Investigation of disinfection regrowth potential assessment of non-thermal plasma on surface materials: A comparative study
Bibliographic record
Abstract
Non-thermal plasma (NTP) presents a promising technique for surface disinfection and microbial decontamination. This study explores NTP's effectiveness on three surface materials—aluminum, glass, and acrylic—by comparing disinfection performance under identical treatment conditions. This investigation introduces the first-ever regrowth potential assessment, evaluating the regrowth of microbes post-NTP treatment. The research examines in depth mechanisms of regrowth and the factors influencing it, utilizing characterization techniques such as Fourier transform infrared spectroscopy (FTIR)-attenuated total reflectance (ATR), scanning electron microscopy (SEM), X-Ray photoelectron microscopy (XPS), and contact angle measurements. The results contribute to the assessment of microbial regrowth potential, providing an informative reference for evaluating the duration of NTP disinfection and helping to improve its practical application. • Evaluates material-dependent NTP disinfection on aluminum, glass, and acrylic surfaces. • Introduces microbial regrowth potential assessment post-NTP treatment. • Highlights hydrophilicity's role in biofilm formation and microbial recovery. • Demonstrates tailored NTP parameters to enhance disinfection while protecting surfaces. • Advances NTP's sustainable application for real-world disinfection challenges.
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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".