Notes: the numbers at the end of each of the controlled and dependent variables refer to their illustration counter part on the next page and the list of apparatus below. (Diagrams drew by pencil and ruler on a separate page)
Apparatus:
- 250ml Standard Issue Glass Beaker
- 100ml Measuring Cylinder
- Bunsen Burner
- Electronic Scale
- Clamp Stand
- Logger Pro Thermometer/ Temperature recording device
- 6 x 100 ml of Water
- 330 ml Pepsi can
- 3 x 100 ml of Milk
- 3 x 30 g of salt
- Computer compatible with Logger Pro
- Logger Pro ‘software’ application
- Tri pod
- Heating mat
- Running water
- Liquid Soap
Having conceiving the design of my experiment, I was ready to commence collecting raw data. Before any else I measured the mass of each liquid and found their density through calculation. The respective masses and densities of each liquid would be recorded in the following tables.
When recording the mass of each substance/liquid from the electronic, the mass of the 250 ml glass beaker had to be subtracted (250 grams). The mass of the liquid would then be divided by the liquid amount (100 cm^3) through the use of the formula builder in Microsoft Excel. Thus the following mass and densities for each substance/liquid were found.
Note: Microsoft Excel through its calculations automatically rounded each density to the nearest second decimal place.
From the table above you can see that pepsi, waster and milk all have similar densities indicating that maybe their heating rates will be similar to another if all other variables remained controlled (ceteris paribus). From this I chose water as my control for all the other substances/liquids as it is the most common liquid to be found and has a density of 1.00 generally (0.97 cc in this case).
Since Logger Pro will record the change in temperature for every second over a course 2 minutes (120 seconds), I chose an interval of 15 seconds for my raw data and the points to be plotted on my graphs. Thus the following table was constructed for each substance and liquid.
Three trial runs will be conducted for each substance and the average temperature for 0, 15, 30, 45, 60, 75, 90, 105 and a 120 second will be calculated through Microsoft Excel by adding each trial and then dividing the total by three. Following this I set up my apparatus as shown by my diagram. To ensure that I had sufficient gas, I placed the gas conduct on full throttle and turned the Bunsen burner flame to a heating flame (‘blue’ flame, this would be the only that I could do to control the heat output upon the liquid tested). I first started with a 100 ml of water measured using the 100ml-measuring cylinder. Simultaneously, I started the recording of data on Logger Pro and placed the Bunsen burner under the tripod to start my investigation. The recording apparatus had a marginal error of ±0.3°C per second (this is mainly due to the Bunsen burner inaccurate heat output).
- I kept the flame under the beaker for 2 minutes so as to get a better idea of the difference in heating rate between the different fluids. After the 2 minutes, I stopped the recording and took the temperature value at 120 seconds as my final temperature. I then repeated this 2 more times before changing the liquid in the beaker and recording that 3 times as well. In between, each trial run and especially when switching each substance/liquid I cleaned both the glass beaker and measuring cylinder with running water and liquid soap to ensure the accuracy of my results. The exact same procedure as for the water trial runs were conducted for pepsi, salt solution, milk and sunflower oil (Raw Data Tables Below).
From all the average temperatures for each substance/liquid a more practical table was created. Thus I would be using the Average Temperature for each substance/liquid as the data points for my first graph (Change in Temperature over Time).
For practical reasons, plotting a graph using the following points above was easier to analyze and find preliminary evidence for my hypothesis. Each table and every column has a marginal error of ±0.6 °C for both my human error and apparatus error: ±0.3 °C accounts for my human errors when starting and stopping the Logger Pro application and the Bunsen burner and another ±0.03 °C accounts for the apparatus error of the Bunsen burner (inefficient heating source) and the Logger Pro application. The temperature-recording device of Logger Pro is particularly heat sensitive thus the set up with clamp stand. If too much heat is conveyed to the apparatus, wild fluctuations in my data would appear causing me to record inaccurate devices. Hence, the total marginal error will be carried forward throughout my whole investigation for each and every calculation and demonstrated through the use of error bars. These limitations and marginal errors will be further discussed in my limitations sections (Graph on the next page with analysis)
- The curves on the graph representing the different temperatures over time for different substances/liquids are individually continuous sets of data. However, all of curves in the group of substances tested are discrete sets of data as they all come from different sources.
The average starting temperatures of each liquid were different, and half of the analysis of the curvature is with the human eye. These two factors combined lead to error in graph analysis.
- From the graph, the heating rates of water and Pepsi are nearly identical. This is logical because Pepsi is a water-based liquid. The only differences are carbonation and artificial flavoring, which would only cause miniscule density differences. Therefore, we can conclude that any water-based liquids, from juice to soda, would have a heating rate almost identical to that of water. The sunflower oil heated the most quickly of all the tested substances. This coincides with our intuition because oil has a lower density than water, causing it to float on the surface of water. Since the density is lower, when heat is applied to it, oil will heat up more quickly than water-based substances. Finally, milk is denser than water and any water-based substance. Because of milk’s density, its heating rate curve should be the least steep, as correctly indicated by the graph.
- However, since I am looking at the relationship between the density of a liquid and its heating rate, this graph proves to be of little use. It does give an indication or hypothesis of how the density/temperature change will look like. The graph and the analysis above confirms to some extent as the density of a substance increases the heating rate of a liquid decreases. However, further graph and analysis have to be drawn and written to confirm this.
- To find the heating rate of the liquids/substances in my investigation further calculations using Microsoft Excel are needed. From my previous table I subtracted the Average Starting Temperature (room temperature at 0s) from the Average Final Temperature (at 120s) and divided the result by a 120 (time in seconds for one trial run) to get my heating rate for each substance/liquid. From this constructed a table compromising of the substance/liquids density and the heating rate found (table below)
From this a graph can be plotted comparing density to the Average heating rate of the liquids tested in this investigation. This graph will include error bars to demonstrate the marginal error through my experiment (Graph on the next page).
- Firstly, we can see from the graph that the data plotted (density and average temperature change) is a discrete set of data. This due to the fact that each point on the graph comes from a different source within the investigation (i.e.: different liquids will have different densities)
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Of the five substances tested, sunflower oil has the lowest density. This is correct intuitively because as we know, when oil is put into a mixture of water, not only does it not mix with the water, but it does not sink is well. Since water has a density of 1 g/cm3, therefore oil must have a density less than that. According to the data I collected, it does indeed have a density less than 1 g/cm3 (.90 g/cm3, to be precise). According to my general knowledge, both milk and Pepsi have a density of 1 g/cm3, but water in this investigation has a density of only .97 g/cm3. However, water should have a density of 1 gm/cm3, so there is some inherent error in my experiment. Also, I think the densities of Pepsi and milk should be slightly higher than that of water, such as 1.03 g/cm3. The salt solution has the highest density (1.18 g/cm3). This makes sense because it is regular water (with a hypothesized density of 1 g/cm3), with 30g of salt added, thereby significantly increasing the density. These factors and variations have affected my trend line and thus the confirmation of my hypothesis.
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Looking at my polynomial trend line, the point that sticks out the most is milk, whose point is quite lower than the trend line. This makes sense because milk is relatively “thick”, so it would not heat up as quickly as other liquids as compared to its density. The lowest average temperature change compared to density is salt water, which also makes sense because of the 30 g of added salt into the water. This salt prevents the water from heating at a normal rate, and also increases the density. It takes more time for the particles in the salt solution to gain energy and velocity and thus increase the thermal energy. Therefore, it is logical that this is the “lowest” point on the graph.
From the downwards, negatively sloping gradient, we can see that an increase in the density of the substances tested lowers the temperature change after two minutes. However, I am doubtful that the trend line formed by Microsoft Excel that the relationship between density and temperature change is polynomial. Thus we can see that the trend line has a line equation of y= 1.764x2-3.967x+2.408 with a R2 value of 0.875. Even though a polynomial trend line best represents the Raw Data, I would have expected a linear trend line. This might be due to my human error (±0.3) for temperature change and time and the electronic apparatus error (Logger Pro) (±0.3). This will be further on analyzed and examined in my Limitations sections and be taken into account in my conclusions and the planning of further investigations.
- In conclusion, even though my data might suffer from large inaccuracies and miscalculations, my hypothesis has proved to be correct as the relation between a liquid’s density and its heating rate is inversely proportional. A liquid with a greater density will have larger heat capacity to overcome and thus a lower heating rate.
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Throughout my investigation, I was able to notice several error or limitations that made my conclusions and analysis somewhat implausible. These limitations included my human (±0.3°C) and electrical/technical error (±0.3°C) in the recording of my data, the Logger Pro thermometer and recording device, the glass beakers used to contain the liquid, the Bunsen burner, the complicated set up of my experiment and the lack of more trial runs. In addition, the density of the liquids/substances used were somewhat too similar as I only chose common liquids that can be found in a normal household. All of these limitations mentioned below would have to be taken into account for further investigations.
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I have empirically estimated a ±0.3°C human error. My estimate comes from both prior experience and also because of my slow human reaction time starting and stopping the Logger Pro application and turning off the Bunsen burner, which led to inconsistent results. My human reaction time and speed is also due to my perception of when the time finished (120 seconds) as I have bad eyesight I might have went overtime or stopped the application Logger Pro too soon. The factor that mainly affected my human reaction speed was also the independent factor of my experiment as it increased I had to move my hand quicker to stop the timer.
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I have also estimated a ±0.3°C electronic error. The Logger Pro thermometer is extremely heat sensitive and shuts off from excessive heat. This sensitivity leads to fluctuations and inaccuracies in the graph plot when using Logger Pro. Another factor that contributed to the inaccuracies of Logger Pro is that it calculated the temperature using only two decimal places and time was only recorded in seconds. However, Logger Pro does give me the advantage of calculating the temperature per unit time in real time and then graphing the results instantly. For further experimentation, I could use a hand held digital thermometer for verification the devices used in this investigation are particularly heat sensitive.
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I used a Bunsen burner as my heat source. This is a very primitive, rudimentary and inaccurate device. It does not output a consistent heat per unit time. I should have used a calorimeter instead, because it is much more accurate and insulated. However, my time was limited and henceforth the Bunsen burner was my only option. Since my experiment and trial runs took course over several days and I was careless, I had no way of controlling the fact that the Bunsen burner, stand and heating rate would be the same over the course of my investigation. Each piece of equipment even though similar and of industrial make might have default that another might not have. Furthermore, there was no way of controlling the heating output of the Bunsen burner. All these inconsistencies and carelessness lead to an exuberant apparatus/technical error of ±0.3°C for each trial run and liquid tested. Moreover, a Bunsen burner lets some heat escape, leading to further inaccuracies, whereas a calorimeter is isolated and a controlled environment with no possible outside factors influencing my results. Therefore, a calorimeter would my a viable option for further experimentation.
- In the set up of my experimentation, I used a clamp stand to hold the substance vertically over the heat source (Bunsen burner). This is both a complicated and inaccurate setup, leading to a higher degree of error. The glass beaker I used is also not ideal. Glass is very malleable under intense heat and could crack or break possibly leading to injuries. Thus for further investigation the use of a calorimeter (made from plastic or any other insulated material) would be a more viable option for more accurate results, analysis and conclusion.
- Finally, to further reduce error in my analysis, I should have completed 5 trials instead of 3 for each of substance. Also, I should have let each trial run for 3 minutes instead of 2, to more accurately measure the change in slope over time and write a more comprehensive and detailed analysis. These controlled variable would be needed to take into account in further investigation.
- The density of the substances used are too similar, especially those of water and Pepsi. I should have chosen liquids with a larger density range for the experiment to be conclusive. Further research should have been done, on which liquids to employ for the expirement. However, in further experimentation I could look up the densities for each of the substances used in order to ensure that my investigation and analysis is conclusive and accurate.
- Taking these errors into account, I may have been able to improve on them in a number of ways in order to further support my conclusion, or even disprove it.
