A Novel Approach to Antiviral COVID-19 Masks

Cristina Ciborowski Escoffery Isabella Dunncristinaciborowski@gmail.com dunni@mfriends.org

Hemil Patel Sydney Yan20hemilpatel03@gmail.com sydneyyan@gmail.com

Shivani Shukla*srshukla@ucsd.edu

New Jersey’s Governor’s School of Engineering and TechnologyJuly 18, 2020

*Corresponding Author

Abstract—In the past few months, the SARS-CoV-2 virus hasinfected millions. To reduce the spread of COVID-19, peopleneed affordable, homemade masks that are as protective as N95respirators. This experiment investigates copper-infused clothmasks as a potential solution. Copper’s antiviral properties areutilized to reduce the probability of infection in a cost-effectiveand accessible manner. In this procedure, copper nanoparticlesare isolated using vinegar, salt, and hydrogen peroxide, and theresultant solution is infused into the cotton masks. Four protocolswere developed to determine the effectiveness of the mask:neutralization, reusability, filtration, and fit. Using Vigna radiataas a model organism, research in these areas led to the conclusionsthat antiviral masks are effective, safe, and comfortable.

I. INTRODUCTION

In December 2019, severe acute respiratory syndrome coro-navirus 2 (SARS-CoV-2) emerged from Wuhan, China. Thisvirus, which causes Coronavirus Disease 2019 (COVID-19)pandemic, was soon deemed a public health emergency of in-ternational concern (PHEIC) by the World Health Organization[1]. In the US alone, over 3.4 million people have been diag-nosed with SARS-CoV-2, and over 138,000 of them have died[1]. Current responses focus on preventative measures such asself-isolation, social distancing, disinfection of surfaces, andpersonal protective equipment (PPE) like masks. As statesbegin loosening restrictions, masks become increasingly im-portant; the most effective N95 masks, however, are reservedfor medical first responders and other health care professionals.As such, many citizens have turned to homemade masks.These masks are far less effective in both fit and filtration,and this paper addresses the inefficiencies of homemade masks[2]. More effective masks are created by utilizing the antiviralproperties of copper. This research maintains a focus on costand accessibility while identifying methods to improve thereusability, filtration, and fit of existing masks.

II. BACKGROUND

A. Transmission Process

The highly-contagious SARS-CoV-2 virus can be trans-mitted via surfaces or aerosols, small respiratory droplets.While talking, sneezing, or coughing, an infected person canproduce aerosols that contain the virus [3]. These aerosolsenter the human respiratory system through the eyes, mouth,and nose. After moving into the body, SARS-CoV-2 travels tothe lungs and targets alveoli, minuscule sacs of air responsiblefor gas exchanges at the end of the bronchioles[4]. The body’simmune response exacerbates the issue: when the immunesystem targets infected cells, healthy cells are also killed inthe process. As a result, the alveoli lose surfactant from type IIalveolar cells and consequently fail. Figure 1 shows a healthyalveolus, whereas Figure 2 pictures a damaged one. In Figure2, protein-rich fluids overflowed the alveolus, which couldresult in symptoms like shortness of breath.

Fig. 1. A healthy alveolus [5]

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Fig. 2. Compromised alveolus [5]

B. Viral Infection

Spike proteins protrude from the SARS-CoV-2 virus en-velope at the cellular level. By helping virions fuse to hostcells, these proteins facilitate cell entry and are the primarydeterminants of cell pathogenesis, the process by which viralinfections lead to disease. As shown in Figure 3, spike proteinsfirst bind to ACE-2 receptors, which assist in the generationof small proteins. When an individual contracts COVID-19,nucleocapsids carrying the RNA of the SARS-CoV-2 virusenter the type II alveolar cell via the ACE-2 receptor. Thespike protein fuses with the receptor and encased viral RNAtakes control of the type II alveolar cell. The nucleus is thencommanded to replicate the virus, releasing these copies intothe alveolus. As a result, this process destroys the type IIalveolar cell and infects neighboring cells.

Fig. 3. Spike protein attaches to ACE-2 receptor allowing for fusion of SARS-CoV-2 virus and host cell [6]

C. Antiviral Properties of Copper

While copper ions’ antiviral properties have been confirmed,the mechanism by which copper inactivates the virus has yet tobe verified. Scientific evidence currently supports two theories.One theory discusses copper’s ability to destroy lipid mem-branes. Copper nanoparticles, which are highly reactive and

capable of penetrating cells or virus particles, oxidize and formhydroxyl free radicals. These free radicals can oxidize anddegrade lipid membranes, preventing normal cellular function.Organelles and intracellular parts may leak out of the cell,which eventually leads to cell death. SARS-CoV-2, which hasa lipid coat, would be therefore rendered unable to infiltratecells and replicate. Another theory examines copper’s potentialability to damage nucleic acids [7]. Copper’s high reactivitymay destroy nucleic acids like RNA after penetrating cellsor virus particles. Viruses like SARS-CoV-2, which have anRNA genetic code, are subsequently unable to infiltrate cellsand replicate.

D. Viability of COVID-19 on Different Surfaces

Recent studies conducted by the National Institute of Al-lergy and Infectious Diseases have shown that SARS CoV-2(pictured in Figure 4) can survive on surfaces for days orhours, depending on the molecular structure of the surface.The virus remains transmissible for three days on plastics,two days on stainless steel, and one day on cardboard [8].However, copper surfaces inactivate virus particles in fourhours, as opposed to multiple days [9]. A study in intensivecare units also showed that only nine percent of coppersurfaces held dangerous microorganisms, while over ninetypercent of plastic surfaces exceeded acceptable standards [10].

Fig. 4. Negative stain TEM images showing corona virus from chicken (Scale:100 nm) [11]

E. Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy

In scanning electron microscopy (SEM), generated electronsare accelerated to produce a focused beam. Through thistechnique, SEMs can image material surfaces at a much higherresolution than optical microscopes: after focusing a beam ofelectrons on the sample, microscopes create sample imagesunder vacuum conditions [12]. SEMs can either measurebackscattered electrons (BSE), where electrons are reflected bysample atoms, or secondary electrons (SE), where electrons onsurface atoms are displaced. While measuring BSEs can revealinformation about atomic numbers, using secondary electronsin SEMs can give a better image of a sample’s surface. Theseimages can reveal the surface topography of samples, andfurther energy dispersive X-ray spectroscopy (EDX) analysiscan ascertain the chemical composition of surfaces. In an EDXtest, the electron beam targets the inner shell of an atom anddisplaces it. Another electron from an outer shell fills thevacancy created by the displaced electron, and the resultantextra energy is emitted in the form of an X-ray. By measuring

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the energy associated with the X-ray, the specific element ofthe sample can be identified [13].

F. Vigna radiata

Vigna radiata, or the mung bean, was chosen as themodel organism for COVID-19 for its rapid germinationtime and sensitivity to copper nanoparticles [14]. Becausethe experimental period of research was short, the three-daygermination time of the mung bean was crucial to obtainsufficient replicates. Additionally, as it was shown that copperbioaccumulation caused growth inhibition, mung beans couldbe used to indicate the presence of copper nanoparticles [14].

G. Solution for Soaking Dimes

A solution of hydrogen peroxide, vinegar, and salt wasused to create copper-based particles from dimes. Acetic acidin the vinegar and salt react to form hydrochloric acid andsodium acetate by the following equation:

HC2H3O2(aq) + NaCl(aq) −−→ HCl(aq) + NaCH3O2(aq)

Hydrogen peroxide is used to remove the nickel plating onthe dime. The dissolution of nickel is given by the equation[15]:

Ni(s) + 2 HCl(aq) + H2O2(aq) −−→ NiCl2(aq) + 2 H2O

Hydrochloric acid is a strong reducing agent that canreduce copper oxide into copper ions and help dissolvecopper in the presence of hydrogen peroxide. This followsthe equations [16][17]:

CuO(s) + HCl(aq) −−→ CuCl2(aq) + H2O

H2O2(aq) + Cu(s) + 2 HCl(aq) −−→ CuCl2(aq) + 2 H2O

Citric acid is a weaker acid that cannot dissolve copper.However, it is still able to reduce copper oxide into thedesired copper nanoparticles, by the following equation [18]:

9 CuO(s) + C6H8O7(aq) −−→ 9 Cu(s) + 6 CO2(g) + 4 H2O

However, after conducting energy dispersive X-ray (EDX)spectroscopy on the cloth samples, it was found that sampleshad inadequate copper content. To increase copper concentra-tion, more dimes may be added or pure copper wires may beused as a source. Additionally, the dimes or copper wires maybe left in solution for longer than twelve hours to increase theamount of copper dissolved in solution.

III. EXPERIMENTAL PROCEDURES

A. Neutralization

The primary objective in creating copper-infused maskswas the neutralization of SARS-CoV-2. Four solutions wereprepared in styrofoam bowls. Solution A was the control,containing only one-half cup of white vinegar. Dimes, whichhave a 91.67% composition of copper, served as the sourceof copper ions in solutions B, C, and D. Solution B consistedof one-half cup of vinegar, two teaspoons of salt, and onedime. Solution C consisted of one-half cup of vinegar, twoteaspoons of salt, one dime, and one teaspoon of hydrogenperoxide. Solution D consisted of one-half cup of vinegar, twoteaspoons of salt, one dime, and two tablespoons of lemonjuice. Each solution was sealed in plastic wrap to prevent theentry of contaminants and given one day for the solution toform. Thirty-two pieces of 100% cotton fabric were cut intorectangles of eight inches by five inches. Eight pieces of fabricwere designated to each sample and soaked in their respectivesolutions for ten seconds. All cloth samples were dried with aConair 1875 Watt Hair Dryer and dampened. Three seeds wereplaced in each cloth. Percent germination was recorded overfour days to measure the antimicrobial properties of copper.

B. Reusability

One dime was soaked in a solution that consisted of one-half cup vinegar, two teaspoons salt, and one teaspoon ofthree percent hydrogen peroxide solution for twelve hours.Five fabric samples were dipped into the solution and dried.Samples were washed once, twice, four times, eight times, ornot at all. Each wash composed of four rinses and one teaspoonof gel hand soap. All cloth samples were dried with an 1875Watt Hair Dryer and dampened. Six seeds were placed in eachfolded sample and put into a sealed Ziploc bag. Ziploc bagswere labeled and placed in sunlight. Percent germination androot length were measured over four days to determine thereusability of copper-infused masks.

C. Filtration

In this procedure, the material with the best ability to filter125 nanometer SARS CoV-2 (Figure 5) was identified. Fivevarieties of fabric samples were tested: one layer of chiffon,one layer of cotton, two layers of chiffon, two layers ofcotton, and one layer of both chiffon and cotton. For thepurpose of this experiment, cotton acted as an example ofmechanical filtration and chiffon for electrostatic filtration.Four samples of each fabric variety were prepared into piecesof six square inches. Each sample was placed on top ofone sheet of white printer paper and sprayed with Krylonfusion all-in-one matte black spray paint for 5 seconds froma distance of approximately half a meter. Spray paint waschosen as the aerosol for this experiment because it provided adark pigmentation that is easily differentiated from the whitepaper background. After the completion of each spray, thecloth samples were removed from the paper immediately andthe paper was left to dry. Once each paper dried, it wasscanned by a Fujitsu fi-7160 scanner and uploaded to ImageJ

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to be compared electronically. The PDFs were compared bymaking the scanned images binary and using the list functionof ImageJ to isolate the number of white and black pixels.Once those numbers were isolated, the percent effectiveness offiltering aerosol particles was determined by taking the numberof white pixels and dividing it by the total number of pixelsin the group.

Fig. 5. To scale image of corona virus [19]

D. Fit

The fit of a mask is critical to its efficacy and can notbe compromised. After numerous experiments and trials, ifthe mask does not fit properly, the integrity of the maskis compromised [20]. Wearing a mask incorrectly createspuckering and prevents the mask from laying flush against theskin. The created gaps can reduce the effectiveness of a maskby approximately sixty percent [21]. With both surgical andhomemade cloth masks, the shape leaves three vulnerabilitiesto contamination. The first problem area being where the earloops connect to the mask on the upper cheek. This presentsa problem because masks are not custom-made to fit specificface shapes. The generic straight edge of a mask folds up onitself instead of laying flush against the cheek. The secondproblem area is around the chin, or the underside of the mask.Once again the generic square shape of most masks does notaccommodate for the roundness of a face, creating large gapsfor airborne particles to enter. The last place to be addressed isthe seal between the top of a mask and the bridge of the nose.While most surgical masks contain bendable wires designedto take the shape of the nose, these wires often do not retaintheir shape and still leave distance between skin and fabric.Homemade masks lack any form of wiring or conformingability. To tackle these areas of concern, three separate sealingmethods were enforced to ensure a customized fit that remainscomfortable while sealing off gaps and reducing puckering.The results of these attempts are pictured in Figure 6.

IV. RESULTS/ANALYSIS

A. Neutralization

In this procedure, the Vigna radiata samples were monitoredevery twenty-four hours for three days. Germination rate was

Fig. 6. The process of making adjustments to a homemade cloth mask tocreate a snug fit.

used to determine the antimicrobial properties of copper, andgermination was defined by the presence of radicles. In SampleA, the control group, twenty-one of the twenty-four mungbeans germinated by the third day. In Sample B, two of thetwenty-four mung beans germinated. In Sample C, one of thetwenty-four mung beans germinated. In Sample D, zero ofthe twenty-four beans germinated. Figure 7 shows the meannumber of seeds per trial for each sample. Using Tukey’s posthoc analysis, an ANOVA test was conducted with the datausing Past, version 4.03. However, all data from Sample Dwas excluded because all of the sample values were zero asno mung beans germinated. This would have resulted in avariance of zero, which would have returned an error. Therewas a significant difference found between each test group (pvalue ¡ 0.05). Originally, evidence supported the conclusionthat copper nanoparticles disrupted the germination rate of

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mung beans. The cotton fabric that was originally soakedin Sample A did not interfere with the germination processof Vigna radiata as it typically takes two to three days forthe beans to sprout. Cloths soaked in Samples B, C, and Dwere thought to be infused with copper, as there were novisible radicles. However, after an SEM analysis, it was foundthat copper ions were not significantly infused into the cottonfabric. Instead, high concentrations of salt, NaCl, had bondedto the cotton fibers and likely prevented germination.

Fig. 7. Mean Germination Rate per Trial

B. Reusability

In this procedure, the durability of cotton mask coppernanoparticles was measured. Experts advise that cotton masksshould be washed daily [22] to inactivate any potential viralparticles, and this procedure determined the extent to whichcopper nanoparticles could survive washes. The 20 secondwashing procedure with hand soap was based on the recom-mended protocol for washing masks [23]. Germination wasmonitored over 3 days, and results are shown in Figure 8.There has been no germination in the negative control withno washes, but beans have germinated in all other samples.

Fig. 8. Graph for Reusability

C. Fit

To fix the ill fit around the sides of a mask, a homemadecloth mask was used because it was the best model of thetarget shape and it resembled what the average person wouldhave access to. The first step was to remove the ear loops tobe repositioned later. The next step was to fold the mask in anaccordion pattern so that all the material gathered at one point

on either side of the mask instead of the prior long, verticalhem. With a needle and thread, a simple straight stitch wassewn to secure the layers together. This process was repeatedon the other edge. After both sides were sewn, the ear loopswere reattached, once again using a straight stitch to ensurethat the new ear loops would not be compromised with regularuse. The next adjustment was adding a gel liner to the bottomof the mask. For the purpose of this experiment, the samesilicon grip used to keep no-show socks on and tube topsfrom falling was used. By using a material commonly foundon clothing, the biocompatibility would not pose as much of aproblem, but it was important to test the level of comfort. Thesilicon lining of an old tube top was cut into the length of theinside of the mask. The gel was hand stitched onto the bottomseam, stopping just short of where the ear loops were attached.Finally, three pieces of pipe cleaner were braided together tocreate a reinforced wire. By creating a small incision near thetop of the mask the pipe cleaner was easily inserted. Sincethe mask was not completely cut, a loop stitch sealed the pipecleaners into place and made sure they would not fall out.

D. Filtration

1) Results: Figure 9 depicts the results of the spray paintaerosol, sprayed across various fabric combinations. The av-erage percent filtration of particles over approximately 175micrometers is displayed beneath each image.

2) Discussion: Protocol ResultsOne major issue the authors of this paper sought to address

with the experimental procedure was the balance between thebreathability and effectiveness of cloth filters. In addressingthis balance it is important to consider that the diameter ofthe virus particles is around 60-140 nanometers [24] whilethe average size of an oxygen molecule is 346 picometers( 289 times smaller) [25]. This is the window in which anoptimal mask’s filtration should lie, with small enough holesto filter out viral particles and the aerosols that carry them, butlarge enough holes to not be restrictive to the breathing of thewearer. With this in mind, the protocol aimed to find the leastrestrictive fabric combination that is still effective in filteringaerosol particles. Because of this, cotton and chiffon werechosen as test materials. Cotton is a very effective mechanicalfilter, a filter that creates a physical barrier to block particulate,although it is less breathable than many other fabrics. Chiffonis a breathable material and effective electrostatic filter, a filterthat uses static electricity to attract particulate towards thefibers of the filter, but a relatively ineffective mechanical filter.Because chiffon utilizes electrostatic filtration it works best atfiltering particulate matter traveling at lower velocities and istherefore most effective when used in conjunction with a layerof cotton which slows the speed of the aerosol particles. Asshown below, the combination of chiffon and cotton is the mostlightweight and breathable filter that has 100% effectivenessin filtering out the spray paint.

3) SEM Results: The cotton fabric was analyzed usingscanning electron microscopy, as shown in Figure 10. Thisenabled a more comprehensive understanding of cotton fabric

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Fig. 9. Filtration protocol results

as a mechanical filter. Each collection of fibers was measuredto be approximately 392 microns in width. Individual fiberswere found to be between 6.782 and 26.529 microns, withan average of 16.105 microns in width. This variation insize created an intricately woven physical barrier that maybe effective in trapping aerosol particles [21].

Fig. 10. Secondary electron scanning electron microscopy image of cotton

E. FIT: Computer Aided Design

Due to the COVID-19 global pandemic this research wasconducted virtually and the authors of this paper were unableto assemble a physical prototype that fit all of the criteria:material, fit, and nanostructure. Instead, two CAD prototypesof the mask were created. The design in Figure 11 ”E” is aneye level view of the mask and it illustrates how the majorityof the fabric gathers together at one point triangularly insteadof along a straight edge like on rectangular masks. The modelin Figure 11 ”B” depicts how the mask appears on a humanskull to demonstrate how these alterations address issues incurrent mask designs. Figure 11 ”C” shows that once the maskis put on, it hugs the face; color-coding is used to highlight thedifferences between this mask and current masks. The purpleis intended to model braided pipe cleaners that would bendto the shape of the nose. The orange liner on the bottom ofthe mask can be seen in Figure 11 ”A” and represents thesilicon, non-slip gel that seals the mask to the skin. The blueparaboloids on either side of the main panel reduce puckeringon the sides of the mask. The flat mask in Figure 11 ”D”shows that there is little overall bulk added to the mask. Theyare still compact and meant to be the same size as a store-bought surgical mask.

V. MATLAB

After the completion of each protocol and determinationof methods to optimize the neutralization, biocompatibility,reusability, filtration, and fit of the masks, the author’s of thispaper wanted to see how the widespread use of an optimizedmask would affect the spread of COVID-19 and the mortalityrate. To achieve this, an accurate simulation of the spread

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Fig. 11. A. View of the bottom of the mask to demonstrate the way it sealsto the chin B. Head on view of the mask C. Side profile to show the nosewire D. Flat view of mask to demonstrate the accordion fold E. Inside viewof mask to show off color coding and all aspects of the mask

of COVID-19 needed to be created. To create a functionalsimulation four groups were included: healthy without immu-nity, infected, contagious, and healthy with immunity. Socialdistancing and PPE such as masks were also accounted for[26].

A. Immunity

When it comes to immunity against COVID-19 there aremany unknowns. Since the virus is relatively new, findingcredible data on how long immunity lasts and the differenttypes of immunity is difficult. In doing research, evidencewas found to support the fact that MERS-CoV and SARS-CoV are comparable [27]. Due to limited SARS CoV-2 datathe information regarding natural and vaccine immunity weretaken from MERS studies. Natural immunity is acquired whenthe body gets sick and recovers from a disease. Vaccineimmunity is aquired second hand by building up a resistanceto the disease without the illness [28]. It was found that naturalimmunity acquired from MERS lasted on average one to threeyears [29,30] which represents the variable Timnat. The datafound for vaccine immunity requires forewarning. The onlyexperiment found to test how long second hand immunity lasts

for MERS was conducted on dromedary camels. The resultstherefore can not be directly applied to humans. Newborncalves, who received the MERS antibody from their mothers,were studied to see how long they retained the antibody for. Onaverage, the camels were immune for five to six months aftergiving birth [31]. This data was used to model the variableTimvac. These two data sets were used to calculate the effect ofnatural immunity versus vaccine immunity in healthy people.To accurately simulate this environment, the incubation andrecuperation periods for SARS-CoV were used. The averageincubation period for the variable Tinc was found to be threeto fourteen days [32] and the average recuperation period forvariable Trec was found to be two to six weeks [33]. Thegraph in Figure 12 shows that while immunity will accumulatein a population, eventually it will wear off whether acquirednaturally or from a vaccine and that more people will acquireimmunity second hand.

Fig. 12. Effect of natural vs vaccine immunity on healthy people

B. Masks

To factor the usage of masks into the MATLAB simulation,two new variables were initialized: one for mask effectivenessand one for the percentage of the population that actively wearmasks. The overall population was split into two groups: thosewearing masks and those not wearing masks. This was accom-plished by first creating the variable PmaskPop to representthe 65% of the US population actively wearing a mask [34]and then creating the function floor (PmaskPop+rand*t). Thisfunction assigns each member of the population a numberspaced evenly between PmaskPop and PmaskPop+1 so thatwhen they are rounded down, PmaskPop% of the populationrounds to one and the rest round to zero. Once these twogroups were established, a condition was set for individualsidentified as wearing a mask to lower their probability of con-tracting COVID-19 based on the variable PmaskEff. PmaskEffwas then added to the code in the disease transfer(DT) sectionby multiplying the variable to mean DT to lower the average

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probability of disease transfer. These changes were used tocreate a graph shown in Figure 13 which demonstrates numberof contagious individuals over time in populations with accessto masks with differing levels of effectiveness. Figure 14further highlights the difference between a population witheffective versus less effective masks. The disease spreads at amuch quicker rate without access to more effective masks, andit takes a shorter time for numbers of healthy people withoutimmunity to drop to 0.

Fig. 13. MATLAB simulation of contagious individuals across multiple maskefficiencies

C. Contagion

Initially, the MATLAB simulation calculated the probabilityof transmission using the probability of wearing a mask,contact with another person, and contracting the disease aftercontact. However, this simulation failed to account for vari-ation in the probability of disease contraction after contact.It was found that the highest rate of COVID-19 transmissionoccurs one to two days after developing symptoms [35]. Phighand Plow variables in the simulation were made to accountfor the overlap between “contagious” and “infected” periods.Studies showed that victims are still contagious while exhibit-ing symptoms of COVID-19, and that the most contagiousperiod was also within the infected period [35]. As a result,the “contagious” and “infected” periods in the simulation werealtered to account for asymptomatic and symptomatic periods.The transmission accuracy of the MATLAB simulation wasimproved by including the period of most contagion. If thesimulated person was in the period of most contagion, theprobability of transmission was increased using the variablePhigh. The probability of transmissions was multiplied byPhigh. Otherwise, the probability of transmission was cal-culated using Plow. A counter variable contagionCount wasincremented each day to simulate people showing symptoms;

this counter was used to determine whether the simulatedperson was the most contagious.

D. Mortality

The MATLAB simulation originally accounted for fourgroups in the affected population of a pandemic: healthywithout immunity, infected, contagious, and healthy withimmunity. The simulation failed to account for the deceasedportion of the population that had not been able to recoverfrom the virus’ deadly symptoms. The mortality rate of SARS-CoV-2 in the United States of America is 3.4% [36]. In order tofactor this into the simulation, the variables “NM1” (number ofmortalities on day one) and “NM” (number of total mortalitiesper day) were initialized and set as parameters. In the sectionof the program that dealt with updating the disease status ofindividuals in the population, specifically the IM array, themortality rate was factored into the code to create a taskthat simulates people dying. The function “rand” randomlychooses a decimal between zero and one; if this number isless than 0.034, the simulated civilian has effectively deceased.Factoring in the mortality rate of SARS-CoV-2 created a moreaccurate simulation.

Fig. 14. COVID-19 Simulation in population of 100

VI. CONCLUSION

A. Accomplishments

Throughout this project, several accomplishments have beenmade. By combining electrostatic and mechanical filtration,filtration efficiency of homemade cloth masks was optimized.Simple and inexpensive protocols improved the fit of anyhomemade mask, and a 3D CAD design mask was createdto model these improvements. Additionally, a cloth surfacecapable of inactivating the model organism was also created.

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A more accurate MATLAB simulation of the COVID-19pandemic was also created by accounting for the usage ofPPE, adding a range of contagion probability, limiting lengthof immunity, and adding variables and functions to accountfor mortality.

B. Error Analysis

One weakness of this investigation was in the filtration pro-cedure. Due to the current pandemic, access to high-precisionscanners was limited, and precisely assessing filtration effi-ciency was consequently difficult. In the filtration experiment,one pixel in ImageJ was assigned to nearly 750 squaremicrometers, which is much larger than both SARS CoV-2and its aerosols. This significantly limited the analysis of smallparticle filtration efficiency. A higher quality scanner wouldallow for higher resolution scans and thus enable the detectionof nanometer scale aerosol contaminants. This would moreaccurately simulate aerosols that carry COVID-19. Secondly,neutralization and reusability experiments could be improvedby utilizing SARS CoV-2, or a better model organism forthis coronavirus. E. coli is one option, as it shares morebiological similarities to SARS CoV-2 than Vigna radiata.Unfortunately, no labs were able to receive the masks andtest them within the time frame of this project. Another issuecreated by using Vigna radiata was that testing germinationcreated only two possible results (either the seed sproutedor it did not) while measuring the growth of E. coli wouldcreate a gradient of possible results. This would allow for amore thorough understanding of how antiviral masks compareto standard home-made masks. In addition, analysis with ascanning electron microscope showed an insignificant numberof copper particles embedded in the fabric. This may suggestthat the copper ions were too small to be detected by the SEM;however it is impossible to support this hypothesis withoutfurther testing. Moreover, while protocol was established tovisibly reduce gaps in the sides of masks, supplies to quan-tify the effects of these changes were not acquired. Finally,as the pandemic currently prevents access to the necessaryequipment, biocompatibility should be tested in the future.Specific concentrations of copper on the mask were unableto be measured and compared with known TD50 standards(140 mcg/dL in the bloodstream [37])

C. Future Extensions

For better filtration testing, a higher resolution scannercould be used. Additionally, reusability and neutralizationexperiments could be improved by obtaining access to E.coli or partnering with a lab that uses live samples ofSARS-CoV-2. This could better determine the efficacy ofhomemade masks, as compared to standard non-antiviralmasks. Copper-infused fabric’s ability to inactivate SARS-CoV-2 particles could be measured more accurately.

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APPENDIXPercent Germination Per Trial

A B C DTrial 1 1.00 0.33 0.00 0.00Trial 2 0.67 0.00 0.00 0.00Trial 3 0.67 0.00 0.33 0.00Trial 4 0.67 0.00 0.00 0.00Trial 5 1.00 0.00 0.00 0.00Trial 6 1.00 0.33 0.00 0.00Trial 7 1.00 0.00 0.00 0.00Trial 8 1.00 0.00 0.00 0.00

Washes vs Germination Rate0x 1x 2x 4x 8x

Day 1 0 1 1 0.5 1Day 2 0 1 1 1 1Day 3 0 1 1 1 1

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Table of ContentsInitialize variables ............................................................................................................... 1Simulation: Subgroup Population vs # Days Passed .................................................................. 1Effect of Different Mask Filtration Efficiencies ........................................................................ 2Effect of Natural vs Vaccine Immunity on Healthy People ......................................................... 4Simulation: Results per Subgroup vs # Days Passed .................................................................. 4Function spreadDisease ........................................................................................................ 5

Initialize variablessims = zeros(50, 150, 5);Tinc = [3,14]; %incubation period [days]Trec = [14,42]; %recuperation period [days]Timm = [365,1096]; %immunity period [days]Pbio = 0.9; % prob of contracting disease after contactPhigh = 0.9; % prob of contracting disease after contact with asymptomatic personPlow = 0.1; % prob of contracting disease after contact with symptomatic personPsoc = 0.2; % prob of running into each other without social distancingPmaskEff = 0.95; % prob of virus transmission if mask is wornPmaskPop = 0.62; % prob of an individual wearing a maskPsoc1 = 0.2; % prob of running into each other with social distancingnday = 150; %number of simulation days%Number of individualsN = 100; %total number of individuals in the populationNC1 = 1; %contagious on day 1NI1 = 0; %infected on day 1NHI1 = 0; %healthy and immune on day 1NM1 = 0; %dead on day 1NH1 = N - (NI1+NC1+NHI1+NM1); %healthy w/o immunity on day 1

Simulation: Subgroup Population vs # DaysPassed

figure;t = 1:150';3for i = 1:50 sims(i, :, :) = spreadDisease(Tinc, Trec, Timm, N, NH1, NI1, NC1, NHI1, NM1, Pbio, Phigh, Plow,Psoc, PmaskPop, PmaskEff,nday); plot(t, sims(i,:,1), 'r',t, sims(i,:,2),'b',t, sims(i,:,3),'g',t, sims(i,:,4),'k',t, sims(i,:,5), 'm'); hold on;endtitle('COVID-19 Simulation with masks');xlabel('Simulation time (days)');ylabel('Number of Individuals per Subpopulation');hold off;

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111112141415

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legend('Healthy w/o Immunity');%, 'Infected', 'Contagious', 'Healthy with Immunity', 'Dead');

ans =

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Effect of Different Mask Filtration Efficiencies%^ Better title might be "Effect of Different Mask Filtration Efficiencies%on Contagious Subgroup"figure;t = 1:150';%PmaskEff=0.95contvals = zeros(70,150,10); % empty array 150 days, 50 sim runs, 10 valsfor j = 1:10 PmaskEff = j/10; for i = 1:70 sims(i, :, :) = spreadDisease(Tinc, Trec, Timm, N, NH1, NI1, NC1, NHI1, NM1, Pbio, Phigh, Plow, Psoc, PmaskPop, PmaskEff, nday); contvals(i,:,j) = sims(i,:,3); %store this simulation in avgcont

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%plot(t, sims(i,:,3),'g'); hold on; end avgcont = mean(contvals(:,:,j)); plot(t, avgcont); hold on;endtitle('COVID-19 With Masks');xlabel('Simulation time (days)');ylabel('Average Number of Contagious Individuals per Subpopulation');hold off;legend('PmaskEff = 0.1', 'PmaskEff = 0.2', 'PmaskEff = 0.3', 'PmaskEff = 0.4','PmaskEff = 0.5','PmaskEff = 0.6','PmaskEff = 0.7','PmaskEff = 0.8','PmaskEff = 0.9','PmaskEff = 1');

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Effect of Natural vs Vaccine Immunity onHealthy People

figure;t = 1:150';Timnat = [365,1095]; Timvac = [150,180];Timmarray = [Timnat,Timvac];contvals = zeros(70,650,2); % empty array 150 days, 50 sim runs, 10 valsfor j = 1:2 Timm = Timmarray(j); for i = 1:70 sims(i, :, :) = spreadDisease(Tinc, Trec, Timm, N, NH1, NI1, NC1, NHI1, NM1, Pbio, Phigh, Plow, Psoc, PmaskPop, PmaskEff, nday); contvals(i,:,j) = sims(i,:,4); %store this simulation in avgcont end avgcont = mean(contvals(:,:,j)); plot(t, avgcont); hold on;endtitle('COVID-19 Immunity Duration');xlabel('Simulation time (days)');ylabel('Average Number of Individuals per Subpopulation Over 70 Runs');hold off;legend('Timm natural', 'Timm vaccine');

Unable to perform assignment because the size of the left side is 1-by-650 and the size of the right side is 1-by-150.

Error in COVIDMasks (line 65) contvals(i,:,j) = sims(i,:,4); %store this simulation in avgcont

Simulation: Results per Subgroup vs # DaysPassed

figure;subplot(2,2,1); % row, column, positionhistogram(sims(i,1:90,1),15);xlabel('Time (days)');ylabel('# Individuals');xlim([0, 100]);title('Healthy w/o Immunity');

subplot(2,2,2);histogram(sims(i,1:90,2),15);xlabel('Time (days)');ylabel('# Individuals');xlim([0, 100]);

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title('Infected');

subplot(2,2,3);histogram(sims(i,1:90,3),15);xlabel('Time (days)');ylabel('# Individuals');xlim([0, 100]);title('Contagious');

subplot(2,2,4);histogram(sims(i,1:90,4),15);xlabel('Time (days)');ylabel('# Individuals');xlim([0, 100]);title('Healthy with Immunity');

Function spreadDiseasefunction subs = spreadDisease(Tinc, Trec, Timm, N, NH1, NI1, NC1, NHI1, NM1,Pbio, Phigh, Plow, Psoc, PmaskEff, PmaskPop, nday) % Compute meanDT, the avg fraction of disease transfers per person per % exposure period meanDT = Pbio*Psoc;

%Intialize vectors for subpopulation in each simulation day % Each of the following vectors has nday elements. Each element is % the number of people in that subpopulation on each simulation day. NH = zeros(nday, 1); %no. of indiv who are healthy w/o immunity on each day NI = zeros(nday, 1); %no. of indiv who are infected on each day NC = zeros(nday, 1); %no. of indiv who are contagious on each day NHI = zeros(nday, 1); %no. of indiv who are healthy w/ immunity on each day NM = zeros(nday, 1); %no. of indv who are dead on each day %Initialize no. of indiv in each status on day 1 %Define the first element in NH, NI, NC, NHI, and NM here. NH(1) = NH1; NI(1) = NI1; NC(1) = NC1; NHI(1)= NHI1; NM(1) = NM1; %Initialize M on day 1 (M = health/disease status) % M is a vector with N element. Each element denotes the health/disease % status of each individual in the population. M = [zeros(NH1,1);... % matrix w number of healthy ppl -1*randi(Tinc,NI1,1);... %^ returns a NI1 x 1 matrix of random integers within Tinc

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%Since NI1 is 0, this matrix will have no values. randi(Trec,NC1,1);... % count down until infected %Since NC1 is 1, a random number within Trec will be selected zeros(NHI1,1);]; % matrix w number of healthy & immune ppl %Initialize IM on day 1 (IM = immunity status) % IM is a vector with N element. Each element denotes the immunity status % of each individual in the population. IM = [zeros(NH1,1);... zeros(NI1, 1);... zeros(NC1, 1);... zeros(NM1, 1);... -1*randi(Timm, NHI1,1)];

%Simulation from day 2 for d=2:nday %start exposure on day 2 %a. Randomly expose population: which pair of individuals would meet %i. Randomly select individuals who would meet nexposure = floor(N/2); %arbitrary no. of people who will go outside % (exposure/traffic) per day DT = zeros(nexposure,1); %initialize vector to store disease transfer events i_all = randperm(N,nexposure); %randomly selected indivs who go out % returns row vector with nexposure unique integers selected % randomly from 1 to N; chooses which people will go outside j_all = randperm(N,nexposure); %randomly selected indivs who also go out contagiousCount = 1;

%ii. Determine if disease transmission would occur % Disease transfer is random % %e.g. largest value: rand=1 + meanDT=.18 = 1.18 --> floor rounds to 1 % %e.g. smallest value: rand=0 + meanDT=.18 = 0.18 --> floor rounds to 0 for e=1:nexposure mask = floor(rand+PmaskPop); if mask == 1 if contagiousCount > 2 DT(e) = floor(rand + meanDT*PmaskEff*Plow); else DT(e) = floor(rand + meanDT*PmaskEff*Phigh); end elseif mask == 0 if contagiousCount > 2 DT(e) = floor(rand + meanDT*Plow); else DT(e) = floor(rand + meanDT*Phigh);

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end

end %Disease transfer: 1 = yes, 0 = no if DT(e) ==1 i = i_all(e); j = j_all(e); % Spread disease if conditions are met if M(i) >0 && M(j) ==0 && IM(j)>=0 % if i is contagious, j is healthy, and j is not immune M(j) = -1*randi(Tinc,1,1); % give person j a count-up from Tinc interval % randi(Tinc,1,1) returns a 1x1 matrix (single % value) of a random integer between 2 & 14 (Tinc) end if M(j) >0 && M(i) ==0 && IM(i)>=0 % if j is contagious, i is healthy, and i is not immune M(i) = -1*randi(Tinc,1,1); % give person i a count-up from Tinc interval end end end %end random exposure

%b. Record number in each subpopulation on the current day % Count the no. of subjects in each subpopulation: NH, NI, NC, NHI. % Use logical indexing and sum() function.

% Healthy w/o immunity (NH) M(i)=0 && IM(i)>=0 log_vec_NH = M==0 & IM>=0; %This line ^ returns a single vector, log_vec_NH, with N %elements. Each element is either 1, if M==0 and IM>=0 at %that index, or 0, if that wasn't the case. NH(d) = sum(log_vec_NH); %This line ^ takes the sum of all the 1's in loq_vec_NH to %get a count for the number of NH people on that day.

% Infected (NI) M(i)<0 log_vec_NI = M<0; NI(d) = sum(log_vec_NI);

% Contagious (NC) M(i) >0 log_vec_NC = M>0; NC(d) = sum(log_vec_NC);

% Healthy with immunity (NHI) M(i)=0 && IM(i)<0

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log_vec_NHI = M==0 & IM<0; NHI(d) = sum(log_vec_NHI);

% Dead (NM) log_vec_NM = IM==5000; NM(d) = sum(log_vec_NM);

%c. Update health/disease status and immunity status for i=1:N %determine if person will die MR=rand; count=0; if MR<0.034 count=1; end %Update M and IM arrays for the simulation of the next day. if M(i)==0 % if the person is healthy IM(i) = IM(i)+1; %healthy without immunity; keep value positive elseif M(i) <0 % they were infected M(i) = M(i) +1; % reduce incubation pd by one day if M(i) == 0 % if they're now healthy, M(i) = randi(Trec,1,1); % start recuperation by choosing pd from Trec interval end elseif M(i)>0 % they were contagious M(i) = M(i)-1; % reduce recuperation pd by one day contagiousCount = contagiousCount + 1;

if M(i)==0 % if they're now healthy if count==0 IM(i) = -1*randi(Timm,1,1); % give immunity for random number of days within Timm interval end if count==1 IM(i) = 5000; end end end end %end update status end %end simulation days subs = [NH, NI, NC, NHI, NM]; % number of ppl in each subpopulationend

Published with MATLAB® R2020a

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ACKNOWLEDGMENTS

The authors of this paper gratefully acknowledge theirproject mentor, Shivani Shukla, for her guidance through-out the experimentation process. Shivani Shukla, a materialsscience and biomedical engineering graduate from CarnegieMellon University, provided many research articles on theantimicrobial properties of copper. Her expertise in the fieldwas crucial to the development of the protocols, experimen-tal process, and fabrication of the mask. Furthermore, herlectures on biocompatible materials and MATLAB syntaxwere essential to ensuring the efficacy of the copper-infusedmasks. In addition, the authors of this paper wish to thankElizabeth A. Clark and Logal Solotky from the MaterialsCharacterization Facility at Carnegie Mellon for their help inconducting scanning electron microscopy. The nanoscale anal-ysis of the copper-infused fabrics provided useful informationon filtration. Dr. Yaw Kai Yan The authors also acknowledgeDean Jean Patrick Antoine, the Director of GSET for hisguidance, and Claire Yan, their Residential Teaching Assistant,for her facilitation of the research throughout the entirety of theproject. Rajas Karajgikar, the Head Counselor, and BenjaminLee, the Research Coordinator were instrumental in providingassistance and resources. Lastly, the authors of this paperwould like to thank Dean Ilene Rosen, the Director Emeritusof GSET, Rutgers University, Rutgers School of Engineering,Rutgers Alumni, Lockheed Martin, and the New Jersey SpaceGrant Consortium for making their GSET experience possibleand funding their scientific endeavours.

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