Effectiveness of Lignocellulose Nanofibers (LNCFS) for removing nitrite, nitrate, and phosphate from Gamishan wastewater
Bibliographic record
Abstract
This study investigates the effectiveness of Lignocellulose Nanofibers (LNCFs) as a sustainable adsorbent for the removal of nitrite, nitrate, and phosphate from aquaculture wastewater, specifically focusing on fish farms in Gamishan. We optimized several key parameters, including pH, contact time, temperature, and adsorbent dosage, to determine the optimal conditions for pollutant removal. The results indicate that the highest removal efficiencies were achieved at a pH level of 6.5, with a contact time of 67 minutes, a temperature of 30 ○ C, and an adsorbent dosage of 400 mg. Specifically, the removal rates were found to be 93 % for nitrate, 95 % for nitrite, and 96 % for phosphate. These findings demonstrate that LNCFs are not only effective in reducing these pollutants but also possess significant potential as a sustainable solution for wastewater treatment in aquaculture systems. The study emphasizes the critical role of optimizing operational parameters to maximize pollutant adsorption. Furthermore, the successful application of LNCFs in aquaculture practices could enhance environmental sustainability by promoting healthier aquatic ecosystems and effectively addressing the challenges posed by nutrient loading in water bodies. This research lays the groundwork for future investigations into the broader applicability of LNCFs in various wastewater treatment contexts, suggesting that their integration into aquaculture practices could lead to substantial improvements in water quality management and ecosystem health. • Lignocellulose nanofibers (LNCFs) effectively removed 96 % phosphate, 95 % nitrite, and 93 % nitrate from aquaculture wastewater. • Optimal adsorption conditions were identified: pH 7, contact time 60 min, and contaminant concentration of 150 mg/L. • Adsorption followed a pseudo-first-order model, achieving equilibrium within one hour, confirming LNCFs as efficient adsorbents. • Freundlich isotherm model (R² = 0.9716) outperformed Langmuir model (R² = 0.9695), indicating favorable adsorption behavior.
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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".