Robust fabrication of thin film polyamide-TiO2 nanocomposite membranes with enhanced thermal stability and anti-biofouling propensity
Bibliographic record
Abstract
Abstract The development of nano-enabled composite materials has led to a paradigm shift in the manufacture of high-performance nanocomposite membranes with enhanced permeation, thermo-mechanical, and antibacterial properties. The major challenges to the successful incorporation of nanoparticles (NPs) to polymer films are the severe aggregation of the NPs and the weak compatibility of NPs with polymers. These two phenomena lead to the formation of non-selective voids at the interface of the polymer and NPs, which adversely affect the separation performance of the membrane. To overcome these challenges, we have developed a new method for the fabrication of robust TFN reverse osmosis membranes. This approach relies on the simultaneous synthesis and surface functionalization of TiO2 NPs in an organic solvent (heptane) via biphasic solvothermal reaction. The resulting stable suspension of the TiO2 NPs in heptane was then utilized in the interfacial (in-situ) polymerization reaction where the NPs were entrapped within the matrix of the polyamide (PA) membrane. TiO2 NPs of 10 nm were effectively incorporated into the thin PA layer and improved the thermal stability and anti-biofouling properties of the resulting TFN membranes. These features make our synthesized membranes potential candidates for applications where the treatment of high-temperature streams containing biomaterials is desirable.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".