The Effect of Cu2+ and Pb2+ in the Feed Solution on the Water and Reverse Solute Fluxes in the Forward Osmosis (FO) Process Using Nanofiltration (NF) Membranes
Bibliographic record
Abstract
The application of nanofiltration (NF) membranes in the forward osmosis (FO) process to remove heavy metal ions from wastewater is an emerging concept. Unlike NF, FO does not require an external driving force. Although the product, a dilute draw solution, must further be processed by NF to produce pure water and reconcentrate a draw solution, the feed to that NF process is “clean”, which minimizes membrane fouling. This paper examines the role of Cu2+ and Pb2+ in the feed solution on the water and the reverse solute fluxes in FO process using novel thin film nanocomposite (TFN) NF membranes. The TFN membranes were fabricated by in situ interfacial polymerization of piperazine (PIP) and 1,3,5-benzenetricarbonyl trichloride (TMC) containing different amounts of dispersed halloysite nanotubes (HNTs) nanoparticles functionalized with the first generation of poly(amidoamine) (PAMAM) dendrimers. The presence of Cu2+ and Pb2+ in the feed solution decreased the reverse flux of MgCl2 by at least 2.5 times compared to the experiments with pure water as a feed. Simultaneously, the water flux also increased. The corresponding rejections of Cu2+ and Pb2+ in the FO process ranged from 94.5% to 98.1%.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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".