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Modeling of mold inactivation via cold atmospheric plasma (CAP)

Summary

This study presents a mathematical formula that predicts how quickly cold atmospheric plasma can kill mold on surfaces. Researchers tested the model using a common mold species and found that when plasma energy matched the mold’s natural growth rate, the mold died completely. The advantage of this approach is that scientists can now predict mold elimination in minutes using calculations instead of waiting weeks for laboratory experiments.

Background

Molds produce mycotoxins responsible for serious health problems and material degradation on food, building materials, and artifacts. Conventional methods like chemical treatment and heating have limitations including toxicity and heat-resistant mold strains. Cold atmospheric plasma (CAP) represents a promising alternative treatment method operating at ambient temperatures without toxic residues.

Objective

To develop a mathematical model using nonlinear logistic equations with density-dependent inactivation rates to predict mold extinction via CAP treatment. The model aims to determine mycelium surface coverage at arbitrary times and validate the approach against experimental data from Aspergillus brasiliensis.

Results

When plasma inactivation rate was comparable to maximum natural growth rate, mold colonies became extinct after finite time. At 72 hours post-inoculation treatment, growth curves remained constant indicating complete inactivation. At 117 hours treatment, a 113-hour stagnation period reflecting mycelium revitalization was followed by resumed growth with modified growth rate.

Conclusion

The analytical solution of logistic equations with density-dependent inactivation rate successfully predicted mycelium coverage and extinction dynamics. The model provides relevant predictions in minutes compared to weeks for real experiments. Future extensions will include modeling varying inactivation rates and real-world applications to building materials.
  • Published in:Applied and Environmental Microbiology,
  • Study Type:Mathematical Modeling Study,
  • Source: 10.1128/aem.02102-24, PMID: 40183547
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