Transformation by plasma technology of cisplatin found in hospital's wastewaters into platinum-containing nanoparticles
Bibliographic record
Abstract
Platinum-containing molecules such as cisplatin figure among oncology's most widely used antineoplastic agents. Cisplatin excreted in the urine usually ends up in municipal wastewater, with a strong toxicological and carcinogenic impact on the environment. Thus, cisplatin should be inactivated before reaching wastewater to attenuate its environmental impact. However, conventional recommended procedures use large quantities of toxic acids, which are not sustainable processes. In this study, a dielectric barrier discharge (DBD) atmospheric pressure plasma reactor is used to degrade cisplatin in wastewater, allowing platinum's recuperation. The article describes the plasma discharge (power, electron temperature, and density) and confirms the most stable operation parameters under Ar and Ar+H2 discharges. Cisplatin is diluted in water or synthetic urine, and plasma treatment is conducted for 30 min. The process degrades cisplatin molecules by conversion into platinum-rich nanoparticles (NPs). These nanoparticles are efficiently recuperated by centrifugation and are characterized by transmission electron microscopy and X-ray photoelectron spectroscopy (XPS). The mass-balance assessment confirms that more than 90% of cisplatin is degraded and recuperated as Pt-rich NPs.
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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".