Adsorption Study of Pb <sup>2+</sup> in a Contaminated Soil Amended With Four Leguminous Husk Wastes
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Bibliographic record
Abstract
ABSTRACT Heavy metal toxicity has become a major threat to living organisms in recent years due to the increase in population and anthropogenic activities. The uptake of lead by the primary producers (plants) is found to affect their metabolic functions, growth, and photosynthetic activity. Globally, various soil amendments have been employed to remediate contaminants in agricultural lands. Soil amendments using food waste could be recognized as cost‐effective, sustainable, and eco‐friendly solutions for “green remediation” strategies. This study aimed to explore how some organic food waste, such as fava, lentil, pea, and soya husk, can adsorb or immobilize the soluble lead (Pb 2+ ) in contaminated soils. Therefore, samples of lead (Pb 2+ ) contaminated soil were amended with and without husk residues and first characterized for their physicochemical properties like porosity, SSA, pH, electrical conductivity, and cation exchange capacity (CEC). Factors influencing heavy metal adsorption, such as initial Pb 2+ concentration, reaction time, and amendment ratio, were assessed. Results showed that the amendments were able to increase porosity, CEC, and electrical conductivity of the soil. Results also indicate that incorporating husks into soil effectively reduces soluble Pb 2+ levels in the soil solution at different concentrations. The adsorption of Pb 2+ in the amended soil follows the Langmuir model, with R 2 of 0.99, while in the non‐amended soil, it follows both the Langmuir (0.87) and the Freundlich (0.88) models. Incorporating 5% of husks is sufficient to achieve Pb 2+ adsorption efficiency of 80%–87%. The controlled and the treated soil samples exhibited a pseudo‐second‐order kinetic model, with high correlation coefficients ( R 2 > 0.99).
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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 | 0.001 | 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 it