User:Javier Vinals Camallonga/Notebook/Javier Vinals notebook/2013/09/04: Difference between revisions
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In addition, we had to find if there were any outliers for Adenosine and inosine absorbances using the Grubb's test. For adenosine, our G value was 1.67, meaning that if any concentration had a G-value bigger than this value, they would be an outlier. For adenosine we found that there were 4 outliers, for the concentrations of 3*10^-5, 2.5*10^-5, 2*10^-5, and for 1.5*10^-5. For this concentrations, the maximum values were 1.75, 1.72, 1.75, 1.67 respectively. For Inosine on the other hand, no outliers were found, since the maximum and minimum G-values for all the concentrations were under the G-value of Inosine, which was 1.46. | In addition, we had to find if there were any outliers for Adenosine and inosine absorbances using the Grubb's test. For adenosine, our G value was 1.67, meaning that if any concentration had a G-value bigger than this value, they would be an outlier. For adenosine we found that there were 4 outliers, for the concentrations of 3*10^-5, 2.5*10^-5, 2*10^-5, and for 1.5*10^-5. For this concentrations, the maximum values were 1.75, 1.72, 1.75, 1.67 respectively. For Inosine on the other hand, no outliers were found, since the maximum and minimum G-values for all the concentrations were under the G-value of Inosine, which was 1.46. | ||
The Grubbs test is helpful, because it helps us discard any value that may not be correct due to error, thus we obtain a better calibration curve with a better R square, which enable us to calculate the concentration of any value in the absorbance more accurately than if the outliers were present in the calibration curve. The best fit line would be more accurate, and it would pass through the points, or at least the points would be very close to the line. | |||
On top of this, we exchanged unknowns with another group, and the goal was to identify the concentration of the sample that was given to us, which was Inosine. | On top of this, we exchanged unknowns with another group, and the goal was to identify the concentration of the sample that was given to us, which was Inosine. | ||
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[[Image:Unknown absorbance vs wavelength Javier Vinals.png]] | [[Image:Unknown absorbance vs wavelength Javier Vinals.png]] | ||
Using the coefficient extinction, and the maximum of the data that was given to us of the unknown, we obtained that the concentration of the unknown was 1.33*10^-5. | Using the coefficient extinction, and the maximum of the data that was given to us of the unknown, we obtained that the concentration of the unknown was 1.33*10^-5. The real concentration of the solution was 1.5*10^-5. Due to the presence of outliers in the calibration curve, when finding out the concentration of the unknown solution, we got an error of 0.17 As it can be seen in our confidence interval, this value is not within the range of the actual value, thus there is an error in the calculation of the concentration due to the outliers in the calibration curve. | ||
==BSA-AuNP== | ==BSA-AuNP== | ||
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BSA solution 0.0104g BSA (MW = 66776g/mol) in 0.0100mL water → 15.6μM | BSA solution 0.0104g BSA (MW = 66776g/mol) in 0.0100mL water → 15.6μM | ||
[[User:Matt Hartings|Matt Hartings]] So, you did your Grubbs analysis ... now what are you going to do with it???!!! That's an important question. How does the calibration curve change after you get rid of points? Also, what is the error on your unknown as determined from the error inherent in the calibration curve? How does your value compare to what was predicted by the group that made it? Are the two values within the error of your measurement? | |||
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Entry title
For today's laboratory, we analyzed the data obtained from yesterday, and then determined if there were any outliers, the standard deviation and the confidence interval 90% and 95%. Calibration Curve and Group work As a large group, determine what wavelengths you want to use for your adenosine and inosine calibration curves (A vs c). Choose two people (one for each molecule) to compile your A(λ) and concentration data from each group. Do a least squares fit to the data and determine the slope of the line (remember the intercept should be zero --- with a concentration of 0 there should be no absorbance). This data, once compiled should be shared with all of the group members (via dropbox). Determine the standard deviation for your data points. Determine the confidence interval for 90% and 95% confidence. Determine if any data can be ruled out using a Q-test. Unknown Groups should exchange unknowns and try to determine the concentration of these unknowns from the calibration curves. In a week, I want you to revisit this data and propagate the error from the calibration curve to your concentration calculation. After making your calculation, find out from the group, whose unknown you are using, what the calculation of their samples should be. In addition to the data analysis, we retook a UV-Vis sample for 0.5*10^-5 and 1.5*10^-5 Adenosine molecule, due to some technichal errors when saving it into our dropbox folder. Therefore we used the dilutions made the day before of these two samples, and rerun a UV-Vis in order to correct our data, and obtain more accurate results for our results. Data AnalysisFor the experiment, we combined the class data of Adenosine and Inosine, and then used this values to find the standard deviation, the confidence interval of 90% and 95%, and used grubb's test to find any outliers "Figure 1"
"Figure 2"
From this extinction coefficient, we found that the molar absorptivity values for Adenosine and Inosine are 14025 and 11007 respectively. "Table 1. Standard deviation of Adenosine Absorbance"
"Table 2. Standard deviation of Inosine Absorbance"
"Table 3. Confindence interval at 95% of Adenosine Absorbance"
"Table 4. Confidence interval at 90% of Adenosine Absorbance
"Table 5. Confidence interval 95% of Inosine absorbance
"Table 6. Confidence interval at 90% of Inosine absorbance"
In addition, we had to find if there were any outliers for Adenosine and inosine absorbances using the Grubb's test. For adenosine, our G value was 1.67, meaning that if any concentration had a G-value bigger than this value, they would be an outlier. For adenosine we found that there were 4 outliers, for the concentrations of 3*10^-5, 2.5*10^-5, 2*10^-5, and for 1.5*10^-5. For this concentrations, the maximum values were 1.75, 1.72, 1.75, 1.67 respectively. For Inosine on the other hand, no outliers were found, since the maximum and minimum G-values for all the concentrations were under the G-value of Inosine, which was 1.46. The Grubbs test is helpful, because it helps us discard any value that may not be correct due to error, thus we obtain a better calibration curve with a better R square, which enable us to calculate the concentration of any value in the absorbance more accurately than if the outliers were present in the calibration curve. The best fit line would be more accurate, and it would pass through the points, or at least the points would be very close to the line. On top of this, we exchanged unknowns with another group, and the goal was to identify the concentration of the sample that was given to us, which was Inosine. "Figure 3"
Using the coefficient extinction, and the maximum of the data that was given to us of the unknown, we obtained that the concentration of the unknown was 1.33*10^-5. The real concentration of the solution was 1.5*10^-5. Due to the presence of outliers in the calibration curve, when finding out the concentration of the unknown solution, we got an error of 0.17 As it can be seen in our confidence interval, this value is not within the range of the actual value, thus there is an error in the calculation of the concentration due to the outliers in the calibration curve. BSA-AuNPThe other week, while doing the experiment we had some problems and mistakes during the reaction, and we ended up with a solution that was not supposed to be obtained, therefore we repeated the experiment again. This procedure was taken from the following reference and has been used by our previous two Experimental Biological Chemistry groups.
- Through calculation, we obtained that 0.001809 L was the necessary amount of BSA solution needed to add, so that the final concentration of gold is 90X that of BSA. Calculations done: First we find the amount of moles of BSA necessary for the reaction. [(2.54*10^-3 moles/L of HAuCL4)*(1L/1000mL)*1mL]/90=2.82 *10^-8 moles of BSA. Now we find the volume of BSA necessary to add to the reaction: (2.82 *10^-8 moles BSA) *(1L/15.6 *10^-6 moles)= 0.001809 L of BSA or 1.8 mL of BSA
- After we took the solution out of the oven, we observed that the entire solution was purple, as expected.
Stock solutions made Gold solution (HAuCl4·3H2O) 0.0100g in 0.0100mL water → 2.54mM BSA solution 0.0104g BSA (MW = 66776g/mol) in 0.0100mL water → 15.6μM Matt Hartings So, you did your Grubbs analysis ... now what are you going to do with it???!!! That's an important question. How does the calibration curve change after you get rid of points? Also, what is the error on your unknown as determined from the error inherent in the calibration curve? How does your value compare to what was predicted by the group that made it? Are the two values within the error of your measurement? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
