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Analytical Chemistry

Analytical Chemistry. Definition: the science of extraction, identification, and quantitation of an unknown sample. Example Applications: Human Genome Project Lab-on-a-Chip (microfluidics) and Nanotechnology Environmental Analysis Forensic Science. Course Philosophy.

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Analytical Chemistry

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  1. Analytical Chemistry • Definition: the science of extraction, identification, and quantitation of an unknown sample. • Example Applications: • Human Genome Project • Lab-on-a-Chip (microfluidics) and Nanotechnology • Environmental Analysis • Forensic Science

  2. Course Philosophy • develop good lab habits and technique • background in classical “wet chemical” methods (titrations, gravimetric analysis, electrochemical techniques) • Quantitation using instrumentation (UV-Vis, AAS, GC)

  3. Chapter 0:The Analytical Process (Example)

  4. 1. Formulating the Question 2. Selecting Analytical Procedures

  5. 3. Sampling

  6. Random Heterogeneous Material

  7. Segregated Heterogeneous Material

  8. 4. Sample Preparation

  9. 5. Analysis

  10. 6. Reporting and Interpretation 7. Drawing Conclusions

  11. representative sampling – random vs. segregated sample preparation extraction, centrifugation, filtering chromatography, stationary phase, adsorption aliquot calibration curve Terminology to Know From the Example

  12. General Steps in a Chemical Analysis • Formulating the Question • Select an analytical procedure (literature search) • Sampling - representative samples (random vs. segregated) • Sample Preparation (extraction, separation, etc) • Analysis of an aliquot (homogeneous phase) • Reporting and Interpretation • Drawing Conclusions

  13. Analyses you will perform • Basic statistical exercises • %purity of an acidic sample • %purity of iron ore • %Cl in seawater • Water hardness determination • UV-Vis: Amount of caffeine and sodium benzoate in a soft drink • AAS: Composition of a metal alloy • GC: Gas phase quantitation titrations

  14. Chapter 1:Chemical Measurements

  15. Chemical Concentrations

  16. Dilution Equation Concentrated HCl is 12.1 M. How many milliliters should be diluted to 500 mL to make 0.100 M HCl?

  17. Chapter 3:Math Toolkit accuracy = closeness to the true or accepted value precision = reproducibility of the measurement

  18. Significant Figures • Digits in a measurement which are known with certainty, plus a last digit which is estimated beaker graduated cylinder buret

  19. Rules for Determining How Many Significant Figures There are in a Number • All nonzero digits are significant (4.006, 12.012, 10.070) • Interior zeros are significant (4.006, 12.012, 10.070) • Trailing zeros FOLLOWING a decimal point are significant (10.070) • Trailing zeros PRECEEDING an assumed decimal point may or may not be significant • Leading zeros are not significant. They simply locate the decimal point (0.00002)

  20. ans = 63.5 Reporting the Correct # of Sig Fig’s • Multiplication/Division 12.154 5.23 36462 24308 60770 Rule: Round off to the fewest number of sig figs originally present 63.56542

  21. Reporting the Correct # of Sig Fig’s • Addition/Subtraction 15.02 9,986.0 3.518 10004.538 Rule: Round off to the least certain decimal place

  22. Rounding Off Rules • digit to be dropped > 5, round UP158.7 = 159 • digit to be dropped < 5, round DOWN158.4 = 158 • digit to be dropped = 5, round UP if result is EVEN158.5 = 158157.5 = 157

  23. ? sig figs 5 sig figs 3 sig figs 1.235-1.02 0.215 = 0.22 Wait until the END of a calculation in order to avoid a “rounding error” (1.235 - 1.02) x 15.239 = 2.923438 = 1.12

  24. Propagation of Errors A way to keep track of the error in a calculation based on the errors of the variables used in the calculation error in variable x1 = e1 = "standard deviation" (see Ch 4) e.g. 43.27  0.12 mL percent relative error = %e1 = e1*100 x1 e.g. 0.12*100/43.27 = 0.28%

  25. Addition & Subtraction Suppose you're adding three volumes together and you want to know what the total error (et) is: 43.27  0.12 42.98 0.22 43.06 0.15 129.31  et

  26. Multplication & Division

  27. Combined Example

  28. Chapter 4:Statistics

  29. Gaussian Distribution: Fig 4.2

  30. Mean – measure of the central tendency or average of the data (accuracy) N   Infinite population Finite population Standard Deviation – measure of the spread of the data (reproducibility) Infinite population Finite population

  31. Standard Deviation and Probability

  32. Confidence Intervals

  33. Confidence Interval of the Mean The range that the true mean lies within at a given confidence interval True mean “” lies within this range x

  34. Example - Calculating Confidence Intervals • In replicate analyses, the carbohydrate content of a glycoprotein is found to be 12.6, 11.9, 13.0, 12.7, and 12.5 g of carbohydrate per 100 g of protein. Find the 50 and 90% confidence intervals of the mean.

  35. Rejection of Data - the "Q" Test A way to reject data which is outside the parent population. Compare to Qcrit from a table at a given confidence interval. Reject if Qexp > Qcrit

  36. Example: Analysis of a calcite sample yielded CaO percentages of 55.95, 56.00, 56.04, 56.08, and 56.23. Can the last value be rejected at a confidence interval of 90%?

  37. Linear Least Squares- finding the best fit to a straight line

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