Environmentally Friendly and Cheap Removal of Lead (II) and Zinc (II) from Wastewater with Fish Scales Waste Remains
Bibliographic record
Abstract
In this article, the physical and chemical properties of pulverized, vinegar treated waste from fish scale remains of fish from Lake Ngami in Sehitwa near Maun, Botswana, were investigated for a possibility of being employed as an environmentally friendly and cheap sorbent material for reducing or removing excess, toxic, heavy metal ions from wastewater before different uses. Lead (II) and Zinc (II) ions were selected as model ions to demonstrate the potential of fish scale waste remains in removing excess toxic heavy metal ions. The pulverized size of the waste was found to be 60 µm, with round and smooth morphology, which are excellent characteristics usually associated with superior sorbents. Furthermore, the fourier transform infrared spectrometer spectrum showed multiple functional groups such as amines, carboxylic, hydroxyl, and carbonyls which are well known to bond well with metals through hydrogen and oxygen bonding. The X-ray diffractogram of the fish scales showed the presence of hydroxyapatite, which has an excellent ion-exchange performance, which exchanges calcium ion site with metals. Multivariate methodologies statistical software, Minitab, were employed for the simultaneous optimization factors that affect sorption studies; initial ions concentration which was found to be 24.45 mg/L, the sorbents dose which was found to be 76.99 mg/L, contact time, which were found to be 62.37 min and solution pH 7.52. The fish scales waste also exhibited high percentage removal efficiencies toward Lead (II) and Zinc (II) removal from real wastewater samples at 81.97% and 80.37% with percentage relative standard deviation of 1.34% and 1.02% respectively.
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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".