Researchers at Duke University have created a low-cost smartphone-based system for measuring the efficacy of homemade facemasks at filtering droplets created during breathing and speech — and have discovered that some materials make matters worse, rather than better.
Since the start of the COVID-19 pandemic, makers across the globe have been turning their efforts to addressing the shortage of personal protective equipment (PPE) by making their own — with homemade masks a particularly popular choice. While these can range from simple cloth masks, there have been a range of designs including ones with in-built LED and E-Ink displays, modified snorkels, special fabrics which produce a virus-killing electric field, and even ones which deploy only when required.
One thing that remains up in the air, however, is just how effective these masks are at filtering the droplets created by breathing and speech and which are responsible for transmitting the virus. That's where the research by Duke University comes in: a simple, low-cost system for testing the efficacy of uncertified masks.
"Mandates for mask use in public during the recent COVID-19 pandemic, worsened by global shortage of commercial supplies, have led to widespread use of homemade masks and mask alternatives," the researchers explain. "It is assumed that wearing such masks reduces the likelihood for an infected person to spread the disease, but many of these mask designs have not been tested in practice."
"We have demonstrated a simple optical measurement method to evaluate the efficacy of masks to reduce the transmission of respiratory droplets during regular speech. In proof-of-principle studies, we compared a variety of commonly available mask types and observed that some mask types approach the performance of standard surgical masks, while some mask alternatives, such as neck fleece or bandanas, offer very little protection. Our measurement setup is inexpensive and can be built and operated by non-experts, allowing for rapid evaluation of mask performance during speech, sneezing, or coughing."
The system is relatively simple: a laser, spread into a flat beam, is captured by the camera on a low-cost smartphone. As a mask-wearer speaks, the droplets that escape the mask disrupt the beam — and each disruption is counted by a machine-learning algorithm, providing a quantifiable measurement as to how effective each mask is at stopping them.
As pointed out in coverage byFast Company, the test revealed a surprising result from one family of off-the-shelf masks: Neck fleeces, often worn by runners, bikers, and others, were found to actually increase the number of particles recorded — possibly, the team surmised, because it was breaking larger particles up into smaller fragments, which could allow them to be carried further than if the wearer didn't have a mask at all.
The team's work, which can be replicated for around $200 in parts, has been published under open access terms in the journal Science Advances.