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

10. 5. Analysis

11. 6. Reporting and Interpretation 7. Drawing Conclusions

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

13. General Steps in a Chemical Analysis 1.Formulating the Question 2.Select an analytical procedure (literature search) 3.Sampling - representative samples (random vs. segregated) 4.Sample Preparation (extraction, separation, etc) 5.Analysis of an aliquot (homogeneous phase) 6.Reporting and Interpretation 7.Drawing Conclusions

14. 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

15. Chapter 1: Chemical Measurements

18. Chemical Concentrations

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

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

21. Significant Figures Digits in a measurement which are known with certainty, plus a last digit which is estimated beakergraduated cylinderburet

22. 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)

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

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

25. Rounding Off Rules digit to be dropped > 5, round UP 158.7 = 159 digit to be dropped < 5, round DOWN 158.4 = 158 digit to be dropped = 5, round UP if result is EVEN 158.5 = 158 157.5 = 157

26. 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 1.12 1.235 -1.02 1.235 -1.02 0.215 = 0.22 0.215 = 0.22 ? sig figs 5 sig figs 3 sig figs

27. 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 x 1 = e 1 = "standard deviation" (see Ch 4) e.g. 43.27  0.12 mL percent relative error = %e 1 = e 1 *100 x 1 e.g. 0.12*100/43.27 = 0.28%

28. Addition & Subtraction Suppose you're adding three volumes together and you want to know what the total error (e t ) is: 43.27  0.12 42.98  0.22 43.06  0.15 129.31  e t

29. Multplication & Division

30. Combined Example

31. Chapter 4: Statistics

32. Gaussian Distribution: Fig 4.2

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

34. Standard Deviation and Probability

35. Confidence Intervals

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

38. 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.

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

41. 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%?

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