Improved surface hydrophilicity and antifouling property of nanofiltration membrane by grafting <scp>NH</scp><sub>2</sub>‐functionalized silica nanoparticles
Bibliographic record
Abstract
Organic fouling has been a major impediment for the widespread application of nanofiltration membranes because it could degrade the performance and shorten the service life of such membranes. In this study, the NH2‐functionalized silica nanoparticles (NH2–SiO2 NPs) were grafted on the surface of commercial NF90 membrane to improve the hydrophilicity and antifouling property with the N‐(3‐dimethylaminopropyl)‐N‐ethylcarbodiimide hydrochloride coupled with the N‐hydroxysuccinimide (EDC/NHS) to activate carboxyl groups on the membrane surface. The surface roughness (Ra) of this modified membrane decreased from value of 47.4 nm of the pristine membrane to 43 nm. The water contact angle of the modified membrane decreased from 48.4° to 15°, indicating the high hydrophilicity of the surface. Moreover, the EDC/NHS membrane showed good stability when it was exposed to the ruinous physical stress (2‐min sonication). The optimized grafting conditions, including the SiO2 concentration (0.5 wt%), the activation time of EDC/NHS (20 min), and the reaction time of NH2–SiO2 NPs with membrane surface (12 h), were obtained by a series of experiments. Under the antifouling test of 36 hours with bovine serum albumin (BSA) and hexadecyltrimethylammonium bromide (CTAB) as foulants, the EDC/NHS membrane lost 42.1% and 49.6% of its initial flux, respectively, which is lower than that of the pristine membrane (52.2% and 68%, respectively). After cleaning with deionized water for 2 hours, the EDC/NHS membrane also showed significant improvement of flux recovery ratio (92% and 87%, respectively) than did the pristine membrane (70.3% and 60%, respectively).
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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".