CHEM 203 CCC Harry S Truman College Lab 6 Kinetics of a Chemical Reaction Report Chem 203 lab and i need help. all you need for the assignment is attached

CHEM 203 CCC Harry S Truman College Lab 6 Kinetics of a Chemical Reaction Report Chem 203 lab and i need help. all you need for the assignment is attached in the document below. Kinetics Lab Simulation: Factors that Affect Rates of Reaction
Due: Thursday, July 16, 2020
Name: ______________________________
Other members of your work group: ____________________________________________
Home much time did you spend on this assignment? _________________________________
To determine the kinetics (rates) of ozone depletion reactions, chemists perform controlled laboratory studies.
In this simulated lab, we will interpret data obtained from such laboratory experiments to study the ozone
depletion reaction:
O3 (g) + O(g) → 2O2(g)
(1)
The data will allow us to:
(1) determine the reaction rate of the uncatalyzed reaction
(2) determine the effect of the presence of chlorine on the kinetics of the reaction
(3) determine the effect of temperature on the reaction rate
Please add your work to this MS Word file, and upload a completed version onto Brightspace, including
all plots and sample calculations. You are strongly encouraged to use a spreadsheet program (MS Excel),
or other mathematical program (matlab, Mathcad, mathematica, R etc.).
The results from 12 experiments are shown below (this is cut and pasted from the OzoneData.xls file available
in Brightspace).
Table 1. Ozone Depletion Kinetics Data
Experiment
Number
1
2
3
4
5
6
7
8
9
10
11
12
[O3]
[O]
[Cl]
molec/cm3
molec/cm3
molec/cm3
1.530E+15
2.780E+15
1.530E+15
2.250E+15
1.350E+15
1.790E+15
1.350E+15
1.350E+15
3.650E+15
1.530E+15
1.530E+15
1.530E+15
1.471E+11
1.471E+11
1.150E+11
2.100E+11
2.100E+11
2.100E+11
1.500E+11
2.100E+11
1.560E+11
1.471E+11
1.471E+11
1.471E+11
0.000E+00
0.000E+00
0.000E+00
0.000E+00
3.100E+11
3.100E+11
3.100E+11
5.300E+11
5.300E+11
0.000E+00
0.000E+00
0.000E+00
T
K
230
230
230
230
230
230
230
230
230
230
298
270
[O3](t=0.50 ms)
molec/cm3
1.530E+15
2.780E+15
1.530E+15
2.250E+15
1.350E+15
1.790E+15
1.350E+15
1.350E+15
3.650E+15
1.530E+15
1.530E+15
1.530E+15
[O3]
molec/cm3
-1.159E+08
-2.106E+08
-9.069E+07
-2.435E+08
-9.084E+08
-9.463E+08
-6.480E+08
-1.469E+09
-1.238E+09
-1.159E+08
-8.937E+08
-4.369E+08
Columns 2 through 5 describe the experimental conditions. Column 6 is the ozone concentration 0.50 ms into
the reaction (1 ms = 1 millisecond). To the number of significant figures shown above, the concentration of
ozone has not changed. But the last column ([O3]) shows the difference between the 2nd and 6th column, so you
can see that there is actually a change in concentration.
Page 1 of 3
Kinetics Lab Simulation: Factors that Affect Rates of Reaction
From this data, perform the following analyses:
A. (6 pts) Experiments 1 through 4 are done in the absence of chlorine. This information can be used to
determine the rate law of the uncatalyzed reaction. Once you have determined your rate law, add two
columns to the above data: one that predicts the rate of the reaction based on your rate law, and one that
shows the % error between your prediction and the experimentally observed rate. (Throughout this
activity, your rate law should reproduce the experimental rates to better than 5%).
My predicted rate law is (include a numerical value for the rate constant):
Complete this table (i.e., cut and paste from your work in MS Excel) by filling in the last three columns
(including the units)
Table 2. Ozone Depletion Data and Results in the Absence of Chlorine Atom
Experiment
[O3]
[O]
3
molec/cm
[Cl]
3
molec/cm
T
3
K
[O3]
(t=0.50 ms)
molec/cm
3
Number
molec/cm
1
1.530E+15
1.471E+11
0.000E+00
230
1.530E+15
2
2.780E+15
1.471E+11
0.000E+00
230
2.780E+15
3
1.530E+15
1.150E+11
0.000E+00
230
1.530E+15
4
2.250E+15
2.100E+11
0.000E+00
230
2.250E+15
Experimental
Rate
[O3]
molec/cm
Predicted rate
% error
3
1.159E+08
2.106E+08
9.069E+07
2.435E+08
B. (7 pts) Experiments 4 through 9 contain sufficient information to determine the rate law of the catalyzed
reaction. Note that when Cl atoms are present, a catalyzed version of the reaction takes place. This
does not mean the uncatalyzed reaction stops. When Cl is present, both the catalyzed and uncatalyzed
reactions occur simultaneously; that is, the actual rate of the reaction is the sum of the rates of the
catalyzed and uncatalyzed reactions. If you take account of this properly, you will find integer powers
in the rate law (i.e. integer orders of reaction).
My predicted rate law for the overall reaction (i.e., the rate of the uncatalyzed plus the rate of the catalyzed
reaction) is:
Page 2 of 3
Kinetics Lab Simulation: Factors that Affect Rates of Reaction
Complete this table (i.e., cut and paste from your work in MS Excel) by filling in the last three columns
(including the units). The rate law you derive here should work whether or not the Cl is present, so it should
work for experiments 1 through 9.
Experiment
Number
[O3]
[O]
[Cl]
T
[O3]
(t=0.50 ms)
[O3]
molec/cm3
molec/cm3
molec/cm3
K
molec/cm3
molec/cm3
1
1.530E+15
1.471E+11
0.000E+00
230
1.530E+15
-1.159E+08
2
2.780E+15
1.471E+11
0.000E+00
230
2.780E+15
-2.106E+08
3
1.530E+15
1.150E+11
0.000E+00
230
1.530E+15
-9.069E+07
4
2.250E+15
2.100E+11
0.000E+00
230
2.250E+15
-2.435E+08
5
1.350E+15
2.100E+11
3.100E+11
230
1.350E+15
-9.084E+08
6
1.790E+15
2.100E+11
3.100E+11
230
1.790E+15
-9.463E+08
7
1.350E+15
1.500E+11
3.100E+11
230
1.350E+15
-6.480E+08
8
1.350E+15
2.100E+11
5.300E+11
230
1.350E+15
-1.469E+09
9
3.650E+15
1.560E+11
5.300E+11
230
3.650E+15
-1.238E+09
Experimental
Rate
Predicted
Rate
% error
C. (7 pts) Experiments 10 through 12 give you sufficient information to fit the rate constant of the
uncatalyzed reaction to the Arrhenius form:
k = Ae-Ea/RT
Determine the activation energy Ea (in J/mol) and the pre-factor A for the uncatalyzed reaction. Ideally,
you should do this using an Arrhenius plot (see for instance http://www.ncsu.edu/labwrite/res/gt/gt-reghome.html for how to do this in Excel). The same instructions are also in the attached file in Brightspace
for this assignment.
Page 3 of 3
Experiment
Number
[O3]
molec/cm
[O]
3
molec/cm
[Cl]
3
molec/cm
T
3
[O3](t=0.50 ms)
K
molec/cm
3
D[O3]
molec/cm
Rate
3
1
2
3
4
5
6
7
8
9
1.53E+15
2.78E+15
1.53E+15
2.25E+15
1.35E+15
1.79E+15
1.35E+15
1.35E+15
3.65E+15
1.47E+11
1.47E+11
1.15E+11
2.10E+11
2.10E+11
2.10E+11
1.50E+11
2.10E+11
1.56E+11
0.00E+00
0.00E+00
0.00E+00
0.00E+00
3.10E+11
3.10E+11
3.10E+11
5.30E+11
5.30E+11
230
230
230
230
230
230
230
230
230
1.53E+15
2.78E+15
1.53E+15
2.25E+15
1.35E+15
1.79E+15
1.35E+15
1.35E+15
3.65E+15
-1.16E+08
-2.11E+08
-9.07E+07
-2.44E+08
-9.08E+08
-9.46E+08
-6.48E+08
-1.47E+09
-1.24E+09
10
1.53E+15
1.47E+11
0.00E+00
230
1.53E+15
-1.16E+08
11
1.53E+15
1.47E+11
0.00E+00
298
1.53E+15
-8.94E+08
12
1.53E+15
1.47E+11
0.00E+00
270
1.53E+15
-4.37E+08
-2.32E+08
-4.21E+08
-1.81E+08
-4.87E+08
-1.82E+09
-1.89E+09
-1.30E+09
-2.94E+09
-2.48E+09
-2.32E+08
-1.79E+09
-8.74E+08
k
calc rate
% error
Experiment
Number
[O3]
molec/cm
[O]
3
molec/cm
[Cl]
3
molec/cm
T
3
[O3](t=0.50 ms) D[O3]
K
molec/cm
3
molec/cm
Rate
k
3
1
2
3
4
1.53E+15
2.78E+15
1.53E+15
2.25E+15
1.47E+11
1.47E+11
1.15E+11
2.10E+11
0.00E+00
0.00E+00
0.00E+00
0.00E+00
230
230
230
230
1.53E+15
2.78E+15
1.53E+15
2.25E+15
-1.16E+08
-2.11E+08
-9.07E+07
-2.44E+08
-2.32E+08
-4.21E+08
-1.81E+08
-4.87E+08
5
6
7
8
9
1.35E+15
1.79E+15
1.35E+15
1.35E+15
3.65E+15
2.10E+11
2.10E+11
1.50E+11
2.10E+11
1.56E+11
3.10E+11
3.10E+11
3.10E+11
5.30E+11
5.30E+11
230
230
230
230
230
1.35E+15
1.79E+15
1.35E+15
1.35E+15
3.65E+15
-9.08E+08
-9.46E+08
-6.48E+08
-1.47E+09
-1.24E+09
-1.82E+09
-1.89E+09
-1.30E+09
-2.94E+09
-2.48E+09
10
1.53E+15
1.47E+11
0.00E+00
230
1.53E+15
11
1.53E+15
1.47E+11
0.00E+00
298
12
1.53E+15
1.47E+11
0.00E+00
270
-2.32E+08
1.53E+15 -8.94E+08 -1.79E+09
1.53E+15 -4.37E+08 -8.74E+08
10
1.53E+15
1.47E+11
0.00E+00
230
1.53E+15
-1.16E+08
11
1.53E+15
1.47E+11
0.00E+00
298
1.53E+15
-8.94E+08
12
1.53E+15
1.47E+11
0.00E+00
270
1.03E-18
1.03E-18
1.03E-18
1.03E-18
1.03E-18
4.81E-15
4.83E-15
4.81E-15
4.83E-15
4.79E-15
4.81E-15
-1.16E+08
k
-2.32E+08
-1.79E+09
1.53E+15 -4.37E+08 -8.74E+08
1.03E-18
7.94E-18
3.88E-18
calc rate
2.32E+08
4.21E+08
1.81E+08
4.87E+08
1.82E+09
1.89E+09
1.30E+09
2.93E+09
2.48E+09
% error
0.00642
0.00146
-0.08360
-0.06678
2.79E-14
2.91E-14
2.79E-14
2.64E-14
2.99E-14
2.82E-14
1/T
lnk
0.004348
-41.417
0.003356 -39.3744
0.003704 -40.0901
w/o catalyst
R=k[O3][O]
w/ catalyst
R=2.49E-15([O3]^0.1338)([O])([Cl]^0.8942)
or
R=k[O][Cl] %error
1.84E+09 -1.04E+00
1.84E+09 3.09E+00
1.31E+09 -1.17E+00
3.14E+09 -6.39E+00
2.33E+09 6.19E+00
5/13/2020
Graphing With Excel – Linear Regression
Graphing with Excel
Linear Regression in Excel
Table of Contents
1.
2.
3.
4.
Create an initial scatter plot
Creating a linear regression line (trendline)
Using the regression equation to calculate slope and intercept
Using the R-squared coefficient calculation to estimate fit
Introduction
Regression lines can be used as a way of visually depicting the relationship between the
independent (x) and dependent (y) variables in the graph. A straight line depicts a linear trend in
the data (i.e., the equation describing the line is of first order. For example, y = 3x + 4. There are
no squared or cubed variables in this equation). A curved line represents a trend described by a
higher order equation (e.g., y = 2×2 + 5x – 8). It is important that you are able to defend your use
of either a straight or curved regression line. That is, the theory underlying your lab should
indicate whether the relationship of the independent and dependent variables should be linear or
non-linear.
In addition to visually depicting the trend in the data with a regression line, you can also calculate
the equation of the regression line. This equation can either be seen in a dialogue box and/or
shown on your graph. How well this equation describes the data (the ‘fit’), is expressed as a
correlation coefficient, R2 (R-squared). The closer R2 is to 1.00, the better the fit. This too can be
calculated and displayed in the graph.
The data below was first introduced in the basic graphing module and is from a chemistry lab
investigating light absorption by solutions. Beer’s Law states that there is a linear relationship
between concentration of a colored compound in solution and the light absorption of the solution.
This fact can be used to calculate the concentration of unknown solutions, given their absorption
readings. This is done by fitting a linear regression line to the collected data.
Creating an initial scatter plot
Before you can create a regression line, a graph must be produced from the data. Traditionally, this
would be a scatter plot. This module will start with the scatter plot created in the basic graphing
module.
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Graphing With Excel – Linear Regression
Figure 1.
Return to Top
Creating a Linear Regression Line (Trendline)
When the chart window is highlighted, you can add a regression line to the chart by choosing
Chart > Add trendline…
A dialogue box appears (Figure 2). Select the Linear Trend/Regression type:
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Graphing With Excel – Linear Regression
Figure 2.
Choose the Options tab and select Display equation on chart (Figure 3):
Figure 3.
Click OK to close the dialogue. The chart now displays the regression line (Figure 4)
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Graphing With Excel – Linear Regression
Figure 4.
Return to Top
Using the Regression Equation to Calculate Concentrations
The linear equation shown on the chart represents the relationship between Concentration (x) and
Absorbance (y) for the compound in solution. The regression line can be considered an acceptable
estimation of the true relationship between concentration and absorbance. We have been given the
absorbance readings for two solutions of unknown concentration.
Using the linear equation (labeled A in Figure 5), a spreadsheet cell can have an equation
associated with it to do the calculation for us. We have a value for y (Absorbance) and need to
solve for x (Concentration). Below are the algebraic equations working out this calculation:
y = 2071.9x + 0.111
y – 0.0111 = 2071.9x
(y – 0.0111) / 2071.9 = x
Now we have to convert this final equation into an equation in a spreadsheet cell. The equation
associated with the spreadsheet cell will look like what is labeled C in Figure 8. ‘B12’ in the
equation represents y (the absorbance of the unknown). The solution for x (Concentration) is then
displayed in cell ‘C12’.
Highlight a spreadsheet cell to hold ‘x’, the result of the final equation (cell C12, labeled B
in Figure 5).
Click in the equation area (labeled C, figure 5)
Type an equal sign and then a parentheses
Click in the cell representing ‘y’ in your equation (cell B12 in Figure 5) to put this cell label
in your equation
Finish typing your equation
Note: If your equation differs for the one in this example, use your equation
Duplicate your equation for the other unknown.
Highlight the original equation cell (C12 in Figure 5) and the cell below it (C13)
Choose Edit > Fill > Down
Return to Top
Note that if you highlight your new equation in C13, the reference to cell B12 has also incremented
to cell B13.
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Graphing With Excel – Linear Regression
Figure 5.
Return to Top
Using the R-squared coefficient calculation to estimate fit
Double-click on the trendline, choose the Options tab in the Format Trendlines dialogue box,
and check the Display r-squared value on chart box. Your graph should now look like Figure 6.
Note the value of R-squared on the graph. The closer to 1.0, the better the fit of the regression
line. That is, the closer the line passes through all of the points.
Figure 6.
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Graphing With Excel – Linear Regression
Now lets look at another set of data done for this lab (Figure 7). Notice that the equation for the
regression line is different than is was in Figure 6. A different equation would calculate a different
concentration for the two unknowns. Which regression line better represents the ‘true’ relationship
between absorption and concentration? Look at how closely the regression line passes through the
points in Figure 7. Does it seem to ‘fit’ as well as it does in Figure 6? No, and the R-squared value
confirms this. It is 0.873 in Figure 7 compared to 0.995 in Figure 6. Though we would need to take
in to account information such as the number of data points collected to make an accurate
statistical prediction as to how well the regression line represents the true relationship, we can
generally say that Figure 6 represents a better representation of the relationship of absorption and
concentration.
Figure 7.
Return to Top
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Sponsored and funded by National Science Foundation
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