Phytoremediation of diclofenac using the Green Liver System: Macrophyte screening to system optimization
Bibliographic record
Abstract
Green Liver Systems employ the ability of macrophytes to take up, detoxify (biotransform), and bioaccumulate pollutants; however, these systems require optimization to target specific pollutants. In the present study, the aim was to test the applicability of the Green Liver System for diclofenac remediation considering the effects of selected variables. As a starting point, 42 macrophyte life forms were evaluated for diclofenac uptake. With the three best performing macrophytes, the system efficiency was evaluated at two diclofenac concentrations, one environmentally relevant and that other significantly higher (10 µg/L and 150 µg/L) and in two system sizes (60 L and 1000 L) as well as at three flow rates (3, 7, and 15 L/min). The effect of single species and combinations on removal efficiency was also considered. The highest internalization percentage was recorded in Ceratophyllum spp., Myriophyllum spp., and Egeria densa. Phytoremediation efficiency with species combinations was far superior to utilizing only a single macrophyte type. Furthermore, the results indicate that the flow rate significantly affected the removal efficiency of the pharmaceutical tested, with the highest remediation efficiency obtained with the highest flow rate. System size did not significantly affect phytoremediation; however, increase diclofenac concentration reduced the systems performance significantly. When planning the setup of a Green Liver System for wastewater remediation, basic knowledge about the water, i.e., pollutant types and flow, must be utilized during planning to optimize remediation. Various macrophytes show diverse uptake efficiencies for different contaminants and should be selected based on the pollutant composition of the 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".