Removal of Pharmaceutical Carbamazepine Using Pulsed Corona Discharge Generated in Water
Bibliographic record
Abstract
Increasing concentrations of pharmaceutical compounds and their metabolites in aquatic environment threatens both aquatic species and human health. Many pharmaceuticals are persistent in the environment and resistant to conventional water treatment; hence, alternate treatment techniques are required to remove these compounds from water. Aqueous phase electrical discharge has been demonstrated to successfully remove a range of pharmaceutical compounds from water. This technique is characterized by the simultaneous generation of highly reactive species and physical effects within the aqueous solution to be treated. The present study investigates and optimizes the efficacy of pulsed corona discharges generated in water for the treatment of carbamazepine (CBZ) in a relatively clean water matrix by varying the solution pH, solution conductivity, gas type injected, and gas flow rate injected. A point-to-plane reactor configuration was employed to produce plasma channel- streamers. In this geometry, the electrical discharges were produced directly within the aqueous phase to be treated by charging a 1nF pulse forming capacitor using a high voltage (25 KV) DC power supply through 10 MΩ resistor to generate discharges within the water at a 30 HZ pulse repetition rate. Solution pH, solution conductivity, gas type, and gas flow rate were optimized to maximize the removal efficacy of CBZ removal by pulsed corona at the laboratory scale. The CBZ degradation rate (concentration decrease over time) was monitored using LC/MS/MS, with overall degradation ranging from 14-94% depending on the experimental conditions. The results demonstrate that higher CBZ removal efficiency is achieved under lower pH and conductivity conditions. Moreover, enhanced degradation efficiency is obtained when gas, and particularly oxygen, is bubbled through the high voltage hollow electrode, especially at higher flow rates.
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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".