Improving plasma sterilization by constructing a plasma photocatalytic system with a needle array corona discharge and Au plasmonic nanocatalyst
Bibliographic record
Abstract
Abstract Efficient sterilization by a plasma photocatalytic system (PPS) requires strong synergy between plasma and photocatalyst to inactivate microorganisms while suppressing the formation of secondary pollutants. Here, we report that a PPS constructed from a needle array corona discharge and Au/TiO2 plasmonic nanocatalyst could remarkably improve the sterilization of Escherichia coli (E. coli) and alleviate formation of the discharge pollutant O3. At 6 kV, the combination of corona discharge and Au/TiO2 achieves sterilization efficiency of 100% within an exposure time of 5 min. At 5 kV and an exposure time of 8 min, the presence of Au/TiO2 improves sterilization efficiency of the corona discharge from 73% to 91% and reduces the O3 concentration from 0.38 to 0.04 ppm, whereas the presence of TiO2 reduces the sterilization efficiency and O3 concentration to 66% and 0.17 ppm, respectively. The Au/TiO2 in the PPS enables a uniform corona discharge, enhances the interaction between plasma, E. coli and nanocatalysts, and suppresses the formation of O3. Further, the Au/TiO2 can be excited by ultraviolet–visible light emitted from the plasma to generate electron–hole pairs, and thus contributes to the formation of reactive radicals and the oxidative inactivation of E. coli. The PPS constructed from a needle array corona discharge and Au-based plasmonic nanocatalyst provides a promising approach for developing high-efficiency sterilization techniques.
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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".