Comparison of Effectiveness of Raw Okra (Abelmoschus esculentus L) and Raw Sugarcane (Saccharum officinarum) Wastes as Bioadsorbent of Heavy Metal in Aqueous Systems
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Bibliographic record
Abstract
Adsorption process had been effective in condensing and concentrating metal ions from aqueous phase to the surface of adsorbent, it is a well established technology that employed the use of synthetic adsorbent which are usually scarce and expensive in waste water treatment. Hence, there is a need to develop new adsorbent which are readily available at low cost to remove metal contaminants in aqueous system. In this work, raw sugarcane waste and raw okra waste which are agricultural by-products were used as adsorbent in the adsorption of Fe(III) Cd (II), Pb (II), Zn (II), Ni (II) from various aqueous solutions. Infrared spectrum of the okra and sugar cane waste were recorded to detect the functional groups that has the binding capability for the metal ion adsorption. Batch studies were performed to evaluate the adsorption process and its was found that the okra waste was able to adsorb 5.05% of Fe(III),), 44.95% of Cd (II),), 65.10% of Pb (II), 38.78% of Zn (II), 57.80% of Ni(II), while the sugarcane waste was able to adsorb 3.61% of Fe (III), 35.06% of Cd (II), 43.50% of Pb (II),), 24.45% of Zn (II), 35.31% of Ni(II). This work proved that raw okra waste was more effective adsorbent material than raw sugarcane waste for the removal of heavy metals from aqueous systems. The Freundlich adsorption model described well the sorption equilibrium of the metal ions however research study have shown that modified form of okra waste was an excellent adsorbent, there is possibility of modifying the raw sugar cane waste for better performance since it has potential of removing heavy metals in waste water.
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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) | 0.000 | 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 it