Computational modeling for PPE filtration: Informed by material characterization, microbial penetration, and particle...

Area of Science:

• Materials Science
• Biomedical Engineering
• Public Health

Background:

• Current characterization of personal protective equipment (PPE) relies on consensus standards.
• There is a need for advanced methods to predict PPE performance, especially after decontamination.

Purpose of the Study:

• To present a novel quantitative approach for characterizing PPE barrier materials.
• To predict the overall filtration efficiency (OFE) of surgical N95 filtering facepiece respirators.

Main Methods:

• Scanning electron microscopy (SEM) and image analysis (Diameter J) to examine filter microstructure.
• Bacterial filtration efficiency (BFE) testing to assess porosity effects.
• Development of a physics-based computational model incorporating material thickness, fiber thickness, and packing density.

Main Results:

• The computational model accurately predicted Staphylococcus aureus filtration efficiency compared to experimental data (ASTM F2101-23).
• The model provides conservative and predictive outputs for OFE across various particle sizes.
• Microscopic analysis revealed structural changes in filter layers after exposure to decontamination chemicals (vaporized hydrogen peroxide, ozone).

Conclusions:

• The developed computational model offers a predictive tool for assessing N95 respirator filtration efficiency.
• This quantitative approach can inform and accelerate decision-making in the design and manufacturing of N95 respirators.
• The model's predictive capabilities are valuable for ensuring the efficacy of PPE.