Effectiveness of Raw versus Activated Coconut Shells for Removing Arsenic and Mercury from Water
Bibliographic record
Abstract
Relatively inexpensive biosorbents, made from coconut shell, were explored as alternatives to high-quality activated carbon for use in small-scale, wastewater treatment in developing economies. Simple charring and activation procedures were followed to produce CaCl2-activated coconut shell charcoal and its effectiveness was compared with raw coconut shell powder for removal of mercury (Hg) and arsenic (As) from contaminated water. From atomic absorption spectroscopy analyses, the removal efficiency of As and Hg with the use of activated charcoal were 67% (vs 65% for the raw form), and 53% (vs 49% for the raw form), respectively, from their corresponding “artificially”-contaminated wastewater. These results suggest that despite the slightly improved removal efficiencies recorded for activated coconut shells, the raw version could equally be used in treating wastewater towards the removal of the toxic metals- As and Hg. In order to understand the chemistry of the adsorption processes, FT-IR spectroscopy was employed to study similarities and differences in chemical compositions of the raw versus activated coconut shells before and after the biofiltration processes. To further investigate the effect of this biofiltration process on the overall quality of water, the physicochemical parameters (pH, conductivity, colour, turbidity, TDS and TSS) were measured on river water samples, pre-treated with the biosorbents. For both the raw and activated coconut shell, there was general improvement, although the conductivity of the water treated with the activated version was slightly elevated, was likely due to leaching of CaCl2 that was used for activation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".