Facemask Fabric Filtration Efficiency

Scientists assessed common household fabrics to determine the best for protection against the coronavirus that causes COVID-19.

Image courtesy of American Chemical Society (Konda et al., ACS Nano, 2020, https://pubs.acs.org/doi/10.1021/acsnano.0c03252); further permissions are directed to ACS.
Schematic showing filtration of aerosol particles using a combination of mechanical and electrostatic filtration from a combination of fabrics.

The Science

Scientists have completed an important and timely study of cloth masks. The study examined the filtration efficiency of fabrics commonly used in these masks. The scientists focused on aerosol particles in a range of sizes relevant to viral transmission through respiratory exposures. The best-performing masks used hybrid designs that include high thread-count cotton and electrostatic layers such as silk or polyester chiffon.

The Impact

This is the first systematic scientific study of how efficiently face mask fabrics filter aerosol particles. The study found that combinations of commonly available fabrics, together with proper fit, should provide significant protection against transmission of aerosol particles. The study resulted in fabric selection guidelines for use by mask designers, hospital workers, and the public. The paper has been downloaded more than 350,000 times as of June 30, 2020 and received wide media coverage.


Due to the COVID-19 pandemic, the Centers for Disease Control and Prevention recommend wearing face masks in public. N-95 masks, which are in short supply, are best reserved for health care workers. As a result, many people have been using cloth masks. However, there is very little scientific data on the effectiveness of cloth masks in filtering aerosol particles. Such aerosols are caused by respiratory droplets that arise from normal activities like breathing, speaking, and coughing. They are a key transmitter of viral infections such as COVID-19. A new study by researchers at Argonne National Laboratory and the University of Chicago looked at more than 15 common household fabrics to investigate their filtration efficiencies against tiny droplets (in the 10 nm to 6 mm size range). The team tested an N-95 respirator and surgical masks for comparison. They also compared the effectiveness of multiple layers of a single fabric and a mixture of multiple fabrics. Among common household fabrics, they found the most effective fabrics to be high thread-count cotton, natural silk, and a polyester-spandex-based chiffon weave. Hybrid combinations, such as high thread cotton, along with silk, chiffon, or flannel also supplied broad filtration coverage.

The study found that fabrics with a tight weave and low porosity, such as those found in cotton sheets with high thread count, performed well. For instance, a commercial 600 thread-count (linear density of the warp and weft of the fabric) cotton performed better than an 80 thread-count cotton. Separately, natural silk, polyester chiffon and other materials that have electrostatic properties provide an electrostatic barrier against droplets. The study found that high thread-count fabric combined with a high-electrostatic fabric filtered aerosols most effectively. The study also revealed that gaps in a mask such as those from improper fit can reduce filtration efficiency by 60 percent. This finding indicates the need for future cloth mask design studies to take into account issues of fit and leakage.


Supratik Guha
Argonne National Laboratory and the University of Chicago


Use of the Center for Nanoscale Materials, a Department of Energy Office of Science user facility, was supported by the U.S. Department of Energy Office of Science, Office of Basic Energy Sciences. One of the researchers also acknowledges the Vannevar Bush Fellowship sponsored by the Office of the Undersecretary of Defense for Research and Engineering and the Office of Naval Research  


Konda, A. et al., “Aerosol Filtration Efficiency of Common Fabrics Used in Respiratory Cloth Masks,” ACS Nano, in press (2020) [DOI: 10.1021/acsnano.0c03252].”

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