Characterization of polyamide thin film composite membranes incorporated silver nanoparticles
Bibliographic record
Abstract
Abstract In this study, the polyamide thin film composite membrane surface has been modified by anti‐microbial silver nanoparticles (AgNPs). The membrane surfaces were interwoven with AgNPs by ultraviolet grafting polymerization method using AgNPs with or without poly(ethylene glycol) (PEG). The membrane surface characteristics were determined by scanning electron microscopy–energy dispersive x‐ray spectrometry images, attenuated total reflection–Fourier transforms infrared spectroscopy, water contact angle values, and anti‐bacterial property. The separation performance was determined based on the flux and the ability to remove calcium ions in water. The anti‐biofouling property was appraised through the maintained flux ratios and the irreversible fouling factors of unmodified and modified membranes during 10 h‐filtration of protein bovine serum albumin (BSA) in an aqueous solution, in which, before filtration of BSA, all membranes were immersed in E. coli bacteria solution for 4 days. The results of the experiments corroborated the hydrophilicity and more anti‐microbial property of the membrane surfaces after being incorporated into AgNPs. The water contact angle decreased from around 49° for the unmodified membrane to 36° and 23° for the AgNP‐modified membranes without/with PEG, while no colonies appeared in the medium containing the AgNP‐modified membranes. The separation property of modified membranes was improved, with both membrane flux and antifouling properties, along with the substantial surge of the anti‐biofouling property. After 10 h of BSA filtration, the fluxes of the modified membranes were maintained at 68% and 78% for AgNP‐modified membranes without and with PEG, respectively. Conversely, this value was only 46% for the unmodified one.
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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".