoother surface. The CNF- g -PSBMA-modified TFNs demonstrated great potential for use in FO filtration, thanks to the incorporation of nature-based green materials, which significantly enhanced the membrane performance.
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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) | 0.001 | 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".