Article

Filtration Efficiencies of Nanoscale Aerosol by Cloth Mask Materials Used to Slow the Spread of SARS CoV-2

Abstract and Figures

Filtration efficiency (FE), differential pressure (ΔP), quality factor (QF) and construction parameters were measured for 32 cloth materials (14 cotton, 1 wool, 9 synthetic, 4 synthetic blends, and 4 synthetic/cotton blends) used in cloth masks intended for protection from the SARS CoV-2 virus (diameter 100 ± 10 nm). Seven polypropylene-based fiber filter materials were also measured, including surgical masks and N95 respirators. Additional measurements were performed on both multi-layered and mixed-material samples of natural, synthetic, or natural-synthetic blends to mimic cloth mask construction methods. Materials were micro-imaged and tested against size selected NaCl aerosol with particle mobility diameters between 50 nm and 825 nm. Three of the top five best performing samples were woven 100% cotton with high to moderate yarn counts and the other two were woven synthetics of moderate yarn counts. In contrast to recently published studies, samples utilizing mixed materials did not exhibit a significant difference in the measured FE when compared to the product of the individual FE for the components. The FE and ΔP increased monotonically with the number of cloth layers for a lightweight flannel, suggesting that multi-layered cloth masks may offer increased protection from nanometer-sized aerosol with a maximum FE dictated by breathability (i.e. ΔP).
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Article Filtration Efficiencies of Nanoscale Aerosol by Cloth
Mask Materials Used to Slow the Spread of SARS CoV-2
Christopher D. Zangmeister, James G. Radney, Edward P Vicenzi, and Jamie Lynn Weaver
ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.0c05025 • Publication Date (Web): 25 Jun 2020
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1
Filtration Efficiencies of Nanoscale Aerosol by Cloth Mask Materials Used to
Slow the Spread of SARS CoV-2
Christopher D. Zangmeister,1* James G. Radney,1 Edward P. Vicenzi,2,1** and Jamie L.
Weaver1,2**
1Material Measurement Laboratory, National Institute of Standards and Technology,
Gaithersburg, Maryland, 20899, USA
2Museum Conservation Institute, Smithsonian Institution, Suitland, Maryland, 20746, USA
*Corresponding author Christopher Zangmeister, ph: (301) 975-8079, e-mail: cdzang@nist.gov
** Second affiliations are via non-funded, guest research appointments at listed institutions
Abstract
Filtration efficiency (FE), differential pressure P), quality factor (QF) and construction
parameters were measured for 32 cloth materials (14 cotton, 1 wool, 9 synthetic, 4 synthetic blends,
and 4 synthetic/cotton blends) used in cloth masks intended for protection from the SARS CoV-2
virus (diameter 100 ± 10 nm). Seven polypropylene-based fiber filter materials were also
measured, including surgical masks and N95 respirators. Additional measurements were
performed on both multi-layered and mixed-material samples of natural, synthetic, or natural-
synthetic blends to mimic cloth mask construction methods. Materials were micro-imaged and
tested against size selected NaCl aerosol with particle mobility diameters between 50 nm and 825
nm. Three of the top five best performing samples were woven 100% cotton with high to moderate
yarn counts and the other two were woven synthetics of moderate yarn counts. In contrast to
recently published studies, samples utilizing mixed materials did not exhibit a significant
difference in the measured FE when compared to the product of the individual FE for the
components. The FE and ΔP increased monotonically with the number of cloth layers for a
lightweight flannel, suggesting that multi-layered cloth masks may offer increased protection from
nanometer-sized aerosol with a maximum FE dictated by breathability (i.e. ΔP).
Keywords: SARS-CoV-2, COVID-19, cloth masks, face masks, personal protection, aerosols,
respiratory protection
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It has been recognized that the global spread of the Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS CoV-2) has limited the supply of medical masks and particulate filtering
facepiece respirators (e.g. N95), so it has been recommended that their use be restricted to
healthcare settings.1 As of this writing the World Health Organization (WHO) and the United
States Centers for Disease Control and Prevention (US CDC) suggest that cloth masks be used in
non-medical settings to reduce the transmission of SARS CoV-2.2,3 The majority of states in the
United States and more than 130 nations, corresponding to over 75 % of the global population,
have issued official guidelines requiring or recommending the wearing of masks in public locations
to mitigate the spread of COVID-19, the disease caused by SARS CoV-2. Thus, non-medical use
of facial coverings will likely consist of masks made from a variety of fibrous cloth materials
utilizing a range of designs and construction techniques. The WHO has stated the need for rapid
dissemination of research investigating the performance of relevant mask parameters – e.g.
material, design and construction – for reducing exposure to micrometer-sized droplets and
nanometer-sized aerosol particles at size ranges relevant to the virus (90 nm diameter core with
20 nm spike proteins).1,3,4 Particle measurements from hosptials in Wuhan, China demonstrated
that viable Covid-19 virus likely exists in particles with diameters between 250 nm and 500 nm.5
Facemasks can provide two modes of protection:6 1) by protecting the localized population
from an infected mask wearer by trapping expelled virus-laden atomized material (droplets and/or
aerosol), and 2) by protecting the mask wearer from ambient virus-laden atomized material by
filtering inhaled air.7 This reduces the risk of both direct and indirect viral exposure, respectively,
thereby decreasing the probability of infection.6
Prior research into cloth masks follows the history of infectious diseases spread by droplets
and/or aerosol and dates back to the 1918 - 1919 influenza pandemic.8,9 After that time the
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publication record on the utility of cloth masks is sparse and most research has focused on the
effectiveness of single use masks available in many developed countries for healthcare or other
occupational settings.10-16 The prior research shows that cloth materials offer limited protection
from particles in the size regime of SARS-COV2.10-12
When particles interact with a filter fiber, it is generally accepted that they are “collected”
by a fiber and retained through van Der Waals forces.17 For small particles, Brownian motion
increases the probability a particle will interact with a filter fiber. At larger sizes, particles can be
intercepted by a fiber when they are within one particle radius. When particle inertia becomes
sufficiently high, the particle may no longer follow a flowing gas streamline resulting in a higher
probability of inertially impacting a fiber. Electrostatic deposition, occurring due to a charge
difference between a fiber and a particle, can also be important in some materials. Collectively,
the sum of these efficiencies – diffusion (ED), interception (ER), impaction (EI) and electrostatic
deposition (EB) – yield the single fiber efficiency (EF = ED + ER + EI + EB). The reader is directed
to the seminal works by Brown,18 Emi,19 Fuchs,20,21 and Liu22 for detailed descriptions of filtration
theory.
The filtration efficiency (FE), is a common metric for reporting particle capture efficiency
of material and is related to EF through18-23
(1)
𝐹𝐸
=
1
exp
(
4
𝐸
𝐹
𝛼𝐿
𝜋
𝐷
𝑓
)
=
1
𝑁
𝐷
𝑁
𝑈
where α, L and Df are the material porosity (i.e. packing density), filter thickness and fiber or yarn
diameter, respectively. The FE can be quantified by measuring the upstream and downstream (NU
and ND, respectively) number density of particles per volume of air (particles cm-3) where particles
ND passed through the filter and particles NU were incident on the filter (see Figure S1 of the
Supporting Information), and the difference between NU and ND represent the particles captured
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by the filter. FE is reported as a percentage (by multiplying Eq. 1 by 100) and is a function of the
particle diameter (Dm), the flow rate through the filter and the filter medium.10-16, 24,25
The variability of test methods (mask materials vs. fitted mask on human-scale forms vs.
standard mask testbed measurements) and the broad range of materials tested makes direct
quantitative comparability between published studies highly challenging.10-16 In studies before
2020, the published FE of cotton-based fabrics were 5 % to 60 % when tested against an NaCl
aerosol with 20 nm Dm 1000 nm, 10 % for spherical 100 nm latex particles, and < 10 % for
100 nm diesel soot.10-12 Generally, the published FE of cloth filter media are much lower when
compared to facepiece respirators that are regulated for use in healthcare and other professional
settings; e.g. the FFP2 (EU standard, EN 149-2001) or the N95 (USA, NIOSH-42C FR84), which
are rigorously tested to ensure FE 94 % and FE 95 %, respectively, for 300 nm Dm NaCl.26,27
Notably, a recently published study suggests that masks from cloth filter media can be constructed
that may afford better protection for the wearer than an N95 respirator for particles between 10 nm
and 200 nm in size due to an enhancement in EB by specific types of cloth arranged in a
multilayered configuration.15
The study reported here aims to aid in the call for research testing the performance of cloth-
based media that are relevant for mask use.3 The FE and differential pressure P, Pa) which is
related to breathability, were measured for 32 relevant cloth materials (14 cotton, 1 wool, 9
synthetic, 4 synthetic blends, and 4 synthetic/cotton blends) that may be used in facemasks. The
FE and ΔP were measured under controlled laboratory conditions utilizing size selected NaCl
aerosol with particle mobility diameters (Dm) between 50 nm and 825 nm, a size regime relevant
for the removal of SARS CoV-2 particles,6,28 and in accordance to standard established filter
testing protocols, Parts 3 and 5 of EN 1822 and ISO 29463, using size selected charged and
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neutralized aerosol, respectively. For comparison, the FE and ΔP of 7 polypropylene containing
materials (2 surgical facemasks, an N95 filter, N95 fabric, a high- and low-density medical wrap
and a purported HEPA vacuum bag), 2 papers (coffee filter and paper towels) and 4-layered
samples of different materials were also measured. The samples in this study were systematically
chosen and characterized by their composition, yarn count, weave type (which has been relatively
understudied),29 and mass with the goal of establishing a relationship between these parameters
and measured FE and ΔP when tested against nanometer-sized aerosol under well controlled
laboratory conditions using established filter testing protocols.
Results and Discussion
Both nanometer-sized aerosol particles and micrometer-sized droplets can be captured by
a filter.17 The FE is a function of Dm, Df, fiber packing density and flow rate.25 Freshly generated
particles may be highly charged but immediately start to neutralize after emission.30 Ambient
aerosols are expected to have a net neutral average charge that follows a Boltzmann distribution
after < 100 min aloft25 (note, lifetimes of nanoparticles span hours to days over this size range).31
Differences in the charge state or distribution of charges of the aerosol may impact the measured
FE with EB typically enhancing FE.32
The distribution of particle sizes filtered by a material can be broadened by using random
arrays or layered structures containing a distribution of fiber diameters.23,33 This has allowed for
the development of filtration media with excellent FE across a broad range of Dm at reasonable ΔP
(e.g. the N95 mask and HEPA filters, see Fig. 1B1,2) and may also give insight into the design and
use of cloth materials for facemasks for the capture of virus-laden aerosols. Therefore, an
understanding of the filter structure (including weave structure, yarn count and yarn mass) and
material composition (natural, synthetic, or blended materials) may be important for the FE of
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cloth materials and may provide a deeper understanding in the complex parameter space that
influences the FE of cloth mask materials.
Figure 1. Transmitted light imagery of nonwoven and woven face mask materials. A1-3) outer,
intermediate, and inner layers of a N95 mask showing randomly oriented spun-bond (A1) and melt-
blown (A2 and A3) synthetic fibers.34 B1,2) Outer and inner surfaces of poplin weave cotton fibers
in lightweight flannel (sample Cotton 10). Note: Transmitted light grayscale intensities have been
inverted so fibers appear lighter relative to voids; all scale bars represent 1 mm with subsections
of 200 µm in length.
There have been several suggestions that FE may be related to textile parameters. Recently,
Konda, et al. (2020)15 indicted an increase in FE as a function of higher yarn count (described as
TPI in the cited article and summarized in their Fig. 3, and which herein will be discussed as yarns
inch-2 unless otherwise noted; 2.54 cm = 1 inch). Other studies have suggested that the cover factor,
defined as the sum of the number of yarns per unit length to the square root of the yarn count for
the warp and weft directions (see SI for definitions), is also related to a fabric’s filtration level (but
not FE directly).35 The cover factor indicates the extent to which the area of a fabric is covered by
one set of yarns, and a fabric with a high cover factor would have a low density of open space
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relative to fabric material for a given area. Cover factor is differentiated from packing density, as
cover factor assumes the yarns are solid and non-porous, thus underestimating a materials porosity.
Other parameters which may play a role are yarn mass, fabric texture, and fabric composition. Past
studies at the National Bureau of Standards (NBS, currently the National Institute of Standards
and Technology, NIST) indicated relationships between yarn count, fabric mass, and weave type
with permeability to air, which is related to the ΔP and the level of breathability through the
material.29
Electrostatics, as discussed by Konda, et al. (2020),15 and Zhao et al. (2020)36 may be
another important parameter and was addressed in this investigation by studying the impact of
particle and cloth charge (see below). A theoretical model of fabric electrostatics was proposed by
Alekseeva, et al. (2007)37 and suggests that fabrics can behave like a capacitor, collecting electric
charge in the gaps (pores) in fibers of the fabric. The specific properties are potentially dependent
on fiber composition, weave type and tightness (related to cover factor), production and processing
methods and can be influenced by atmospheric conditions (humidity, pressure, and temperature).38
The electrostatics of cloth materials are more commonly (although not always) discussed for
synthetic as opposed to natural fibers.39
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Figure 2. Measured filtration efficiency (FE) as a function of particle mobility diameter (Dm) for
an N95 respirator (black), the N95 base fabric (orange), a surgical mask (pink) and a twill (blue).
The solid bold lines and circles represent the base sample while the dashed light lines and squares
correspond to the re-neutralized samples. Olive triangles correspond to the twill FE measured with
an aerosol particle mass analyzer and re-neutralization. Uncertainties in FE are ± 5 %. See
discussion in text. Corresponding distributions of NU and ND can be found in Section S3 of the
Supporting Information.
The FE of 41 samples 32 cloth materials (14 cotton, 1 wool, 9 synthetic, 4 synthetic
blends, 4 synthetic/cotton blends), 2 paper materials and 7 polypropylene-based fiber filter
materials were measured at 15 equally log-spaced mobility diameters (Dm) resulting in Dm
spanning 50 nm Dm 825 nm. Particles were charge neutralized (as recommended by Parts 3
and 5 of the EN 1822 mask filtration protocol) using a soft X-ray source and size selected by a
differential mobility analyzer.27 For comparison, seven of the samples were re-neutralized after
size selection in accordance with the ISO 29643 filter testing standard. Particle number densities
upstream (NU) and downstream (ND) of the sample were recorded by condensation particle
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counters (CPC) for 30 s at 1 Hz at each Dm. These data were averaged and 1σ standard deviations
were calculated at each Dm bin. Measured 1σ standard deviations in N were 2.5 % with a CPC
accuracy of 3 % for N < 2 x 104 cm-3 (independently calibrated by optical methods),40 and 2
% day-to-day variability in FE for the same sample and 1 % sample-to-sample variability in FE,
resulting in an assumed 5 % combined uncertainty for FE; see Figures S30 and S19 in the
Supporting Information for day-to-day and sample-to-sample variabilities, respectively. The ND
was scaled based upon the CPC counting mode (single particle versus photometric; see Figure S2
and corresponding discussion in the Supporting Information) and the FE was calculated from Eq.
1.
Representative FE as a function of Dm are shown in Figure 2 for four of the tested samples
(an N95 respirator, the base fabric that is used to make an N95 respirator, a surgical mask and a
65 %/35 % cotton/polyester twill Polyester/cotton blend 3, twill weave, 229 yarns inch-2).
Importantly, the measured data capture the FE under conditions where leaks are absent. The
aerosol after size selection had moderate net charge (q, where +1 q +4 due to charge
neutralization prior to the measurement).41 The impact of particle charge on FE was tested by also
measuring the FE of 7 samples that were re-neutralized after size selection (dashed light lines and
squares in Figure 2 and blue traces in Figures S20, S23, S38, S41, S51, S52, S53, and S54 of the
Supporting Information). The measured data indicate that particle charge does not impact FE of
the measured cloth materials. In addition, the FE of Polyester/Cotton blend 3 was quantified by
passing the size selected aerosol through an aerosol particle mass analyzer (APM) and
subsequently re-neutralizing (olive triangles in Figures 2 and S39 of the Supporting Information).
The tandem DMA-APM effectively removes the presence of particles with q > +1 that may exit
the DMA.42,43 With the exception of the surgical mask that uses polypropylene layers for filtration,
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aerosol re-neutralization (and mass selection) did not affect FE (all values were within 1σ
uncertainty), see Figure 2, suggesting that EB is not significant for these cloth materials under
current conditions and is in agreement with previous observations.10
The FE values presented here are likely higher than typically measured on a mask testing
device as required for regulatory compliance (e.g. US 42 CFR 84 or EN143) and should be
interpreted as the maximum expected FE of the material for mechanical processes (ED, ER and EI).
Unless otherwise noted, the reported values are shown for aerosols that were not re-neutralized
after size selection (see additional sample FE curves of re-neutralized aerosol in S3 of the
Supporting Information).
Previous studies have reported values of FEmin and the most penetrating particle size
(MPPS, i.e. Dm at FEmin) for mask comparability.10-16 In this study FEmin and MPPS were
determined by converting FE to penetration efficiency (PE = 100 % - FE) and fit to a logarithmic
bi-Gaussian distribution (see Eq. 2).44 A bi-Gaussian distribution was chosen as it describes the
shape of the data over the measured range and is loosely related to the functional form of FE versus
Dm (see Eq. 1). The interpolated bi-gaussian fits are shown for all measured samples in Section S3
of the Supporting Information with a representative data in Figure 2.
An N95 mask is constructed of a multi-layered assemblage of polymer fibers with a
distribution of Df to efficiently bind particles of all Dm resulting in FE that are nearly invariant
with Dm with an average FE of (99.9 ± 0.1) % (1σ standard deviation across the 15 measured Dm;
see Figure 2 and S46). Except for the sample cut from a N95 mask, the FE of each tested material
had a consistent and typical U-shaped curve (increased inertial impaction and interception with
larger Dm vs. increased diffusion at smaller Dm),25 see Figure 2, that was exclusive for each
material tested (see Section S3 of the Supporting Information for all N and FE vs. Dm plots). Some
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materials, e.g. sueded polyester (Polyester 3, poplin weave, 152 yarns inch-2), had a high FE (75
%) at Dm < 200 nm and low FE (< 25 %) at Dm > 500 nm. In comparison, a heavy chiffon (Polyester
4, plain weave, 152 yarns inch-2), exhibited a FE that was only weakly dependent on Dm, but
averaged 30 % across the entire Dm range. A third poplin weave, light chiffon polyester
(Polyester 6) of the same yarn count and base-fiber of the previous two fabrics mentioned had an
FE < 25% across the range of Dm sizes measured. All three have different areal masses (85 mg
inch-2, 107 mg inch-2, and 38 mg inch-2, respectively). These data suggest that FE has a complex
interplay between fiber type, sample mass and construction methods (weave or bond structures).
A detailed correlation analysis of these parameters for several of the woven samples is discussed
below.
Figure 3. Filtration efficiency (FE) as a function of number of fabric layers for a cotton fiber
poplin weave in a lightweight flannel (cotton 10). A) Particle number densities per volume of air
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for the upstream (NU, black) and downstream (ND) particle counters as a function of number of
layers (colored lines). Shown NU was measured for a one-layer sample but was representative of
the NU measured for all samples. B) Calculated FE as a function of Dm and number of layers
(colored lines), C) Measured differential pressure (∆P, Pa) across the material as a function of
number of fabric layers. Gray line and plot maximum correspond to NIOSH recommended
maximum differential pressure across filter mask for exhaling (245.2 Pa, 25 mm H2O) and inhaling
(343.25 Pa, 35 mm H2O), respectively.27, 45 D) Minimum measured FEmin as a function of number
of layers. E) Quality factor (QF) as a function of number of fabric layers. Horizontal line shows
QF = 3 (WHO recommendation).3 F) Maximum penetrating particle size (MPPS, nm) as a function
of number of fabric layers. Lines in B) correspond to bi-Gaussian fits of the data while lines in C)
and D) are shown to guide the eye.
Initial investigations of the efficacy of cloth masks during the 1918 influenza pandemic
showed increasing the number of cloth layers in a facemask (1 layer to 8 layers) was important in
the reduction of microbial growth on plates placed downstream of fabrics and exposed to aqueous
aerosol droplets containing bacteria.8,9 Another study showed an analogous benefit by increasing
the number of mask layers up to four layers of cotton gauze.9 A similar trend was observed in this
study for a lightweight flannel (Cotton 10) using 1 to 5 layers of cloth (see Fig. 3). Figures 3A and
3B show N (particle concentration, particles cm−3) and FE as a function of the number of layers,
respectively. The ΔP (see SI S1, Fig. S1) across the fabric and FEmin increase monotonically with
the number of layers present (see Fig. 3C and 3D). More than four layers of this sample exceeded
the NIOSH recommended ΔP during exhalation through a fitted mask (245.2 Pa, 25 mm H2O).46
The quality factor (QF) was also calculated as a function of the number of layers, see Fig. 3E. The
QF is a commonly used factor to evaluate filter performance under common experimental
conditions23
(2)
𝑄𝐹
=
ln
(1
𝐹𝐸
𝑚𝑖𝑛
/100)
∆𝑃
QF increases by increasing FE or reducing P. QFs reported in the literature utilize both ln and
log10.23,36,47 The ln form was used in this study in accordance with the WHO recommendation.3
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The QF was nearly invariant with the number of fabric layers, consistent with multiplicative FEmin
and additive changes in P with the number of fabric layers. Lastly, the maximum penetrating
particle size (MPPS), defined as the Dm of FEmin, is shown in Fig. 3F, is 300 nm for a single layer
and slightly increases with the number of fabric layers to 350 nm for four layers of fabric.
The P and FE curves, were measured for 38 cloth materials spanning 50 nm Dm 825
nm. The FEmin and MPPS were calculated by fitting the distribution to a bi-Gaussian from which
QF was determined (see Figure 4 for FEmin and QF and Figure S5 in the Supporting Information
for MPPS; average MPPS across all samples was (252 ± 45) nm). The samples, listed above and
in Tables S1 and S2 in Section S2 of the Supporting Information, were classified by fiber content
as natural, synthetic, synthetic blend, synthetic/cotton blend, paper, and polypropylene based. The
P and FE of the cloth and paper samples were measured utilizing two layers of the respective
materials and as envisioned when constructing a multi-layer mask. The Synthetic blend 3 (waffle
weave, towel) and medical-grade materials (high-density and low-density wraps, HD and LD,
respectively) were measured as a single layer due to sample thickness (Synthetic blend 3) or per
the manufacturer’s use instructions (wraps). The N95, N95 fabric and HEPA vacuum bag were
constructed of multiple, separable thin layers of synthetic fibers and were used as received. As of
this writing, the medical-grade wraps are regulated for specific use in healthcare settings and are
not currently approved for use in masks in the United States.48 However, they have been utilized
in emergency situations by frontline healthcare workers in the United States and are included only
to aid in comparability to cloth and other tested materials. Additionally, while the HEPA vacuum
bag tested here contains polypropylene, some HEPA filters may be made from inorganic glasses.
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Figure 4. Measured samples categorized by type from left to right: cotton (orange), synthetic
(pink), synthetic blend (blue), synthetic/cotton blend (olive), paper (green), and polypropylene-
based (light orange). A) Yarn mass (mg yarn-1), B) Differential pressure across sample P, Pa),
C) Minimum filtration efficiency (FEmin, %), D) Quality factor, QF (kPa-1). The average most
penetrating particle size (Dm) across all samples was (252 ± 45 nm with individual values plotted
in Figure S5 in the Supporting Information. Uncertainties in B) and C) are ± 4.9 Pa (2x manometer
read uncertainty) and ± 5 % (expanded uncertainty) while uncertainties in D) were propagated
from B) and C). Abbreviations: Polyester (poly) and Cotton (Cott).
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The areal mass yarn-1, (see Figure 4A), a normalized mass value and not an absolute value
for each yarn set, spanned almost an order of magnitude across the range of cotton and synthetic
samples. The measured ΔP, (shown in in Fig. 4B) also spanned a broad range and was nearly zero
for open weaves – e.g. Cotton 11 (muslin cotton) and Rayon – and near 334 Pa (34 mm H2O) for
the most tightly woven samples (e.g. Cotton 4, down proof ticking), which also was one of the
highest yarn count samples measured. The FE of the measured samples ranged from 100 % for
a cut-out section from an N95 mask, to 0 % (within measurement uncertainty) for Polyester 6 (light
chiffon) and Cotton 11 (muslin). Porosity, which was reported for the fabrics measured by Konda,
et. al (2020),15 was not calculated for these samples as the length scale of smaller fabric pores (>
150 nm) was below the resolution of the imaging instrument (≈ 20 µm, see Supporting Information,
S4 Figs. S56 and S57).
Of the fabrics tested, the top three fabrics with the highest FEmin were 100 % cotton: Cotton 4
(down proof ticking), Cotton 8 (woven hand towel), and Cotton 10 (lightweight flannel). The FEmin
of Cottons 4 and 8 did not exhibit a statistically significant difference (p > 0.05), assuming 5 %
uncertainty. Cotton 4 (229 yarns inch-2) exceeded the NIOSH exhalation P limit (245 Pa, 25 mm
H2O).46 Notably, it approached the maximum 343 Pa (35 mm H2O) inhalation P limit
recommended for mask use by NIOSH.27 For comparability of cloth to other tested materials, the
measured FEmin of 2-layers of a high performing cloth (Cotton 8, FEmin = 32 %) was lower than
those measured for the low- (FEmin = 70 %) and high-density (FEmin = 86 %) medical-grade wraps,
the N95 mask (FEmin > 99.9 %), and the HEPA vacuum bag (FEmin = 94 %). Cotton 8 also had a
similar FEmin to the two tested surgical masks (30.5 %) and coffee filter (34.4 %). The other top
cotton fabrics were of average (≈ 150 yarns inch-2) to low (≤ 100 yarns inch-2) yarn counts, with
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the lowest being Cotton 8 (≈ 100 yarns inch-2). These materials had 24 % FEmin 32 %, and 57
Pa (5.8 mm H2O) P 106 Pa (10.8 mm H2O).
The other two fabrics in the top five were Synthetic blend 2 (twill weave, synthetic, 200
yarns inch-2) land Polyester 5 (100 % polyester, poplin weave, 230 yarns inch-2). The FEmin
performance of these two fabrics do not exhibit a statistically significant difference (see Table S2
in Section S1 in the Supporting Information) assuming an uncertainty of 5 %. The synthetic blend
had a moderately high yarn count of and the second highest P (300 Pa, 30.6 mm H2O) of the all
samples. While it met the NIOSH inhalation ΔP limit,45 it exceeded the exhalation limit.46
Polyester 5 had a higher yarn count and the P (104 Pa, 10.6 mm H2O) was well below NIOSH
limits. No trends were observed between the type of fabric weave and the measured FEmin. The top
five fabrics had weaves ranging from plain (Cotton 4), to block (Cotton 8), to poplin (Cotton 10,
Polyester 5) and twill (Synthetic blend 2). Descriptions of weaves can be found in Table S38 of
the Supporting Information.
Visual inspection of all top performing, highest FEmin, cotton samples showed some
amount of fiber raised from the weave structure. For Cotton 9 – which fell within the top half of
FEmin performance – and Cotton 10, the raised fibers were nap formed during manufacturing. Nap
is an intentional textural feature made from directionally oriented raised fibers that protrude from
the 2-dimensional plane of the fabric. Directionality can be lost without intervention (i.e. brushing)
as the result of use and the fibers can form aperiodic patterns similar to those seen in fiber-web
fabrics (e.g. Fig. 1). Imaging of the two flannels showed they were more heavily napped on their
outer side as opposed to their inner side and were the most heavily napped/raised fiber textured of
almost all the materials measured (see Figs. S17 and 19 in S3 of Supporting Information for
imaging of each measured sample and Fig. 1B1,2). These two flannel, Cotton 10 and Cotton 9, had
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similar yarn counts (≈ 150 yarns inch−2) and yarn widths: Cotton 10 - (weft) 0.26 mm ± 0.04 mm,
(warp) 0.18 mm ± 0.03 mm; Cotton 9 - (warp) 0.19 mm ± 0.02 mm, (weft) 0.30 mm ± 0.03 mm).
The other top performing cotton fabrics exhibited a similar, raised fiber texture, although not to
the same extent. This raised fiber texture was not observed for Polyester 5 or Synthetic blend 2.
Additional textural features were observed as raised fibers for Cotton 8 (woven hand towel) and
are likely related to fabric construction.
The fabric with the lowest FEmin was Polyester 6 (lightweight chiffon), followed by, in
ascending FEmin, Cotton 11 (muslin), Polyester 1 (knit), Rayon, and (Polyester/cotton blend 1, 65
% polyester/35 % cotton). Four out of the five fabrics with the lowest FEmin were synthetic. Two
fabrics, Polyester 6 and Cotton 11, had visually open weave structures compared to all other fabrics
analyzed (see See Figs. S21 and S31 in S3 of the Supporting Information). All had yarn counts <
250 yarns inch-2. Rayon, Polyester 1, and Polyester 6 had no apparent raised fibers, while
Polyester/cotton blend 1 and Cotton 11 had very few observable raised fibers. All were highly
breathable, with P < 88.2 Pa (9 mm H2O).
The QF is a relative metric for assessing the overall performance of a filter through the
combination of FEmin and ΔP (see Eq. 2 and Figure 4D). For measurements at similar flow rates,
higher QF typically implies improved performance; the WHO recommends utilizing cloth masks
with QF > 3.3 QF ranged from > 50 kPa-1 for the N95 mask and HEPA filter to < 0.3 kPa-1 for
Nylon, Cotton 11 (muslin) and Polyester 6 (lightweight chiffon). Paradoxically, two of the highest
QFs were for materials with low FEmin and ΔP: Rayon QF = (10.8 ± 2.5) kPa-1, FEmin = 2.1 %
and ΔP = 1.9 Pa – and Synthetic blend 1 – cotton/spandex, (13.5 ± 2.5) kPa-1, 15.3 %, 13 Pa. These
QFs are similar to the medical grade wraps (QF 13), surgical masks (average QF 9) and the
N95 fabric (QF 8) which all had significantly higher FEmin. For these low ΔP materials, FEmin
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can be improved by constructing masks with many layers (compared to the 2 layers used presently)
while maintaining QF (e.g. Fig 2E); however, the number of layers required to obtain the desired
FEmin will likely limit functionality in other ways (e.g. FEmin > 30 % would require 34 layers of
Rayon). Whereas, Cotton 8 – woven hand towel, (6.2 kPa ± 0.3) kPa-1, 32 %, 62 Pa – had a
moderate QF, FEmin and ΔP, respectively.
The FEmin and ΔP data were statistically evaluated using correlation calculations to
determine if relationships existed between measured values and textile parameters for both 100 %
cotton and 100 % polyester fabrics. No statistically significant relationships were found for
parameter sets with a sufficient number of samples (n = 5 for 100% polyester and n = 11 for 100%
cotton). Details of analyses and results can be found in Tables S34 to S37 in S5 of Supporting
Information. Sample sizes, after they were controlled for outliers and samples with easily
discernable weave patterns and yarn counts were relatively small (2 < n > 9) and may have
attributed to the non-significant results. A larger data set from additional samples collected from
similarly controlled populations may provide more insight into the effect of these variables on
FEmin and ΔP.
In their recent paper Zhao, et al. (2020)36 related the measured electrostatic properties of
common household fabrics (many similar to those studied here) to the fiber chemistry. A similar
discussion can be had relating the mechanical filtering ability of a textile to the fiber sources, yarn
widths, and textile construction. Mechanical filtration of aerosols by fiber filters is partially
dependent on the fiber diameter49 and the range of diameters of the fibers.50 Fiber shape may also
be an important factor.51 For synthetic based apparel textiles, the fibers used to construct yarns
tend to be set and have a relatively small variability in width and shape.39 Generally natural fibers
have more variability, which can be influenced by many factors. For plant-sourced materials (e.g.
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cotton) these factors can include plant type, growing conditions, and post-harvesting processing
methods.52 Cotton has been reported to have fiber diameters from 1 μm to 22 μm with a similar
range of fiber width variability (≈ 20 μm) found within singular crops. For animal-sourced
materials (e.g. wool) factors may be animal type/breed,53 and seasonal nutrition levels of the
animals.54 Sheep wools have been reported to have fiber diameters spanning < 17 μm to 40 μm,55
with intra-breed variability of 20 μm. Although this in range fiber diameter isn’t as broad as
those reported for high filtering synthetic nanofibers (spanning nm to μm),56 they do provide a
broad fiber diameter range and can be used to form fiber-webs for particle filtration. These two
features are those often found in filter media that have been shown to have good FEs.57
In this study many woven fabrics were found to form fiber-webs, with the highest
concentration being apparent for the 100% cotton flannel samples. These webs, the result of fibers
being raised away from the woven structure of the textile, can result in an increase of material
disorder, thereby disrupting flowing gas streamlines and providing more surfaces with which the
aerosols can interact. This may provide a mechanism to explain why the medium range yarn count
(≈ 150 yarns inch-2) Cotton 10 out-performed other fabrics with higher yarn counts (e.g. Cotton 6
and Cotton 13) and/or synthetics with visually tighter weaves (e.g. Polyester 5). Additionally, this
suggests that cover factor, discussed above, may not be a good individual indicator of FE for
fabrics displaying these textures. Interestingly, the cotton batting sample (Cotton 14) showed a
relatively low FEmin despite consisting of fiber-webs. More research is needed to understand these
observations.
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Figure 5: Comparison of ΔP, FEmin, and QF for single component layers and mixed layered
materials. Samples and mixed samples are coordinated by color. A) top 3 samples are single layers
and bottom 4 are mixtures using fabric samples either similar, or identical to those reported in
Konda et al. (2020).15 Samples marked with * were rubbed together for 30 s while wearing latex
gloves to aid in sample charging as in Zhao, et al. (2020).36 Pink diamonds show data from Konda,
et al. (2020),15 B) top 3 samples are single layers and bottom 2 are mixtures using fabric samples
exclusive to this investigation.
In addition to measuring materials from a single type of cloth, two-layered (multi-material)
structures were also investigated in accordance with recent findings by Konda et. al (2020)15 who
measured FEmin> 95 % for masks comprised of combinations of two layers of a synthetic and
natural fabric using NaCl aerosol that was not size selected. Combinations of cotton and synthetic
fabrics identical, or nearly identical, to those outlined in Konda et al. were tested here, and the
present results were not consistent with the prior findings (Fig. 5A). Due to the low-pressure
differentials measured, QF value in Konda et al. were over 1000 kPa-1, 250-500 times higher than
measured in this study, see values in Fig. 5A.15 Additional combinations that were exclusive to
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this investigation (see Fig 5B) were also tested. Konda et. al hypothesized that the measured FEmin
resulted from dissimilar layered fabrics increasing electrostatic charge (and hence EB) at the
interface between the two layers. In this study an attempt was also made to increase the charge
between the layered structures by rubbing the materials against one another wearing latex gloves
for 30 s as described recently by Zhao et al. (2020)36 and by Perumalraj (2015)58 for woven
fabrics. This did not significantly impact the measured FEmin (see Figure 5A). Subsequently, the
multi-layered samples also did not exhibit a significant difference in the measured FEmin when
compared to the product of the individual FEmin for the components (sum of individual efficiencies
in Eq. 1), see Fig. 5A. Zhao et al. (2020)36 showed that intense rubbing of materials using latex for
30 s increased the FE for some synthetics, but did not increase FE for cotton samples. The high
FE reported by Konda et. al (2020)15 is an intriguing observation that is inconsistent with the
results presented here and may be an artifact of sampling a freshly generated NaCl aerosol (e.g.
Forsyth et al. (1998))59 that has not reached charge equilibrium (either through the passage of time
or utilizing a charge neutralizer prior to exposing the aerosol to the sample). Thus, while their
FEmin may be applicable to freshly emitted virus-laden particles (e.g. from a cough etc.), they are
likely less representative of neutral or weakly-charged particles or those that persist in the ambient
environment.
The presented measured data may be utilized to highlight cloth materials that have a
reasonable (based on NIOSH guidelines) ΔP and a high FEmin. These data are discussed below for
the best performing cloth samples; microscopic images of each are shown in Figure 6 and data is
tabulated in Table 1. The top performing samples show a broad array of structure. This information
plus that presented above reinforces the fact that visual appearance is not necessarily indicative of
a fabric’s filtration and pressure differential properties as previously suggested.15 For example,
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the top performing cloth was Cotton 8, which had a visually loose weave, a high FEmin (32.0 % ±
1.6 %) and low ΔP (61.8 Pa ± 4.9 Pa), and a resulting QF of (6.25 ± 0.27) kPa-1. In comparison,
Cotton 6, which had a visually tighter weave and high yarn count (812 yarns inch-2), had a lower
FEmin (20.3 ± 1.02) % and a higher ΔP (128.5 ± 4.9) Pa, with a resulting low QF of (1.77 ± 0.04)
kPa-1. Another high yarn count fabric (Cotton 13, 600 yarns inch-2, and the same weave type as
Cotton 8) did not make this top five list and has statistically the same FEmin (Cotton 13 FEmin =
19.7% ± 1.0 %) as Cotton 8 despite having 200 less yarns inch-2.
Other two-layered cloth materials that offer a similar combination of FEmin and ΔP are
shown in Table 1. Using the reasonable assumption that the monotonic change in ΔP with the
number of layers (e.g. Cotton 10, lightweight cotton flannel shown in Fig. 3C) is similar for other
cotton materials, it can be hypothesized that the following material combinations and structures
may provide the best breathability and FEmin: a six-layered mask (by extrapolation from the change
in ΔP observed in Fig. 3C) of Cotton 8 or a four-layered assembly of lightweight flannel (sample
Cotton 10, FEmin = 48 % and ΔP = 216 Pa). Note, these hypothesized constructions are based upon
measured FEmin and ΔP data and do not consider the other potential guidelines that may affect
mask performance, such as the current WHO recommendation of utilization of mixtures of
hydrophobic and hydrophilic layers3 and the effects of mask fitment.13
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Figure 6: Transmitted light images of top performing fabrics based on FEmin and P that fall
within NIOSH guidelines and listed Table 1: A) Cotton 8 (hand towel, 100% cotton, block
weave), B1) and B2) Outer and inner surfaces of Cotton 10 (light flannel, poplin weave, 100%
cotton), respectively, C) Polyester 5 (apparel fabric, poplin weave, 100% polyester), D) Cotton 6
(inner surface, pillowcase, 100% cotton, satin weave see SI for image of other side), and E)
Polyester 2, (soft spun, 100% polyester, plain weave). Note: Transmitted light grayscale
intensities have been inverted so fibers appear lighter relative to voids; all scale bars represent 1
mm with subsections of 200 µm in length.
Table 1. Top fabrics based on FEmin and that fall within NIOSH P guidelines. Samples and
sources, yarns inch-2 (reported as threads inch-2 (TPI) in the table), FEmin, ΔP and QF for 2 layers
of select samples. Uncertainties are 1σ.
Sample & weave
Source/
Descriptor
TPI
(yarn inch-2)
FEmin
(%)
∆P
(Pa)
QFb
(kPa-1)
Cotton 8, block
Hand Towel
102a
32.0 ± 1.6
61.8 ± 4.9
6.25 ± 0.27
Cotton 10, poplin
Lightweight
Flannel
152
24.3 ± 1.07
106.0 ± 4.9
2.62 ± 0.07
Polyester 5, poplin
Poplin
Apparel Fabric
229
21.4 ± 1.08
104.0 ± 4.9
2.32 ± 0.06
Cotton 6, satin
Pillowcase
812
20.3 ± 1.02
128.5 ± 4.9
1.77 ± 0.04
Polyester 2, plain
Soft Spun
Apparel Fabric
152
20.2 ± 1.02
177.6 ± 4.9
1.27 ± 0.03
aSample is constructed of a complex design that results in a variable TPI across the fabric area. Reported
value is an estimated average TPI.
bThe WHO recommends utilizing cloth masks with QF > 3.3
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Non-woven, synthetic fabrics utilized in masks, such as the N95, are often constructed
using a melt-bond process. Part of the goal of this process is to stabilize and compress (e.g.
calendaring) the manufactured fiber-webs to enhance filtration. Similar steps to engineer the
processing of natural fabrics may be necessary to improve, and help maintain, the performance of
the above suggested natural fiber materials. Cloth materials with similar microscale structure
could be pursued in future investigations. Comparisons between studies measuring cloth FE is
challenging due to experimental parameters that may influence mask performance (e.g. sample
flow rate, aerosol generation scheme, charge neutralization and variability in protocols). Future
work in this area should use established techniques and officially recognized methods, such as EN
1822 or ISO standard methods. Based upon the findings presented here other important future
research areas of cloth masks may include: 1) investigating the relationship between fiber
diameters, diameter ranges, and sources with FE and ΔP of select best performing fabrics, 2) an
examination of the extent of napping and/or raised fibers on FE and ΔP, and 3) the effect of
washing/decontamination on FE , 4) the impact of seams 5) the influence of relative humidity
and/or 6) high face velocities such as encountered during coughing or sneezing on FE.
Conclusion
Cloth materials considered in the construction of facemasks to reduce the transmission of
SARS CoV-2 were evaluated for key factors in mask performance: the filtration efficiency (FE),
differential pressure P) and fabric construction parameters. The results indicate that there is a
complex interplay between fabric type, weave and yarn count and the filtration of nanometer-sized
aerosol particles. The best performing cloth materials had moderate yarn counts with visible raised
fibers. No measured cloth masks performed as well as an N95. The measured data of mixed cloth
assemblies were in contrast to recent measurements by Konda et al., where mixed assemblies of
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multiple materials did not synergistically impact the measured FEmin beyond the product of
individual FEs.15 The measured data indicate that particle charge does not impact FE for both
natural and synthetic fabrics.
Importantly, the presented data were measured under idealized conditions that eliminate or
minimize leaks, which are an important determinant in mask performance.13 Other factors that may
influence mask performance include: fiber width, relative humidity, fabric moisture content, seams
across the fabric, fabric nap/texture, and washing and/or degradation of the fabric.60 Extending the
measured particle size to mimic larger droplets will also aid in understanding fabric filtration.
The method of measuring FE using a combination of aerosol size selection and particle
counting is widely available and may enable other research laboratories to perform similar
measurements under controlled conditions enabling quantitative comparability of materials. The
combination of microscopy and FE applied to other mask materials may allow rapid screening of
a broad array of relevant materials or combinations to construct a detailed database for the
optimization of FE to reduce exposure to virus-sized particles.
Materials and Methods
Samples. Samples were acquired from multiple sources by the authors and those acknowledged.
Samples were cut into a circular shape of 2.5 cm diameter using a cleaned stainless-steel form and
scissors. Samples were used as received and were equilibrated at 22 °C and at ambient relative
humidity, 30 % to 50 %, prior to measurement. Most samples were measured in two layers, see
text, and were loaded into the sample assembly as individual layers to avoid the potential of perfect
registration between the two layers, which may reduce the measured FE.
The measured samples did not shed particles under the flow conditions described in this study.
However, materials that meet the HEPA filtration standard may be constructed from multiple types
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of materials, including layered polymers or glass (SiO2) fibers. Using mask materials that meet the
HEPA filter standard under conditions they were not designed for, such as cutting or the physical
deformation of the filter membrane, may cause the shedding of nano- or microscale fibers from
the filter increasing the likelihood of inhalation or ingestion by the mask wearer.
The fabric characterization experiments presented can be split into two sets: 1) material
microscopy image analysis for fabric structure to assess material yarn count, weave and mass
measurements, and 2) the filtration efficiency of aerosolized material.
Material imaging. Imaging was completed with an USB digital microscope (Innovation Beyond
Imagination) equipped with 8 brightfield LED lights, a color CMOS sensor, and a high speed 24-
bit DSP. Images were collected in diffuse reflected light (LED, ring illuminated) and transmitted
light (LED lightbox, Tiktec Labs, using size A4, or A5). All images were calibrated to a millimeter
length scale, and resolution was calculated to be 20 µm (see SI S4 for details). Calibration was
completed for each fabric type imaged. This was necessary as the differing thicknesses of the
fabrics resulted in utilization of slightly different fields of view for each sample set. Preliminary
processing, yarn counts, and yarn width measurements were completed in ImageJ (v. 1.8.0_112
with Fiji).61,62 All images were length-scale calibrated and converted to an eight-bit gray scale
before analyses. Additional image processing was completed in Digital Surf® (Mountain Labs, v.
8), and was utilized to create the inverted grayscale images shown in Figure 1. Reflected and
transmitted light images, yarn widths and yarn count data can be found in the SI S3.
Weave types and yarn count, width, and mass measurements. Reported weave types were
either determined by visual inspection following the descriptions outlined in Table S38 in Section
S7 of the Supporting Information or listed as described by the manufacturer. Two or more different
sample sites were analyzed for each fabric for determination of yarn widths, and, unless otherwise
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stated, an n = 20 yarns were measured. Measurements were completed manually and recorded
using the measure subroutine in ImageJ. Widths were measured between yarn crossing junctions
(i.e. not at the overlap of the yarns). Yarn counts were completed by measuring 1 mm across either
axis of an image (starting in a void space) and then counting the number of yarns which fall under
that line. Both front interior and exterior faced yarns were counted. Mean, standard deviation
(reported to 1σ), minimum, and maximum values are reported for the warp and weft yarn widths
(see Section S3 of the Supporting Information). Warp and weft yarn directions were utilized in
this study following textile industry practices. Warp yarns are those placed on a loom before being
interwoven with weft yarns (see Fig. S6 in the Supporting Information). Identification of these
yarns was completed by either visual inspection of the selvedge and/or by analyzing the weave
and looking for reed marks (marks left by a tool used to separate and space yams) in the samples.
Yarn counts were also conducted separately in the warp and weft directions. Reported yarn counts
are the average result of n = 5 measurements across 2 or more different sample sites. In cases
where there was no clear warp:weft yarn structure (e.g. knits, block and waffle weave samples), a
single value is reported for the number of yarns counted in along 1 mm of the horizontal direction
of the image. Mass yarn-1 measurements are relative to a 1 cm2 area and were calculated by
dividing measured masses by the total number of yarns (sum of warp and weft yarns) counted for
the same size area.
Filtration efficiency. FE measurements mimicked Parts 3 and 5 of the EN 1822 mask filtration
protocol (aerosol remains un-neutralized after size selection) or the ISO 29463 testing standard
(aerosol is re-neutralized after size selection); see Figure S1 in the Supporting Information.
Aerosol was generated from a 10 mg mL-1 aqueous solution of NaCl using a constant output
atomizer supplied with dry (dew point < -75 °C), HEPA-filtered air (25 psig). Of the 2.2 L min-1
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flow that was generated, 0.3 L min-1 was sampled and conditioned using two silica gel diffusion
dryers (desiccant was replaced daily prior to data collection). The aerosol was then passed through
a soft X-ray charge neutralizer and size selected, at a mobility diameter (Dm), using a differential
mobility analyzer (DMA) and a 10:1 sheath:aerosol flow. Under these experimental conditions,
the range of aerosols selected was 50 nm Dm 825 nm. The 0.3 L min-1 of aerosol exiting the
DMA was then mixed with 2.7 L min-1 HEPA-filtered dilution air (ambient laboratory air with
30 % relative humidity) and either passed to a condensation particle counter (CPC) or through a
25 mm plastic filter holder (with stainless steel filter backing) to a CPC to measure the upstream
(NU) and downstream (ND) particle number densities, respectively. Both CPCs sampled at 1.5 L
min-1 and the face velocity at the filter holder was 6.3 cm s-1 and in line with NIOSH guidelines.
Conductive (carbon black impregnated) silicone tubing was used throughout the experiment to
prevent aerosol savaging. The material under test was held in a plastic filter holder with a polymer
gasket that electrically isolated the material. No attempts were made to electrically ground the
tested material.
The downstream CPC had been previously calibrated by a spectroscopic method using
ammonium sulfate aerosol, see Radney and Zangmeister (2018).40 Using this method, the CPC
calibration is better than 3 % for number densities (N) < 2 × 104 cm-3, see additional details in the
SI. At larger values of N, NU and ND deviated from each other, so they were compared multiple
times daily using the described arrangement without fabric in the filter assembly. Both CPCs (TSI
3775) also switched between a single particle counting mode and a photometric counting mode at
5 × 104 cm-3. The transition between modes was set by the manufacturer and automated based
on the rate of change of N as a function of time. The transition regime of two instruments behaved
similarly but their set points may have been slightly offset under some measurement conditions.
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Two methods were used to account for this variation: 1) measurements with 4.5 × 104 cm-3 ND
5.5 × 104 cm-3 were excluded from the determination of the minimum FE and 2) separate
comparison curves between NU and ND were used for ND 4.5 × 104 cm-3 and ND 5.5 × 104 cm-
3 (see Fig. S2 in the Supporting Information).
Experimental and data acquisition were controlled by custom software written in our laboratory.
Dm (15 samples equally log spaced spanning 50 nm Dm 825 nm) was set by a computer and
the particle counts were allowed to stabilize for 30 s. The NU and ND were then recorded for 30 s
at 1 Hz after which the next Dm was sequentially selected. The 30 s data (representing one technical
replicate) was then averaged and a 1σ standard deviation calculated. The FE was then calculated
from Eq. 1 with the relative uncertainty in FE including the propagated 1σ uncertainties in NU and
ND. The total time required to collect an FE curve for a single fabric sample was 15 min.
For all presented FE, we assume an uncertainty of 5 % that derives from: 1) measured 1σ standard
deviations of N are 2.5 %, 2) CPC accuracy is 3 % (we assume this is constant for all N and
not just N < 2 × 104 cm-3), 3) an 2 % day-to-day variability in FE for the same sample and D)
and 1 % sample-to-sample variability in FE for the same material.
The calculated FE curves were then converted to penetration efficiency (PE = 100 % - FE) and fit
as a function of Dm to a logarithmic bi-Gaussian distribution (i.e. a logarithmically transformed bi-
Gaussian distribution)
(3)
𝑃𝐸
=
𝑃𝐸
𝑚𝑎𝑥
×
exp
(
(
log
10
(
𝐷
)
log
10
(
𝐷
𝑎𝑒
)
)
2
2
(
log
10
𝜎
1
)
2
)
𝐷
𝐷
𝑚
𝑃𝐸
𝑚𝑎𝑥
×
exp
(
(
log
10
(
𝐷
)
log
10
(
𝐷
𝑎𝑒
)
)
2
2
(
log
10
𝜎
2
)
2
)
𝐷
>
𝐷
𝑚
where PEmax is the maximum penetration efficiency of the material. To be consistent with other
reports, we report all data as the Filtration Efficiency (FE) as shown in Eq. 1.
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The differential pressure (∆P, Pa) across the filter sample was measured using one of two
manometers with ranges of 0 Pa to 245.17 Pa and 0 Pa to 490.3 Pa; to convert from Pa to mm H2O
to Pa, divide by 9.80665 Pa.63 The reported P across the filter represents the difference between
the filter assembly with and without a filter present. Uncertainty in P was calculated as twice the
manometer read error (± 4.9 Pa, 1σ).
Outlier and Correlation Calculations. 100 % cotton (n = 11) and 100 % polyester (n = 5) sample
sets were selected for correlation calculations by controlling for fiber type (cotton or polyester)
and utilizing data sets on fabrics only with definite yarn counts. The data sets consisted of the
following parameters: FEmin (%), ΔP (mm H2O), weft and warp yarn widths (mm), weft and warp
yarn counts (yarn inch-1), yarn count (yarns inch-2), and areal yarn mass (mg yarn-1). Parameter
outliers were determined by calculating the 1st (25 %) and 3rd (75 %) quartiles, determining the
interquartile range from these values, calculating the upper and lower bounds of the range, and
then rejecting any data value that fell outside of these bounds. Results from these calculations
along with other descriptive statistical values for each parameter analyzed is presented in Tables
S34 to S35 in Section S5 of the Supporting Information.
The remaining data was tested for normal distribution using the Shapiro-Wilk Test for Normality.64
Normality test results were employed to determine whether to use a parametric (Pearson Product
Moment Correlation, normally distributed data sets, Eq. 4) or a non-parametric (Spearman Rank-
Order Correlation, non-normally distributed data sets, Eq 5) correlation equation on pairs of data
sets.
(4)
𝑟
𝑝
=
𝑛(
𝑥
𝑖
𝑦
𝑖
)
(
𝑥)
(
𝑦
𝑖
)
[𝑛
𝑥
𝑖
2
(
𝑥
𝑖
)
2
][𝑛
𝑦
𝑖
2
(
𝑦
𝑖
)
2
]
(5)
𝑟
𝑠
=
6
(𝑟𝑔(
𝑥
𝑖
)
(𝑟𝑔(
𝑦
𝑖
))
2
𝑛(
𝑛
2
1)
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Where xi and yi are raw values for the ith set, n is the number of observations, and rg(xi) and rg(yi)
are converted ranks for defined values. Where xi and yi are raw values for the ith set, n is the
number of observations, and rg(xi) and rg(yi) are converted ranks for defined values.
These tests were selected under the assumptions that: 1) variables are independent, 2) that the
values in the non-parametric sets are interval variables and 3) that there is a monotopic relationship
between variables. Paired sets where both data sets failed the Shapiro-Wilk test and paired sets
where only one failed the Shapiro-Wilk test were treated with the Spearman test, and the Pearson
test was only utilized with pairs that both tested positive for normality. Pearson coefficients (rp)
and Spearman coefficients (rs), and their corresponding p-values p and ρs, respectively) were
calculated in SigmaPlot (version 14).65 Results are reported in Tables S36 and S37 in Section S5
of the Supporting Information. Calculated coefficients were evaluated for significance based on
the following criteria: variables with p-values below 0.050 and positive (+) coefficients tend to
increase together; pairs with p-values below 0.050 and negative coefficients (-) have one variable
that tends to decrease while the other increases. There is no significant relationship between two
variables for pairs where the p-values are greater than 0.050.65 Presented correlation results should
be interpreted cautiously given the small number of observations in this study per the controlled
population sizes.
Supporting Information
Supporting Information is available free of charge at ACS Nano. The Supporting Information
includes FE measurement description and schematic, measurements of particle counts for NU and
ND and FE as a function of Dm, description of method of image analysis, microscopic images, yarn
widths, number of yarns in the weft and warp directions, and number of yarns cm-2 for each
measured sample.
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Acknowledgements
The authors wish to acknowledge Gail Porter for the donation of samples for this work and Pamela
Chu, David LaVan and Rob Dimeo at NIST for technical discussions and organization and the
design and implementation of the TOC artwork, respectively. The authors would also like to thank
Darcy Gray and Nik Dukich of Tom Bihn, Inc. for supply of some samples and technical
discussions of suitable fabric materials.
Funding Sources
C.Z., J.R., and J.W. were funded by the National Institute of Standards and Technology. E.V.
was funded by the Smithsonian Institution.
Disclaimer
Trade names and commercial products are identified in this paper to specify the experimental
procedures in adequate detail. This identification does not imply recommendation or endorsement
by the authors or by the National Institute of Standards and Technology, nor does it imply that the
products identified are necessarily the best available for the purpose. Contributions of the National
Institute of Standards and Technology and the Smithsonian Institution’s Museum Conservation
Institute are not subject to copyright.
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... Their utility has been highlighted by the ongoing COVID-19 pandemic, where masks have significant utility as part of larger mitigation strategies (Hendrix 2020;Lyu and Wehby 2020;Stutt et al. 2020;Abboah-Offei et al. 2021;Bazant and Bush 2021;Gettings et al. 2021). The pandemic has also renewed interest in assessing the effectiveness of masks, including extensive consideration of common materials for use by the general public (Zangmeister et al. 2020;Zhao et al. 2020;Bagheri et al. 2021;Radney et al. 2021;Rogak et al. 2021;Zangmeister et al. 2021); the reuse of high performing masks (Lu et al. 2020;Ma et al. 2020;Rubio-Romero et al. 2020); the environmental impact of discarded masks (Fadare and Okoffo 2020;Dharmaraj et al. 2021;Hartanto and Mayasari 2021); and the testing methods and standards associated with various types of masks (Rengasamy et al. 2017;Corbin et al. 2021;LaRue et al. 2021;Zoller et al. 2021). ...
... By contrast, the ASTM F2299/F2100 test method, which employs polystyrene latex (PSL) spheres, and methods based on the PortaCount (e.g., ) measure particle counts and thereby target count-or numberbased filtration efficiencies (NPFE). Academic/research studies often use systems composed of size-resolved particle counting instruments, such as scanning mobility particle sizers (SMPSs) (Li et al. 2012;Tang et al. 2018;Lu et al. 2020;Zangmeister et al. 2020;Corbin et al. 2021) and optical particle sizers (OPSs) (Tang et al. 2018;Rogak et al. 2021;Bement et al. 2022). The counting nature of these sizing instruments is well suited to compute NPFE. ...
... The mobility diameter depends on the aerodynamic drag between the particle and the gas (DeCarlo et al. 2004). Mobility diameter distributions are measured by the SMPSs within the PFEMS system used in this study (Corbin et al. 2021) and many others, e.g., Bałazy et al. 2006;Lu et al. 2020;Zangmeister et al. 2020;Hao et al. 2021). The mobility diameter is often similar to the volume-equivalent diameter (it is larger by a factor that depends on the additional drag experienced by aspherical particles (Hinds 1999b;DeCarlo et al. 2004), see Equation (2) for reference) and is a physical measure of diameter related to diffusion. ...
Article
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The particle filtration efficiency (PFE) of a respirator or face mask is one of its key properties. While the physics of particle filtration results in the PFE being size-dependent, measurement standards are specified using a single, integrated PFE, for simplicity. This integrated PFE is commonly defined with respect to either the number (NPFE) or mass (MPFE) distribution of particles as a function of size. This relationship is non-trivial; it is influenced by both the shape of the particle distribution and the fact that multiple practical definitions of particle size are used. This manuscript discusses the relationship between NPFE and MPFE in detail, providing a guide to practitioners. Our discussion begins with a description of the theory underlying different variants of PFE. We then present experimental results for a database of size-resolved PFE (SPFE) measurements for several thousand candidate respirators and filter media, including filter media with systematically varied properties and commercial samples that span 20%–99.8% MPFE. The observed relationships between NPFE and MPFE are discussed in terms of the most-penetrating particle size (MPPS) and charge state of the media. For the sodium chloride particles used here, we observed that the MPFE was greater than NPFE for charged materials and vice versa for uncharged materials. This relationship is observed because a shift from NPFE to MPFE weights the distribution towards larger sizes, while charged materials shift the MPPS to smaller sizes. Results are validated by comparing the output of a pair of automated filter testers, which are used in gauging standards compliance, to that of MPFE computed from a system capable of measuring SPFE over the 20 nm–500 nm range.
... As a response to the current COVID-19 pandemic, woven cloth materials have been examined in terms of the protection they provide against the transmission of particles of certain sizes, particularly in the "Greenfield gap" from 0.25-0.5 μm [23][24][25]. To date, numerous experimental researchers have investigated the performance of woven cloth in face masks [26][27][28][29][30]. Christopher et al. [26] measured 32 available mask cloth for protection from the COVID-19 virus, and proposed that filtration efficiency and pressure were very relevant to those of single layer. ...
... As a response to the current COVID-19 pandemic, woven cloth materials have been examined in terms of the protection they provide against the transmission of particles of certain sizes, particularly in the "Greenfield gap" from 0.25-0.5 μm [23][24][25]. To date, numerous experimental researchers have investigated the performance of woven cloth in face masks [26][27][28][29][30]. Christopher et al. [26] measured 32 available mask cloth for protection from the COVID-19 virus, and proposed that filtration efficiency and pressure were very relevant to those of single layer. Wang et al. [28] investigated the filtration performance of carbon woven fabric on the removal of PM 1.0 particles. ...
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To enhance the design process of high-performance woven fibers, it is vital to clarify the evolution of particle dendrites, the dynamic pressure drop, and the capture efficiency with respect to dust loading during the non-steady-state filtration process. A general element (orthogonal elliptical fibers) of woven filter cloths is numerically simulated using the 3D lattice Boltzmann-cell automation (LB-CA) method, where gas dynamics is solved by the LB method while the solid particle motion is described by the CA probabilistic approach. The dendrite morphologies are evaluated under various particle diameters, aspect ratios, packing densities, and inlet fluid velocities. For submicron particles in the “Greenfield gap” range, it is revealed that the normalized pressure drop is an exponential function of the mass of deposited particles, and the rate of increase is exactly proportional to the perimeter of the elliptical fibers. Moreover, the normalized capture efficiency is a linear function of the deposited mass. It is not advisable to increase the packing density too much, as this might simply increase the pressure drop rather than enhancing the normalized capture efficiency. It is also worth noting that the fitting slope is more likely to grow linearly once the aspect ratio exceeds 1.6, indicating that orthogonal elliptical woven fibers offer higher capture efficiency than normal orthogonal cylindrical woven fibers. The work is beneficial to gain insights into the angular distribution of particle dendrites, as well as the prediction of dynamic growth of pressure drop and capture efficiency of the elliptical fiber. These efforts could help to deepen the understanding and realize assistant designing for the filtration performance of woven fiber in the future.
... If purpose-made filter materials are not available, then there is a wealth of literature on the filtration efficiency and breathability of common materials. 12,14,27 As with all face masks, the effectiveness is dependent on all aspects of the design-fit, filtration, and breathability-working in concert to trap airborne particles. ...
... Accordingly, this methodology should be considered alongside the multitude of studies which focus only on the filtration efficiency of materials. 12,14,27 When designing a high-quality AM face mask, a mask designer may use the design methodology presented to refine the mask frame design, select a wellestablished, highly efficient filter material, and perform rigorous quantitative fit testing on the resultant AM mask design. ...
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In response to the respiratory protection device shortage during the COVID-19 pandemic, the additive manufacturing (AM) community designed and disseminated numerous AM face masks. Questions regarding the effectiveness of AM masks arose because these masks were often designed with limited (if any) functional performance evaluation. In this study, we present a fit evaluation methodology in which AM face masks are virtually donned on a standard digital headform using finite element-based numerical simulations. We then extract contour plots to visualize the contact patches and gaps and quantify the leakage surface area for each mask frame. We also use the methodology to evaluate the effects of adding a foam gasket and variable face mask sizing, and finally propose a series of best practices. Herein, the methodology is focused only on characterizing the fit of AM mask frames and does not considering filter material or overall performance. We found that AM face masks may provide a sufficiently good fit if the sizing is appropriate and if a sealing gasket material is present to fill the gaps between the mask and face. Without these precautions, the rigid nature of AM materials combined with the wide variation in facial morphology likely results in large gaps and insufficient adaptability to varying user conditions which may render the AM face masks ineffective.
... It demonstrates a filtration capability by creating an electrostatic charge difference between the fiber and particle. [27] Surgical mask helps block large droplets, splashes and sprays on face and mucosa which might contain deadly viruses and bacteria. Surgical masks also lead to reduction in exposure of infective saliva and respiratory secretions. ...
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The legendary Greek philosopher, Aristotle once said that “Man is by nature a social animal.” Biological transmission of any disease is linked to the social contact of human beings. Respiratory infections are the best example of it. Pandemics of respiratory viral illnesses in history have taught a lesson of simple measures to protect ourselves by using face masks. Since the last nine centuries, scientists have struggled to come up with the masks giving 95%–99% of protection against respiratory pathogens. Through this article, we aim to review the evolution of the mask through times, with the objective of finding its effectiveness in preventing infections and also its role as a source of infection. Various online databases were searched to find articles that provided description of evolution of the mask.
Article
After the rapid spread of SARS-Cov-2 virus, the use of masks was suggested by the world health organization (WHO) to reduce the virus transmission, whose primary mode of transmission was suggested to be through respiratory droplets. The recommended face coverings were single use surgical and respirator masks made of non-woven materials. With the increased demand for masks worldwide, the environmental impacts of mask disposal and the pollution caused by microplastic fibers of the non-woven materials was presented. This challenge necessitates the need for the development of a novel reusable mask reducing the environmental effects, while providing the necessary personal protective properties. Based on the ASTM F2299 standard test method, the performance, i.e., particle-size dependent filtration efficiency and pressure drop were studied for 20 samples with multilayer knit fabrics of natural and synthetic fibers (inner layer of pure cotton, cotton-nylon and cotton-polyester, middle layer of Lycra, and outer layer of superhydrophobic polyester). The results show that all the samples had an efficiency of >94% and 87-99% for large (250 nm–1 μm) and small (100–250 nm) particles, respectively. The best performing structure has a material composition of 41% superhydrophobic polyester, 26% natural cotton, 24% nylon and 9% Lycra. The filtration efficiency, pressure drop, and quality factor for this sample are 97.8% (for 100 nm particles), 4.04 mmH2O/cm² and 4.77 kPa⁻¹, respectively. It was also demonstrated that the developed mask maintains its performance after 50 wash/dry cycles, verifying its reusability. It should be noted that charge neutralizer was not used in the experimental setup of this study which might have led to enhanced results for the filtration efficiency of small (100-250 nm) particles due to the dominance of electrostatic attraction. However, several samples were tested by the third-party company who uses a certified testing equipment based on ASTM F2299, and similar results were obtained.
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Worldwide attention has been paid to effective protection strategies against the COVID-19 pandemic. Filtering masks are generally kept for a certain period of shelf-life before being used, and frequently, they are used repeatedly with recurrent storages. This study investigates the effect of storage temperature and humidity on the structural characteristics and charges of an electret filter, associating with the filtration performance in terms of efficiency and pressure drop based on a practical use-storage scenario. For the repeated use conditions with recurrent storage, humid storage conditions significantly deteriorated the filtration efficiency as hygroscopic particles quickly wetted the surface and masked the surface charges. The high temperature rapidly deteriorated the filter charges and caused a lowered electrostatic filtration efficiency. In a heated condition, the web became fluffier, yet it did not directly affect the pressure drop or mechanical filtration efficiency. The approach of this study is progressive in that rigorous analysis was performed on examining the particle morphology and internal structure of filter media with varied storage conditions to link with the filtration performance and the effective lifetime. This study intends to provide a scientific reference guiding a desirable storage condition and replacement cycle of filtering masks considering the actual use habits and storage environment.
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During the COVID-19 pandemic, reusable masks became ubiquitous; these masks were made from various fabrics without guidance from the research community or regulating agencies. Though reusable masks reduce the waste stream associated with disposable masks and promote the use of masks by the population, their efficacy in preventing the transmission of infectious agents has not been evaluated sufficiently. Among the unknowns is the effect of relative humidity (RH) on fabrics’ filtration efficiency (FE) and breathability. This study evaluates the FE and breathability of several readily accessible mask materials in an aerosol chamber. Sodium chloride aerosols were used as the challenge aerosol with aerodynamic particle diameter in the 0.5 to 2.5 µm range. To mimic the variability in RH in the environment and the exhaled-breath condition, the chamber was operated at RH of 30% to 70%. The face velocity was varied between 0.05 m/s and 0.19 m/s to simulate different breathing rates. The FE and pressure drop were used to determine the quality factor of the materials. Among the tested materials, the 3M P100 filter has the highest pressure drop of 140 Pa; the N95 mask and the 3M P100 have almost 100% FE for all sizes of particles and tested face velocities; the surgical mask has nearly 90% FE for all the particles and the lowest pressure drop among the certified materials, which ranks it the second to the N95 mask in the quality factor. Other material performance data are presented as a function of relative humidity and aerosol size. The quality factor for each material was compared against reference filtration media and surgical masks. Multiple layers of selected materials are also tested. While the additional layers improve FE, the pressure drop increases linearly. Additionally, the certified materials performed approximately three times better than the highest performing non-certified material.
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Motorbikes are by far the dominant mode of transportation in Ho Chi Minh City (HCMC). They solve mobility problems but represent a health risk since riders are directly exposed to noxious exhaust fumes. Hence, face masks emerge as a solution to reduce exposure to harmful particles. The manufacturers of these masks report that they can significantly reduce particle exposure on roads with vehicular traffic. Such reports are usually based on laboratory assessments, with limited data from field experiments. To evaluate the performance of the masks commonly worn by HCMC commuters under quasi-real exposure conditions, we tested the total inward leakage of particles (i.e., including penetration through the filter media, and leaks from the face seal and exhalation valve if the mask is equipped with one) of six representative masks mounted on manikins at the curbside of two busy roads during high traffic time periods. Several particle metrics, including mass and number concentrations, active surface area, and abundances of equivalent black carbon and particle-bound polycyclic aromatic hydrocarbons were measured to determine the protection level provided by masks against distinct types of particles. As part of this study, through a set of measurements using the same instrumentation we found that commuters are exposed to a mix of freshly emitted particles and aged particles, including contributions from sources other than motorbike exhaust, such as trash burning and street food stalls. Ultrafine particles, especially those in the nucleation mode (< 50 nm), turned out to be the dominant fraction in terms of number concentration. This study focused its evaluation on these particles. We found that no mask can completely remove all particles under practical conditions. It is largely due to inappropriate mask fitting. Performance efficiency of 60-80% was achieved by an N95 respirator, a reusable valved filtering mask, and a locally manufactured carbon-layer sandwiched mask. Surgical and cloth masks achieved efficiencies of 25-60%. The results show that any face mask provides some level of protection. Efforts should be made to provide end users with practical information on the effectiveness of masks under real conditions, and informing on how to best fit each mask to increase effectiveness.
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Wearing surgical or N95 masks is effective in reducing the infection risks of airborne infectious diseases. However, in the literature there are no detailed boundary conditions for airflow from a cough when a surgical or N95 mask is worn. These boundary conditions are essential for accurate prediction of exhaled particle dispersion by computational fluid dynamics (CFD). This study first constructed a coughing manikin with an exhalation system to simulate a cough from a person. The smoke visualization method was used to measure the airflow profile from a cough. To validate the setup of the coughing manikin, the results were compared with measured data from subject tests reported in the literature. The validated coughing manikin was then used to measure the airflow boundary conditions for a cough when a surgical mask was worn and when an N95 mask was worn, respectively. Finally, this study applied the developed airflow boundary conditions to calculate person‐to‐person particle transport from a cough when masks are worn. The calculated exhaled particle patterns agreed well with the smoke pattern in the visualization experiments. Furthermore, the calculated results indicated that, when the index person wore a surgical and a N95 mask, the total exposure of the receptor was reduced by 93.0% and 98.8%, respectively.
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Face masks serve to protect the respiratory system from unwanted aerosol droplets, in which various types of pathogens or pollutants are present. They are particularly important during a pandemic, like SARS-CoV-2 pandemic we are witnessing. The efficiency of filtration of aerosol droplets, which contain the virus particles, is generally unsatisfactory, especially in conditions of extremely virulent environments, for the most of commercially available masks. Therefore, the challenge is to produce masks with increased filtration efficiency, in order to reduce the percentage of virus penetration through the mask. Hence, it is crucial to correctly define the possibilities and limitations of today's most commonly used epidemiological masks, in order to successfully define completely new concepts of face masks manufacturing, which would enable the most effective protection not only of medical workers but also patients, especially in areas where virus concentrations are extremely high. Also, it has been shown that, in addition to the concentrations of infectious pathogens in a given environment, the conditions in which infection with a given pathogen occurs, such as temperature and humidity within a given contaminated space, are also important.
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The COVID-19 pandemic is currently causing a severe disruption and shortage in the global supply chain of necessary personal protective equipment (e.g., N95 respirators). The U.S. CDC recommended use of household cloth by the general public to make cloth face coverings as a method of source control. We evaluated the filtration properties of natural and synthetic materials using a modified procedure for N95 respirator approval. Common fabrics of cotton, polyester, nylon, and silk had filtration efficiency of 5–25%, polypropylene spunbond had filtration efficiency 6–10%, and paper-based products had filtration efficiency of 10–20%. An advantage of polypropylene spunbond is that it can be simply triboelectrically charged to enhance the filtration efficiency (from 6 to >10%), without any increase in pressure (stable overnight and in humid environments). Using the filtration quality factor, fabric microstructure, and charging ability, we are able to provide an assessment of suggested fabric materials for homemade facial coverings.
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Masks and testing are necessary to combat asymptomatic spread in aerosols and droplets
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The ongoing COVID-19 outbreak has spread rapidly on a global scale. While the transmission of SARS-CoV-2 via human respiratory droplets and direct contact is clear, the potential for aerosol transmission is poorly understood1–3. This study investigated the aerodynamic nature of SARS-CoV-2 by measuring viral RNA in aerosols in different areas of two Wuhan hospitals during the COVID-19 outbreak in February and March 2020. The concentration of SARS-CoV-2 RNA in aerosols detected in isolation wards and ventilated patient rooms was very low, but it was elevated in the patients’ toilet areas. Levels of airborne SARS-CoV-2 RNA in the majority of public areas was undetectable except in two areas prone to crowding, possibly due to infected carriers in the crowd. We found that some medical staff areas initially had high concentrations of viral RNA with aerosol size distributions showing peaks in submicrometre and/or supermicrometre regions, but these levels were reduced to undetectable levels after implementation of rigorous sanitization procedures. Although we have not established the infectivity of the virus detected in these hospital areas, we propose that SARS-CoV-2 may have the potential to be transmitted via aerosols. Our results indicate that room ventilation, open space, sanitization of protective apparel, and proper use and disinfection of toilet areas can effectively limit the concentration of SARS-CoV-2 RNA in aerosols. Future work should explore the infectivity of aerosolized virus.
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We identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness. Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets. Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.
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