Your browser doesn't support javascript.
Filtering efficiency measurement of respirators by laser-based particle counting method.
Illés, Balázs; Gordon, Péter.
  • Illés B; Department of Electronics Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Muegyetem rkp. 3-9, H-1111, Budapest, Hungary.
  • Gordon P; Department of Electronics Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Muegyetem rkp. 3-9, H-1111, Budapest, Hungary.
Measurement (Lond) ; 176: 109173, 2021 May.
Article in English | MEDLINE | ID: covidwho-1091704
ABSTRACT
Respirators are one of the most useful personal protective equipment which can effectively limit the spreading of coronavirus (COVID-19). There are a worldwide shortage of respirators, melt-blown non-woven fabrics, and respirator testing possibilities. An easy and fast filtering efficiency measurement method was developed for testing the filtering materials of respirators. It works with a laser-based particle counting method, and it can determine two types of filtering efficiencies Particle Filtering Efficiency (PFE) at given particle sizes and Concentration Filtering Efficiency (CFE) in the case of different aerosols. The measurement method was validated with different aerosol concentrations and with etalon respirators. Considerable advantages of our measurement method are simplicity, availability, and the relatively low price compared to the flame-photometer based methods. The ability of the measurement method was tested on ten different types of Chinese KN95 respirators. The quality of these respirators differs much, only two from ten reached 95% filtering efficiency.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Measurement (Lond) Year: 2021 Document Type: Article Affiliation country: J.measurement.2021.109173

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Language: English Journal: Measurement (Lond) Year: 2021 Document Type: Article Affiliation country: J.measurement.2021.109173