RAPID: Highly Customizable, Breathable N95 Mask Design Utilizing Kirigami-enabled Filters and Sensor Platforms to Maximize Comfort and Monitor usage Patterns
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2034626
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$200,000Funder
National Science Foundation (NSF)Principal Investigator
Max ShteinResearch Location
United States of AmericaLead Research Institution
Regents of the University of Michigan - Ann ArborResearch Priority Alignment
N/A
Research Category
Infection prevention and control
Research Subcategory
Barriers, PPE, environmental, animal and vector control measures
Special Interest Tags
N/A
Study Type
Unspecified
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Intellectual Merit:
This project proposes to develop a rapidly manufacturable novel N95 class respirator design platform that decouples the relationship between comfort and filtering efficiency. The majority of N95 respirator masks are worn improperly even by trained medical personnel, caused by improper donning and fit when optimizing for comfort instead of filtering efficacy. The proposed N95 respirators would enable the monitoring and minimization of face touching and track cleaning cycles and usage patterns. The project will circumvent several fundamental shortcomings in the design of current N95 style respirator masks, as well as their fit and wear protocols. The proposed design maximizes compactness, comfort, and manufacturability, while enabling real-time monitoring of face-touch events. The research team will use the latest advances in knitting technology that controllably place stiff and compliant elements in a seamless and semi-customized manner, sensing algorithms that can run on widely deployed wearable technology, as well as novel kirigami/origami-inspired sensor platforms and mechanical enhancement for filter effectiveness. Despite the advanced nature of these elements, the project will use current hardware and manufacturing capacity and plans to quickly transition to cost-effective implementation.
Broader Impact:
Studies have implied that gaps (as caused by an improper fit of the mask) can result in over a 60% decrease in the filtration efficiency, implying the need for future cloth mask design studies to take into account issues of "fit" and leakage, while allowing the exhaled air to vent efficiently. The knitting-enabled approach used in this project allows for N-95 class of masks to be manufactured with a wider variety of size and fit options, using industrial capacity that has not been used to date for medical-grade masks. Successful implementation of these designs will result in greater numbers of PPE produced and made available to healthcare workers. Although the proposed manufacturing process will be more costly initially, the mask construction will meet the necessary standards to allow for reusability and continued fit over time; significantly reducing its per-use cost. Furthermore, this approach to achieving semi- or fully customized fit of something as varied as the human face can be more broadly adapted to a variety of wearable garments and devices.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
This project proposes to develop a rapidly manufacturable novel N95 class respirator design platform that decouples the relationship between comfort and filtering efficiency. The majority of N95 respirator masks are worn improperly even by trained medical personnel, caused by improper donning and fit when optimizing for comfort instead of filtering efficacy. The proposed N95 respirators would enable the monitoring and minimization of face touching and track cleaning cycles and usage patterns. The project will circumvent several fundamental shortcomings in the design of current N95 style respirator masks, as well as their fit and wear protocols. The proposed design maximizes compactness, comfort, and manufacturability, while enabling real-time monitoring of face-touch events. The research team will use the latest advances in knitting technology that controllably place stiff and compliant elements in a seamless and semi-customized manner, sensing algorithms that can run on widely deployed wearable technology, as well as novel kirigami/origami-inspired sensor platforms and mechanical enhancement for filter effectiveness. Despite the advanced nature of these elements, the project will use current hardware and manufacturing capacity and plans to quickly transition to cost-effective implementation.
Broader Impact:
Studies have implied that gaps (as caused by an improper fit of the mask) can result in over a 60% decrease in the filtration efficiency, implying the need for future cloth mask design studies to take into account issues of "fit" and leakage, while allowing the exhaled air to vent efficiently. The knitting-enabled approach used in this project allows for N-95 class of masks to be manufactured with a wider variety of size and fit options, using industrial capacity that has not been used to date for medical-grade masks. Successful implementation of these designs will result in greater numbers of PPE produced and made available to healthcare workers. Although the proposed manufacturing process will be more costly initially, the mask construction will meet the necessary standards to allow for reusability and continued fit over time; significantly reducing its per-use cost. Furthermore, this approach to achieving semi- or fully customized fit of something as varied as the human face can be more broadly adapted to a variety of wearable garments and devices.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.