Adsorption of Herbicide onto Fly Ash Sample from Aqueous Solution
Bibliographic record
Abstract
Applying of low cost adsorbent for herbicide capturing is an important area of research in environmental field. The present work reports the adsorption potential of fly ash, a waste from power stations, for removal of herbicides namely paraquat and alachlor from aqueous solution. Batch experiment was employed to investigate the adsorption of herbicide in aqueous solution. The maximum capacity of the fly ash to adsorb paraquat and alachlor was found to be 2.02 and 1.70 mmole.g-1, respectively. The coefficient of adsorption on the basis of 3 models: Freundlich, Langmuir and Sips isotherm models were found. The results shown that the equilibrium data are better fitted by three-parameter models than two-parameter models. The experimental data agree to adsorption isotherms in the order of Sips > Langmuir > Freundlich isotherms.
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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) | 0.001 | 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".