Sustainable valorization of electrocoagulation sludge in ceramic kaolinite membrane fabrication and its application to seawater pretreatment for SWRO desalination
Bibliographic record
Abstract
This study explores the sustainable valorization of dye electrocoagulation sludge (EC-Sludge) as a novel additive to enhance kaolinite-based flat ceramic membranes for seawater pretreatment prior to reverse osmosis desalination . Driven by the demand for low-cost, efficient, and eco-friendly membranes, incorporating up to 30 wt% EC-Sludge (containing aluminum hydroxide), significantly improved kaolinite membrane properties. The optimal membrane (including 15 wt% EC-Sludge) exhibited an average pore size of 1.43 μm, porosity of 39.30 %, permeability of 2580 L m −2 h −1 bar −1 , and mechanical strength of 21 MPa, demonstrating its suitability for microfiltration (MF) applications. MF tests of raw seawater confirmed the excellent pretreatment performance of the kaolinite/sludge membrane, achieving 95.24 % turbidity rejection, 81.14 % TOC removal, and 71.20 % COD reduction, while effectively lowering the SDI from 5.63 to 3.47. Fouling analysis revealed predominantly reversible cake layer formation, with up to 80 % flux recovery post-cleaning. The findings highlight the replicability and ecological potential of this approach for advancing sustainable desalination technologies .
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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".