1. Introduction

1.1 Background Research 

As the COVID-19 pandemic continues and an increasing number of people are being infected with COVID-19, according to Associate Professor Loh Xian Jun, from A* STAR's Institute of Materials Research and Engineering (IMRE), "a simple cloth mask is effective in curbing the spread of droplets from inside-out. That is, it protects those around the person coughing." (Loh, 2020). Masks have been required to be worn by many governments of many countries worldwide. Along with this new measure came people sporting many different types of masks. Some of these masks include surgical masks, N95 respirators, homemade and store-bought cloth masks, bandanas and neck gaiters. Various materials are used to make masks, such as cotton, woven fabric, polymers with non-woven fabrics and for N95 respirators, synthetic plastic fibre, similar to those used in synthetic and polyester shirts. According to the journal BMJ Open, "most of the fabrics commonly used for non-clinical face masks are effective at filtering ultrafine particles. N95 masks were highly effective, although a reusable HEPA vacuum bag actually exceeded the N95 performance in some respects" (O'Kelly, 2020).

Since these masks are made of different materials, we predicted that different masks would have different levels of effectiveness. Some masks may protect others from the wearer's sneeze more effectively than others. Hence, we have decided to conduct this experiment to determine the effectiveness of each type of mask in reducing the spread of potentially virus-carrying saliva droplets through it. For this experiment, we will be using water mist to act as the droplets and measuring the distance the water mist had travelled from the mask. 

To measure the distance the water mist travelled, we will use a technique known as Schlieren photography to get an image of the droplets passing through the mask. During Schlieren photography, the illumination pattern is imaged onto a geometrically congruent cutoff pattern (essentially a multiplicity of knife edges) with focusing optics, while a camera system images density gradients lying between the illumination pattern and the cutoff pattern. (see Figure 1.1 below)

Figure 1.1.1 - A Schlieren imaging setup (Source: Wikipedia)

In the flow of uniform density (when there is only surrounding air), this will halve the photograph's brightness. However, in a flow with varying density (when there are water molecules in the air from the water mist), the distorted beam focuses imperfectly, and parts that have been focused in an area covered by the knife-edge are blocked. The result is a set of lighter and darker patches corresponding to positive and negative fluid density gradients in the direction normal to the knife edge. In theory, the darker patches would represent the droplets while the lighter patches would represent the surrounding air, which would allow us to visualise the distance travelled by the droplets easily. According to a fluid dynamicist and mechanical engineer at the National Institute of Standards and Technology (NIST), “These measurement systems excel at showing how air moves around, so it was clear to me that I could use these tools to create qualitative video content that illustrates the importance of wearing a face covering and the pros and cons of various kinds of homemade face coverings in an easily understandable way” (Staymates, 2020).

We also found an alternative method which is by spraying coloured water mist through each mask at high speed and onto a piece of paper below. From the stains of the coloured water mist on the paper, we can find out the spread of the water mist with the point system indicated on the paper marked with lines on the paper. Each mask's points are calculated by the number of points on the left and right added together, and the mask is sprayed at the centre point "0". The total spread is then calculated by the number of points multiplied by 0.7 cm. 0.7cm is the spacing between each line on the paper. We can then compare the spread of the different masks and determine which of them has the least spread of water mist sprayed through them. Should the Schlieren photography method not work, we will use this method instead.

Figure 1.1.2 - The paper used for the experiment which has been marked with points with an overlay showing the method to calculate the spread of water mist 

By wearing the mask that has the least spread of water mist through it, others will be less likely to catch the COVID-19 virus. According to Jeremy Howard, a research scientist at the University of San Francisco in California, "You don't have to do much math to say this is obviously a good idea". (Howard, 2020) 

1.2 Research Questions

 


We wanted to find out the effect of the different types of masks on the spread of water mist sprayed through the mask to help allow people to make an informed decision when choosing a mask of which to wear to protect themselves and others from the COVID-19 virus.

1.3 Hypothesis

We think that the N95 mask will have the least spread of water mist sprayed through it because the N95 mask had the highest protective efficacy (approximately 80% to 90% reduction) of the various masks examined in research conducted by the American Society of Microbiology.

1.3.1 Independent Variables 

The independent variable is the type of mask used because we are testing the effectiveness of different types of masks in obstructing water mist from passing through them. 

1.3.2 Dependent Variables 

The dependent variable is the spread of the water mist that travels through the mask and onto the paper below.

1.3.3 Constants 

We decided on three key constant variables, among many others.

• Force used to spray the bottle to ensure the same speed of water mist travelling through each mask

• The volume of water sprayed to ensure the same volume of water mist travelling through each mask

• Distance from the spray bottle to mask, to ensure the same speed and volume of water mist travelling through each mask

• Type of liquid, so that the size of the particles passing through the mask is the same.