Experimental Study on the Elimination of Colour and Organic Matter from Wastewater Using an Inexpensive Biomaterial, Chitosan
Bibliographic record
Abstract
Abstract Chitosan has been investigated as an inexpensive, biologically derived adsorbent and/or primary coagulant for two reactive azo dyes in textile wastewater. At natural pH, complete elimination of colour was achieved from 0.1 g/L aqueous solutions of the textile dyes Procion Orange MX-2R and Procion Red MX-5B with a dose of 6 g of chitosan per litre of dye solution. However, when pH was lowered (to 4.8 and 5.5 respectively), a dose of only 1 g of chitosan per litre was necessary to eliminate colour and drastically reduce TOC (total organic carbon) and chemical oxygen demand for the same concentration of dyes. This allowed about an 80% reduction in sludge volume production. Addition of sodium phosphate dibasic and potassium sulfate improved the dye removal at higher pH. Colour removal decreased significantly with or without added salts as pH was adjusted above 7. Equilibrium adsorption experiments showed that both dye solutions follow the Freundlich isotherm, but not the Langmuir isotherm. Kinetics measurements show a better fit to the pseudosecond-order Lagergren model than to the first-order Lagergren model. Brunauer-Emmett-Teller, or BET, surface area analysis and scanning electron microscope micrographs were included for better understanding of the nature of the chitosan surface with and without adsorbed dye. Chitosan appears to be a natural, clean and excellent product for the adsorption of Procion Orange MX-2R and Procion Red MX-5B in mildly acidic conditions.
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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) | 0.001 | 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) | 0.001 | 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".