Sterilization/disinfection using reduced-pressure plasmas: some differences between direct exposure of bacterial spores to a discharge and their exposure to a flowing afterglow
Bibliographic record
Abstract
The use of plasma for sterilization or disinfection offers a promising alternative to conventional steam or chemical approaches. Plasma can operate at temperatures less damaging to some heat-sensitive medical devices and, in contrast to chemicals, can be non-toxic and non-polluting for the operator and the environment, respectively. Direct exposure to the gaseous discharge (comprising an electric field and ions/electrons) or exposure to its afterglow (no E-field) can both be envisaged a priori, since these two methods can achieve sterility. However, important issues must be considered besides the sterility goal. Direct exposure to the discharge, although yielding a faster inactivation of microorganisms, is shown to be potentially more aggressive to materials and sometimes subjected to the shadowing effect that precludes the sterilization of complex-form items. These two drawbacks can be successfully minimized with an adequate flowing-afterglow exposure. Most importantly, the current paper shows that direct exposure to the discharge can lead to the dislodgment and release of viable microorganisms from their substratum. Such a phenomenon could be responsible for the recontamination of sterilized devices as well as possible contamination of the ambient surroundings, additionally yielding an erroneous over-appreciation of the inactivation efficiency. The operation of the N2–O2 flowing afterglow system being developed in our group is such that there are no ions and electrons left in the process chamber (late-afterglow regime) in full contrast with their presence in the discharge. The dislodgment and release of spores could be attributed, based on the literature, to their electrostatic charging by electrons, leading to an (outward) electrostatic stress that exceeds the adhesion of the spores on their substrate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".