We used various instruments to create a calibration curve showing the relationship between absorbance and molarity of a potassium permanganate solution. Stock and daughter solutions were created through serial dilutions and transferred to spectrophotometer cells to measure absorbance at 535nm. The resulting data was plotted on a graph, where increasing absorbance correlated with increasing molarity. Some points fell outside the linear regression, possibly due to imprecise dilution techniques. Removing these outlier points resulted in a cleaner linear regression to better interpret the calibration curve relationship.

1. University of Puerto Rico <br />Cayey Campus<br />Jessica Diaz Rivera<br />RISE Program<br />Prof. Elena Gonzalez<br />Laboratory #2: Spectroscopy: Calibration curve<br /> In the laboratory about calibration curve given by Wilfredo Resto we used different instruments to know the relationship between the absorbance and molarity. We put 50ml of KMnO4 (stock solution) in the beaker of 10ml, and pipetted 2ml in a volumetric flask.We then had to dilute to the mark with de-ionized water. We use a burette of 50ml and rinsed it with the prewet solution to create the granddaughter solution. These solutions needs to be transferred to a different volumetric flask of 25ml and diluted in de-ionized water up to the mark. This mixture is going to throw in a cell and put into the spectrophotometer with the wavelength of 535 to know the absorbance. Calculate the molarity with the formula M1V1=M2V2. With the resulting data you can make a graph to see the relationship between absorbance and molarity. If the absorbancy increases then molarity will also increase. In this case, we did a lineal regression, but since we had sufficient numbers we were able to erase the numbers that fell outside of the linear regression. Some of the possible reasons for the decrease was that maybe the person that did mixture adequately or precisely when using the burette did not perceive the required amount. In conclusion linear regression damage is removed from the plots so that, in this way, it does not affect it and we can make a good interpretation of the graph.<br />