Photocatalytic Adsorbent for the Removal of MicroPollution from Industrial WWTPs
Bibliographic record
Abstract
Recently, concern about emerging contaminants such as micro-pollutant including pharmaceuticals and perfluorinated compounds has increased extensivel.Eliminating these substances from wastewater is complicated, and conventional treatment methods are not suitable for removing micro-pollutant.This study investigated a heterogeneous photocatalytic degradation process to remove micro-pollutants from aqueous solutions.As a photocatalytic adsorbent, Fe/Mn-SiO2 nanocomposites and Fe-AC were prepared to remove pharmaceuticals and perfluorinated compounds.In addition, process parameter optimization (catalyst dosage, UV power intensity, pH, H2O2) has been investigated.Spectroscopic and microscopic properties of the catalysts were analysed with BET, SEM, TEM, EDS, XRD, XPS, etc.Moreover, the reusability of photocatalytic adsorbents, and the field applicability in actual waste water was studied.According to this research, Fe/Mn-SiO2 and Fe-AC can be a practical approach for the treatment of micro-pollutants such as pharmaceuticals and perfluorinated compounds in aqueous environments.
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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) | 0.001 | 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".