Spring 2010 updated March 24 Determination of Aromatics in Gasoline by Gas Chromatography- Mass Spectrometry: Comparison of Grades and Brands

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1 Chem F413W Spring 2010 updated March 24 Determination of Aromatics in Gasoline by Gas Chromatography- Mass Spectrometry: Comparison of Grades and Brands Introduction Gasoline is a complex mixture of volatile organic compounds (1). Alkanes, alkenes, and aromatics are the major classes of compounds in traditional gasoline. In the Lower-48, EPA regulations have caused the aromatic content to decrease from traditional levels that were as high as 4% benzene, 15% toluene, and 15% xylenes (2). These regulations are not in effect in the State of Alaska. The diversity of refineries serving Alaska is unusually narrow. There are two refineries in North Pole that serve Fairbanks (Flint Hills and Petro Star). In addition, a Tesoro refinery in Kenai is reported to ship gasoline north. The brands of gasoline in Alaska include Fred Meyer, Safeway, Texaco, Chevron, and Sourdough. Most brands have three grades of gasoline: regular, premium, and super premium. The approximate octane ratings are 87, 88.5, and 90. You will analyze a dozen samples. They will represent the three grades from four different brands. Your laboratory goal will be to estimate the concentrations of benzene, toluene, three xylene isomers, and ethyl benzene in the headspace above the gasoline by capillary column gas chromatograph using a mass spectrometer detector (3). You will identify these compounds by using the automated mass spectral library searching ability of the GC-MS software. You will quantify percent composition by the peak area normalization method (4). You will assume that the detector response factor is the same for all substances and that all components have been eluted and detected. The problem to be solved has two components. You will attempt to establish if there is a correlation between octane rating (grade) and aromatic composition. You will attempt to identify which gasolines have a common refinery source. Day 1 Bench chemistry: Experimental CAUTION: Gasoline is a hazardous mixture of alkanes, alkenes, and aromatics. Some of the gasoline could contain ethers and alcohols. Gasoline is extremely flammable. The aromatic components of gasoline are toxic and potentially carcinogenic. Latex gloves afford no contact protection. If you need gloves, use a solvent resistant variety. MSDS sheets for toluene, xylenes, benzene, and gasolines are available in the lab loose-leaf MSDS notebook and on the web at and

2 Work as a group of two. Put about 2 ml of each gasoline sample into a clearly labeled large headspace GC autosampler vial. Seal the vial using a screw cap and Teflon-faced cap liner. (The syringe will sample the head-space above the liquid.) Day 1 at the GC-MS You will use the Agilent 7890GC with 5975C mass selective detector and a CTC Analytics headspace autosampler for these analyses. Make a sequence file for the twelve samples using the method 413gshsa.m. Program the sequence so that all of the 12 samples will be run in duplicate. Randomize appropriately. At the end of the sequence, add a line using the method gas-save.m. Load the autosampler tray. Use a GC program with the following conditions: Column: HP5-MS: 30 m long, 0.25 mm ID, 0.25µm film thickness, 5% phenyl silicone polymer stationary phase. Injection port temperature: 200 o C Carrier gas: He, 1 ml / min Oven temperature: 30 o C for 12 minutes, ramp at 4 C/min to 90 o C Syringe: 2.5 ml headspace Injection volume: 250 µl Split injection: split ratio = 50:1 Mass spectrometer: total ion scan 40 amu amu, no solvent delay, source 230 o C, quadrupole 150 o C Start the GC run and establish that the chromatography is going properly. The run time is 28 minutes. I will instruct you on how to load chromatograms from the hard drive, display them, get a mass spectrum at any point on the chromatogram, and do qualitative analysis by spectral matching with reference spectra from the library. The generally accepted criterion for a match is to have a numerical score of 90 (on a scale of 100). We will also show you how to make a selected ion chromatogram, which will help in finding the retention times for the analytes. Day 2 After the GC runs are completed Examine the 1st, 8th, 16th, and 24th stored chromatograms. Use the spectral search routine and selected ion chromatograms to determine the retention times for benzene, toluene, the xylenes, and ethylbenzene. (The mass spectra for the xylenes and the ethyl benzene are similar, so a definitive match may not be made.) Your goal is to establish that the retention times of the aromatic compounds of interest do not change dramatically. The chromatogram at the end of this handout (Appendix 1) may also be helpful in getting started. Examine all 24 printed chromatograms. Identify unusual features such as ghost peaks or profiles that look very different from most other chromatograms. Verify that the

3 y-axis range for each chromatogram is similar to the others. (You should see no problems, but this examination of all of the chromatograms is a good habit to develop.) Tabulate the information about peak area percentages for the aromatics. Examine the chromatograms and the tabulation of peak areas to see if there are clear differences in the chromatograms among brands or among grades. Only then you should begin the arithmetic of analyzing the data by analysis of variance (ANOVA). Data Workup Because you did not use any quantitative standards, you will make an approximate analysis of composition by assuming that the % of total area value in the Area Percent report is equal to the % by weight in the gasoline. This assumption is good enough for making comparison of the relative composition of aromatics in the gasoline samples. It is not good enough to ensure high accuracy in the absolute analysis values, however. When you examine the data, do exploratory data analysis before doing the ANOVA calculations. A single plot of the data should allow you to visualize trends or interactions. (See the class handout for two factor ANOVA for an example of exploratory data analysis for a design similar to the one used in this experiment.) The matrix of the three grades and four brands gives a design called two-factor ANOVA with replication. This type of design allows simultaneous investigation of the effect of two variables and any interaction between the variables. At the risk of oversimplifying the analysis of a data set such as this, let me note three things. You have twelve sets of duplicated data, enabling you to calculate an estimate of random error. You have four samples of regular gasoline, four samples of premium, and four samples of super premium. You should be able to figure out a way to determine if the aromatic content of the three grades varies in a systematic way. You have three samples of brand A, three samples of brand B, three samples of brand C, and three samples of brand D. You should be able to figure out a way to determine if the aromatic content of the four brands varies in a systematic way. Do the ANOVA calculations. Appendix 2 has instructions for using Excel and for calculating minimum significant difference. A good approach is to do ANOVA calculations for benzene, toluene, the sum of the eight-carbon aromatics, and the sum of all of the aromatics. If some of the aromatics are present in only a fraction of the samples, you will probably not do the ANOVA for that aromatic. Interpret the data from the ANOVA table, keeping in mind your exploratory data analysis. Report Write a "full" report. A full report should have "introduction", "experimental", and "results and discussion" sections. Your audience is a senior chemistry major who has not done this experiment.

4 The report should communicate observations about the five topics listed below, at a minimum. Integrate related topics appropriately and consider the focus of the report given in the last paragraph of the introduction. 1. Report your analyses results, including estimates of random error. 2. Is there compelling evidence that the concentrations of the aromatic compounds correlate strongly with grade of gasoline? Convince the reader with pictures and possibly numbers. 3. Is there compelling evidence that some of the brands come from the same refinery? Is there compelling evidence that some of the brands come from different refineries? Convince the reader with pictures and possibly numbers. 4. There are chromatographic bands for a number of aromatic compounds. Did you use the individual band areas or did you use the sum (or product or quotient) of areas? Why did you make this decision? 5. Is there anything interesting that you would communicate to the man on the street about different brands or grades of gasoline? The "experimental" section should be brief, with just enough detail so that someone could generally reproduce your experimental program. Although you have acquired the data with another student, you should do the data evaluation and report writing as an individual. Literature Cited 1. Snyder, C. H.; The Extraordinary Chemistry of Ordinary Things, p Haekman, S. K.; Environ. Sci. Technol. 1992, 26 (6), D. C. Harris, Quantitative Chemical Analysis, 7 th ed., p or Skoog, D. A., Holler, F. J., Crouch, S. R.; Principles of Instrumental Analysis, 6 th ed., p Skoog, D. A., Holler, F. J., Crouch, S. R.; Principles of Instrumental Analysis, 6 th ed., p Schedule Experiment Date Experimenters Report Due March 22, 24 SE, CS Apr 2, 2010 March 29, 31 JC, SH Apr 9, 2010 April 5, 7 AC, AT Apr 16, 2010

5 Appendix 1 Sample Chromatogram Supelco Chromatography catalog, 1999, page 508. Note that the stationary phase for this separation (Petrocol DH) is a 100% methyl polysiloxane, while the stationary phase used in the Chem 413 experiment is a 5% phenyl / 95% methyl polysiloxane.

6 Appendix 2 Chem F413W Spring 2010 How To Do Two Factor ANOVA With Replication In Excel You have a handout that shows the result of doing two factor ANOVA. The experiment examined the effect of three catalysts and four temperatures on a yield of a chemical reaction. Each combination of catalyst and temperature was duplicated. The summary data tables and ANOVA table were given, but the method of using Excel to get these data was not. Here is the method. Enter the data, including the row labels and column labels into the spreadsheet. Each replicate occupies a row. T=10 T=20 T=30 T=40 Cat A run Cat A run Cat B run Cat B run Cat C run Cat C run Click on the Data tab. Click on Data Analysis. In the Analysis Tools window, click on Anova: Two-Factor with Replication. OK. In the Wizard, enter the following: Input Range: Select the data set and the row and column labels. Rows/Sample: 2 (in general, this is the number of replicates) Alpha: 0.05 (the default value) Output Range: enter the cell that will be at the top-left of the calculated values. OK The output will consist of summary data, showing averages and standard deviations by catalyst and by temperature, and an ANOVA table. The ANOVA table tells you if a significant temperature effect or a significant catalyst effect is observed. The large F and the small P-values tell you that sample (row, catalyst) and column (temperature) effects are both significant. The interaction of temperature and catalyst is significant at a level where there is a 7.8% chance for a false positive. The next step is to calculate the 'minimum significant difference in means' for temperature and for catalyst. The exact answer to this question is complicated, so I'll suggest an approximate solution for this design (and for the aromatics in gasoline design). Averages that differ by 2.0 times the pooled standard deviation (S p ) can be called statistically different. Where do we get the S p value? Take the square root of the MS (within), or (1.85) 0.5 in this case. ANOVA Source of Variation SS df MS F P-value F crit Sample E Columns Interaction Within Total

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