Response Surface Methodology Modeling Correlation of Polymer Composite Carbon Nanotubes/Chitosan Nanofiltration Membranes for Water Desalination
Bibliographic record
Abstract
Quick population growth and worldwide industrialization is creating serious issues in accessing safe drinking water, which necessitates the exploration of operative and economical water treatment methods. This study aims to develop chitosan and carbon nanotube (CNT)-incorporated nanofiltration polyethersulfone (PES) membranes via the phase inversion method that have effective salt rejection capability. Various membranes, i.e., pristine PES, PES─0.75 wt % chitosan, PES─0.1 wt % CNTs, and PES─0.1 wt % CNT/chitosan composites, were fabricated and characterized. The composition, surface texture, and cross-sectional microstructures of the synthesized membranes were investigated by using attenuated total reflection–Fourier-transform infrared spectroscopy, atomic-force microscopy, and scanning electron microscopy, respectively. The chitosan/MWNTs containing a PES membrane showed excellent water flux and salt rejection. This composite membrane registered a maximum water flux of 80.26 L/m 2 ·h and ∼95.5% salt rejection at 40 °C and 4 kg/cm 2 of feed water pressure, as validated by ANOVA analysis. Response surface methodology showed a complete fit for the experimental analysis. This study suggests that the designed membrane can be used in practice to treat brackish water.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".