Chemically crosslinked electrospun chitosan/poly (vinyl alcohol) membranes with encapsulated zeolite for organic dye removal
Bibliographic record
Abstract
Safe water is a basic human right, yet about 25 % of the global population lacks reliable access. Rapid urbanization and industrialization, especially in developing countries, have worsened water pollution, causing environmental and health issues. To achieve global safe water goals, affordable and effective wastewater treatment solutions are needed. To this end, zeolites are highly effective for wastewater treatment due to their unique adsorption properties; however, their efficacy can be significantly hindered due to a propensity to aggregate. Herein, electrospun chitosan (CS)/polyvinyl alcohol (PVA) membranes are fabricated to encapsulate sodium Y (NaY) zeolites, preventing their unwanted aggregation. By utilizing 1,1’-Carbonyldiimidazole (CDI) as a green crosslinker, NaY could be effectively immobilized within the electrospun membranes. CDI crosslinking enhanced the mechanical properties of the composite membranes, with improved Young's Modulus, ultimate tensile stress, and toughness as compared to non-crosslinked membranes. After optimizing the electrospinning process and formulation, as a proof of concept membrane modules were prepared and tested for the filtration of methylene blue (MB) as a model organic dye. Continuous filtration tests showed effective MB removal, achieving a 95 % removal rate and 287 LMH flux at an initial MB concentration of 50 mg/L, using a 1 g/L adsorbent dosage. Crucially, these membranes showed good mechanical stability and potential to be regenerated/recycled, yielding only a 13 % decrease in performance after five adsorption/desorption cycles. We anticipate that this approach could lead to the development of functional membranes for removing organic dyes and other emerging contaminants from water, directly improving access to safe, clean water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".