Water Treatment Using High Performance Antifouling Ultrafiltration Polyether Sulfone Membranes Incorporated with Activated Carbon
Bibliographic record
Abstract
Membrane fouling is a continued critical challenge for ultrafiltration membranes performance. In this work, polyether sulfone (PES) ultrafiltration (UF) membranes were fabricated via phase-inversion method by incorporating varying concentrations of APTMS modified activated carbon (mAC). The mAC was thoroughly characterized and the fabricated membranes were studied for their surface morphology, functional groups, contact angle, water retention, swelling (%) porosity, and water flux. The hydrophilicity of mAC membranes also resulted in lower contact angle and higher values of porosity, roughness, water retention as well as water flux. Also, the membranes incorporated with mAC exhibited antibacterial performance against model test strains of gram-negative Ecoil and gram-positive S. aureus. The antifouling studies based on bovine serum albumin protein (BSA) solution filtration showed that mAC membranes have better BSA flux. The higher flux and antifouling characteristics of the mAC membranes were attributed to the electrostatic repulsion of the BSA protein from the unique functional properties of AC and network structure of APTMS. The novel mAC ultrafiltration membranes developed and studied in present work can provide higher flux and less BSA rejection thus can find antifouling applications for the isolation and concentration of proteins and macromolecules.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".