Evaluation of plasma‐activated water characteristics and its process optimization
Bibliographic record
Abstract
Abstract Plasma‐activated water (PAW) is an emerging technology for the disinfection of foods and it is also widely evaluated for its applications in medicine. The long‐lived reactive oxygen species such as hydrogen peroxide and ozone are mainly responsible for the disinfecting properties of Ar/O2 PAW. In this study, PAW characteristics were evaluated with respect to the process conditions and the post‐treatment time to understand the effect of process conditions and the time stability of PAW. PAW was generated using a continuous flow dielectric barrier discharge Ar/O2 atmospheric pressure plasma system at different PAW treatment times. PAW properties were evaluated based on the concentration of hydrogen peroxide, ozone, pH, and the disinfection of Escherichia coli. From the time stability analysis, it was found that the hydrogen peroxide was more stable than ozone in PAW when stored at room temperature for 2 days. The E. coli inactivation was mainly attributed to the H2O2 and ozone concentration than pH. The optimum process condition was found as 104 ml/min water flow rate, 20‐min treatment time and 4 slm gas flow rate for maximum reactive species concentration in PAW. Practical Applications There is a need for non‐chemical disinfection method of fresh fruits and vegetables, as the present chemical‐based disinfection methods are inefficient in controlling food‐borne outbreaks and the residual toxicity of these chemicals. Plasma‐activated water (PAW) is an emerging technology, which has the potential in disinfecting microorganisms. The aim of the work was to analyze the disinfection properties and the time stability of PAW. It is evident from the results that PAW is effective in disinfecting Escherichia coli and its reactive species degrade with time. This will facilitate the application of PAW as a disinfectant for fresh fruits and vegetables without any residual toxicity to the food and the environment. Further, from the optimization studies, understanding of the influence of process parameters on the PAW characteristics was derived which will be helpful to scale‐up of this technology.
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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 | 0.001 |
| 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".