Application of Extracellular Polymeric Substances Extracted from Wastewater Sludge for Reactive Dye Removal
Bibliographic record
Abstract
This study aimed to investigate the adsorption of three commercial reactive dyes using extracellular polymeric substances (EPS) extracted from the waste sludge of a beer wastewater treatment plant in Hanoi, Vietnam. EPS was extracted from sludge by the HCHO-NaOH method and was characterized by measuring kaolin flocculation activity, dry weight, chemical composition, and functional groups. Adsorption of dyes on EPS was conducted by Jartest at different pH values, contact times, and EPS dosages. The EPS was composed of 25% sludge by weight. The FTIR analysis showed the presence of amine and carboxyl groups in the EPS structure. The removal efficiencies of reactive dyes were high at pH values below 6, a contact time of 30 to 60 min, and EPS dosage of 200 – 250 mg/L. At optimum condition, removal efficiencies of 85%, 99%, and 99% were obtained for Reactive Yellow 176 (RY 176), Reactive Blue 21 (RB 21), and Reactive Red 241 (RR 241), respectively. The adsorption process could be described by both Langmuir and Freundlich models. The maximum dye adsorption capacities for RY 176, RB 21, and RR 241 were 0.50 g/g, 0.72 g/g, and 0.95 g/g, respectively. It is concluded that EPS in wastewater sludge could be utilized as an effective adsorbent for dye removal, thereby enhancing the value of sludge in wastewater treatment. • EPS of wastewater sludge was extracted by HCHO-NaOH and used for dye removal. • EPS extracted by HCHO-NaOH was composed of 25% of sludge weight. • Removal of RY 176, RB 21, and RR 241 were 85%, 99%, and 99%. • The high adsorption capacity of dye was obtained at a pH of less than 6.
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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".