Preparation and characterization of a novel highly hydrophilic and antifouling polysulfone/nanoporous TiO<sub>2</sub>nanocomposite membrane
Bibliographic record
Abstract
In this research, novel ultrafiltration nanocomposite membranes were prepared by incorporating self-synthesized nanoporous titanium dioxide (NTiO2) nanoparticles into polysulfone. The surface of the nanoparticle was treated with a silane-based modifier to improve its distribution in the host polymer. Atomic-force microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, Brunauer-Emmett-Teller, transmission electron microscopy, energy-dispersive x-ray spectroscopy, porosity and contact angle tests were conducted to characterize the properties of the particles as well as the fabricated nanocomposite membranes. The effects of the nanoparticle incorporation were evaluated by conducting ultrafiltration experiments. It was reported that the membrane pure water flux was increased with increasing NTiO2 loading owing to the high porosity of the nanoparticles embedded and/or formation of enlarged pores upon addition of them. The antifouling capacity of the membranes was also tested by ultrafiltration of bovine serum albumin fouling solution. It was found that both water flux and antifouling capacity tended to reach desired level if the NTiO2 added was at optimized loading.
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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".