Removal of Two Pharmaceutically Active Compounds from Aqueous Phase Using an Engineered Floodplain Filtration System
Bibliographic record
Abstract
Abstract This study investigated the removal of two model pharmaceutically active compounds (PhAcs), viz., ibuprofen and triclosan, in lab‐scale engineered floodplain filtration (EFF) system. Biodegradation experiments were performed to acquire knowledge about the degradation of the targeted PhAcs, at an initial concentration of 350 µg/L. Biodegradation results showed that the two compounds were bio‐transformed to >70% after 15 days of incubation. Column tests were performed in a statistically significant manner to determine the adsorptive potential of the suggested filler layer in the EFF (C/C0), by varying the flow rate and initial concentration of the compound. It was observed based on the F and p‐values that the main effects (F = 3163, p < 0.005) were more significant than the interactive effects (F = 9561, p < 0.05) for both ibuprofen and triclosan removal. Besides, by performing the Student's “t” test, it was concluded that the flow rate plays a major role in determining the rapidness of achieving complete breakthrough than the initial concentration of both the compounds. The data obtained from column studies under biotic conditions indicated that the removal mechanism for PhAcs is mainly biotransformation based, and that an EFF system may be effectively used to remove these emerging compounds during ground water recharge for water recycling.
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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".