Real textile wastewater treatment using nano graphene‐based materials: Optimum pH, dosage, and kinetics for colour and turbidity removal
Bibliographic record
Abstract
Abstract Textile effluent is one of the most hazardous types of wastewater for both the environment and human health when discharged without proper treatment. This work stands out as one of the first to evaluate the parameters for the application of graphene oxide (GO) to treat real textile wastewater. A comparative analysis was conducted to investigate the removal efficiencies of turbidity and apparent colour from raw textile wastewater using GO. The effects of different parameters, such as GO dosage, pH, and contact time were discussed, considering a removal mechanism based on the salting out effect. Results regarding treatment using GO followed by centrifugation showed that in >1 hour nearly 90% turbidity was decreased, and an apparent colour removal efficiency over 76% was recorded, which is twice the value obtained with the conventional treatment applied in textile mills. Over 60% chemical oxygen demand was reduced. Tests using GO followed by sedimentation also revealed promising results, showing removal efficiencies of 66% and 88% for apparent colour and turbidity, respectively. These results suggest that GO could be promising for real wastewater treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".