Highly hydrophilic and antifouling poly(vinylidene fluoride) hybrid ultrafiltration membranes incorporated with silver‐loaded iron‐based metal–organic frameworks
Bibliographic record
Abstract
Abstract Silver‐loaded iron‐based metal–organic frameworks (Ag@FeBTC) are synthesized and incorporated with poly (vinylidene fluoride) (PVDF) to make hybrid ultrafiltration (UF) membranes and are efficiently utilized to treat water containing macromolecular pollutants. The chemical functionality and semi‐amorphous nature of the Ag@FeBTC are validated by FT‐IR and XRD, respectively. The morphology and elemental composition of PVDF/Ag@FeBTC membranes are probed by SEM and EDX mapping. The creation of pores is visibly seen in cross‐sectional SEM images and the improvement in roughness is noticed in AFM images. Because of the improved hydrophilicity of Ag@FeBTC on the PVDF membrane matrix, the contact angle is reduced to 33.9°. The excellent dispersion of Ag@FeBTC on the PVDF membrane matrix is observed through elemental mapping. The PVDF/Ag@FeBTC hybrid membranes exhibited higher pure water flux (160 L m−2 h−1) and greater than 90% rejection as well as flux recovery ratio for foulant removal (BSA and HA). The antimicrobial property of PVDF/Ag@FeBTC hybrid membranes is probed using a zone of inhibition test against E. coli and S. aureus, and the results revealed that the hybrid membranes possess superior antibacterial behavior. The long‐term stability of MOF in hybrid membranes is confirmed by AAS. Overall characterization and performance results of the hybrid PVDF/Ag@FeBTC UF membrane clearly demonstrated its potential use for water treatment applications.
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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".