ADVA MED PHARM

Abstract: In this experiment, a spectrophotometry computer simulation was used to create two standard curves for different samples in solution, and those standard curves were used to calculate the concentration of two unknowns for each sample from a given absorbance.

Introduction: Spectrophotometry is the process by which the properties of a sample are analyzed using light. One of the more common spectrophotometric applications is measuring concentration of a sample dissolved in solution. This is done using the Beer-Lambert Law. The equation for the Beer-Lambert Law is A= abc, where A is absorbance, a is absorptivity, b is the path length travelled by the light through the sample, and c is the concentration of analyte dissolved in the sample. Since, a and b can be kept constant, and A and c are directly proportional to one another, the Beer-Lambert Law can be easily used to determine the concentration of an unknown using a standard curve. By plotting known absorbance and concentration data, a line of best fit can be placed over the data, and the equation can be used to calculate the concentration of an unknown based on measured absorbance.

Method: The computer simulation used for this experiment was the University of Colorado Physics Education Technology (PhET) Beer’s Law Interactive Simulation, which can be found at the URL https://phet.colorado.edu/sims/html/beers-law-lab/latest/beers-law-lab_en.html. The cuvette size was adjusted to 1 cm for both parts of the experiment. The first part of the experiment measured transmittance at wavelength of 427 nm through potassium chromate at known concentrations of 0 μM, 100 μM, 200 μM, 300 μM, and 400 μM. The second part of the experiment measured transmittance at wavelength of 740 nm through copper (II) sulfate at known concentrations of 0 mM, 50 mM, 100 mM, 150 mM, and 200 mM. The gathered data were entered into a Microsoft Excel Spreadsheet and graphed into a scatter plot. A trendline and trendline equation for the two plots were generated using excel and the equations were used to calculate the concentration of two unknowns for each substance from a given absorbance.

Results: Tiles with given values are white. Tiles with measured values are green. Tiles with calculated values are orange.

The equation for the standard curve for the potassium chromate was y = 0.0034x – 0.0003 with and R2 value of 1. The equation for the standard curve for the copper (II) sulfate was y = 0.0059x + 0.0008 with and R2 value of 1. For potassium chromate, the given absorbances of 0.97 and 0.11 yielded a calculated concentration of 285.38 μM and 32.44 μM, respectively. For copper (II) sulfate, the given absorbances of 1.22 and 0.25 yielded a calculated concentration of 206.64 mM and 42.24 mM, respectively. These values were then checked using the simulation.

Discussion: In both cases, the R2 value of 1 indicates a standard curve which matches the data perfectly (or perfectly within Excel’s three significant digits), which indicates a high degree of confidence when using the standard curve to make calculations. Additionally, we can see from the equation that the standard curves are perfectly linear, as denoted by the y=mx+b form. This is good because the Beer-Lambert Law, which was used in this experiment is linear as well, where y is A, x is c, and m is a constant, ab. The y-intercept is zero within three significant digits, so can be ignored for the purposes of the experiment. Since this experiment was done with a computer simulation, the data works perfectly, however, if this experiment were performed in an actual lab, the curves would likely not be perfectly linear due to numerous minor interfering circumstances, such as cuvette cleanliness, spectrophotometer variation, detector sensitivity, or poorly mixed standard solutions. However, the computer simulation seems to have given very reliable data, as the calculated values match up very well with the verification measurements made after the experiment, with one notable exception. For copper (II) sulfate, the calculated measurement of 206.64 mM could not be checked, as the simulation only allowed for a maximum concentration of 200 mM. This also highlights that this calculation was made using extrapolation of data to find a value outside the standard curve, while all other values fell within the curve. Traditionally, extrapolation has a higher instance of error than interpolation, but this value is still fairly close to the curve, so is likely still fairly accurate.

Potassium Chromate

Absorbance

0 100 200 300 400 0 0.33894491514662123 0.68235445677884132 1.0222763947111522 1.3615107430453626

Concentration (μM)

Absorbance

Copper (II) Sulfate

Absorbance

0 50 100 150 200 0 0.29593532059143263 0.58536085326299092 0.88140463477623787 1.1732774798310077

Concentration (mM)

Absorbance

Lab 4: HPLC In the Time of COVID

Purpose

This lab will require you to use an HPLC simulator to create standard curves which you will use to calculate the concentration of compounds in an unknown solution.

Procedure

1. Go to the link below.

https://www.multidlc.org/hplcsim/4_0_0/

2. Set the mobile phase to be an isocratic 60% methanol in water solution.

3. Leave the default settings for the chromatographic, general, and column properties.

4. Change the included compounds and their respective concentrations as needed to solve the problem below.

5. Pull the maximum signal value from the top of the appropriate peaks.

Calculations

You’re given a solution containing unknown concentrations of two unknown compounds. You run them through an HPLC using the above settings and obtain two peaks. One peak is centered at 0.7697 minutes and has a peak height of 410. The second peak is centered at 1.1167 minutes with a peak height of 470.

1. Run various standards through the HPLC to find out which compounds elute at the same time as your unknown compounds. This will identify which compounds they are.

2. Once you have identified your compounds. Create a standard curve for each one. Each standard curve should contain at least five data points, with at least two data points at higher and lower concentrations than the appropriate unknown (i.e. don’t do four lower and one higher). Note that the simulator seemingly does not tell you the peak height or the area. You’ll need to eyeball the chromatogram on the screen and take your best guess.

3. Calculate the concentrations of the unknowns.

Report

Write a lab report with the following sections: abstract, introduction, methods, results, discussion.

Abstract: Summarize the entire report in no more than 150 words.

Introduction: Discuss the concepts of liquid chromatography.

Methods: Write out exactly what you did so that someone else could repeat it exactly.

Results: Include data tables and graphs. Put your results into words (don’t include individual measurements).

Discussion: Discuss the meaning of your results. Were your standard curves linear? Should they be? What do you think they might look like if you were doing this experiment in real life? Do you trust your calculated concentrations for the unknowns?

How might this experiment be much more difficult if you were given unknown solutions in real life and had to identify their identity by retention time?

Submit

Submit both your report and spreadsheet files.

Abstract: In this experiment, a spectrophotometry computer simulation was used to create two standard curves for different samples in solution, and those standard curves were used to calculate the concentration of two unknowns for each sample from a given absorbance.

Introduction: Spectrophotometry is the process by which the properties of a sample are analyzed using light. One of the more common spectrophotometric applications is measuring concentration of a sample dissolved in solution. This is done using the Beer-Lambert Law. The equation for the Beer-Lambert Law is A= abc, where A is absorbance, a is absorptivity, b is the path length travelled by the light through the sample, and c is the concentration of analyte dissolved in the sample. Since, a and b can be kept constant, and A and c are directly proportional to one another, the Beer-Lambert Law can be easily used to determine the concentration of an unknown using a standard curve. By plotting known absorbance and concentration data, a line of best fit can be placed over the data, and the equation can be used to calculate the concentration of an unknown based on measured absorbance.

Method: The computer simulation used for this experiment was the University of Colorado Physics Education Technology (PhET) Beer’s Law Interactive Simulation, which can be found at the URL https://phet.colorado.edu/sims/html/beers-law-lab/latest/beers-law-lab_en.html. The cuvette size was adjusted to 1 cm for both parts of the experiment. The first part of the experiment measured transmittance at wavelength of 427 nm through potassium chromate at known concentrations of 0 μM, 100 μM, 200 μM, 300 μM, and 400 μM. The second part of the experiment measured transmittance at wavelength of 740 nm through copper (II) sulfate at known concentrations of 0 mM, 50 mM, 100 mM, 150 mM, and 200 mM. The gathered data were entered into a Microsoft Excel Spreadsheet and graphed into a scatter plot. A trendline and trendline equation for the two plots were generated using excel and the equations were used to calculate the concentration of two unknowns for each substance from a given absorbance.

Results: Tiles with given values are white. Tiles with measured values are green. Tiles with calculated values are orange.

The equation for the standard curve for the potassium chromate was y = 0.0034x – 0.0003 with and R2 value of 1. The equation for the standard curve for the copper (II) sulfate was y = 0.0059x + 0.0008 with and R2 value of 1. For potassium chromate, the given absorbances of 0.97 and 0.11 yielded a calculated concentration of 285.38 μM and 32.44 μM, respectively. For copper (II) sulfate, the given absorbances of 1.22 and 0.25 yielded a calculated concentration of 206.64 mM and 42.24 mM, respectively. These values were then checked using the simulation.

Discussion: In both cases, the R2 value of 1 indicates a standard curve which matches the data perfectly (or perfectly within Excel’s three significant digits), which indicates a high degree of confidence when using the standard curve to make calculations. Additionally, we can see from the equation that the standard curves are perfectly linear, as denoted by the y=mx+b form. This is good because the Beer-Lambert Law, which was used in this experiment is linear as well, where y is A, x is c, and m is a constant, ab. The y-intercept is zero within three significant digits, so can be ignored for the purposes of the experiment. Since this experiment was done with a computer simulation, the data works perfectly, however, if this experiment were performed in an actual lab, the curves would likely not be perfectly linear due to numerous minor interfering circumstances, such as cuvette cleanliness, spectrophotometer variation, detector sensitivity, or poorly mixed standard solutions. However, the computer simulation seems to have given very reliable data, as the calculated values match up very well with the verification measurements made after the experiment, with one notable exception. For copper (II) sulfate, the calculated measurement of 206.64 mM could not be checked, as the simulation only allowed for a maximum concentration of 200 mM. This also highlights that this calculation was made using extrapolation of data to find a value outside the standard curve, while all other values fell within the curve. Traditionally, extrapolation has a higher instance of error than interpolation, but this value is still fairly close to the curve, so is likely still fairly accurate.

Potassium Chromate

Absorbance

0 100 200 300 400 0 0.33894491514662123 0.68235445677884132 1.0222763947111522 1.3615107430453626

Concentration (μM)

Absorbance

Copper (II) Sulfate

Absorbance

0 50 100 150 200 0 0.29593532059143263 0.58536085326299092 0.88140463477623787 1.1732774798310077

Concentration (mM)

Absorbance