Removal of pollutants from aquaculture wastewater using chitosan/bentonite composite beads
Bibliographic record
Abstract
Aquaculture systems produce ammonia nitrogen as a by-product of aquatic animal protein metabolism. The dropwise method was used in this study to prepare composite beads based on the chitosan (CS) biopolymer and bentonite (Bt) clay to remove ammonia nitrogen from aquaculture wastewater. The composite beads were characterised using Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), Brunauer–Emmett–Teller analysis and environmental scanning electron microscopy. According to the FTIR analysis, CS was successfully immobilised on the surface of Bt. Furthermore, the XPS analysis revealed that the chemical surface of the chitosan/bentonite (CSBt) composite was rich in CS elements. To remove ammonia nitrogen, three samples of aquaculture effluents were collected and characterised in terms of physico-chemical and chemical aspects. The ammonia nitrogen removal from aquaculture effluents was 100, 91.8 and 87.7% for initial concentrations of 0.56, 1.72 and 2.13 mg/l, respectively. Ammonia nitrogen sorption tends to reach equilibrium in approximately 120 min. The findings also show that the kinetic data correlated well with the pseudo-second-order equation. pH values increased slightly after adsorption, and the same trend was observed for total hardness and alkalinity. As a result, the CSBt composite is a promising adsorbent for ammonia nitrogen removal from aquaculture effluents.
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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) | 0.000 | 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".