Lab Substrate Concentration & Enzymes
Introduction:
Enzymes are biological catalysts. They are generally large proteins made up of several hundred amino acids.
Enzymes catalyze thousands of chemical reactions that occur in living cells. Enzymes are highly specific so each one
speeds up only one particular chemical reaction. Many kinds of enzymes are found in each cell but because they are
used over and over there may be only a small amount of each enzyme present.
In this lab activity, you will study the enzyme catalase and its substrate hydrogen peroxide. Catalase accelerates the
breakdown of hydrogen peroxide (H2O2) into water (H2O) and oxygen (O2). The chemical equation for this reaction is:
2 H2O2 ----Catalase-------> 2H2O + O2
Catalase is found in both plant and animal tissues. It is especially abundant in plant storage organs such as potatoes
and the fleshy parts of fruits. Catalase is extremely important in cells because it prevents the accumulation of hydrogen
peroxide. Hydrogen peroxide is a strong oxidizing agent, which tends to disrupt the delicate balance of cell chemistry. If
too much hydrogen peroxide accumulates, it will kill the cell.
Several factors affect the action of enzymes: salt concentration, pH, temperature, enzyme poisons, radiation, the
concentration of enzymes, and the concentration of the substrate.
This lab deals only with how an enzyme is affected by different concentrations of substrate - specifically, what effect will
different concentrations of a substrate (H2O2) have on an enzyme (catalase in Pig Liver).
OBJECTIVES
In this experiment, you will
 Use an Oxygen Gas Sensor to measure the production of oxygen gas as hydrogen peroxide is destroyed by the
enzyme catalase or peroxidase on substrate concentrations.
 Measure and compare the initial rates of reaction for this catalases enzyme when different concentrations of
Substrate are used.
Safety Notes:
1. Hydrogen peroxide can damage your clothes. Rinse any spills with water immediately.
2. Keep hydrogen peroxide out of your eyes. Wear safety glasses!
3. Report any accidents of spills to your instructor.
Title:
The effect of hydrogen peroxide concentration (%) on the rate of oxygen production (% /sec)
of enzyme catalase in pig livers, which were soaked in different hydrogen peroxide
concentrations for 180 seconds
Hypothesis:
As the hydrogen peroxide concentration increases, the rate of change in oxygen production by
enzyme catalase in pig liver will increase.
This happens because the increase in solute concentration will create more chances for the
enzyme catalase to collide with the enzyme substrates, hydrogen peroxide. This should eventually
lead to the increase in decomposition performance in pig liver, and therefore, the rate of change in
oxygen produce, since oxygen is a product of decomposition.
Null Hypothesis:
The hydrogen peroxide concentration does not have any effect on the rate of change in oxygen
production by pig liver through decomposition.
Table 1: Experimental Variable Table:
Independent Variable
Dependent Variable
Control Variables
180 seconds of time for
reaction
10ml of Hydrogen
peroxide amount
1cm3 of pig liver size
Room Temperature
Temperature of H2O2
Type of liver
Control group
Hydrogen peroxide concentration
Rate of change in oxygen production by
catalase enzyme in pig liver per cent
Units
Second (±1 sec)
Units
Range
%,±0.1% 0.0,0.1,0.3,0.5,3.0
%/sec
n/a
ml(±0.2ml)
Cm3 (±0.0001 cm3)
°C
°C
n/a
Enzyme catalase of pig liver in 0.0% hydrogen peroxide concentration
group
Materials: (per group)
25 ml graduated cylinder
30ml beaker
hydrogen peroxide ( 0%, 0.1%, 0.5%, 1%, 3%)
forceps
Ruler
Cube of Liver (1cmX1cmX 1cm)
Scalpel
Cutting Board
Vernier O2 Gas Sensor
400 mL beaker
10 mL graduated cylinder
three 18  150 mm test tubes
250 mL Nalgene bottle
Procedure:
1. Obtain and wear goggles.
2. Connect the O2 Gas Sensor to LabQuest and choose New from the File menu. If you have an older sensor that
does not auto-ID, manually set up the sensor.
3. On the Meter screen, tap Rate. Change the data-collection rate to 0.2 samples/second and the data-collection
length to 180 seconds.
4. On cutting board cut a cube of liver (1cmX1cmX 1cm)
5. Obtain H2O2 concentrations (0%, 0.1%, 0.5%, 1%, 3%)
6. Pour 10ml of the 0% hydrogen peroxide into ,acmacmac
7. algene bottle. Place the O2 Gas Sensor into the bottle as shown in Figure 1. Gently push the sensor down into the
bottle until it stops. The sensor is designed to seal the bottle with minimal force.
8. Drop Liver cube into
9. Start data collection (O2 Production in parts per million (ppm) for 180 seconds.
10. When data collection is complete, a graph of O2 gas vs. time will be displayed. Remove the O2 Gas Sensor from
the Nalgene bottle. Rinse the bottle with water and dry with a paper towel.
11. Perform a linear regression to calculate the rate of reaction.
a. Choose Curve Fit from the Analyze menu.
b. Select Linear for the Fit Equation. The linear-regression statistics for these two data columns are displayed for
the equation in the form y = mx + b
c. Enter the absolute value of the slope, m, as the reaction rate in Table 1.
d. Select OK.
9. Repeat steps 1-8, four more times using the other hydrogen peroxide solutions (0.1%, 0.5%, 1%, 3%) and a new
Liver cube for each trial
10. Store the data from the first run by tapping the File Cabinet icon
11. Tap Run four, and select All Runs. All three runs will now be displayed on the same graph axes.
12. Obtain class data for each concentration of hydrogen peroxide used by all groups in the class and record this data
in Table II.
Table 2 (Group raw data): The concentration of hydrogen peroxide (%), the pig liver size(cm3) for
each trial, and the rate of change(%/sec) in oxygen production by catalase enzyme in pig liver after
180 sec of soaking in different levels of hydrogen peroxide
Concentration of hydrogen peroxide
%
±0.1%
Pig liver size
Cm3
±0.0001 cm3
Rate of change in oxygen production
by enzyme catalase pig liver for each
concentration of hydrogen peroxide
group
%/sec
±0.00000001 %/sec
0.0
0.1
0.5
1.0
3.0
1.0000
1.0000
0.9600
1.1700
1.3000
-0.00105700
0.00026410
0.00010870
0.00761600
0.03206000
*Brief observation of the table above: the rate of change in oxygen production generally increased as
the hydrogen peroxide concentration increased, however the 0.5% concentration group did not follow
the trend, this might be caused by the minor reduction in its pig liver size than the expected 1cm3
value by 0.04 cm3. In addition, the negative value of rate of change in oxygen production might be
caused by the uncertainty of oxygen probe, the change is so small that the oxygen probe wasn’t
sensitive enough to detect, thus producing a negative value.
Table 3(class data): The concentration of hydrogen peroxide (%), the rate of change (%/sec) in
oxygen production by catalase enzyme in pig liver after 180 sec of soaking in each different level of
hydrogen peroxide, the average and standard deviation of rate of change in oxygen production of
each hydrogen peroxide concentration
Rate of change in oxygen production by enzyme catalase in pig liver
(%/sec) for each concentration of hydrogen peroxide group
±0.00000001 %/sec
Concentration of hydrogen
peroxide
0.0
0.1
0.5
1.0
3.0
%
±0.1%
1
-0.00280000
-0.00007850
0.00042750
0.00358100
0.01295000
2
-0.00007505
-0.00013900
0.00107700
0.00097980
0.00982400
3
-0.00085870
0.00044500
0.00161300
0.00435300
0.02660000
4
-0.00153400
-0.00001733
0.00153300
0.00330700
0.02685000
Number
5
-0.00100700
-0.00074150
0.00054700
0.00285700
0.02550000
of trials
6
-0.00076470
-0.00009300
0.00401900
0.00519000
0.01095000
7
-0.00121800
-0.00046720
0.00035690
0.00751800
0.00932600
8
-0.00001120
0.00077800
0.00207800
0.00816200
0.02220000
9
-0.00105700
0.00026410
0.00010870
0.00761600
0.03206000
Average of each data set of
rate of change in oxygen
-0.00103618
-0.00002276
0.00130667
0.00484042
0.01958444
production by catalase
enzyme in pig liver
Standard deviation of each
data set of rate of change in
0.00082710
0.00044902
0.00121638
0.00247593
0.00879312
oxygen production by
catalase enzyme in pig liver
Average of each data set of
rate of change in oxygen
production by catalase
-0.00081571
-0.00002276
0.001306678
0.00484042
0.01958444
enzyme in pig liver without
outliers
Standard deviation of each
data set of rate of change in
oxygen production by
0.00053088
0.00044902
0.00071319
0.00247593
0.00879312
catalase enzyme in pig liver
Without outliers
Data highlighted in yellow is the group data
Data highlighted in pink are identified as outliers (see the process of outlier identification below in table 4)
*Observation: Looking at the average of rate of change in oxygen production, the increasing trend of
rate of change in oxygen production as the concentration of hydrogen peroxide increased. In group
0.0%, the average rate of change of oxygen production is -0.00103618% per sec; however, in group
0.1%, the oxygen production increased to -0.00002275% per sec, it amount continued to increase as
the concentration of hydrogen peroxide increased, for example, in group 0.5%, the amount increased
to a positive number, 0.00130667% per sec. To briefly conclude from the graph above, the results
support the hypothesis.
Table 4: Outlier identification
±2 Standard Deviation table for rate of change in oxygen production by enzyme catalase in pig liver
Concentration of
hydrogen peroxide
%
±0.1%
0.0
0.1
0.5
1.0
3.0
Average of each
rate of oxygen
production by
catalase enzyme
in pig liver
Standard deviation
of each rate of
oxygen production by
catalase enzyme in
pig liver
%/sec
±0.00000001 %/sec
%/sec
±0.00000001 %/sec
-0.00103618
-0.00002276
0.00130667
0.00484042
0.01958444
+2 standard
deviation
-2 standard deviation
0.00061802
0.00087529
0.00373943
0.00979228
0.03717068
0.00082710
0.00044902
0.00121638
0.00247593
0.00879312
-0.00269038
-0.00092079
-0.00112609
-0.00011144
0.00199820
*The ±2 standard deviation values listed in the outlier identification table above are values that are
used to identify outliers in the class data set. This is because 68% of all data fall within ±1 standard
deviation of the mean, and 95% for ±2 standard deviations from the mean. So all valid data points
should fall within ±2 standard deviations of the mean. If any value in the data set is greater or less
than ±2 of the mean, then it should be considered as an outlier. With the help of the ±2 standard
deviations, 2 data points in the data set in table 3 are identified as outliers because they are not in the
range of +2 standard deviation to -2 standard deviation. The 2 data points are highlighted in pink in
Table 3.
Table 5: R-values Table
R-values of each data set of rate of change in oxygen production by catalase enzyme in pig liver
Concentration of
hydrogen
peroxide
%
±0.1%
Average of each
rate of change in
oxygen production
by catalase
enzyme in pig liver
%/sec
±0.00000001%/sec
Average of each
rate of change in
oxygen production
by catalase enzyme
in pig liver
without outliers
%/sec
±0.00000001%/sec
0.0
-0.00103618
-0.00081571
0.1
-0.00002275
-0.00002275
0.5
0.00130667
0.00130669
1.0
0.00484042
0.00484042
3.0
0.01958444
0.01958444
r-value
0.99709332
0.99994242
*The r-value (correlation coefficient) represents how well average of each rate of change in oxygen
production by catalase enzyme in pig liver correlates with Concentration of hydrogen peroxide
respectively. With the number close to +1, both the R-values of average of change in oxygen
production indicate that there are significantly high positive correlation between dependent variable
(the average of change in oxygen production) and the independent variable (change in oxygen
production). However, the r-value of the average without outliers is slightly higher than the one with
outliers, this shows that without the outliers, the independent and dependent variables are more
correlated. It shows success in removing outliers.
Table 6: T-test Table
P-values of selected data hydrogen peroxide concentration groups of rate of change in oxygen
production by enzyme catalase in pig liver (%/sec)
Rate of change in oxygen production by enzyme catalase in pig liver
%/sec; ±0.00000001 %/sec
Concentration
of hydrogen
peroxide
%
0.0
0.1
0.1
0.5
0.5
1
1
3
±0.1%
P-values
-0.00280000 -0.00007850
-0.00007505 -0.00013900
-0.00085870 0.00044500
-0.00153400 -0.00001733
-0.00100700 -0.00074150
-0.00076470 -0.00009300
-0.00121800 -0.00046720
-0.00001120 0.00077800
-0.00105700 0.00010870
0.00523176
-0.00007850 0.00042750
-0.00013900 0.00107700
0.00044500 0.00161300
-0.00001733 0.00153300
-0.00074150 0.00054700
-0.00009300 0.00401900
-0.00046720 0.00035690
0.00077800 0.00207800
0.00010870 0.00010870
0.00723540
0.00042750 0.00358100
0.00107700 0.00097980
0.00161300 0.00435300
0.00153300 0.00330700
0.00054700 0.00285700
0.00401900 0.00519000
0.00035690 0.00751800
0.00207800 0.00816200
0.00010870 0.00761600
0.00143640
0.00358100 0.01295000
0.00097980 0.00982400
0.00435300 0.02660000
0.00330700 0.02685000
0.00285700 0.02550000
0.00519000 0.01095000
0.00751800 0.00932600
0.00816200 0.02220000
0.00761600 0.03206000
0.00018023
*This T-test for the data sets of rate of oxygen production of different hydrogen peroxide groups is to
see if the data sets are significantly different. The data groups chosen to compare to see if each
group’s data set has significant difference with the group next to it, for example, group 0.1% is next to
0%, group 0.5 is next to 0.1, 1% group is next to 0.5, and group 3% is next to group 1%. The t-test
shows that all the data sets are significantly different. The data sets are significantly different as long
as the p-values are lower than 0.01. The P-values of the 4 groups above show that they have
significant differences.
Observation:
Uncertainty justification:
Concentration of hydrogen peroxide: ±0.1%; informed by school technician
Solution amount: ±0.2ml; the smallest increment of the test tube
Volume uncertainty: 0.05cm*0.05cm*0.05cm = 0.000125cm » 0.0001cm (0.05 smalles increment of the
ruler used divded by 2)
Rate of change in oxygen production: ±0.00000001 %/sec, data recorded by software Loggerpro
3
3
Qualitative results:
Below are images of experimental groups of 0.1% and 3% hydrogen peroxide when pig liver cubes
were soaked and the enzymes are reacting on the solute. As seen in the image, in group 0.1%, there was a
small amount of bubbles floating on the surface of the liver. In group 3.0%, bubbles were all over the place.
However, as recorded, but not shown in the image, in the control group of 0.0% hydrogen peroxide, there was
no bubbles present.
Image of group 0.1% hydrogen perioxide
Image of group 3.0% hydrogen perioxide
Sample Calculations for Processed Data


Pig liver size: Width*length*height
o Ex: pig liver size for group 0%
o Volume= 1.0*1.0*1.0=1cm3(uncertainty: ±0.00000001)
Rate of change in oxygen production (slope m)
o The values are derived from the slope of a time vs. oxygen gas graph on Loggerpro
o Ex: the rate of change in oxygen production of group 1.0% hydrogen peroxide is shown in the
following graph, for example: M(Slope): 0.007616(%/sec)

o
Averaging data from repeat trials of each group is calculated in MS Excel
o ex: the standard deviation for 0% group data set
o

o
Standard deviation of each group data is calculated in MS Excel
o ex: the standard deviation for 0% group data set
o


o
±2 standard deviation
o +2 SD=average+2*standard deviation
 ex: +2 SD for group 0%
 +2 SD= -0.00103618+2*0.00082710=0.00061802
o -2 SD= average-2*standard deviation
 ex: -2 SD for group 0%
 -2 SD= -0.00103618-2*0.00082710=-0.00269038
R-values (MS Excel used)
o Ex: R-values for average of rate of change in oxygen production (with outliers)
o

o
P-values (MS Excel used)
o Ex: P-values of group 0% and 0.1%
o
o
Process Data:
Raw Data (DCP Aspect 1)
Graph 1 (scatterplot for all data points):
* The data points circled in red are considered outliers
Observations:
The data points circled in red are considered outliers. The justification of outliers is presented
in table 4 above. The graph includes outliers that will be omitted in the following two graphs.
The linear best-fit line of the average data shows that as concentration of hydrogen peroxide
increased, the rate of change in oxygen production increased as well. The strong positive correlation
between the variables also proves the high value of r-value (0.99) shown in Table 5). In addition, the
0.96 of R2 value (coefficient of determination) shows that the average data points correlate strongly
with the trend. This happened because the hydrogen peroxide solute concentration increased,
creating more opportunities for enzyme catalase (‘s active site) to collide with enzyme substrate,
which is hydrogen peroxide. More collision leads to higher rate of reactions. As a product of the
reaction, oxygen increased in amount as the solute concentration increased.
Processed Data (DCP Aspect 2/3)
Graph 2 and 3 (Bar graph of average data with and without outliers)
* Graphs 2 and 3, with and without outliers have no big differences in terms of the average values
and error bars (the calculation of the error bar is based on the values of standard deviation of each
data set). The bars with increasingly large value show that the average rate of change in oxygen
production by catalase in pig liver increased as the concentration of hydrogen peroxide increased.
However, the error bars of groups 0%, 0.1% overlap, group 0.1% and group 0.5% overlap, group
0.5% and group 1% overlap. The overlapping of error bars shows that the average values of 0% and
1%, 0.1% and 0.5%, and 0.1% and 1% groups are not significantly different. However, group 0% and
1% , 0% and 0.5%, 0.1% and 1% ‘s non overlapping error bars show that these groups have
significant differences. On the other hand, although the error bar of 3% group has a relatively big
error bar compared to other groups’, it doesn't overlap with any other data groups, its average and
data set are reliable and are significant different from the other groups’.
DISCUSSION, EVALUATION & CONCLUSION
Discussing and Reviewing (DCE Aspect 1)
The aim of this lab was to investigate how different percent concentrations of hydrogen peroxide
solute affect the rate of oxygen production (%/sec) of enzyme catalase in pig livers. By looking at the
different rates of oxygen production in different percent concentrations, the experimenters were able
to determine the effect of solute concentration on the rate of enzyme activities. The data collected
from the experiment indicated that as the percent concentration of hydrogen peroxide increased, the
rate of oxygen production by enzyme catalase in pig livers increased. This showed that more solute
concentration causes more collisions between the active site of the enzyme and the substrate (H 2O2),
thus catalyzing the decomposition reaction of H2O2 into water and oxygen, leading to the increased
rate of oxygen production by catalase.
The hypothesis stated in the beginning of the lab is supported by the results of the lab. Clearly
shown in Table 3, the average rate of oxygen production by catalase of the control group 0% H 2O2 is
-0.0008%/sec without outliers. The groups with increasingly higher solute concentration follow the
rising trend. Group 0.1% moved up to -0.00002 %/sec. Group 0.5% had 0.0007 %/sec, 1% group had
0.0025%/sec, and group 3% had 0.0098%/sec. The best-fit line drawn based on the average values
of oxygen production by each percent solute concentration in graph 1, shows that as the solute
concentration increased, the average rate of oxygen production by catalase also increased as a
result. In addition, the R2 value of 0.96 in the graph shows that the average data points have strong
correlation with the best-fit line. Graph 2 and graph also show similar results in the representation of
bar graphs. The error bars in draw based on the data set’s standard deviation indicate that group data
like 0% and 0.1%, 0.1% and 0.5%,0.5% and 1% have no significant differences. However, the Pvalues from t-tests and R-values show otherwise. The t-tests compare the data sets, mentioned
above as having significant differences. The resulted p-values of these data sets with values all lower
than 0.01 show that they are significantly different. In addition, the R-value (calculated in table 5) of
0.99, representing the correlation between the average of rate of oxygen production and percent
concentration of H2O2 , shows the significantly high positive correlation between the independent and
dependent variables.
The results of the experiment correspond to the current understanding of the effect of solute
concentration on enzyme activities. According to Damon, if there is a constant amount of enzymes,
as it was assumed in the 1cm3 pig liver cube, the rate of reaction will increase as the concentration of
a substrate increases. This is because if there are more reactant molecules, or H 2O2, there will be
more random collisions between the enzyme substrate and the active site on enzyme. However, the
increases in enzyme activity will stop at some point where all enzymes are working at tis maximum
rate (Damon, 68). In our experiment, catalase was the enzyme; hydrogen peroxide concentration
was the solute concentration. When the solute concentration was as low as 1%, the average rate of
oxygen production change was -0.00002. However, as the solute concentration increased, causing
more chances for collision with substrate H2O2, the rate of reaction increased, thus leading to the
increase in rate of oxygen production, as oxygen was a product of the decomposition reaction of
H2O2, The increase of rate of oxygen production didn’t stop even with group 3% because not all
enzyme catalase were used.
As the H2O2 concentration increased, the rate of change in oxygen production decreased. This not
only corresponds to scientific understanding of the topic, it is also supported by an experiment
conducted by Elisha Joy Bryson, a student in University of Pennsylvania. The lab states, “A higher
concentration of substrate will yield an increase in the rate of the reaction”. In the lab, the
independent variables were in the unit of ml instead of percentage. The H2O2 concentration with the
most H2O2 ml substrates (6ml) had the highest reaction rate of 0.663 kPa/s, the H2O2 concentration
with the least H2O2 ml substrates (2ml) had the lowest reaction rate of 0.208 kPa/s (Bryson).It
basically shows that high solute concentration leads to high rate of oxygen production, thus leading
high reaction rate. Elisha’s conclusion is the same as the conclusion drawn from this lab results.
Evaluating Procedures and Suggesting Improvements: (DCE Aspect 2)
A) ACCURACY, PRECISION and RELIABILITY
The overall data of the lab can be seen as reliable. The small p-values (all lower than 0.01)derived
from the t-test tables show data sets to be significantly different. The R-value of 0.99 indicates strong
positive correlation between the independent and dependent variables, thus, implying the data to be
accurate. In graph 1, the linear trend with a R2 value of 0.96 shows that the data plots average rate of
oxygen production correlates strongly with the trend line, showing the validity of the upward positive
trend. In the same graph, the data points of group 0%, 0.1%, 0.5% and 1% are closely stacked
together, showing the small range of the data sets, proving its precision. However, group 3%’s data
points are scattered. This can also be seen in bar graph 3. The group 3% has a huge range of data
set. The reason why its error bar doesn't overlap with others might be because of the large interval
between 3% and 1% compared to the small interval between 1% and 0.1% or 0.1% and 0%.
Moreover, the error bars of 0% and 1%, 0.1% and 0.%,0.5% and 1% overlap, showing no significant
difference. However, this might be cause by the close intervals between the independent groups. The
oxygen probe was not sensitive enough to detect the difference in rate of oxygen between the
groups. Something to be noted is the presence of outliers, according to the ±2 standard deviation
rule(shown under table 4), one data point from the control 0% group and one data point from group
0,5% do not fall within the range of “mean±2*standard deviation” , and are considered outliers. In
general, the data are accurate and reliable because of the high value of R-value, small p-values from
t-tests. However, the precision is in question due to the indication of overlapping error bars in graph 2
and 3, that show large standard deviation and range.
The data collected are not precise. This can be seen in the error bar in graph 2, before the
removal of outliers. The error bars are drawn based on the standard deviation. The graph clearly
shows that the error bars of group 1% and 0.1, 0.1% and 0.5%, and 0.5% and 1% overlap, implying
that they have no significant difference due to its big standard deviation value. This indicates that the
standard deviation of each group is huge and the data are imprecise, leading to a large range of data
points for each group. Though group3%’s error bar doesn't overlap with any other’s, it might be
caused by the huge interval of its independent variable with the previous 1% variable (consider that
1% and 0,5% have 0.5% of interval, whereas 3% and 1% have 2% of interval). This can explain the
why the 3% error bar has a large error but doesn't over lap with others. The imprecision and big
standard deviation can be caused by human errors during the lab and the uncertainty of the rate of
oxygen production. The oxygen probed used to detect oxygen production rate is highly accurate that
it has an uncertainty of ±0.00000001. Normally, it should be highly precise, However, considering the
small differences between the average rates of oxygen production of the experimental groups, it is
rather reasonable to have overlapping error bars.
Although the operation on Loggerpro can be confusing at times, there should be no major
systematic error in the data collection. Overall, the methodology of the lab is explicit and clear.
According to observation, there was no major error that shifted the direction of the data points.
Many random errors, including human errors, were presented during data collection. During the
experiment, the temperature of H2O2 and the room, in which the lab was conducted in, fluctuated.
Since the raw data is combined between two classes, the operation of trials for the same
experimental group didn't happen at he same time, presumably, not with the same temperature,
which is one of the major factors that could have had an impact on the rate of enzyme activity.
Because high temperature leads to faster kinetic energy, causing to more collision between H 2O2 and
catalase, thus, increase the rate of oxygen production. The temperature might have changed the
results, and contributed to the large standard deviation of the data collection. A human error would be
the amount of H2O2 for each experimental group. Operators might have distributed slightly more or
less amount of H2O2 to the Nalgene bottle due to the ±0.2 ml uncertainty on the graduated cylinder.
The increase of solute amount might have increased the rate of oxygen production, thus, leading to
bigger standard deviation of data. In addition, since the procedure did not explicitly say to wash the
H2O2 10ml graduated cylinder after one experimental group is used, many did not wash the
graduated cylinder that contained H2O2 of the previous concentration. Therefore, the following
experimental group might have contained a bit H2O2 concentrations from the previous group. This
together could have contributed to the presence of outliers and the great standard deviation of the
data. Moreover, some operators might have forgotten the time and prolonged the time for chemical
reactions. The dependent variable of this lab is percent over time, the time should not matter much.
However, there might be changes in the rate of oxygen production after certain period of time, which
is not concerned by this lab and might have contributed to the large standard deviation. Lastly, the
different sizes of the pig liver caused by human errors. It was almost impossible for operators to cut
out pieces of pig livers with the exact dimension of 1cm*1cm*1cm with a scalpel and ruler. As
recorded in the raw data Table 2 above, the volume of pig liver for each experimental group differed.
Larger pig liver contained more catalases, which could in turn speed up the rate of oxygen
production, creating large variation in data, because more enzyme catalases can collide more often
with hydrogen peroxide. Generally, random errors may have caused slight changes in control
variables, making the independent variable, hydrogen peroxide concentration, not the only variable
that affected the dependent variable. All these errors, together, caused the large value of standard
deviation, thus, imprecision to the lab.
B) EXPERIMENTAL WEAKNESSES AND LIMITATIONS
In general, the lab was well controlled. The procedure was well-written and gave detailed
instructions. However, it was hard to keep all the control variables constant. As discussed above,
many random errors, which might have caused variation in data, occurred and contributed to the
large standard deviation values. Outliers were also caused by these random errors, which resulted in
uncontrolled time for reactions, different sizes of pig liver, fluctuation in room temperature and the
temperature of hydrogen peroxide, and the amount of hydrogen peroxide in each experimental group.
These inconsistent control variables might have been factors affecting the difference in the rates of
oxygen production, causing the large variation in data shown in the form of overlapping error bars.
These random errors are also cause for outliers in data.
In addition, the range of the independent variable from 0% to 3%, including the jump from 1%
to 3%, is unreasonable. A better arrangement of dependent variable could be 0%,1%,2%,3%, with
even intervals. The uneven intervals caused confusion in data interpretation. Moreover, current
scientific understanding shows that the activity of enzyme will plateau at some point when all
enzymes are working. However, this effect is not shown in the lab results. To test the effects of
hydrogen peroxide concentration on rate of oxygen production in more depth, one can include a wider
range of solute concentration until the rate of oxygen production plateaus. Furthermore, there were
only 9 trials in the experiment, 9 trials for each group wasn’t good enough for a more accurate data
processing. Considering the small size sample, if one trial were considered outliers, 1/9 of the data
would become invalid, leaving only 8/9 data. Therefore, more trials can create higher precision.
Lastly, the equipment used. The oxygen probe used to detect rate of oxygen production had really
small uncertainty, however, it is too sensitive that it could have detected relatively drastic changes
caused by minor changes in the environment. This is evident in the negative value of rate of oxygen
production in group 0%, which should not have haven’t since 0% of H 2O2.shouldn't cause
decomposition of H2O2. In addition, cylinders used to measure the 10ml of hydrogen peroxide had an
uncertainty of ±0.2ml, which was not high considering the total amount of hydrogen peroxide needed
for each trial was only 10ml.
Out of all the limitations stated above, the random and human errors had the greatest impact
on data. This is because the random errors contributed a lot to the huge standard deviation, causing
the data to be imprecise. Additionally, it might have caused the outliers. Since the writer of this lab
was not present in the group who operated the results with outliers (since the raw combined data
from two classes) , though there was no observation of human errors, major human errors might have
taken place in the other class. These elements combined eventually caused the presence of outliers
and large variation in data.
The results of this experiment should have high reproducibility. Other scientists who are willing
to conduct this experiment will probably draw similar conclusion from their results. In graph 1,
although the data points of 1% and 3% are scattered, the trend of the average rate of oxygen
production still shows strong positive correlation. In the same graph, it can be observed that as the
concentration of hydrogen peroxide increased, the rate of oxygen production increased as well. The
conclusion correlates with recent scientific understanding of solute concentration’s effect on enzyme
activities that high solute concentration increases enzyme activities. Also, looking at the high Rvalues and p-values of the lab results, the results produced by this lab should be accurate. Hence, if
conducted properly, other scientists should probably observe general trend, though not necessarily
the exact same results. This is because the results of this lab are imprecise due to random errors.
Depending on making less or more random errors, other scientists might get either more or less
precise result.
C) EXPERIMENTAL IMPROVEMENTS
As the results of this lab were not precise, many can be done to improve it. The following
points are efforts to keep the control variable of the lab consistent throughout the lab for each
experimental group. Firstly, make the temperature of H2O2 and the temperature of the room
consistent throughout the experiment. Instead of splitting classes to do the experiment, the teacher
can combine the two classes and operate the experiment at the same time to ensure consistency in
temperature. Another method can be used is to put all the solutes in a container with the 20 degree
Celsius and use a thermometer to keep track of the temperature. As for the room temperature, to
prevent wind movement, experimenters should close all the windows and doors and turn on air
conditioners and keep the temperature constant at 25 degree Celsius. Secondly, to ensure the
amount of H2O2is to find a graduated cylinder with smaller uncertainty. For example, a graduated
cylinder with only ±0.01ml uncertainty, instead of ±0.2ml. Thirdly, to reduce differences in time
allowed for chemical reactions, the best way is for all the operators to be highly focused during the lab
and check the stopwatch constantly. Fourthly, the size of the pig liver. In order to get accurate
1cm*1cm*1cm cubes with its mold, the operators can use a machine called French Fry Cutter that
can cut cubes into 1cm*1cm*1cm cubes. With this machine, the dimensions of the pig liver cubes will
be very accurate. Lastly, the procedure should clear instruct that the operators should wash the
graduated cylinder that contains different H2O2 percent concentrations before it is used for the next
experimental group. The procedure can also instruct the operators to prepare one washed graduated
cylinder for each experimental group to prevent different percent concentrations of H 2O2 to mix with
one another. As the control variables, including the temperatures of the room and H2O2, the pig liver
size, amount of H2O2, the time for reaction, accuracy of percent concentration of H2O2, are kept
constant through the steps discussed above, the standard deviation of data should decrease, thus,
increasing the imprecision and validity of data.
Methodologically, there can also be a lot of improvements. The independent variables can
have a larger range with even intervals. For example, the independent variables would be 0%, 1%,
2%, 3%, 4%, 5%, 6%, 7%,8%,9%,and 10%. Additionally, there can be 30 trials for each experimental
group instead of 9 trials in this experiment. This can also make the results more precise.
Concluding: (DCE Aspect 3)
The experiment showed that as the hydrogen peroxide concentration increased, the rate of
oxygen production by enzyme catalase in pig liver increased as well. In Table 3, the average rate of
oxygen production by catalase of the control group 0% H2O2 is -0.0008%/sec. The groups with
increasingly higher solute concentration followed the rising trend. Group 0.1% moved up to -0.00002
%/sec. Group 0.5% had 0.0007 %/sec, 1% group had 0.0025%/sec, and group 3% had the largest
rate of 0.0098%/sec. The data supported the positive correlation between the dependent and
independent variables.
Reference Cited:
Bryson, Eilisha Joy. "Enzyme Catalyst Lab - Formal Report."Http://www.sas.upenn.edu/. N.p., 11
Feb. 2014. Web. 08 Aug. 2007.
Experiment Title:
Student name:
SL
Submitted to Turn it In for plagiarism check:
DATA COLLECTION & PROCESSING
Teacher:
Ms. Tyler
Y
N
Total
HL
Date:
ASPECT 1. Recording raw data
Records appropriate quantitative & associated qualitative raw data, including units & uncertainties where relevant.
Student
checklist
Teacher
evaluation
Complete (2 marks)
a. Includes descriptive table titles that summarize the investigation including the independent & dependent variables (& if
appropriate, essential control variables).
b. Includes tables with correct column & row headings.
c. The correct units are listed with column & row headings (units should not be included with the raw data).
d. Appropriate uncertainties have been estimated for all raw data & stated with the units (in column & row headings).
e. All uncertainties are stated as absolute values (not percent) to one significant digit.
f. Data is recorded with the same precision as the uncertainty (i.e. with the same number of decimal places as the uncertainty). *
g. The raw data is appropriate for the research question investigated & logical. Data must be collected independently.**
h. If significant outliers are present, they are identified in raw data & omitted from Aspect 2. data processing.
i. Justifies choice of uncertainty (considers precision/uncertainty of equipment plus any random errors that may significantly affect the variation
Partial
(1 mark)
Records
appropriate
quantitative
data &
associated
qualitative raw
data but with
some mistakes
or omissions.
(Some of the
points are
omitted.)
Incomplete
(0 marks)
Does not record
any appropriate
quantitative raw
data or raw data
is incomprehensible.
(None of the
points are
completed.)
in data e.g. parallax error when measuring instruments cannot be clearly aligned with the measurement being made, human reflex when timing, etc).
j. Relevant qualitative data that may enhance and support the interpretation of the results has been included.
ASPECT 2. Processing raw data
Processes the quantitative raw data correctly &, where relevant, includes uncertainties & error bars.
Student
checklist
Teacher
evaluation
Complete (2 marks)
a. If necessary, processes raw data into a form suitable for graphical representation (e.g. adding, subtracting, averages, % change, rate
etc.).
b. Processed data is recorded in a correctly formatted table (see aspect 1 above).
c. Calculates variation within trials by establishing standard deviation.
d. Determines if a significant difference exists between levels of treatment by performing a t-test
e. Includes clearly labeled sample calculations for each method of data processing used. (show one sample for each calculation made).
f. or if data is already in a form suitable for graphical presentation plots a suitable ‘best-fit’ curve or line graph.
DATA COLLECTION & PROCESSING continued on the next page
1
Partial
(1 mark)
Incomplete
(0 marks)
Processes
quantitative raw
data, but with
some mistakes
&/or omissions.
No processing of
quantitative raw
data or major
mistakes are
made in
processing.
(Some of the
points are
omitted.)
(None of the
points are
completed.)
ASPECT 3. Presenting processed data
Presents processed data appropriately & where relevant, includes errors and uncertainties.
Student
checklis
t
Teacher
evaluatio
n
Complete (2 marks)
a. Includes descriptive graph titles that make reference to the independent & dependent variables (& if appropriate,
essential control variables).
b. For graphs - Both axes are labeled with the correct units.
c. For graphs - Scale is even
d. For graphs - is sufficiently large (use at least half an A4 page) .
e. For graphs - Points are plotted accurately.
f. For bar graphs uncertainty in graphical analysis is accounted for by applying error bars which are correctly drawn.
Explains under the graph if error bars are not significant for one or both variables.
g. For scatter-plot uncertainty in graphical analysis is accounted for by drawing an appropriate line or curve of best fit
h. A brief description of trends/relationships observed in graphs is included.
Comments:
~ signifies that point was attempted but needs improvement to be considered complete
* Point h is a requirement for Aspect 3 if a Conclusion & Evaluation is not submitted for assessment.
19
Partial
(1 mark)
Incomplete
(0 marks)
Presents
processed data
appropriately,
but with some
mistakes &/or
omissions.
Presents
processed data
inappropriately
or incomprehensibly.
(Some of the
points are
omitted.)
optional*
(None of the
points are
completed.)
Experiment Title:
Student name:
Submitted to Turn it In for plagiarism check:
CONCLUSION & EVALUATION
Teacher:
Ms. Tyler
Total
SL
Y
HL
N
Date:
ASPECT 1. Concluding
States a thorough conclusion, with justification, based on a reasonable interpretation of the data.
Student
checklist
Teacher
evaluation
Complete (2 marks)
a. Research question is answered by correctly stating the relationship between the variables
b. Conclusion is justified by describing the trends or patterns revealed by the data and by referring to numerical data from
processed results.
c. States whether the hypothesis is supported or refuted by the data collected in this investigation.
d. If known physical quantities were calculated compares the experimental value(s) with the referenced accepted value/s. May
choose to calculate the percent discrepancy (error) between experimental and accepted value/s
e. If literature is used to determine accepted values then it must be referenced
Partial
(1 mark)
Incomplete
(0 marks)
States a
conclusion
based on
reasonable
interpretation of
the data.
States no
conclusion or
the conclusion
based on an
unreasonable
interpretation
of the data.
(Some of the
points are
omitted.)
(None of the
points are
completed.).
ASPECT 2. Evaluating procedure for controlling & measuring variables
Evaluates experimental weaknesses, limitations & reproducibility.
Student
checklist
Teacher
evaluation
Complete (2 marks)
Partial
(1 mark)
Incomplete
(0 marks)
Identifies some
weaknesses &
limitations, but
the evaluation
is weak or
missing
Identifies
irrelevant
weaknesses &
limitations.
Accuracy & Precision
f. Accuracy of data collected is discussed. (Note: only required if known quantities were calculated, sources of systematic error were evident or
the quality of procedure/equipment used was questionable).
g. Precision of data collected is discussed [spread/variation (or uncertainty) in raw data indicated by standard deviation values].
h. Correctly identifies major systematic errors, if present [i.e. if all data points have shifted in same direction ]. The direction of any
systematic errors should be specified.
i. Correctly identifies sources of random error & discusses whether they had a significant impact on the data collected.
Experimental weaknesses and limitations
j. Fairness & validity is discussed (i.e. were there any unforeseen control variables / were all control variables kept constant, thus ensuring a fair
test that produced valid results, where changes measured in the dependent variable were a true reflection of manipulating the independent variable) .*
k. Other significant weaknesses (revealed during or after the experiment) are identified (e.g. management of time, large spread in data, etc.).
l. Possible causes for any outliers (if identified) are discussed.*
m. Significant limitations (apparent before the experiment) in the ‘quality’ of procedure for manipulating & measuring variables is
discussed (data range & the number of data points manipulated/measured within the range, number of trials &/or experimental techniques).
n. Significant limitations in the equipment used is discussed [precision (& if questionable, accuracy) of equipment]. [Note: precision of
equipment essentially refers to the number of decimal places (or power of 10) that could be measured with the instrument.]
o. Has some appreciation of the significance of weaknesses & limitations. (List in order of significance.)
Reproducibility & reliability of the experiment
p. Reproducibility of experiment is discussed i.e. whether another scientist could repeat the experiment & expect to get the same
results (Think: whether the method was detailed, explicit & easy to follow; whether the results were precise; and whether the results were accurate).
q. The reliability of the relationship/trend observed is discussed (depends on the accuracy & precision of raw data, & how close plotted
data points are to the line/curve of best fit).
CONCLUSION & EVALUATION continued on the next page
20
(Some of the
points are
omitted.)
(None of the
points are
completed.)
ASPECT 3. Improving the investigation (can be discussed with aspects 1 & 2 or in separate section)
Suggests realistic improvements for all identified errors, weaknesses and limitations.
Student
checklist
Teacher
evaluation
Complete (2 marks)
a. Suggestions on how to remove all systematic errors (if present) are made.
b. Suggests how the effect of all random errors could be removed or minimized. [Note: you can nearly always specify – repeat more
trials (except when there is a very high level of precision in data/very low uncertainty).]
c. Suggest how the control of variables could be improved (to ensure a fairer test of the variables, thus improving the validity of the results).
d. Suggests how other significant weaknesses (e.g. management of time, large spread in data, etc.) could be improved.
e. Suggests how limitations in the procedure &/or equipment used could be improved (to increase accuracy &/or precision of raw data,
and hence, improve the reliability of the trend/relationship observed).
f. Improvement of all weaknesses & limitations identified in Aspect 2 are addressed.
g. Realistic, clearly specified improvements are suggested. Do not simply state generally that more precise equipment should be used.
Comments:
~ signifies that point was attempted but needs improvement to be considered complete
21
Partial
(1 mark)
Suggests only
superficial
improvements.
(Some of the
points are
omitted.)
Incomplete
(0 marks)
Suggests
unrealistic
improvements.
(None of the
points are
completed.)