Removal of Zn, Pb, and Ni heavy metals from aqueous system using efficient modified-banana peel adsorbent
Bibliographic record
Abstract
Heavy metals contamination of water is a serious and complex environmental problem due to rapid industrialization, bioaccumulation and non-degradability. Therefore, the reuse of agricultural waste in the process of purifying water from pollutants is an attractive and promising method. Almost inexpensive materials are used to purify water, thus achieving the desired economic and environmental goal. In this study banana peel (BP) was used before and after modification by 0.1M sulphuric acid (H2SO4) to enhance the removal of Zinc Zn(II), Lead Pb(II) and Nickel Ni(II). The effect of various parameters was studied Such as pH, contact time, adsorbent dosage and initial metal ion concentration at 5 ppm. All these parameters were studied in batch experiments for a comparative study. The removal percentage was found to be 84% Zn (II),78%Pb (II) and 72% Ni (II) before modification and 92% Zn (II) , 94% Pb (II) and 96% Ni (II) after modification at initial concentration of 5 ppm. The data obtained from sorption isotherms were described with Langmuir and Freundlich isotherm models but were found to be well fitted for the Langmuir model. The correlation coefficient values R2 for Langmuir were at the range (0.996-0.999) while that for Freundlich were at the range (0.912-0.972). The rate of adsorption follows Pseudo-second-order kinetics. This work proved the high banana peels efficiency as an adsorbent agent for heavy metals removal from aqueous solutions.
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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) | 0.001 | 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 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".