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Surface Energy of Filtration Media Influencing the Filtration Performance against Solid Particles, Oily Aerosol, and Bacterial Aerosol.

Seojin JungJaejin AnHyungjin NaJooyoun Kim
Published in: Polymers (2019)
Particulate airborne pollutants are a big concern to public health, and it brings growing attention about effective filtration devices. Especially, particulate matters smaller than 2.5 µm can reach the thoracic region and the blood stream, and the associated health risk can be exacerbated when pathogenic microbials are present in the air. This study aims at understanding the surface characteristics of nonwoven media that influence filtration performance against solid particles (sodium chloride, NaCl), oily aerosol (dioctyl phthalate, DOP), and Staphylococcus aureus (S. aureus) bacteria. Nonwoven media of polystyrene (PS) fibers were fabricated by electrospinning and its pristine surface energy (38.5 mN/m) was modified to decrease (12.3 mN/m) by the plasma enhanced chemical vapor deposition (PECVD) of octafluorocyclobutane (C4F8) or to increase (68.5 mN/m) by the oxygen (O2) plasma treatment. For NaCl particles and S. aureus aerosol, PS electrospun web showed higher quality factor than polypropylene (PP) meltblown electret that is readily available for commercial products. The O2 plasma treatment of PS media significantly deteriorated the filtration efficiency, presumably due to the quick dissipation of static charges by the O2 plasma treatment. The C4F8 treated, fluorinated PS media resisted quick wetting of DOP, and its filtration efficiency for DOP and S. aureus remained similar while its efficiency for NaCl decreased. The findings of this study will impact on determining relevant surface treatments for effective particulate filtration. As this study examined the instantaneous performance within 1-2 min of particulate exposure, and the further study with the extended exposure is suggested.
Keyphrases
  • public health
  • staphylococcus aureus
  • health risk
  • spinal cord injury
  • machine learning
  • combination therapy
  • risk assessment
  • particulate matter
  • artificial intelligence