The Progression of Perovskite Light Emitting Diodes (LEDs) in our Future

Blog, Journal for High Schoolers, Journal for High Schoolers 2021


J. Guzman, R. Schweyk, and L. Traore


Over 1.5 billion incandescent and III-V Light Emitting Diodes (LEDs) are currently in use in the United States today. Whether used in headlamps, flashlights, or lamp lights, these LEDs are consuming up 136 billion kilowatt-hours on a yearly basis in the United States. However, there is a possible solution concerning the new progress of perovskite LEDs. If we were to replace all the light sources in the United States that are dominated by III-V LEDs and incandescent light bulbs with the current progression of perovskites, would the enegry savings outweigh the financial cost, and what would this look like for the future of perovskite LEDs. Limited to their operational stability, perovskites as they stand are not a viable replacement. However, with the rapid evolution of perovskite LEDs as well as their superior optoelectric properties, it is probable that in the near future they will play a fundamental role in our daily lives.

In order to understand the cost effciency of perovskites, one must consider the economies of scale. When a company mass produces perovskites, instead of considering each and every material created in a single perovskite, one must take into account buying in bulk quantities. Not only this, but one must examine the advantages in energy consumption for perovskites in comparison to III-V and incandescent LEDs.

In this investigation, we will analyze the viability of replacing III-V LEDs and incandescent light bulbs with next-gen perovskite LEDs with an emphasis on the current operation stability of perovskite LEDs, how the stability will improve in the next 5-10 years, and when they reach an operational stability that rivals or surpasses that of III-V and incandescent technologies. Overall, this report will demarcate when it will make financial sense to switch to perovskite LEDs on a national scale.

Background and Motivation

To understand what perovskite LEDs (PeLEDs) are, one must become familiar with what is available on the market. As of now the majority of the world utilizes Incandescent lights or LED bulbs as light sources. A light-emitting diode (LED) is a semiconductor light source that emits light when current flows through it. Before understanding the physics governing PeLEDs, we need to understand the ambipolar charge transport comprised of both holes and electrons that lead to light emission within PeLEDs.

Electrons can be described as subatomic particles with a net negative charge. Their most influential role is in the process of bonding individual atoms, known as atomic bonding. Electrons can be found in every element as they occupy the outer orbitals of an element. It is also important to understand electron flow, or in other words, current. This is essential when considering a semiconductor, as when a voltage is applied to a semiconductor, the protons (positively charged subatomic particles) are held stationary whereas the electrons flow with some drift velocity. An abundance of these charge carriers creates majority carriers, whereas a scarcity of these charge carriers creates minority carriers. This in turn induces a current through an electric field. This will be key in this investigation regarding some of the issues that currently face perovskite LEDs, including ion migration and Joule heating. Although similar to an electron in magnitude, holes have a net positive electronic charge associated with them. In essence, a hole represents the absence of an electron. Both however are charge carriers that are necessary for the current flow in ambipolar-based semiconductors.

Figure 1. Typical Peled device structure including price breakdown for each layer.

Electrons in the semiconductor recombine with holes as electron-hole pairs, releasing energy in the form of photons thus leading to light emission. While perovskite light-emitting diodes depend upon the perovskite active layer which is in the form of ABX3, in lead halide perovskites, A represents a cation, B represents lead, and X represents a halogen. In the most common lead halide perovskites, halogens tend to most regularly be chlorine, fluorine, iodine, and bromine, the cations usually represent cesium or methylammonium, and as stated above, B represents lead. For example, CsPbBr3 is a typical lead halide perovskite.

Perovskites were first invented in the early 1950s only emitting red, but later green emission was possible. However, the color blue was and still is challenging to create, being 150 times less stable than green and red. Blue’s instability limits a wide color palette for perovskites to display, therefore impeding perovskite commercialization. One of the particular advantages of PeLEDs over current generation LEDs is that PeLEDs emit light with very narrow emissions resulting in ultra-high color purity. Thus, Lead Halide Perovskites are actively researched for their light emission capabilities and their promise for next-generation lighting and display technologies.

Figure 2. Joule heating that occurs in the electron transport layer, perovskite layer, and hole transport layer

Utilizing the knowledge above, we can now understand the fabrication process needed to build the most basic lead halide perovskite LED. The process begins with adding the bottom electrode ITO (indium tin oxide) to the glass substrate which is often used to create a conductive, transparent coating for the bottom layer. Next, the PEDOT: PSS layer is spun onto the ITO, or otherwise known as the hole transport layer, which is a polymer mixture composed of multiple sulfonyl groups created by sulfonic acid. One of the most key components of the perovskite LED includes the perovskite active layer itself. Created by the ABX3 structure, lead halide LEDs contain the most common compound in the perovskite layer as CsPbBr3 (cesium lead bromide), created by mixing CsBr (cesium bromide) and PbBr2 (lead bromide). It is also necessary to add TPBi, a man-made compound that plays the role of the electron transport layer, after the perovskite layer. Additionally, LiF (lithium fluoride) is added mainly to improve device performance and to align energy bands. Finally, the top electrode layer is added, Al (Aluminum). Towards the end of the fabrication process, LiF, Al, and TPBi layers are evaporated for the best stability of the perovskite. The culmination of these steps provides what we now can classify as the most basic perovskite LED (lead halide perovskite LED).

Considering all these factors required in the progression of perovskite LEDs, each and every component is essential in creating the most basic perovskite. In this report, we will further examine the cost analysis as well as the plausibility of our proposal. Given their current operational stability issues, we will predict how far into the future that these perovskite LEDs will be a suitable replacement for all the current incandescent and III-V LEDs in the United States.

Methods and Results

To analyze when the US can transition to perovskite LEDs rather than incandescent and III-V LEDs, one must consider the output power of these PeLEDs. White light is the current most often utilized color of light, consisting of a superposition of three colors: red, blue, and green light. Once one determines the amount of power each color individually consumes, we can sum up the wattages resulting in a final approximation of the amount of power white light would need to be at its best efficiency.

I. Power Analysis

When considering the power of each individual light source, the equation P=IV will be necessary to keep in mind. Boiled down to four specific steps, at this point one can thoroughly investigate the necessary amount of power needed to allow for a certain color of light to be emitted. First, one must gather the peak EQE (external quantum efficiency), the ratio between the amount of photons leaving the system to the amount of electrons entering, of the color[] Once this is gathered, use the EQE to characterize how much power the PeLED is consuming at that given operating point using J-V-L graphs. Similarly, one must do the same and compare this voltage number to a graph correlating J (current density) to voltage. This is one of the most important steps in the process as J is measured most commonly in mA/cm^2 which will be relevant in the step that follows. When understanding the equation P=IV, P represents power, I represents current, where V represents voltage. Then, one must input the data gathered into this equation. Although plugging in one’s value for V is simple, current is a bit more complicated. Since we are given units in mA/cm^2, one must multiply this value by the device area each perovskite substrate is created on, leaving with the overall current. Only then, will one be able to complete the calculation and solve for power.

After a thorough investigation of these criteria, we can begin the exact calculations. Take for instance red perovskite LEDs. Having a peak EQE currently at 17.8%, the corresponding voltage at the best efficiency occurs at 2.2 V. Similarly, the corresponding J value results in 8mA/cm^2, but must be multiplied by an area of 2.25 cm^2 as explained above. This has a result of approximately 41mW. Now, we can apply these same steps to green and blue perovskite LEDs. Green perovskite LEDs have their peak EQE at 21.63% being the highest of the three. With a voltage of 3.5 V and a current density value of 5mA/cm^2 both at the best efficiency of the perovskite, this results in a total of 63 mW. Although blue perovskite LEDs are still in development as are the others, blue perovskite LEDs have a long way to go in terms of stability and EQE as it is a very difficult color to produce. Containing a peak EQE of 10.11%, a voltage most efficient at 3.5 V, and a J value most efficient at 15 mA/cm^2, the power results in 118 mW .

Table (1) Values used to calculate the total power of red, green, and blue perovskites LEDs

The data above is reasonable as one must consider the amount of energy each color consumes as a total. Utilizing the equation  E= \frac{h c}{\lambda} , the colors with the largest wavelengths will result in the least energy, and understanding the equation  P= \frac{E}{t} , the greater the energy, the greater the power. When analyzing each color with this information, blue has the smallest wavelength and therefore consumes the largest amount of power. In contrast to blue, red has the longest wavelength so, in turn, has the least amount of power, whereas green falls in between the two.

Since all of these perovskites are currently not at a competitive EQE or stability to replace incandescent and III-V LEDs, it is important to understand how much they need to improve. State-of-the-art green LEDs reside at an approximate 38.4% EQE whereas red LEDs reside at 35.48%. The reason why these values are at such a high percentage is for the very reason of optical lenses. Optical lenses are utilized to ultimately direct light in a single forward direction. EQE is only measured by the forward light emission of the perovskite LEDs. Keeping this into consideration, when perovskite LEDs do indeed reach this level, it will be the logical solution as a replacement. However, this now begs the question of when we will be able to replace all current incandescent and III-V LEDs with perovskite LEDs in the future, and what does the future of blue perovskite LEDs look like.

II. PeLED Operational Lifetime and EQE Estimates:

In order to predict commercialization of PeLEDs, one must consider the following factors: blue’s current and predicted EQE, Red, green, and blues’ projected operational lifetime in 10 years and their operational lifetime using ion migration suppression methods.

Figure 3. Ion migration and the movement of halide ions.

Blue EQE Prediction:

Currently, blue perovskite LEDs are not at their maximum external quantum efficiency. In comparison to green and red which have come close to reaching their maximum EQE, blue is still behind. Knowing that green and red’s projected maximum EQE is around 25%, blue is expected to reach this maximum of 25% as well. In order to predict when blue perovskite LEDs would reach their maximum EQE, this investigation first gathered blue EQE from the years 2016-2021. With the information collected this research created a graph displaying the points to form a line of best fit. To create the line one had to find the average between the points. This average made the equation of Y= -825.58/x +50. Note that when using this equation one must use the last two digits of the year for “X”, for example in 2016, use 16 for x. This equation grows asymptotically because blue’s EQE would eventually plateau and approach a maximum of 25%. This means that in the year 2034 (12 years), blues predicted EQE will reach 25 percent (view equation (A) for solution and figure (6) for growth).

In addition, all red, green, and blue perovskite LED colors are significantly behind the operational lifetime of an LED thus withholding perovskite commercialization. This however is temporary as advances and predictions can be made to show when perovskite LEDs will surpass LEDs. First, the research started by predicting each color’s operational stability within the next ten years. This would help understand how rapidly perovskites are truly growing. In order to do so, research gathered papers that reported each color’s operational stability throughout the years.

Predicted Blue PeLED Operational Lifetime in 10 years:

Starting with blue, in 2019, blue’s operational stability was 14.5 minutes, 51 minutes in 2020, and 81.3 minutes in 2021. With this data, one can create a line of best fit to average the points, create a growth rate, and predict future operational stability. Once done, research was able to average the data to formulate an equation of Y=33.4x-619.1, note that to again predict the years following, one must use the last two digits of the year. With this information, one replaced 31 to our x, representing 2031(10 years from now). This means that by the year 2031, blue would reach a predicted operational lifetime of 7 hours (view equation (B) for solution and figure 7 for growth).

It is important to notice that blue’s operational lifetime growth is linear. This is extremely valuable since blue is the most challenging color to create due to its large bandgap which will be further explained once compared to red and green’s operational lifetime growth.

Predicted Red PeLED Operational Lifetime in 10 years:

Just like blue, the investigation took similar steps to predict red perovskites’ operational stability in 10 years. Again, one collected red’s previous operational stability throughout the years using research papers to predict a growth rate. In 2017, red operated for 16 hours; it increased to 30 in 2018 and currently in 2021, 317 hours. This data is a bit different from blue since it grows exponentially. Therefore instead of using a line of best fit that is linear, research made it exponential. This has to do with the fact that red is a much easier color to accomplish than blue allowing it to advance faster. With that said, the red exponential equation is  y=0.0000273735 \times 2.6169^{x} found by the average between points. With this equation, one imputed the year 31, representative of 2031 into the x of the equation. This shows that by the year 2031 red is predicted to operate at 726,735 hours, an extremely noticeable difference between our current operational stability( view equation (C) for solution and figure 8 for growth).

Predicted Green PeLED Operational Lifetime in 10 years:

Again the research repeated the same process, this time gathering green’s perovskite operational stability to help figure out what the operational lifetime would look like in 10 years. The research found that in 2016 the green perovskite’s operational stability was ten hours, 2 years later in 2018 the LEDs operated for 46 hours, and finally, in 2021 the perovskites operated to their current maximum of 208 hours. This allowed the creation of a line of best fit that was exponential, being the average of the points  y=0.0033 \times 1.169^{x} . Then use the equation to input the last two digits of the year into x as 31, representing 2031(10 years from 2021). Results in green’s operational lifetime to be 39,105 hours (view equation (D) for solution and figure 9 for growth). This operation stability for the year 2031 is a bit smaller than red’s considering that red is the most efficient.

Knowing that the blue’s bandgap is the widest, it directly correlates to the growth rate. The bandgap denotes the minimum energy required to excite an electron into a state that allows it to conduct current in the conduction band. The valence band is the lower energy level, and if there is a gap between this level and the higher energy conduction band, energy must be added to allow electrons to flow. As described, green and red have smaller band gaps which cause longer operational stability and exponential growth. Blue however has a wider bandgap which causes shorter operational stability and linear growth. Blue requires more energy to emit a photon because of the higher energy band gaps ultimately compromising the efficiency of perovskites.

Figure 4. Red, green, and blue Perovskites’ band gap width in comparison to one another.

III. PeLED Commercialization Viability

Red PeLED Commercialization:

Now that sufficient equations for each color have been gathered, predictions of commercialization can be considered. In order to find when each color can accomplish commercialization, one must acknowledge that an average LED can last 6 ×106h. Using this information, one can use the red perovskite equation to input 6 ×106h as our “ Y “ representing the operational stability to help find “X” our year. Doing so one can use  0.0000273735*2.16953^{x}=10^(6)*6h , this answer would result in 33~34, representative of the year 2034. This means that by the year 2034 the red perovskite should be able to compete with LEDs. (view equation (E) for solution)

Figure 5 displays blues highest EQE throughout the years and its recent progress. Figure 6 displays the equation ​​Y= -825.58/x+50, the line of best fit from figure 5, and predicts in which blue perovskite reaches maximum EQE. Figure 7 Displays the equation Y=33.4x-619 To help predict the operational lifetime of the blue perovskite in 10 years.

Figure 8 Displays the equation y=0.0000273735 x 2.6169^x to help predict the operational lifetime of the red perovskite in 10 years. Figure 9 displays the equation y=.0033 x 1.69116^x To help predict operational lifetime of the green perovskite in 10 years.

Green PeLED Commercialization:

One can repeat the process for the green perovskite where we utilize our equation to input our desired operation lifetime. By replacing our “Y“ as 6 ×106h simplified to  10^{6}*6 =.00336293*1.69116^{x} is equivalent to 40 representing 2040 years of commercialization. By the year 2040 green perovskites are estimated to commercialize (view equation (F) for solution)

Blue PeLED Commercialization:

Lastly, for the blue perovskite, one must solve for the estimated year which was  (33.4x)-619=10^{6}*6h equal to the year

Figure 8 Displays the equation  y=0.0000273735 x 2.6169^{x} to help predict the operational lifetime of the red perovskite in 10 years. Figure 9 displays the equation  y=.0033 x 1.69116^{x} . To help predict operational lifetime of the green perovskite in 10 years.

179659, 179659 representative of the year 181,659.25. This year appears different in comparison to the other perovskite predicted commercialization year in which they were in the years 2000. To convert the number as one did for red and green one added 2000 which resulted in blues predicted commercialization year to be 181,659.25. ( view equation (G) for solution)

Blue is immensely behind, however, this is without taking into account advances that will be made in the future. For one, one must consider the fact that these numbers do not utilize ion migration joule heating suppression methods in addition to new research and studies that will specifically help advance the blue perovskite.

IV. Pathways to enhance PeLED performance and stability

Ion Migration Suppression Strategies:

As of now, there are many methods utilized to counteract migration and joule heating. One being B-site engineering. B site engineering is doping B-site cation ions with metal ions like Mn2+. Traps in the mid-gap can prevent electrons from recombining with holes, ultimately limiting the perovskite as light cannot emit. Reducing traps allows electrons to fall freely without encountering any obstructions. The addition of Mn2+ reduces the trap density, which results in less ion migration. Mn2+ can lower defect density (the number of defects ) in perovskites, reducing ion migration and resulting in greater operational stability. With that said, a study that utilized this method caused blue perovskites to operate 1,440 times longer than their original undoped perovskites. This study also applied B-site engineering to red perovskites allowing them to operate for 305 times longer than the undoped perovskites.

The second method was using Precursor Solution Composition Optimization. There are three crystallinity orders in a perovskite (substrate). There is amorphous, which means there is no discernible order. There is crystalline, where everything is in order and visually is similar to a checkerboard. Lastly, Polycrystalline indicates that halide ions can be arranged and slightly distributed in tiny groupings. There are pockets of small groups in polycrystalline thin films that will be in order. However, grain boundaries will exist inside the limits of those groupings.

In a polycrystalline material, a grain boundary is a point where two grains, or crystallites, meet. Grain boundaries are two-dimensional defects in the crystal structure that reduce the material’s electrical and thermal conductivity. This indicates that there are flaws in the device that cause it to degrade. Halide ions, such as bromide, chloride, or iodide, can easily cross grain boundaries since they have the lowest activation energy.

These halide ions are at grain boundary cusps, and because they are not strongly attached to the group, they can be whisked away by the electric field, resulting in ion migration. Halide ions are being swept across and concentrated at some point due to the electric field being applied to the layer, which is producing the most segregation. Instead of employing a cesium bromide, we can use a different method. CsTFA-derived films have a flatter energy landscape (a more homogenous energy level distribution for charges), a more stable crystal structure, superior optical characteristics, and reduced ion migration as compared to the CsBr method.

As a result, such grain boundaries get passivated, or the lead halide ions become more difficult to respect within the magnetic field. This causes tighter films where there are no bubbles, (effectively a sheet compared to cesium bromide with defects), less ion migration occurs. A research group utilized Precursor Solution Composition Optimization which allowed for green perovskites to operate 17 times more efficiently.

Applying Precursor Solution Composition Optimization to Green PeLEDs:

Our research can then apply these numbers to the current undoped operational lifetime. Precursor Solution Composition Optimization allows green perovskites to function 17 times more efficiently. Therefore one first had to find a number multiplied to 17 to then calculate to 10^(6)*6 h resulting in 352,941h. One can then calculate the year our “X” in which green will be able to reach 352,941h. Solving for the year 35 representative of in the year 2035, green will be able to commercialize ( view equation (H) for solution).

Applying B-site Engineering to Red PeLEDs:

Applying B site engineering red perovskites will be able to operate 305 times longer in comparison to our original predictions. One must find the minimum number that can be multiplied with 305 (305 representing how many times more the perovskite would last being doped) to be equivalent to 10^(6)*6h.

Set of equations A-J is processes taken in order to predict blue EQE (A), Blue perovskite estimated operational lifetime in ten years(B), Red perovskite estimated operational lifetime in ten years (C), Green perovskite estimated operational lifetime in ten years (D), Predicted Red perovskite commercialization year (E), Green perovskite commercialization year (F), Blue perovskite commercialization year (G), Green perovskite estimated commercialization year using Precursor Solution composition optimization(H), Red perovskite estimated commercialization year using B-site Engineering(I) and Blue perovskite estimated commercialization year using B-site Engineering(J).

This number is 19,672h, now replacing it for “Y” representing operational lifetime, into our red perovskite equation. Let us solve for “X” resulting in 26 representing the year 2026. This method allowed for the reduction of 7 years, in comparison to not using any ion migration suppression methods ( view equation (I) for solution).

Applying B-site Engineering to Blue PeLEDs:

The same process can then be repeated for the blue perovskite. Instead, however, ion suppression made the operational lifetime 1440 times longer. The research found the minimum number that could be multiplied to 1440 (1440 representing how many times more the perovskite would operate for, being doped) to be equivalent to 10^{6}*6h . Resulting in 4166h, which was used to represent Y operational lifetime into our blue perovskite equation. Then research proceeded to solve for X as our estimated year. Doing so, formulated the number 143 representing the year 2143 when the blue perovskite would be able to commercialize (view equation (J) for solution). Although this is significantly sooner than the undoped blue perovskite commercialization prediction there are still other factors one must consider. Since perovskites are rapidly evolving there are still numerous studies and ion suppression methods that can be applied. With this in mind, new research can be applied to all color’s estimated commercialization year. Regardless of red and green being able to commercialize sooner than blue, blue is still a color with great improvements expected over the next few years.

V. Modeling the Economies of Scale towards Mass Production

To evaluate the total cost of mass-producing perovskite LED materials in the project of replacing all light sources in the US, we would need to apply economies of scale to our calculations. Economies of scale is the total average cost savings obtained by an enterprise for a greater quantity of production. As production increases, the total average cost decreases, resulting in a lesser total cost than the sum of the price per unit. In the generic model, the variables P1 and P2 represent the cost of production, and the variables Q1 and Q2 represent the quantity of production, and with an increase in the quantity of production (Q value), there is a gradual decline in the cost of production.

Figure 10 describes the asymptotic model developed from TPBi costs at 2g and 5g and describes the decreasing growth rate of costs as production increases.

Our model evaluating costs, considering the impacts of economies of scale, was developed from a quotation by the Stanford Congreve Lab for TPBi, providing numbers for price at different numbers of grams. From these numbers, we were able to gather initial price numbers and establish a trend. The numbers initially provided were $566.00 for 2 grams and $1341.00 for 5 grams, thus $283.00/g and $268.20/g respectively, while the cost for 1 gram is between $600 and $700. The development of a model for economies of scale necessitated the development an asymptotic graph model based on these numbers, given costs for 2 grams and 5 grams respectively, to calculate the price per gram for 10,000 grams and price for bulk production of 1 billion at this rate.

For an increase in gram quantity by a factor of 2.5, there is a 5.23 % decrease in cost. This number was achieved by subtracting 268.20 from 283, the respective price per gram for 2 grams and 5 grams, and evaluating that percentage of the resulting 14.8. By this model, we can calculate a projected $169.69/g for 10,000 grams, and a projected $1.70/g for 1 billion. The asymptotic model for TPBi prices for increasing gram quantities follows this trend. Having calculated our model for the TPBi layer, we can then apply it similarly to each layer, which would be expected to follow a similar price trend.

To apply these numbers, we must consider the fact that for TPBi, 10,000 grams amounts to 1,000,000 substrates. For the ITO layer, $250.00 is the approximate price for 100 substrates, and by an increase in substrate by a factor of 250, there is a 0.05% decrease in cost per substrate. For 1,000,000 substrates, the cost can be estimated to be $237.48, a mere fraction of a cent per substrate at that quantity. Similarly, an approximate $125.00 corresponds to the quantity of 100 substrates for the PEDOT layer, and by the same process, the total cost for 1,000,000 substrates can be estimated to be $118.74. For the CsPbBr3 layer, the original costs were found to be for $38.00 CsBr and for $10.64 for PbBr2 for 1 substrate, and once more by the same process, the cost for 1,000,000 substrates can be estimated to be $34.29 for CsBr and $9.60 for PbBr2. For the LiF layer, $1 corresponds to the price of LiF for 1 substrate, and for 1,000,000 substrates, the cost can be calculated to an approximate $0.90. Finally, $0.27 corresponds to the price for 1 substrate in the Aluminum layer, and the cost for 1,000,000 substrates can be similarly estimated to $0.24. These costs were originally gathered from Sigma-Aldrich and Ossila.

Finally, from our calculated value of 3 to 4 cents for the cost of 1 substrate for a perovskite LED, we can apply our model to find this number at mass production, which would amount to an approximate fifth of a cent for a gram increase by a factor of 2.5. At production of 1 billion, the price would be about 11 million USD.

VI. Financial, Performance, and Energy Analysis of Transitioning to PeLED-based Lighting

After taking a cumulative approach to this research of perovskite LEDs, the ultimate question of if the price of perovskite LEDs is worth the energy consumption can finally be answered. Considering the equation  P = \frac{E}{t} where P equates to power, E equates to energy, and t equates to time, it is possible to calculate the energy of each color perovskite LED given power and time. Finally, one must combine all these values to find the total energy the US would have to use to power lighting with perovskite LEDs. Considering these perovskites will be at the level where they can compete with current LEDs, one must assume each color perovskite is at their maximum EQE of 25%. The calculations proceed as follows:  ((0.063*1.1)+(0.041*1.4)+(0.118*2.5))*(60*60*6*360*328,200,000) . By multiplying each power value by specific numbers to approximate them to be around 25% EQE, this would give us a better and more accurate representation of the future. Multiplying this by 3,600 gives our value in hours rather than seconds, and final multiplying by the amount per year for every person in the US. This equates to approximately  1.5*10^{15} kWh. Compared to the 136 billion kWh the US uses in energy consumption a year to fuel light emission, this does not seem to be a logical fit currently, but with joule heating methods along with the rapid improvements of these perovskite LEDs, they are sure to reach the stability and energy of regular LEDs in the future.

VII. Cumulative Research Prediction of White Peled

Taking into account power analysis, operational stability, and economies of scale, research is sufficient to help predict how much power a white PeLed would utilize, how long the peled would operate for, and how much the product would cost the U.S as a whole. As previously stated the blue Peled has an operational lifetime of 81 min noticeably behind the green and red Peled operational lifetimes, therefore if one were to utilize multiple blue peled in one white Peled it would help increment lifetime. For example, to create a white PeLED one red, green, and blue peled is needed. However one could utilize ten blue PeLEDs to increment the lifetime by 10 times as long as only one blue peled is used at a time. Nevertheless, there are limitations such as the cost of having multiple PeLEDs in one unit or utilizing an extreme amount of power to operate. Therefore as all colors currently stand research would only utilize 173 blue LEDs. Particularly, 173 blue perovskites in one white Peled because this would allow for the operation to increase to 207 hours (This lifetime being close to green Peleds current operational stability which is at 208 hours). Research multiplied the power needed to light up a blue Peled by 173. Finally adding the power needed to operate one green and one red Peled. The power for this Peled would equate to 20.518 kW. According to calculations 173 blue PeLEDs, one red and one green in one unit to supply the U.S population would cost 128 million. As they currently stand, they would not be the best alternative.

However, research can also calculate the lifetime of the white Peled in 16 years. It is important to consider how the white Peled would change in the future. Particularly in 16 years, since the green perovskite has reached operational stability of 1 million. At this time the blue perovskite would operate for a total of 10 hours, and could potentially operate for 1,000 hours using 100 blue Peled in one unit. This amount of blue Peleds would take less energy as time progressed, the power calculating to a total of 11.9 kW to operate one white LED in the year 2037. The cost of the white Peleds is 127 million dollars to fully transition. This number is lower in cost for a longer operational lifetime and less power needed compared to where they currently stand. It is also important to consider that in the future there could be new advances to combat ion migration which could also improve power, operational lifetime, and cost.

Challenges Encountered

We are currently facing many problems when considering the stability of PeLEDs. At the moment being unstable, the goal for the future of perovskite LEDs is to eventually increase the stability of each perovskite, however, there are some issues that need to be solved that are preventing us such as ion migration, joule heating, etc.

Before understanding the problems with ion migration, we first must understand the meaning of an electric field. An electric field is a field that physically surrounds electrically charged particles which allows for the repulsion and attraction of other electrically charged particles. Ion migration occurs in the perovskite layer, where either cations or anions, typically halide ions (anions) in lead halide perovskites, as seen in figure 3, approach either the negative or positive side of the electric field, which creates unevenness throughout the perovskite. Once they congregate near the terminals, any sort of light emission will be uneven which in turn compromises the performance of the device. This is solely due to the electric field.

Joule heating occurs in the electron transport layer, the perovskite layer, and the hole transport layer. Joule heating allows for and creates heat, which is produced by current flowing in the material. The problem with this is that it can raise the temperature of the material by up to 40 degrees celsius which also, in turn, allows for degradation of the material and a decrease in instability. Any material is optimized at 200k, which progressively regresses as temp increases, which decreases current since electrons are more scattered. No flow amounts to no current, which results in no light emission. This is solely based on a thermal effect.

Another contributing factor to perovskite degradation is bandgap width, this can cause problems with operational lifetime, particularly with the color blue. The reason why red is the most efficient color is due to its small band gap width. Blue for example has the widest band gap width out of the three colors, this means that there is more energy that must move an electron. As for red, which has the smallest band gap width, the electron does not need as much energy to the dropdown. With smaller band gaps, we are able to have greater stability for red and green. For blue, since the color has a wider bandgap, occasionally the electron gets trapped in the middle of the bandgap; this area is called the midgap. Here the perovskite cannot emit light, as light emission does not occur in the mid-gap, only when the electron falls. This is one of the major problems faced as band gap width is not something that can be changed. Unlike green and red which have longer operational lifetimes, blue currently faces this problem and will continue to face it. In addition, the traps in the mid-gap also affect the operational lifetime to not only the blue perovskite. Therefore jeopardizing particularly the blue perovskite lifetime.

Another challenge encountered was the lack of perfect accuracy for our model of economies of scale. The economies of scale model utilized to calculate the cost values at various quantities is an imperfect measure of estimates. The model is based on very small quantities of TPBi, thus it could have been made more accurate if costs for larger quantities had also been provided for comparison. Additionally, all estimates are based on the model for TPBi, so they are expected to have slightly more inaccurate estimates for the other materials. However, although it does not specifically account for such inaccuracies, the model is itself an estimation and only approximates the trend in cost change at larger scales.


Whether keeping into account each individual color of perovskite LEDs or the culmination of them all together, they most undoubtedly will play a fundamental role in our future. Each aspect explained upon in the preceding sections of this paper show countless evidence of the rapid evolution of these perovskites when considering either the power of each color, their operational stability, or the price of each perovskite.

The power analysis played an equally important role in determining the reliability of each perovskite. Considering the equation P=IV, the peak EQEs of each color, applying J-V graphs, and combining them collectively to get a final value for power, all portray the importance of the power analysis. As the rise of each color’s EQE continues, so will the efficiency of perovskites, which perhaps may be the most important factor in transitioning from III-V and incandescent LEDs to perovskites. With joule heating methods, perovskites are improving at exponential rates being very promising to their future.

By utilizing previous perovskite operational lifetimes there was sufficient information to predict if Peleds are worth the transition. Starting with the prediction of EQE for the blue perovskite, where research proved to show growth is rapid. Green and red perovskites too have shown extreme n operational lifetime improvements for the future. While the blue perovskites do seem to struggle, there are advances such as B-site engineering and precursor solution composition optimization to combat their short operational stability. Most importantly, the current white Peleds would not be a reasonable transition with the blue perovskite being the largest setback. However, with time we can be sure to see white Peleds thrive.

A significant consideration to evaluating the cost of transitioning to lighting by perovskite LEDs was economies of scale. At the high number of production necessary for this replacement, the total financial estimate could not be made accurately by multiplying the price per unit by the intended number of units, as such a calculation would overestimate the actual cost. Thus, the reduction in increase was calculated based on the original prices gathered from Sigma-Aldrich and Ossila. The economies of scale assumed for these calculations was a continued 5.23 % decrease in cost per gram for a quantity increase by a factor of 2.5, and by the model we used, this amounts to the LED cost of a fraction of a cent for a gram increase by that trend. Currently, to supply the U.S population with next-generation lighting, it would cost $128 million.


  1. Aneer Lamichhane, Nuggehalli M. Ravindra, Energy Gap-Refractive Index Relations in Perovskites, Materials, 10.3390/ma13081917, 13, 8, (1917), (2020).
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  6. Ma, Dongxin. “Chloride Insertion–Immobilization Enables Bright, Narrowband, and Stable Blue-Emitting Perovskite Diodes.” ACS Publications, 9 Mar. 2020, Accessed 27 July 2021.
  7. Ren, Zhenwei. “High-Performance Blue Perovskite Light-Emitting Diodes Enabled by Efficient Energy Transfer between Coupled Quasi-2D Perovskite Layers.” Onlinelibrary, 2021, adma.202005570. Accessed 27 July 2021.
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Investigating the Extent to which Discrepancies in SpO2 Readings Occur due to Differences in Patient Skin Pigmentation

Blog, Journal for High Schoolers, Journal for High Schoolers 2021


Hillary Khuu, Jun Hyun Park, Carolina Pavlik, Sylvia Chin


Pulse oximeters measure the level of oxygen saturation, also known as SpO2, in a person’s arterial blood to diagnose patients with diseases such as anemia, chronic obstructive pulmonary disease (COPD), and sleep disorders. They were developed in 1974 and are the primary method for measuring oxygen saturation. However, some studies show that pulse oximeters may display inaccurate readings when used on individuals with darker skin tones. In light of the COVID-19 pandemic, where pulse oximeters have been frequently used as self-diagnosing tests, solving technical issues with pulse oximeters has become ever so crucial.

Our project aims to discover the extent to which pulse oximeters work more faultily on individuals with darker skin tones and to determine what factors cause inaccurate readings. To obtain an intimate look into pulse oximetry use in hospitals today, we repurpose a dataset of 400 patients and cross-analyze several demographic points to pulse oximeter accuracy. Furthermore, we explore engineering methods to minimize discrepancies in SpO2 readings that may occur due to variances in patient skin pigmentation by creating our own pulse oximeters.


Pulse oximetry uses a technique called photoplethysmography, which uses red and infrared light to detect variations in blood volume [3]. A pulse oximeter shines a red light (wavelength of around 660 nm) and an infrared light (wavelength of around 940 nm) through the finger it is placed on, and a photo sensor on the opposite side of the finger absorbs the lights. Digital programs can determine the ratio between oxygenated and deoxygenated blood based on these light absorption levels and therefore provide patients with their SpO2 levels. This process is possible because healthy, oxygenated blood cells have a bright red color, whereas unhealthy, deoxygenated blood cells have a darker color, and thus absorb a different amount of red and infrared light. A healthy level of oxygen saturation hovers between 90% and 100%. An SpO2 reading of 94% indicates that 94% of a person’s red blood cells are healthy, and 6% lack oxygen [2].

There are two methods of pulse oximetry: reflective and transmissive. Reflective pulse oximetry is a process in which light is emitted through a finger, bounces off reflective material on the opposite side of the finger, and is then received by a photodiode located on the same side as the light emitters. Transmissive pulse oximetry, the more popularized and accurate method, involves the lights being emitted through a finger and a photodiode receiving them on the opposite side of the finger [4].

The accuracy of FDA-cleared pulse oximeters is between 2% and 3% of arterial blood gas values. Factors that may affect the accuracy of pulse oximeter readings include poor circulation, skin pigmentation, skin thickness, skin temperature, tobacco use, and fingernail polish [7].

In the 1970’s, when pulse oximeters were being created and tested, the population on which they were used was not racially diverse. Recently, research reports have concluded that pulse oximeters are more likely to miss low oxygen levels on darker skinned individuals compared to lighter skinned individuals. These researchers used the Fitzpatrick scale, a method of categorizing skin tone based on the ability to burn and tan. Their findings suggest that these devices may not be equally accurate on all skin tones. One explanation as to why the potential for racism in pulse oximetry has not been publicized until recently is because of the current U.S. Food and Drug Administration (FDA) requirements. As of 2021, the FDA requires at least two test subjects or 15% of subjects (whichever amount is larger) to be “darkly pigmented” [8].

Methods and Materials

  • Dataset from BMC Pulmonary Medicine journal’s “A multicentre prospective observational study comparing arterial blood gas values to those obtained by pulse oximeters used in adult patients attending Australian and New Zealand hospitals”
  • 10 of Newark’s MCL053PD red LED
  • 4 of Newark’s OP165A infrared emitter
  • 4 of Newark’s TEFD4300 photodiode
  • 1 of Newark’s A000066 Arduino Uno Board
  • 10 of Newark’s MCF 0.25W 10K ohm resistor
  • 10 of Newark’s MCCFR0W4J0331A50 330 ohm resistor
  • 1 of Newark’s 759 jumper wires
  • 4 of Newark’s SSL-LX5093UWW white LED
  • 1 USB-A to USB-B cable
  • 1 I2C 16×2 Arduino LCD Display Module
  • 1 breadboard
  • Male to female jumper wires

Our project focused on examining the relationship between pulse oximetry readings and potential racial bias due to variations in skin pigmentation. To investigate, we contacted Janine Pilcher of “A multicentre prospective observational study comparing arterial blood gas values to those obtained by pulse oximeters used in adult patients attending Australian and New Zealand hospitals,” which was published in the BMC Pulmonary Medicine journal, and obtained her dataset of 400 patients [5]. While her research tested the accuracy of pulse oximeters (which provides the SpO2 value) against arterial blood gas tests (which provides the SaO2 value) for determining oxygen saturation levels, her team recorded information on skin tone as well, making the dataset viable for our project.

We used the Fitzpatrick scale as a reference for determining different levels of human skin pigmentation. The Fitzpatrick scale classifies human skin color into six categories that are dependent on the skin’s melanin concentration and reaction to UV rays. A low number on the scale generally indicates a lighter skin tone that burns more often than tans, while a high number generally indicates a darker skin tone that tans more often than burns [1].

We repurposed Pilcher’s dataset and organized the data by demographic information (i.e. Fitzpatrick type, gender, hospital location). We applied formulas to calculate the average percent error between the SpO2 and SaO2 measurements. We converted these computations into charts and plotted the patient Fitzpatrick scale skin types against the average percent error between SpO2 and SaO2 measurements to interpret whether there were discrepancies in the accuracy of measurements of different skin tones.

Additionally, we furthered our understanding of how pulse oximeters function by assembling our own pulse oximeters with instructions from Giulio Pons’ “Really Homemade Oximeter Sensor” [6]. Although the guide was excellent, some modifications were required due to accessibility issues. For example, instead of using a pre-assembled KY-039 sensor as the basis of the light emitting and sensing portion of the circuit, our group individually connected a red LED, infrared LED, and photodiode to create the sensor. Also, some parts of the code were edited to fit the parameters of our own pulse oximeter. Eventually, we were able to construct a working pulse oximeter that successfully detects heart rate as well as SpO2 readings.


We reorganized a dataset of 400 patients from Pilcher’s study in Australia and New Zealand, which compared the accuracy of pulse oximeters to arterial blood gas tests. From this dataset, we were able to organize patients on their Fitzpatrick scale classification, gender, hospital location, and percent error between SpO2 and SaO2 readings.

When creating our pulse oximeters, a significant challenge we faced in ensuring accurate results was correctly positioning the LEDs and photodiode. The red and infrared LEDs had to be placed on the top of the finger and the photodiode on the bottom so that the lights could be absorbed properly. Because of accessibility issues due to COVID-19, a casing could not be 3-D printed for the pulse oximeter, which required us to manually position the sensors, as well as tape certain components together for stability. Correctly placing the light emitters and sensors was crucial since incorrect placement could result in inaccurate readings or no readings at all. Also, the lack of at-home soldering material required us to intertwine the metal wires rather than solder them together, which proved to be difficult and tedious at times. As shown below in Figure 1, one member of our research group obtained a heart rate of 91 beats per minute and an SpO2 reading of 95%.

Figure 1: Our Homemade Pulse Oximeter

Some studies show that pulse oximeters display results with larger errors when used on individuals with darker skin complexions. However, a slight downtrend in percent errors is visible as the Fitzpatrick scale increases (see Figure 4), disproving our hypothesis. These contradictory results can be greatly attributed to the fact that the Australian and New Zealand study mostly conducted tests on patients that were types I through IV on the Fitzpatrick scale, with only one patient that was a V category and zero patients that were a VI category, as shown in Figure 3. Therefore, since most data points were obtained from patients with lighter skin tones, the study does not demonstrate a holistic view of the global community. Furthermore, Figure 5 shows that the average percent error varied for each gender, and Figure 6 shows that the average percent error varied for each hospital location. Such discrepancies in factors other than skin tone could also have contributed to the contradictory downtrend in average percent errors as skin tone became darker.


While the results of our research suggest that patients between types IV and V on the Fitzpatrick scale obtain more accurate pulse oximeter results than those between types I and III, different results may have been obtained if an equal number of patients from each Fitzpatrick scale were tested and the population size of each category was substantially greater. Our results highlight the necessity of diverse test subjects and new FDA requirements. The FDA requires only two or 15% of patients to be “darkly pigmented” but fails to specify what qualifies as “darkly pigmented” [8]. To clarify, the FDA should list specific Fitzpatrick scale numbers. In addition, the requirements should be adjusted so that the test subjects in each Fitzpatrick scale category make up around 15% of the total in order to reach equal representation. These adjustments should be applied to all IoMT devices, not solely pulse oximeters, to remove biases based on racial traits.

Additionally, the study was conducted in only the Australian and New Zealand regions, which further strengthens our belief that further research should be conducted on a diverse pool of subjects in order to represent all groups of the world. While our data suggests that percent errors between SpO2 and SaO2 readings decline as Fitzpatrick scale increases, we would need to globally expand our project to definitively trust the results.

Furthermore, while the six categories of the Fitzpatrick scale are beneficial for simplicity, they are oversimplified and do not account for important nuances between darker skin pigmentations as type V and VI were added many years after the lighter tones [9]. The Fitzpatrick scale is the current scientific standard, and while it has room for improvement, it is superior to its predecessor, the Von Luschan’s chromatic scale (VLS). VLS consists of 36 categories, as opposed to the Fitzpatrick scale’s 6, so each category is far more specific, making it more challenging to have a classification for every skin pigmentation. Also, the large number of categories has led to inconsistent results when classifying skin tones. However, one benefit to VLS is that it categorizes skin based on pigmentation. In contrast, the Fitzpatrick scale classifies based on the ability to burn and tan, which may not always correlate with the expected skin pigmentation and does not correspond to any race [10]. Therefore, the FDA should require that the race of the subjects be recorded in conjunction with their Fitzpatrick scale classification to ensure equal representation in the research.

Future Directions

Through our project, we hope to contribute to the mitigation of racial bias within the medical field in order to improve the lives of all patients. Because COVID-19 has limited human interaction and the ability to work hands-on, we were not able to reach as far into our goals as originally planned. However, future work would include obtaining our own dataset with an equal number of individuals from each Fitzpatrick category to ensure a diversely represented pool of subjects. Testing pulse oximeters on a diverse population would allow us to detect potential discrepancies in SpO2 readings between Fitzpatrick categories.

Furthermore, we would continue to develop stringent recommendations for FDA requirements to ensure safety and accuracy for all patients regardless of skin tone. In addition, we would consult with pulse oximetry experts on their opinions and experiences with inaccurate SpO2 readings that result from variances in skin pigmentation as a method of confirming our hypothesis. We would then continue to modify our pulse oximeters so that skin pigmentation would no longer have any effect on pulse oximeter accuracy. We would test our novel pulse oximeters on the aforementioned group of diverse subjects to confirm the efficiency and accuracy of our successful modification. To further eliminate racial biases and inequities in healthcare, we intend to expand access to our pulse oximeters by making them inclusive and affordable for everyone.


  1. Beveridge, Chloe. “Determining your skin type on the Fitzpatrick scale.” Current Body Editorial, 4 May 2018, Accessed 21 July 2021.
  2. Chan, Edward D., et al. “Pulse oximetry: Understanding its basic principles facilitates appreciation of its limitations.” ScienceDirect, vol. 107, no. 6, 2013, pp. 789-99, Accessed 21 July 2021.
  3. Cheriyedath, Susha, M.Sc. “Photoplethysmography (PPG).” Edited by Yolanda Smith, B.Pharm. News Medical, 27 Feb. 2019, Accessed 21 July 2021.
  4. Nitzan, Meir et al. “Pulse oximetry: fundamentals and technology update.” Medical devices (Auckland, N.Z.) vol. 7 231-9. 8 Jul. 2014, doi:10.2147/MDER.S47319
  5. Pilcher, Janine et al. “A multicentre prospective observational study comparing arterial blood gas values to those obtained by pulse oximeters used in adult patients attending Australian and New Zealand hospitals.” BMC pulmonary medicine vol. 20,1 7. 9 Jan. 2020, doi:10.1186/s12890-019-1007-3
  6. Pons, Giulio. “Really Homemade Oximeter Sensor.” Project Hub, 13 May 2020, Accessed 21 July 2021.
  7. “Pulse Oximeter Accuracy and Limitations: FDA Safety Communication.” U.S. Food and Drug Administration, 19 Feb. 2021, Accessed 21 July 2021.
  8. “Pulse Oximeters – Premarket Notification Submissions [510(k)s]: Guidance for Industry and Food and Drug Administration Staff.” U.S. Food and Drug Administration, 4 Mar. 2013, Accessed 15 July 2021.
  9. Sharma, Ajay N., and Bhupendra C. Patel. “Laser Fitzpatrick Skin Type Recommendations.” National Center for Biotechnology Information, 11 Mar. 2021, Accessed 8 Aug. 2021.
  10. “Von Luschan’s chromatic scale.” Wikipedia, 20 June 2021, Accessed 1 Aug. 2021.

Analysis of Sociocultural and Optical Influences on Olfactory Memory

Blog, Journal for High Schoolers, Journal for High Schoolers 2021


Madison Abbassi, Jonathan Mulenga, Subah Mahbub, Esther Chung, Teyon Herring, Eric Guo, Dante Aguilar, Arindam Saha


The field of olfaction is a rising topic of interest, with researchers globally making new discoveries about olfaction and its connections with our other basic senses. Studies over the past few decades have uncovered the effects of age, disease, color, and other sensory stimuli on people’s “smell memories” and their differences in smell perception. Research into olfaction is particularly relevant today given the COVID-19 pandemic: many who experienced hyposmia or anosmia during the pandemic have reported diminished happiness, along with disruptions to their normal cooking and eating patterns. For this study, our research group decided to use surveys due to our virtual setting and designed two surveys to examine potential areas impacting the perception of olfaction: the first survey tested image quality and alternative coloration of images; the second survey investigated relationships between participants’ sociocultural backgrounds and their preferences for certain smells. We deployed our surveys across different online forums to get a random, diverse group of participants.

Based on the findings from our prototype surveys, we expected our results to show an association between higher quality or brightly colored images and stronger olfactory perception. We also hypothesized that our sociocultural survey respondents will have stronger and more favorable perceptions of odors that they are most familiar with – whether that is due to frequent exposure or common use of the ingredient in a cultural dish that matches the participant’s cultural background.

Background/Literature Review

Olfaction, the sense of smell, plays a complex role in our daily lives, subtly influencing everything from our eating patterns to our social habits. During the current COVID-19 pandemic, some patients have reported experiencing anosmia – total loss of smell – or hyposmia – reduced ability to smell – as a direct result of the virus. Gaining a better understanding of how we can stimulate smell memories even when a respondent is unable to physically smell the odor will potentially help with future technologies or programs for the smell-impaired. A respondent to our survey who had lost her sense of smell post-COVID described the experience as losing “half the essence of life”, furthering the importance of olfaction-related research: a deeper understanding of arguably the most disregarded sense can help others to continue experiencing this “essence of life,” whether it is through novel technologies or by restoring smell for those who have been impaired by illness. As previous olfactory studies have done, this is best achieved by interacting with study participants, and more specifically to our study, by investigating the conditions that optimize olfactory memory/smell recall.

Prior studies in the area of olfaction research have established the effect of color on strengthening olfactory memory [1], linking changes in the color of a food item’s appearance with differences in reported odor perception. Professor Mariana Obrist’s lab collected in-depth responses from survey participants describing how they experience smell. The reported adjectives used by participants to describe scents (including heavy, penetrating, sweet, reassuring, and erotic, to name a few) suggested (1) a strong connection between smell perception and perception in other senses, and (2) a close relationship between intensity of smell memories and emotions associated with that smell.

However, there has not been much prior research on correlations between sociocultural background and ability to recall the odors of culturally linked ingredients such as kimchi, turmeric, cardamom, chilli pepper, and coffee. Furthermore, research linking smell to other senses is rather new, and there is a lot more to be explored in that space. To that end, we designed and deployed surveys aiming to further investigate the smell experience and its relationship with both cultural and multisensory factors.

Research Methodologies

Survey Design:

For our study, we designed and deployed surveys to measure participants’ ability to recall odors with a particular stimulus. After a few preliminary test surveys, we finalized two Google Form surveys: a multisensory survey (testing the impact of visual stimuli on odor recollection) and a multicultural survey (testing for correlations between cultural backgrounds and ability to recall the smell of certain culture-specific ingredients).

Both of the surveys were designed to maximize the efficiency of survey responses from a psychological understanding and approach. The beginning of the survey started with easy questions, including general information about one’s demographics or sexual orientation. Working off the momentum from the easier questions, the survey questions gradually progressed to harder questions, which required more reflection and activation of smell memories [3]. Pictures were added as part of survey questions to draw in more attention from the survey participants and randomization of question order was implemented in the survey to limit response biases (more specifically, participants responding in ways they believe researchers want them to rather than giving honest answers) [2]. The questions were in the form of multiple-choice questions, short responses, and linear scale questions. Two question varieties were utilized to maximize the accuracy of our conclusions. The question varieties are: same questions with different objects which were repetitive but reinforced the conclusions of an idea), and multiple variables accounted into one question format (for easier analysis of the data). Both of the surveys are explained in detail below:

Multisensory Survey:

To evaluate smell relationship from a multisensory aspect, we incorporated questions related to exposure, taste, and visual senses. To test how exposure affects smell recall, survey respondents were asked to rate how much they were exposed to and how often they consumed a particular food item on a scale of 1-5. The highlight of the multisensory survey was to find a connection between sight and ability to recall a certain smell. The normally colored image of a food item was set as the control and as a basis to compare the other variables’ results. Versions of each control image were made into a quality/pixelation, black and white, and multiple alternative colors: neon green, pink, and purple were chosen to be applied to the control as most food items on the survey do not naturally have such colors. The changes in visuals were obtained using Picsart app (for alternative color of the control), Pinetools website (or changing image quality through changing the pixelation of the control), and Adobe Spark (for black and white image of the control). We asked the survey respondents to rate their ability to recall the smell of the food item on a scale of 1-5 based on the different changes of the control image displayed in the survey.

Fig. 1: Some examples of visual stimuli that were used in the surveys.

Multicultural Survey:

In order to analyze the impact of cultural grouping on recalling certain smells, we chose universal along with culture-specific ingredients dominant in 7 different cultures to be incorporated in the survey. There are a total of 26 ingredients present in the survey from Asian, African, American, Latin American, European, Middle Eastern and South Asian cultures. The cultural groupings were made based on the previous responses of our initial surveys. We predict that respondents of the survey will potentially have stronger and more favorable recalling of odors that they are most familiar with from their respective cultures. For example, since a dominant ingredient from South Asia is cardamom, we predict that the smell of cardamom could potentially be more strongly recalled from that cultural group. To test this potential hypothesis, we asked our participants how intensely they can recall the smell of a particular ingredient on a scale of 1-7. Secondly, we asked the participants how strongly they liked a particular ingredient on a scale of 1 to 7. General demographic questions were added as well to evaluate the different cultures and racial associations of the survey respondents.

Survey Deployment

In order to reach a diverse sample of responses, we applied a three-pronged approach to deploying our surveys to multiple parties: Facebook survey exchange groups, Reddit forums such as r/samplesize and r/takemysurvey, and circulating it among our own communities. Facebook survey exchange groups are a community of random people from across the globe who are willing to take a survey in exchange for you completing a survey yourself. Reddit forums follow a similar concept as having random people from across the globe without the exchange system. Having members of the team located in different parts of the United States surrounded by a multitude of diverse people presented us with another method of survey data collection.

Website (UI)

Google Forms is an editable survey document with many bugs in participant survey filling. Users reported that question order was incorrect in multiple different ways informing us that each time the survey was taken, questions were moved without our command. Another inefficiency we discovered was the constant repetition of questions that could be condensed into a much neater, easier to read format. To eliminate these disadvantages from our survey we created our own website.

Creating our own website provided us the ability to have total control over survey creation allowing us to make our desired survey design. In addition to this upgrade, we were able to create a user-friendly interface giving each survey participant easy access to either of the two surveys and a nice display of both questions and answer choices. Also with total control of the survey, we were able to add more functionality to the survey. The website has a randomization feature that mixes all survey questions excluding the demographic questions to eliminate participants from figuring out the survey’s objective and adding bias into their responses. Another feature is a timer which allows us to see which questions take the most time and analyze the flow of the survey. A final bonus to having our own website is cross-platform compatibility giving participants the opportunity to fill the survey on any device of their choosing as long as they possessed a wifi-connection.

Fig. 2: Multisensory survey questions taken from the website.

Keywords – Vue.js, Firebase, Multisensory (MS), Multicultural (MC),

– Firebase Image

Fig. 3: Firebase database containing all survey responses.

Vue.js is a coding tool that builds interactive user interfaces and allows for easy integration and collaboration with other services and functionalities. Vue.js draws heavily from HTML, Javascript, and CSS, which are the basic tools of any web designer, thus providing an easy learning curve to those with prior experience. The benefits of using Vue.js include an easier front-end designing process, as seen through our website’s drop-down menu along with formatting of questions. Compared to old-school HTML, establishing the layout of the website is simplified with pre-built features such as v-row and v-col. Furthermore, Vue.js simplifies many aspects of survey design, which can be seen in the data storage of survey responses along with the creation of question templates and types. Vue.js eliminates the need to establish traditional formatting tools, such as Float-based or Grid-based layout on HTML.

Firebase is a service developed by Google for data storage and data CRUD (Create, Read, Update, Delete). Firebase ensures data security because of its client-side coding properties, while also creating numerous future add-ons through Google’s offerings of extensions that can apply onto the

The process of connecting Firebase and Vue.js together involves creating a Firebase project on Firebase console, then connecting the web API keys into the Vue.js code, which then opens up all firebase commands to be used in Vue.js, such as creating data collections and using code to CRUD data as needed in each survey.

After the connecting of Firebase and Vue.js, Firebase serves as the database and thus is the source that we draw from when analyzing data results. By activating Firebase’s blaze plan, we set up gcloud for our survey and can export all documents on the console of Google Cloud platform’s import/export tab, thus giving us our final .csv file, which could be used for further analysis.

Fig. 4: Google Cloud Platform to export/import data for .csv files.
Fig. 5: Code for Survey1, which as seen is a .vue file.

Data Analysis

Data analysis of the survey responses were conducted through Google Colab and Google Sheets. Each provided advantages in making our conclusions in an efficient and organized manner. Google Collab was able to support statistically complicated analyses, such as correlation tests, and some visuals while Google Sheets produced graphs, charts, and relative R-values much faster.

In order to make better use of Google Colab for data analysis, a series of packages were used, mainly NumPy, Matplotlib, and Pandas. The Pandas package was especially useful in transferring our csv files as datasets into Google Colab and in providing easy access to manipulate the data. NumPy was utilized to faster perform trigonometric, statistical and algebraic functions while Matplotlib was used for data visualization with its ability to transfer data as plots.

Several correlation tests (Pearson’s R, Spearman’s Rho, Kendall’s Tau) were used in order to enhance our understanding of our data outcomes and the relationships between certain responses. However, Pearson’s R test was preferred because it gave extra information not only about the strength of the relationship between two variables, but also the type of relationship (in this case, linear). Pearson Product Moment Correlation aims to draw a line that best fits the data of the two variables called a regression line. Pearson’s r value measures how far away each data point is from the regression line. When Pearson’s r value is 1 or -1, this signifies a perfectly linear regression line with no data points showing any variation to the line. This would show an association between our two survey questions. When Pearson’s r value is close to 0, this would show great variation between the data points and the linear regression line therefore showing no association between the two survey questions. Xi represents the x-values of a variable or in our case, responses to one survey question. Yi represents the y-values of a variable or in our case, responses to another survey question.  \bar{X} represents the mean of the x-values and  \bar{Y} represents the mean of the y-values. Figure 6 shows the equation for Pearson’s correlation coefficient given these variables. A R-value higher than 0.50 is accepted to indicate a strong correlation between two variables.


Figure 6: Pearson’s equation, which is used to solve for the correlation coefficient.

The correlation tests mentioned above were limited to only comparing two questions at a time, a limitation which was addressed by the “analysis of variance” test (ANOVA) [4] that finds if there is a statistically significant difference among the means of multiple groups and checks if one or more factors have an impact on these differing means. Questions the ANOVA one-way test can help answer include:

  • Among several sample groups, were differences in performance due to different situations and not by chance?
  • How statistically significant is the difference between several samples?
  • What is the probability that one group performs significantly different from the other two groups? [5]
Fig. 7: Comparison of means.

ANOVA uses the F-value (defined in Figure 7) to investigate the ratio of two variances, measuring dispersion from the mean, as shown in the equation above. Larger values represent greater dispersion while lower values mean low variability. A higher F-value relative to a larger context means that group means are relatively close together. However, ANOVA cannot affirmatively describe which groups are significantly different, it rather gives us a direction on which groups to look at.

We also made use of p-values calculated for each ANOVA test in the Scipy package. P-values not only show variance among groups but also calculate how likely our data would have occurred by random chance rather than because of underlying reasons. Lower p-values, less than 0.05, mean that our results are significant and that groups did respond differently. [6]

Altogether, with correlation, ANOVA, and p-value tests, our group was able to isolate several significant statistics that demonstrated strong relationships between different variables of data.


Multicultural Responses

With an intent to find a correlation between racial groups, who may be exposed to different cultural dishes, and cultural foods, the question “How intensely can you recall the smell of (cultural food) on a scale of 1 to 7?” was asked and analyzed based on race by the ANOVA tests. Among the results, kimchi, sauerkraut, and dumplings had a p-value less than 0.05, indicating that variance was very high among racial groups and that it was statistically significant.

Fig. 8: Averages of responses to “How intensely can you recall the smell of (cultural food) on a scale of 1 to 7?” organized based on race. The highest-varying cultural foods (kimchi, sauerkraut, dumplings) among racial groups based on the ANOVA test.

In the figure above, kimchi was the most prominent in Asian/Pacific Islander races while sauerkraut was in white races. However, as shown by the data from dumplings, comparing races to how vividly they can recall the smell of cultural foods is not always straightforward and not the best indicator of culture’s relationship with smell memory. Therefore, comparing cultural groups to each cultural food through the ANOVA test may show more relevant and accurate correlations.

Fig. 9: Averages of responses to “How intensely can you recall the smell of (cultural food) on a scale of 1 to 7?” organized based on culture. The highest-varying cultural foods (kimchi, sauerkraut, dumplings) among cultural groups based on the ANOVA test.

Again, when comparing cultural groups with cultural foods, Asians were better able to recall the smell of kimchi, which further affirms our prediction that kimchi would be better recalled by aAians possibly due to higher exposure to it. Interestingly, we can now make the more nuanced observation that the smell of sauerkraut is better recalled by people who identify with the Middle East, where sauerkraut is more commonly consumed. Unfortunately, dumplings again fell short in having a strong correlation for one set cultural group as we had predicted.

Therefore, there are sporadic correlations between cultural groups, how much they are exposed to a certain food based on their culture, and their cultural foods: some cultural foods are more strongly tied to a culture than others, leaving no set conclusion.

Multisensory responses

In our multisensory survey, we focused on how the amount of exposure of a certain food and how the color and image quality of a food picture can impact how vividly a respondent can recall the smell of that food.

Impact of Exposure

In Fig. 10, Pearson’s r-value was 0.603, showing high correlation between exposure of oranges to smell recall from a regular-colored image of an orange. This trend of higher-exposure-higher-smell-recall was apparent across multiple items in the multisensory survey, implying that there is a relationship between the two factors.

Image Quality

As shown in the figure above and as demonstrated by the high Pearson’s r-value, the pixelated and regular images of peppermint show to have a high correlation, indicating that responses to the two related questions were similar. This trend was similar across other pixelated and regular objects with little variation. Therefore, image quality, as demonstrated by pixelated images, was not a significant factor in smell recall evoked by images.

Color: No color/black and white images

Figure 12: Averages of responses to “How strongly does this picture remind you of the smell of (objects)?” organized based on foods. This data shows that among different foods, colored images had better smell recall and black and white images had the greatest impact on smell recall.

With a side-by-side comparison in Figure V, we can now observe the differences of how vividly participants were able to recall a certain smell based on the type of image they were presented with. Overall, it can be observed that regular-colored images, in red, had the highest averages in smell recall, shortly followed by pixelated images. Interestingly, alternative colors results, in grey titled “alternative” always ranked the lowest. Black and White was also lower than the regular-colored images and the pixelated images. This trend being consistent no matter the object strongly implies that photos with a change in color, are likely to evoke lower levels of smell recall, ultimately showing that color does have an impact on smell recall.


Due to limited time and resources, we were unable to get a large and diverse sample size to effectively get the most accurate results. For example, most of the surveys were deployed to predominantly U.S.-based groups so we were unable to get respondents who fully identified with a different culture without the secondary influence of mainstream American culture. We were also unable to explore more different cultural sub-categories such as East Asian, Central Asian, Eastern European, Southern European, etc due to the limited data set. Therefore, we had to generalize the categories to 7 different broader cultural categories, limiting the insights that we could draw from our research on how cultural association influences smell recall. Furthermore, the simplified and general format of Google Forms in both surveys may have influenced slight response biases as it is easier for participants to potentially figure out the results we were trying to extract through the visuals and question types – even if the questions were randomized.


From our work, we have concluded that in order to create an ideal visual stimulus for smell recall, image quality and coloration must be prioritized. Using suboptimal image quality or altered color, such as black-and-white images or unconventional coloration, will negatively impact a subject’s ability to recall the target odor. We conclude that food-related odor memories are triggered optimally when the visual stimulus depicts a food item with natural coloration (for example, a purple steak would be suboptimal for stimulating odor memories). We also noted a positive correlation between the subjects’ reported sociocultural group and an increased ability to recall the odors of ingredients specific to that group, as compared with the broader sample’s scores. For one example, the Middle Eastern and South Asian cultural groups performed much higher on their ability to recall the smell of cardamom, which is more widely used in many Middle Eastern and South Asian cultures. These findings are in line with our earlier hypotheses.

Among the tested factors, changes in a visual stimulus’s coloration had the greatest impact on our subjects’ ability to recall odors, followed closely by image quality. We believe that these findings may be applicable to future treatments developed for the smell-impaired that aim to trigger olfactory memories even when the physical sense of smell has been damaged.

Implications/Future Directions

There are several steps we could take to further improve our survey. One main priority is making it accessible in more languages, which would allow us to avoid bias. Releasing non-English surveys will allow us to reach study participants who might be more immersed in their respective cultures, rather than implicitly requiring knowledge of English to participate in our survey. Requiring a knowledge of English could limit our results, because most English speakers have some degree of exposure to a mainstream culture or belong predominantly to English-speaking sociocultural groups, which may introduce bias into the groups that we are able to examine. We would try and implement an international reach by adding a Google Translate extension available through Firebase to have our surveys available in various languages on the website. We would also like to perform all future deployments of our surveys on the completed website, which will allow for more efficient survey deployment and accessibility on mobile devices. Furthermore, having a wider reach can allow access to more secluded cultural groups and give us a deeper understanding of how culture plays an impact on olfactory senses.


  1. Osterbauer, R. A., Matthews, P. M., Jenkinson, M., Beckmann, C. F., Hansen, P. C., & Calvert, G. A. (2005). Color of scents: chromatic stimuli modulate odor responses in the human brain. Journal of neurophysiology, 93(6), 3434–3441. h ttps://
  2. Ponto J. (2015). Understanding and Evaluating Survey Research. Journal of the advanced practitioner in oncology, 6(2), 168–171.
  3. Jones, T. L., Baxter, M. A., & Khanduja, V. (2013). A quick guide to survey research. Annals of the Royal College of Surgeons of England, 95(1), 5–7.
  4. Editor, M. B. (2016, May 18). Understanding Analysis of Variance (ANOVA) and the F-test. Retrieved August 12, 2021,from Minitab Blog website:
  5. Singh, G. (2018, January 15). Analysis Of Variance (ANOVA) | Introduction, Types & Techniques. Retrieved August 12, 2021, from Analytics Vidhya website:
  6. Mcleod, S. (2019, May 20). P-Value and Statistical Significance | Simply Psychology. Retrieved August 12, 2021, from Study Guides for Psychology Students – Simply Psychology website:
  7. Stojiljković, M. (2021, March 15). NumPy, SciPy, and Pandas: Correlation With Python. Real Python.

Information Sciences

Blog, Journal for High Schoolers, Journal for High Schoolers 2021


Kevin Bachelor , Hannah Yousuf, Alexander Kong, Isabella Coenen, Meher Brar


Information science involves the study of processes for storing and retrieving information, especially scientific or technical information. Our project’s goal is to use a multimedia educational website to teach different groups of people about these ideas. Our focus will be on applications of information science within social media. This website will include different ways of teaching including information science, such as a cartoon on internet safety aimed at younger kids, some short papers exploring the ethics of common practices of social media giants, a parent guide to internet safety with a focus on social media and privacy, and video tutorials to help older people use different social media platforms, with an emphasis on safety and practical usage, as well as some other video tutorials in which we explain how data gets traded behind the scenes in social media. The website can be accessed at:

The published website can be accessed at:


Social Media Ethics

A staggering 50,000 child predators are online at any given time [1]. This terrifying statistic reveals that every parent’s worst fears for their children online have a very real chance of manifesting in the form of unwanted sexual advances, grooming, online harassment, and countless other grim scenarios. Since most children in today’s modern world have access to the potentially volatile internet, we felt called to educate the group which we saw as most at risk for dangerous schemes and unethical practices- young children. After exploring the most successful methods of educating young children, we decided to make a visual learning aid in the form of a colorful cartoon. However, educating young children on internet safety habits would be essentially useless without also informing parents of their options as their child’s gatekeeper to the online world, so we composed a parent guide to the cartoon, modeled after movie review guides, to include parents in this learning process.

Social Media Data Videos

Social media giants such as Facebook and Snapchat claim to be free. However these websites are raking in exorbitant profits. For instance, just last quarter, Snapchat earned 982.11 million dollars in revenue: their most profitable quarter to date [2]. The main way these companies earn their profit is through personal data collection. Companies take this valuable data and sell it to numerous companies who use it for everything from ads to insurance prices. 72% of Americans today use at least one type of social media [4]. Since social media giants attempt to keep this data selling policy under the door, we wanted to begin sharing this debatably unethical practice with the public. We decided to do this by developing tutorial and general info videos to attempt to educate the general public about social media, so they will be better equipped to make informed decisions about their personal data.

Instagram Politics

In the aftermath of a volatile election and deadly pandemic, both of which were exacerbated by the spread of misinformation on social media, we recognized the enormous role social media now plays in shaping society as a whole. The potential of bias in social media and news outlets remains a major question, so we decided to put the algorithm of the second most popular social media app of the decade, Instagram, to the test. The algorithm, or code, of Instagram is largely secretive. It is assumed that the Instagram algorithm works by analyzing the type of content you are interacting with, and presents you with similar content that it guesses you will find entertaining. With this assumption in mind, we hypothesized that an individual who has clear political leanings would be shown content mostly related to the party they belong to. We predicted that if they were to have a change of heart and show interest in the other side, Instagram would reflect this switch, since it is in Instagram’s best interest to present content that best matches a person’s interests, increasing interaction, the number of ads consumed, and hence revenue. In order to test this hypothesis we came up with a methodology that composed of saturating an Instagram account’s feed with posts of one political party and then interacting with the other side a certain amount in order to see how the algorithm responds.

Blog Post & Tutorials

Blog posts and videos are important aspects of internet usage. They show the audience that content creators are reliable with providing information to a greater audience. Composing blog posts imply that creators cherish the information that they are sharing with others. Although blog posts provide a concise way of spreading information, videos have a much higher engagement rate. People are more inclined to click on videos and possibly refer them to others. In order to ensure that social media will make the world a better place, people first need to learn how to use certain platforms effectively. Blog posts and tutorial videos are not nuanced methods of teaching in today’s society, however they are extremely effective in ensuring that social media is used properly and safely. A hub for all tutorials for each social media platform will prove to be an accessible and beneficial tool for those who are trying to learn the functions of social media. Blog posts and Videos are instrumental in the effort to create more practical internet usage.

Snapchat is a worldwide social media platform that allows users to message one another using advanced features. Snapchat is recognized for its outstanding camera quality, functuational filters and lenses, and many other high-tech digital features. These classifications of the app were intended to embolden a more communicative “content” stream. Because Snapchat’s strength is within engagement, marketers are able to grasp the puncturing gap between Millennials and Generation Z.

Twitter is a microblogging social media that allows its users to like, comment, and tweet (post) on blogs and/or topics. Twitter grants it’s users the ability to nurture the development of research, grow your own platform ( ie. your business, blog, etc), and to furnish trending and non-trending discussions. Both platforms allow users to become more distinguished within society’s political and social grounds.


Social Media Ethics

Our team began this project by developing a script for the cartoon and sketching a rough storyboard of what we envisioned. The cartoon, entitled Welcome to the Jungle, metaphorically relates the wildness of a jungle to the virtual world of the internet. While young children may not fully grasp this metaphor, we hoped to frame the internet as full of possibilities- both exciting and worrisome ones. The storyboard was designed using the notability app on an iPad. After the initial storyboard underwent review and was finalized, the cartoon was illustrated using color pencils. While this was in production, we began writing the parent guide to Welcome to the Jungle. This guide was designed to parallel the popular format of movie guides for parents, so that it would be in a familiar and easy to navigate layout.

Algorithm Fluidity

Our team developed the following methodology to use in gathering data for all 6 of our studied accounts:

First, create an instagram account on a personal computer. The account should be set for a male, named Billy Joe, with the birthday 1/01/1985. Make the account on private mode and ensure location services are turned off. Like 10 posts from 1 of the 10 predetermined political leaders for the political leaning which you are saturating the account for. After that, go to the discover page, refresh the page, and record the percentage of posts (out of the first 50 posts,) that are directly related and in support of the account’s initial political ideology. Record any details about the radicalness of the political posts, as well as the nature of the nonpolitical posts. Next, like all posts out of the first 50 on the discover page related to the account’s initial political saturation. Repeat this process 9 times, carefully documenting everything. After 10 cycles of liking political posts, the account should be ≥40% saturated with either liberal or conservative content. After this, repeat the initial liking cycle, using accounts from the opposite political leaning instead. Like x percentage of posts from the opposite party. For instance, a lib2con.20 account mimics a liberal individual engaging with content that is 20% conservative. So, like 2 posts from the new ideology, and the remaining 8 from the initial leaning. This process is the same for the 40% and 60% accounts. Finally, record the percentage of both political ideologies out of the first 50 posts on the explore page, along with any notable observations about the content being displayed. This process was repeated for the 6 accounts that data was collected from.

Potential Sources of Error: All of this data was gathered on a personal computer located in California. While location services were turned off for the final few accounts, it was on for the initial testing. The instagram algorithm could have possibly been influenced to display more of a certain political ideology by location.

While we developed guidelines for determining whether or not a post could be considered political, there is a chance that our personal political biases influenced the decision to count a post as either liberal or conservative.

Blog Post & Tutorials

Our group used screen mirroring and screen recordings to create our videos. We used a cable that would connect an iphone to mac in order to achieve the full visuals of creating an account. We used a software called “QuickTime Player”and prompted screen recording. Before officially recording, we downloaded the social media platform that we would be working on from the app store, then we began the process of creating a specific account on that platform while recording. Once we finished recording the videos, we then switched to a different application for the intention of editing. These applications are “IMovie” and “Adobe Premiere Pro”. Other social media platforms required some differentiation. For example, instead of downloading the Twitter app, we created the account by going to the website: Once we went to the web version of the platform, we began with recording the process of creating a Twitter account. The videos and blog posts encompass useful and engaging information that teach the population more practical internet usage.

Social Media Data Videos

In order to do this, we began creating a website to store all of the different types of things we wanted to share and present in the end. We planned to do this using HTML and CSS, and a design tool called Sigma, however we eventually found a better site html5up that allowed us to start with a template, and just change the HTML and CSS to make it all fit our project.. We were then able to move on to the videos, but we needed to make sure our information was all correct and no false assumptions had been made. In order to make sure of this, we did a lot of research and organized it into video topics. We then wrote the draft for the first video. After this we recorded, completed the graphics and edited together the video.


Social Media Ethics

Our group plans on publishing the multimedia website which contains both the cartoon and comic book. Once the website is published, we plan on promoting it with resources available through Stanford University so that our targeted audience of young children and their parents have access to these materials. Future work in this project could involve composing several papers discussing the ethics of questionable social media practices such as data mining and facial recognition. We also want to explore options for creating educational resources for older children, who are just as in-need of vetted internet safety advice.

Algorithm Fluidity

Due to time constraints, we were only able to complete 6 accounts, 3 liberal and 3 conservative, from 20-60%. Because of the small sample size it is difficult to come up with a definitive conclusion. But from the trends that are observable within the data we have, it seems as though the algorithm does not respond drastically to a change in the amount of interaction with the other side. It seems to push a maximum of around 6-9 posts (or 12-18% of the explore page) related to the opposite ideology no matter how much you interact with the other side. This insinuates that the algorithm is fairly rigid and it is difficult to switch what you are seeing even if your beliefs change.

Given more time we would complete all the accounts we had planned and even create multiple of each type of account so that we could have more reliable data. Additionally, we would create more firm guidelines on what constitutes a liberal or conservativ post. There were definitely inconsistencies in each of our evaluations and that may have heavily affected our results.

Social Media Data Videos

We plan on turning the rest of our research into more videos. The video we have at the moment is explanatory and helpful, but it would be nice to have more, as that doesn’t really cover the ethical or more technical material that we originally planned to cover. On top of this, we want to increase the quality of future videos, possibly with more visuals, and more rehearsal.

Blog Post & Tutorials

We intend on spreading and sharing the blog posts and video tutorials on our website, where we can strive to increase the number of elders on social sites by providing information on how to use these platforms. Other than teaching people how to use social media in an effective manner, these posts teach new social media users how to maintain safety and practicality while being active on social media. Before starting this project, our team had realized that although there were an ample amount of websites that teach how to use a specific social media platform, there was not a website that included tutorials for all platforms in one place. Future work will involve creating more blog posts and tutorials on other social media programs, such as-Tik-Tok- and Facebook.


Overall, the goal of our website is to educate people on all different aspects of social media, from how to use it safely, to the possible ethical drawbacks of social media. Our future plans would be to share the website and give it exposure so that we can share our work with as many people as possible. We could also potentially add new media and continue to work on the format and layout of the website itself. We hope that our work can open people’s eyes to aspects of social media they may never have thought about before, and show people the crossover between the information sciences and social media.

Works Cited

  1. GuardChild. (2013, June 11). Internet Statistics.
  2. Published by Statista Research Department, & 26, J. (2021, July 26). Snap revenue per Quarter 2021. Statista.
  3. Pew Research Center. (2021, April 26). Demographics of social media users and adoption in the United States. Pew Research Center: Internet, Science & Tech. 3
  4. Youens, A. (2020, April 21). The Complete Data Privacy Timeline. AE.
  5. MacMillan, D. (2019, June 24). How to stop companies from selling your data. Washington Post.
  6. Steve, S. (2014, March 10). The Data Brokers: Selling your personal information. CBS News.
  7. Auxier, B., Anderson, M., Perrin, A., & Turner, E. (2020, July 28). Parenting Children in the Age of Screens. Pew Research Center: Internet, Science & Tech.

Creating A Smell Print Through Artificial Scents

Blog, Journal for High Schoolers, Journal for High Schoolers 2021


Jude Kamal, April Mares, Serena Pulopot, Jasmine Vargas, Victoria Whipple, Caeley Woo, Devon Baur


Smell is the oldest of humanity’s five senses, and it’s used for almost everything – identification, safety, emotions, relationships, and memories. Despite this, it’s one of society’s most undervalued senses [20]. As a result of the COVID-19 pandemic, people are increasingly beginning to recognize the importance of our olfactory system. To help continue highlighting the necessity of smell, we created an installation in the Architecture and Design Museum in Los Angeles. This installation attempts to craft a unique smell experience connected to their memory of this event, a phenomenon known as a smell print, by exposing the museum-goers to a new manufactured smell free of cultural biases. In addition, the installation encourages visitors to think of smells and space in a way they hadn’t before.


Smelling is an incredibly quick process: in just two synapses, smell travels to the highest region of the brain [25]. The regions of the brain that are involved with olfaction are connected to the memory and emotion portions of the brain. As a result of this, smell and memory often become intertwined, with each person having their own unique smell print — the specific memory that they have attached to a smell [21]. Often, these memories go back many years, sometimes all the way back to early childhood; in this aspect, smell related memories diverge from typical memory patterns, as most people tend to suffer from a period of pre-adolescent amnesia where most memories before adolescence tend to be inaccessible.

However, most people are unaware of this link between smell and memory because most people tend to undervalue smell as a whole. In a 2019 survey of adults, smell was the sense that people would be most willing to lose [20]. These results weren’t limited to just adults — in a 2011 survey, 53% of young people (ages 16-22) would rather lose their sense of smell than their technological devices [20]. Part of the reason for this lack of appreciation for smell is that although we process smell almost instantly, smell often doesn’t pass through the conscious part of our brain. Certain smells that we’ve experienced hundreds of times before — like our bedroom or car — our brain has deemed as safe and we no longer consciously process these smells in a phenomenon known as “nose-blindness.” Other times, we become so bombarded by different smells that we can no longer distinguish any of the smells. Since we often aren’t consciously aware of what we’re smelling, people mistakenly conflate that with smell being unimportant.

But, for people whose sense of smell is altered, the value of smell can’t be overstated. Parosmia is when smells are distorted, like coffee smelling like rotting garbage. Parosmia can lead to a loss of identity, as people no longer feel like they are living their own life [9]. Parosmia can also impair daily life, causing people to avoid eating and anything that triggers unpleasant smells. Similarly, anosmia, the inability to smell, can have extremely negative impacts. Sadly, anosmia and depression are linked because both anosmia and depression impact some of the same brain levels, and in studies done on rats, impaired olfactory bulbs actually resulted in chemical changes in serotonin and dopamine [36]; as a result, anosmia both mimics the symptoms of depression and dovetails into it. Despite these incredibly real issues, many parosmics and anosmics report feeling misunderstood by their friends and family who don’t understand the severity of their condition; others even report doctors being unaware of what anosmia and parosmia was [28].

Now, with COVID-19, these once niche terms of “anosmia” and “parosmia” are becoming more commonplace as select COVID-19 cases result in both anosmia and parosmia. Collaborating with Los Angeles’ Architecture and Design Museum, we hope our Smell. Print. exhibit keeps the conversation about smell going and exposes more people to the tremendous importance of smell.

Methods & Materials

To efficiently create the exhibit our group divided into two teams — research and art.

Research Team: Smell Stimulus Survey

The research team’s initial objective was to analyze research articles and focus on those that were survey based. Our mentor instructed us to find inspiration for research questions we could use for our own study. We were to look for research we found most interesting and take notice of anywhere we saw an information gap so that we could add to that body of knowledge. Inspired by the question we were given — “How can we explore the implications of different scents?” — we were able to build our own research.

Through Zoom, we established constant communication with other members of the group. We utilized Google Documents to record our initial survey responses from our first trial set of questions. Our original questions were as follows: does this smell take you back to a memory, is it one memory or multiple, how strong is this memory, what is the date of the memory, and from what point of view did you see the memory. The questions we were given had to be answered based on the item’s odor, and the items initially included sunscreen, printer paper, an old book, chapstick, laundry detergent, and a blown out match.

Using Google Documents, we recorded our responses into a table and answered each question. From our responses, we noticed that some questions were unclear and some items were difficult to find; therefore, using Zoom we communicated with our mentor to give her the feedback we had about our experience answering the initial questions. From there, the research team was able to modify the questions, rewriting the questions to be answered specifically instead of the unstructured way they were originally posed. The questions later changed to be as follows: does this smell take you back to a memory, is it one memory or multiple, how vivid is the memory (on a scale of slightly to very vivid), what is the date of the memory, how does this memory make you feel (in a scale of 1[very bad]-10[very good]), how long did it take you to remember, and what is the significance of the memory (scale of slightly-very).

Once the questions had been modified, we made another trial run of the questions, but we explored two different modes: survey and interview. Using Google Forms, we were able to add the new questions, along with the new items, including sunscreen, printer paper, an old book, chapstick, laundry detergent, and dirt, to smell and then answer the new questions. For the interview approach, two members of the group were asked the same questions as on the Google Forms survey but used to record their responses verbally. We did this to understand which method would give us better results. The survey was more convenient due to time constraints and our goal for the number of participants we wanted to answer the questions. Based on the two different methods, our group members gave us feedback on the questions and the items they were asked to smell. We decided to eliminate printer paper and chapstick and replace them with fresh herbs, spice and add 2 more items, citrus, and crayon. With the new items, we also modified the questions to be clearer and more understandable. The new questions became as follows: please describe the memory in a short paragraph, how long did it take you to remember, is it one specific memory or many memories that are difficult to place, what is the date of the memory, and what brand/item did you use. These questions were then made into a survey using Google Forms, and we sent it out to as many people as possible.

After 48 hours of collecting data, we obtained over 40 responses. The responses were carefully analyzed to find the common words that participants used to describe the memories they associated with the items’ odors. From these common words, we decided to create word maps to display the most frequently used words participants used to describe the memory they had associated with each item they smelled. We also focused on the date and the time it took each participant to remember and added them to the word maps. From the eight word maps that were created, sunscreen, fresh herbs, old books and laundry detergent were displayed physically in the exhibit with the rest being available in the digital exhibit.

The scent machine, a key attraction of the exhibit meant to aid in museum goers’ experience of scent, runs on Python code. In order for the device to function, it must be given raw serial commands. Natlie Cygan, a Stanford student responsible for coding our machine, and Richard Hopper, a Research Software Engineer from SCHI Labs, provided insight on the function of the device: the machine switches off every sixty seconds and must be controlled using an iMac.

Design Team

Qr Code and Space Proposal

We decided to create an interactive component for the exhibit. To do so, we displayed QR codes around the exhibit space, following figure 1’s structure. Figure 2 shows how we visualized the exhibit space.

FIGURE 1 ( A visitor would use their mobile phone to scan a QR code)
FIGURE 2 (Exhibit Space Visualization)

Smemory Theater Video Approach

As part of the installation, we decided to create a narrative-style video to illustrate the experience of becoming anosmic, particularly the gaps in memory that form as a result of anosmia. One of the main components of the video was a miniature kitchen (shown in figure 3) that was built dollhouse style: the walls of the kitchen were made out of cardboard that we painted white; the floor and shelves were made out of popsicle sticks we cut in half; cereal boxes, toothpicks, and beads were used to make the kitchen cabinets; the tile backsplash and marble countertops were printed out; the stove top was made out of cardstock and silver foil; the fridge, dishwasher, and sink were made out of silver foil and toothpicks; the chairs and kitchen table were made of chopsticks, toothpicks, cardboard, cardstock, and nail polish; the gallery wall was made out of our photos and pictures of our paintings that we printed out; small items around the kitchen were made of clay, cardstock, popsicle sticks, chapstick caps, and scrabble tiles. Similarly, the playground slides used during the memory sequence were built in a miniature style out of chopsticks, toothpicks, and cardstock.

FIGURE 3 (The kitchen used in the Smemory video)

The other main component of the video included memories that were shot on an iPhone 8 using members of our group and our families. AfterEffects was used to stylize the videos and to digitally change the color of select objects during the video. To create the final video, iMovie was used to splice together the clips. Sniffing sound effects were used, and a recording of the hr-Sinfonieorchester conducted by Lionel Brunguier playing the Allegro con Grazia from Tschaikovsky’s 6th Symphony was used for the background music.

Animations Proposal And Approach

To create our frame-by-frame animated videos, we learned and explored the Adobe Creative Cloud Programs, including Adobe After Effects, Adobe Premiere Pro, Adobe Illustrate, Adobe Animate, and Adobe Media Encoder. We also used iMovie to splice some of our clips together. For file compression we used an application called HandBrake. We used Adobe Creative Cloud Programs over an open source program, Blender, because the Adobe system offers program adaptive files that makes the creation process more efficient. Figure 4 includes an example of our storyboards for the animations.

FIGURE 4 (“The Beach”; a final storyboard for our “Sunscreen” scent.)


Research Team

Forms response chart. Question title: How good do you think your sense of smell is?. Number of responses: 44 responses.
GRAPH 1 (How good do you think your sense of smell is? This graph shows the first question the Google survey asks. The x-axis is the scale from 0-5, 0 representing very weak and 5 representing very strong. The y-axis is the number of participants. The bars show the frequency of people who selected that value.)

Final QR Code Designs and Exhibit Space

Using Adobe Illustrator, we designed QR code frames (Figure 9). Within these frames sat a QR code linked to a webpage on the main exhibit’s website (Figure 10). The QR code was created with an online QR Code generator. We ended up with these designs to make it obvious that a visitor would have to scan a QR Code.

FIGURE 9 (Icons outlined in pink represent our eight scents. Icons outlined in blue represent the scents physically displayed at the exhibit. Icons outlined in green are not scent related: a. Represents Laundry Detergent b. Represents Old Books C. Represents Sunscreen D. Represents Fresh Herbs E. Represents Spices F. Represents Dirt G. Represents Lemons H. Represents Crayons I. Link to Main Website J. Link to Smemory Theater Video)
FIGURE 10 (Example of how a QR code would be placed within a QR code frame)

Our final physical exhibit space is in Los Angeles, California in cooperation with Los Angeles’ Architecture and Design Museum. The exhibit runs from August 6, 2021 to August 20, 2021 on select days. Figure 11 displays the final organization of our work.

Final Smemory Theater Video

In the end, the Smemory Theater video explored the associated memories someone might have with the smell of certain objects in a space that’s very familiar to them: their kitchen. The video moved through the kitchen, focusing in on different objects — coffee, lemons, dirt in a flower pot, a cookbook, and paprika — as the unseen narrator smelled them. These smells then each triggered their own memory, and the video cut away to “Smell-Memory Land,” which was signified by a slide that transported the viewer to the “Smemory Theater.” Here, different memories played out: stirring coffee, making lemonade, walking in a forest, reading an old book, and sprinkling paprika on a deviled egg.

However, the video soon took a sad turn. The narrator lost their sense of smell, and with it, all the smell-related memories. To signify this, the objects turned gray, and when the video cut to Smell-Memory Land, the landscape was gray as well because the memory was no longer there; now, there was something preventing the memory from playing out — a missing slide, a broken ladder, a toppled slide, or no Smemory Theater at all. With all the smell-related memories now all gone, the entire kitchen faded to gray as it was now nothing more than a kitchen.

Final Smell Print Animations

We created eight frame-by-frame animated videos for each smell: Sunscreen, Fresh Herbs, Laundry Detergent, Old Books, Crayons, Spices, Lemons, and Dirt. Figure 16 displays shots of the animations and explanations of the audio.



SUNSCREEN: “A Day at the Beach”

AUDIO: Ambient Beach Sounds

Mother: “Time to put sunscreen on!”

Child: “No, no, it’s so cold!”

FRESH HERBS: “Helping in the Kitchen”

AUDIO: Pasta Boiling

Friend 1: “What’s the next ingredient?”

Friend 2: “I think basil would be a good addition, go get some from the plant please.”

Friend 1: “Okay!”



Voice 1: “Hey, you made it!”

Voice 2: “Thanks for coming!”

Voice 3: “Don’t cry, it’ll be okay.”

Voice 4: “Right on time!”

Voice 5: “Look how old you’ve gotten.”

OLD BOOKS: “Learning to Play”

AUDIO: Simple Jingle Bells tune that transitions to a more advanced version of Jingle Bells

CRAYONS: “Elementary”

AUDIO: Ambient Classroom Music

Child: “Teacher! Look at my drawing!”

Teacher: “Wow, is that a dinosaur? You did a fantastic job!”

SPICES: “Spicing it up”

AUDIO: CHEF humming

Ambient pan noises and sizzling

LEMONS: “Lemons make Lemonade”

AUDIO: Ambient outside noises

Car driving by and honking

DIRT: “Rebellion in the Mud”

Child 1: “Aw, there’s no mud!”

Child 2: “Here, we can make some!”

Mother: “What are you guys doing?”

FIGURE 16 (Smell Print Animations (listed in no particular order).)



As we created our exhibit, we recognized there is a clear connection between smell and memory. Most of our Smell Stimuli Survey participants show this connection, as the recall time for “Immediately” for our scents ranged from 52.6% to 81.1%. Incoming feedback on our exhibit further suggests that we can successfully use technology to create art that intrigues an audience and encourages them to appreciate their sense of smell. We understand, however, that experiences and smells we worked with may not be universally recognized and contain American cultural bias.

Future Directions

Future work including ties of olfaction to memory would help to add onto and clarify the research. Relatively, there is not a lot of data on smell prints and memory, so additional research on memories and smell would be very helpful in expanding the field. Additionally, more research about how anosmia and parosmia work would be very helpful as it would support the research of the tie of memory to smell and how the absence or alteration of smell affects memory recall. More research and studies will hopefully occur in the future due to the mass experience of parosmia and anosmia as a result of COVID-19. This pandemic, while an incredible tragedy, has brought awareness to the importance of smell as a sense and will hopefully inspire more research in the field of olfaction.


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