Engine emission researchers retool to identify effective N95 mask alternatives
Researchers at the University of Wisconsin-Madison are leveraging tools used to measure the particulate from combustion engines to measure the filtration efficiency for alternative N95 masks to help improve supply during the COVID-19 pandemic.
As our world faces massive shortages of N95 masks, there is growing concern for the safety of healthcare workers who rely on these specialized masks and other personal protective equipment (PPE) to treat COVID-19 patients. Now, research in an unusual area — internal combustion engines — at the University of Wisconsin-Madison is aiding in the development of alternative N95 masks in response to this urgent need.
Dave Rothamer, a mechanical engineering professor at UW-Madison and an expert in internal combustion engines, is leveraging the tools he uses to measure the particulate matter emitted from combustion engines for a new purpose: He has converted the instruments so that he can measure the filtration efficiency of different candidate materials for face masks.
His goal is to identify materials for alternative masks that meet the National Institute for Occupational Safety and Health (NIOSH) standard for N95 masks, which is 95% filtration efficiency of 0.1 micron particles.
Rothamer and his graduate student Stephen Sakai are performing filtration efficiency and pressure drop measurements using the NIOSH standard for a wide range of natural and synthetic fabrics.
“This screening will identify materials or combinations of materials that provide high filtration at an acceptable pressure drop—and that will open up a range of manufacturing solutions to help address the mask shortage,” Rothamer said.
As COVID-19 spread, faculty, staff and students across the UW-Madison campus mobilized around an effort to address the need for N95-compatible materials. The group reached out to Rothamer, whose research involves studying particulate matter like the soot that’s formed during combustion in diesel engines.
“It turns out that soot particles from an engine are about same size as coronavirus particles,” Rothamer said. “And this means that we can uniquely apply our specialized instrumentation to contribute to solutions in this crisis.”
Particle size (diameter) plays a crucial role in the ability of a medium to filter those particles out of the air. Very small particles—0.005 microns or less—are easily trapped because they readily diffuse to a solid surface. And larger particles—1 micron or larger—are big enough that they inevitably become stuck to the fibers of a filter as they try to pass through.
Coronavirus particles, as well as particulate matter from engine exhaust, fall into an intermediate size range, making it more challenging to effectively filter them out. The coronavirus is 0.1 to 0.2 microns in size.
To address this filtration challenge, Rothamer is drawing on research expertise and techniques that have enabled auto manufacturers to significantly reduce engine particulate emissions. For example, when the engine industry was working to develop diesel particulate filters, researchers began to investigate engine exhaust particulate matter in great detail—and, in particular, the distribution of those particles as a function of their size, or diameter.
Rothamer and colleagues in the Engine Research Center at UW-Madison have instruments that can measure particle concentration in discrete size ranges from 0.01 to 1 micron. And, in the past, they have used these tools to characterize the filtration efficiency of potential particulate filters for engine exhaust.
That strong foundation is a valuable head start on an urgent effort to identify alternate materials for N95 masks.
“By applying these advanced experimental capabilities to test various natural and synthetic fabrics, I’m hopeful that we’ll be able to quickly zero in on the best materials for alternative masks and help make a difference in people’s lives during this pandemic,” Rothamer said.
– Edited by Chris Vavra, associate editor, Control Engineering, CFE Media and Technology, firstname.lastname@example.org.
Original content can be found at Control Engineering.
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