Recent Advances in Thin Film Composite (TFC) Reverse Osmosis and Nanofiltration Membranes for Desalination
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Bibliographic record
Abstract
Reverse osmosis (RO) by polymeric membranes is known to be among the successful technologies for brackish and seawater desalination. For the development of these polymeric RO membranes, two different techniques have been used – the phase inversion method for asymmetric membranes, such as cellulose acetate membrane and the interfacial polymerization for thin film composite (TFC) membranes. Despite the high quality of the water produced by TFC–RO process, TFC membranes are susceptible to fouling. After a long period of academic and industrial researches, it is generally accepted that hydrophilicity, surface charge, and surface roughness of the TFC membrane surface are the major factors which affect the membrane susceptibility to fouling. As a result, several recent studies have focused on tailoring these properties with the aim of producing TFC membranes with reduced fouling as well as enhanced flux and salt rejection for use in desalination.In this study, authors present a comprehensive summary of the most recent findings in the literature focusing on the enhancement of TFC RO and nanofiltration membrane performance in desalination applications, by tailoring membrane characteristics. The authors would then present a new concept for the preparation of TFC membranes by interfacial polymerization on porous polysulfone support using novel additives, namely, surface modifying macromolecules (SMMs). Hydrophilic SMMs (LSMMs) were synthesized both ex–situ and in–situ within the organic solvent of the TFC system. The RO performance results showed that the addition of the ex–situ LSMM significantly decreased the salt rejection of the membrane and slightly reduced the flux, while in the case of the in–situ LSMM, salt rejection was improved but the flux declined at different rates for different LSMM concentrations. The membrane prepared by the in–situ LSMM exhibited less flux decay over an extended operational period.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it