Biostimulation of Chlorella vulgaris with Indole-3-Acetic Acid and Epigallocatechin Gallate Enhances the Removal of Amoxicillin and Cephalexin from Water: Kinetics and Efficiency Evaluation
Bibliographic record
Abstract
This study investigated the ability of Chlorella vulgaris to remove amoxicillin and cephalexin from water and evaluated the effects of indole-3-acetic acid (IAA) as a biostimulant and the combined application of epigallocatechin gallate (EGCG) and sodium bicarbonate (NaHCO₃) as catalytic enhancers.Batch experiments were conducted using different initial concentrations of both antibiotics over a 13-day exposure period.The two antibiotics exhibited distinct removal behaviors.Amoxicillin showed a relatively linear reduction pattern and followed apparent first-order kinetics, characterized by a lower removal rate constant and a longer half-life.In contrast, cephalexin exhibited a non-linear, biphasic removal behavior, involving an initial slow adsorption phase followed by rapid biodegradation, and therefore did not fit well to a single first-order kinetic model.The presence of catalytic systems significantly enhanced antibiotic removal compared with non-catalytic treatments.IAA promoted algal activity and enzymatic pathways, whereas the EGCG + NaHCO₃ system achieved complete removal (100%) of both antibiotics under optimal conditions.The superior performance of EGCG + NaHCO₃ is attributed to synergistic redox reactions and buffering effects that create favorable pH and oxidative conditions for algal metabolism.Overall, the integration of catalytic enhancement with microalgal bioremediation represents a sustainable and environmentally friendly approach for the removal of pharmaceutical contaminants from wastewater.
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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) | 0.000 | 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 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".