Improved biodegradation of pharmaceuticals after mild photocatalytic pretreatment
Bibliographic record
Abstract
Abstract The combination of photocatalysis and biodegradation was investigated for the removal of nine selected pharmaceuticals as a means to reduce loadings into the environment. The combined process, consisting of a resource‐efficient mild photocatalysis and a subsequent biological treatment, was compared to single processes of intensive photocatalysis and biological treatment. The UV‐TiO 2 based photocatalysis effectively removed atorvastatin, atenolol and fluoxetine (>80%). Biological treatment after mild photocatalytic pretreatment removed diclofenac effectively (>99%), while it persisted during the single biological treatment (<50%). Moreover, the biodegradation of atorvastatin, caffeine, gemfibrozil and ibuprofen was enhanced after mild photocatalytic pretreatment compared to biological treatment alone. The enhanced biodegradation of these pharmaceuticals appeared to be triggered by the biodegradation of photocatalytic products. Mild photocatalysis followed by biological treatment is an effective and resource‐efficient combination for pharmaceutical removal that could substantially reduce the loading of pharmaceuticals into the environment.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".