Development of Iron Doped Activated Carbon for Pharmaceuticals Removal and Adsorbents Regeneration by UV in Water
Bibliographic record
Abstract
Pharmaceutical compounds in water streams have many difficulties in removal, primarily related to their low concentration (ppb-ppm level) in water [1].Among numerous methodologies developed, the adsorption process is promising as it is effective, easy, and inexpensive [2,3].A novel iron doped PAC (Fe-PAC) have been synthesized to remove the adsorbed pharmaceutical compounds by the photo-oxidation reaction of Fe impregnated in PAC.In this study, an experiment was conducted to repeat the process of regenerating Fe-PAC after adsorbing diclofenac.A small amount of UV-radiative energy was intermittently applied to Fe-PAC, where the material retained 91% of diclofenac (50 ppm) removal ability even after the 10th cycle.Unlike conventional PAC regeneration methods, which consume much thermal energy [4], it is a revolutionary study to remove micropollutants by supporting a minuscule proportion of Fe ions over carbon structure and regenerating under a 32 W UV-C radiation system.
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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 | 0.001 | 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".