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

Abstract

This work assesses the current characterization framework of single-use personal protective equipment (PPE) per recognized consensus standards and presents a novel quantitative approach to refining characterization of barrier materials and predicting PPE performance. Scanning electron microscopy (SEM) and image analysis software (Diameter J) were used to examine the microscopic fiber and pore structure of filter layers of surgical N95 filtering facepiece respirators, before and after exposure to chemicals used in decontamination modalities (vaporized hydrogen peroxide or ozone). The effect of porosity on penetration was assessed by bacterial filtration efficiency (BFE) testing. Results from these experiments were incorporated into a physics-based computational model of overall filtration efficiency (OFE). Material thickness, fiber thickness, and packing density were introduced as inputs into a sequence of mathematical expressions to calculate OFE for filtration layers from surgical N95 respirators. OFE derived from the computational model was compared with experimental data for Staphylococcus aureus filtration (per ASTM F2101-23). The resulting output from the model is conservative and predictive when compared with experimental results to assess OFE and filtration efficiency relative to specific particle-size ranges. The model functions may be used to help inform or expedite design or manufacturing decision-making on surgical N95 respirators.