Synergistic effect between ultraviolet irradiation and electrochemical oxidation for removal of humic acids and pharmaceuticals
Bibliographic record
Abstract
Abstract The fate of pharmaceuticals in the aquatic environment is significantly affected by the presence of humic acids (HA). In this work, the synergistic effect of electrochemical oxidation (EO) and ultraviolet irradiation (UVI) was evaluated for HA removal and for the simultaneous degradation of three pharmaceuticals (carbamazepine, propranolol and sulfamethoxazole) in presence of HA. The effectiveness of EO, UVI and their combination for HA removal was assessed using different operating parameters, such as type of anode (Nb/BDD and Ti/IrO 2 ), supporting electrolyte (NaCl, NaBr and Na 2 SO 4 ), current density (8.1, 16.1, 28.2, 40.3, and 48.4 mA/cm 2 ), pH (3, 7 and 9) and NaCl electrolyte concentration (7, 14 and 21 mM). The use of non‐active anode Nb/BDD, NaCl electrolyte and combination EO‐UVI was the most efficacious option for HA removal, due to the production of hydroxyl radicals as well as active chlorine species (HClO, Cl ● and ClO − ) generated by anodic oxidation and by UVI. The effectiveness of the EO process was enhanced coupling EO with UVI, however the energetic consumption increased. The composition of the electrolyte was the pivotal parameter since a complete degradation of the pharmaceuticals was achieved by both processes EO and EO‐UVI using NaCl as electrolyte; this is attributed to the indirect oxidation by electrogenerated active chlorine which dominates the pharmaceuticals degradation.
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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 | 0.001 |
| 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".