Heavy Metal Removal (Copper and Zinc) in Secondary Effluent from Wastewater Treatment Plants by Microalgae
Why this work is in the frame
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Bibliographic record
Abstract
Microalgae is used for the removal of heavy metals from a wastewater treatment plant discharge. Laboratory-scale experiments are described that characterize the heavy metal uptake of copper and zinc by three microalgae strains: Chlorella vulgaris, Spirulina maxima, and a naturally growing algae sample found in the wastewater from a wastewater treatment plant (containing Synechocystis sp. (dominant) and Chlorella sp. (common) and a few cells of Scenedesmus sp.) Tests were conducted using untreated and autoclaved secondary effluent as a substrate. In the untreated secondary effluent trial, the microalgae removed up to 81.7% of the copper, reaching a lowest final concentration of 7.8 ppb after 10 days. Zinc was reduced by up to 94.1%, reaching 0.6 ppb after 10 days. The removal rates varied significantly with the microalgae strain. Higher heavy metal removal efficiencies were obtained in the autoclaved secondary effluent than the untreated secondary effluent, suggesting microorganisms already present in secondary effluent contribute negatively and compete with microalgae for nutrients, hindering microalgae growth and uptake of heavy metals. Inoculated samples showed decreased heavy metal concentrations within 6 h of initial inoculation, suggesting microalgae do not require long periods of time to achieve biosorption of heavy metals.
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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 | 0.000 | 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) | 0.000 | 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