Robust Polymer Nanocomposite Membranes Incorporating Discrete TiO2 Nanotubes for Water Treatment
Bibliographic record
Abstract
Polyethersulfone (PES) is a polymeric permeable material used in ultrafiltration (UF) membranes due to its high thermomechanical and chemical stability. The hydrophobic nature of PES membranes renders them prone to fouling and restricts the practical applications of PES in the fabrication of water treatment membranes. The present study demonstrates a non-solvent-induced phase separation (NIPS) approach to modifying PES membranes with different concentrations of discrete TiO2 nanotubes (TNTs). Zeta potential and contact angle measurements showed enhanced hydrophilicity and surface negative charge in TNTs/PES nanocomposite membranes compared to unmodified PES membranes. To discern the antifouling and permeation properties of the TNTs/PES membranes, steam assisted gravity drainage (SAGD) wastewater obtained from the Athabasca oil sands of Alberta was used. The TiO2 modified polymer nanocomposite membranes resulted in a higher organic matter rejection and water flux than the unmodified PES membrane. The addition of discrete TNTs at 1 wt% afforded maximum water flux (82 L/m2 h at 40 psi), organic matter rejection (53.9%), and antifouling properties (29% improvement in comparison to pristine PES membrane). An enhancement in fouling resistance of TNTs/PES nanocomposite membranes was observed in flux recovery ratio experiments.
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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".