User:Daniel-Mario Larco/Notebook/AU Biodesign Lab - 09/03/2013/2013/09/04
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Adenosine and inosine have different absorption spectra. We will be observing changes in UV-Vis spectra to determine changes in concentration of both adenosine and inosine. In order to do this, we will need to know the molar absorptivity (ε) of both of these molecules. Just as each molecule has a characteristic absorption at each wavelength, this (per-wavelength) absorption can be quantified by a molar absorptivity. Or ... for a given concentration a molecule will absorb a very specific amount of light at a precise wavelength. A molecule doesn't have just one molar absorptivity; there is a molar absorptivity to describe each wavelength in a molecular absorbance spectrum.
BSA-AuNP This procedure was taken from the following reference and has been used by our previous two Experimental Biological Chemistry groups.
* In this experiment the stock solution had a concentration of 2.54mM
* In this experiment the appropriate volume of BSA needed was calculated and found to be 0.001809 L (1.809mL).
* At the end of the 3 hours, the tube containing the solution was removed from the oven. The solution had taken a purple color without any filaments this time. The solution was transferred to another plastic tube, labelled and stored.
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
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 order to determine ε for any substance (molecule, protein, gold nanoparticle), you need to determine how the absorbance of that substance changes with concentration.
Taking our Beer's Law relationship: (The darker the beer, the stronger the brew!)
where b is the path length (1cm), we can see that A is directly proportional to c and that if we plot A vs C, the slope will be ε.
That will be your task for the day. To determine molar absorptivity of both adenosine and inosine by plotting graphs of A (at one specific wavelength; I suggest you use a peak value from the spectrum) vs c.
We are also going to pool data from all of the groups to develop a full calibration curve.
We are going to determine standard deviations from the group's data, determine a confidence interval, and perform a Q-test to remove any outlying data.
The collected data was put into a spreadsheet program in order to make the calibration curve for adenosine and inosine using the maximum absorbances. The following curves were obtained:
We also used the class data to find a full calibration curve for both
The Molar Absorptivity was calculated by dividing the Absorbance by the corresponding concentration for all the data in the class and the average was taken.
Table 1. Molar absorptivity for Adenosine and Inosine
The spreadsheet program was used to calculate the standard deviations, the 90% and 95% confidence intervals for the absorbances recorded by the class for each of the concentrations
Table 1. Adenosine Std. Dev. and Confidence Intervals
Table 2. Inosine Std. Dev. and Confidence Intervals
We performed a Grubb's test for outliers in order to check if any of the recorded absorbance values were outliers. Here are the results of the Grubb's test for suspected outliers.
Table 3. Grubb's Test for Outliers Adenosine and Inosine
For adenosine, there sample size was 5. At 95% confidence interval, the critical G value is 1.67. For inosine, the sample size was 4. At 95% confidence interval, the critical G value is 1.46. In a Grubb's Test, any value G that is greater than the critical value at the particular confidence interval can be considered an outlier. In our results, there were no points on the Inosine graph that could be considered outliers. On the other hand, there were a few points on the Adenosine graph that could be considered as outliers. Those points were (1.50E-05,0.304) , (2.00E-05,0.406) , (2.50E-05,0.442) , and ( 3.00E-05,0.558) where (Concentration, Absorbance).