On performance and anti-fouling properties of double-skinned thin film nanocomposite hollow fiber membranes in forward osmosis system
Bibliographic record
Abstract
This study involves the preparation of a double-skinned thin film composite (TFC) and thin film nanocomposite (TFN) hollow fiber (HF) membrane for forward osmosis (FO) applications. The porous substrate consisted of a Polyvinyl chloride (PVC) / Polycarbonate (PC) blend HF membrane. Interfacial polymerization (IP) was then applied to coat a polyamide (PA) layer on the lumen surface and the porous substrate's outer surface. In addition, the impact of the outer PA active layer and the addition of nanoparticles to the outer selective layer on the FO flux and internal concentration polarization (ICP) were studied. By adding the second active layer to the substrate, water flux, reverse salt flux and ICP decreased. Also, the decline of water flux decreased over time due to the fouling agent. To compensate for the decrease in water flux in the double-skinned membrane, graphene oxide (GO) nanoparticles with 0.05% and 0.1%wt were added to the outer active layer. Addition of 0.1%wt graphene oxide nanoparticle to the outer active layer can help to improve water flux about 78% without spoiling the reverse salt flux . Moreover, the performance of double-skinned membranes against osmotic dilution process for oily wastewater treatment was investigated. The findings of this study demonstrated that the novel double-skinned TFN HF membrane exhibited high FO performance with low ICP and fouling.
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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".