Efficiency of Sawdust from Selected African Indigenous Wood spp. as a Low-cost Adsorbent for Removal of Copper Ion from Contaminated Water
Bibliographic record
Abstract
Aims: This study investigates the use of sawdust from 3 hardwood species as low-cost adsorbent for the removal of copper from contaminated water. Study Design: The experimental design used for this study was 3 x 2 x 4 factorial experiment; the different sawdust species, two baselines (treated and untreated) and four levels of pH and time as factors were combined and used for the study. Methodology: Test was carried out to investigate the effect of sawdust pre-treatment on their adsorption capacity in the removal of Copper ions from contaminated water at different pH levels; the sawdust samples were sieved through a screen size of 850 μm after which a portion of each species sawdust was subjected to pre-treatment by boiling while the other portions were maintained as control samples (untreated). Results: The results shows that adsorption capacity for both treated and untreated samples were 69.75±13.78%, 68.60±19.48%, 69.34±23.08%, 74.79±17.79%, 74.52±22.30% and 76.90±18.21% for Alstonia boonei, Erythrophleum suaveolens and Ficus mucuso respectively. Conclusion: The contact time and pH showed no significant difference between the treated and untreated samples. Sawdusts from the selected wood species are suitable to be used as adsorbent towards the removal of copper from contaminated water.
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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".