Non‐thermal plasma decontamination of microbes: a state of the art
Bibliographic record
Abstract
Microbial decontamination is a critical concern in various sectors, from healthcare to food processing. Traditional decontamination methods, while effective to a degree, present limitations in terms of environmental impact, efficiency, and potential harm to the target material. This review investigates the emerging realm of non-thermal plasma (NTP) as a promising alternative for microbial decontamination, emphasizing its mechanisms, reactor designs and applications. The mechanism decomposed into physical, chemical and biological effects of plasma, are elaborated upon to provide a foundational understanding of the intrinsic principles of plasma decontamination. Except for the generation type of NTP, reactors and other parameters by which NTP achieves microbial decontamination, emphasizing the design considerations and parameters that influence its efficacy. Moreover, the latest applications of NTP in decontaminating air, water, and surfaces, supported by the latest research findings in each domain are explored. Additionally, the perspectives on the future research tendencies of NTP decontamination and disinfection are highlighted with potential avenues for exploration and innovation. Through this comprehensive review, the aim is to underscore the potential of NTP, particularly DBD plasma, as a versatile, efficient, and environmentally friendly method for microbial decontamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".