Green Electrospun Membranes Based on Chitosan/Amino-Functionalized Nanoclay Composite Fibers for Cationic Dye Removal: Synthesis and Kinetic Studies
Bibliographic record
Abstract
Chitosan/poly(vinyl alcohol)/amino-functionalized montmorillonite nanocomposite electrospun membranes with enhanced adsorption capacity and thermomechanical properties were fabricated and utilized for the removal of a model cationic dye (Basic Blue 41). Effects of nanofiller concentrations (up to 3.0 wt %) on the morphology and size of the nanofibers as well as the porosity and thermomechanical properties of the nanocomposite membranes are studied. It is shown that the incorporation of the nanoclay particles with ∼10 nm lateral sizes into the polymer increases the size of the pores by about 80%. To demonstrate the efficiency of the adsorbents, the dye removal rate is investigated as a function of pH, adsorbent dosage, dye concentration, and nanofiller loading. The highest and fastest dye removal occurs for the nanofibrous membranes containing 2 wt % nanofiller, where about 80% of the cationic dye is removed after 15 min. This performance is at least 20% better than the pristine chitosan/poly(vinyl alcohol) membrane. The thermal stability and compression resistance of the nanocomposite membranes are found to be higher than those of the pristine membrane. In addition, reusability studies show that the dye removal performance of this nanocomposite membrane reduces by only about 5% over four cycles. The adsorption kinetics is explained by the Langmuir isotherm model and is expressed by a pseudo-second-order kinetic mechanism that determines a spontaneous chemisorption process. The results of this study provide a valuable perspective on the fabrication of high-performance, reusable, and efficient electrospun fibrous nanocomposite adsorbents.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".