Controlling biofouling of reverse osmosis membranes through surface modification via grafting patterned polymer brushes
Bibliographic record
Abstract
Thin film composite (TFC) polyamide membranes are extensively used as selective barriers in reverse osmosis processes. The major challenge faced with TFC membranes is significant fouling on the surface, which restricts the overall purification performance. To address the fouling problem, we developed novel fouling-resistant surface coatings via polyelectrolyte [poly(allylamine hydrochloride)/poly(styrene sulfonate)] layer-by-layer self-assembly, functionalized with patterned antimicrobial and antifouling/fouling-release polymer brushes. Two types of different polymer brushes, among antimicrobial poly(quaternary ammonium), antifouling poly(sulfobetaine) and fouling-release poly(dimethylsiloxane) (PDMS), were selected and grafted in a checkerboard pattern, with a square feature of 2 µm. The successful patterning and incorporation of different polymer brushes on the membrane was confirmed through X-ray photoelectron spectroscopy analysis. Grafting with sulfobetaine and PDMS significantly increased the hydrophilicity and lowered the surface energy of the membrane, respectively. The fouling-resistant property of the modified membrane was evaluated via static protein (bovine serum albumin) deposition and bacterial (Escherichia coli) cell adhesion tests. Surface modifications proved to diminish protein adhesion and exhibited 70–93% reduction in bacterial cell attachment. This observation suggests that the modified membranes have strong antifouling properties that inhibit the irreversible adhesion of organic and bio-foulants on the membrane surface.
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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 | 0.001 |
| 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".