Dual-layered ultrafiltration membrane with MgO nanoparticles via co-casting technique for arsenate removal
Bibliographic record
Abstract
Conventional membrane technologies for arsenic removal often struggle with inefficiencies due to arsenic neutrality, high-pressure needs, low water flux, limited contact area, and scaling issues. To address these challenges, we introduce a novel dual-layered flat sheet adsorptive membrane via a co-casting technique, incorporating thermally activated MgO nanoparticles (MgO 650° C ) into the selective layer. This co-casting technique enables both strong adhesion and enhanced adsorption performance. The membrane with MgO 650° C : 2.0 ratio exhibited excellent performance, combining an arsenate adsorption capacity (184.56 mg/g), efficient water permeability (167.39 L/m 2 .h.bar), and rejection efficiency of over 99 %. The adsorption behavior followed a pseudo-second-order kinetic model, implying that chemisorption is the dominant mechanism. The application of four adsorption isotherm models with kinetic analysis provides a comprehensive understanding of the adsorption mechanism. This is further supported by EDX elemental mapping, which confirmed the formation of As–O–Mg complexes on the membrane surface. Additionally, the membrane can be easily regenerated using a 0.1 M NaOH solution coupled with the ability to maintain performance over multiple cycles, underscoring its reusability. This work presents a scalable, regenerable, and highly adsorptive membrane platform, establishing a new benchmark for arsenic removal efficiency in membrane-based water treatment systems.
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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) | 0.000 | 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".