Fast and efficient removal of caffeine from water using dielectric barrier discharge
Bibliographic record
Abstract
Abstract Caffeine is a well-known central nervous system stimulant, which can cause anxiety, insomnia and nervousness. Domestic wastes of caffeinated drinks, beverages and chocolates are the major sources for entry of caffeine in the environmental system. Caffeine has been widely detected in natural water resources. The current study describes a method for efficient removal of caffeine from aqueous solution by a laboratory scale dielectric barrier discharge (DBD) in open air. Caffeine concentrations in various sample solutions were monitored by high-performance liquid chromatography, and the degradation products were identified by directly injecting the sample to mass spectrometer. The consequences of varied parameters such as input power, initial concentration and initial pH of the solution on the degradation of caffeine were investigated. Removal efficiency of caffeine from aqueous solution was 72.6% and 96.6% for the initial concentrations of 100 and 1 µg/mL, respectively, at initial pH 7 after 4 min treatment in DBD plasma system with 60 W input powers. Caffeine removal efficiency was less in acidic solutions (initial pH 4), and insignificant degradation was observed in alkaline solutions (initial pH 10). Furthermore, the degradation of caffeine was also enhanced by increasing the input power in DBD system. The DBD system used in this study has been considered to be fast, effective and economical. It was operated at atmospheric condition in open air without using catalyst, expensive gases or organic solvents, and significant degradation of caffeine was achieved in a short (4 min) treatment time.
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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".