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. 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: %Cu in pre- and post-1982 pennies
GC: Gas phase quantitation using an internal standard
titrations

4. Chapter 1: Chemical Measurements

7. Example, p. 15: convert 0.27 pC to electrons

8. Chemical Concentrations

9. Example, p. 19: Molarity of Salts in the Sea
Calculate molarity of 2.7 g NaCl/dL
[MgCl2] = M. How many grams in 25 mL?

10. Dilution Equation
Concentrated HCl is 12.1 M. How many milliliters should be diluted to 500 mL to make M HCl?
M1V1 = M2V2
(12.1 M)(x mL) = (0.100 M)(500 mL)
x = 4.13 M

11. Chapter 3: Math Toolkit
accuracy = closeness to the true or accepted value (given by the AVERAGE)
precision = reproducibility of the measurement (given by the STANDARD DEVIATION)

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

13. Rules for Determining How Many Significant Figures There are in a Number
All nonzero digits are significant (4.006, , )
Interior zeros are significant (4.006, , )
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 ( )

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

15. 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

16. Reporting the Correct # of Sig Fig’s
Addition/Subtraction in Scientific Notation
Express all of the numbers with the same exponent first:
1.632 x x x 106

17. Reporting the Correct # of Sig Fig’s
Logs and anti-logs

18. Rounding Off Rules digit to be dropped > 5, round UP 158.7 = 159
digit to be dropped < 5, round DOWN = 158
digit to be dropped = 5, make answer EVEN = = 158.0
BUT =

19. Wait until the END of a calculation in order to avoid a “rounding error”
? sig figs
5 sig figs
3 sig figs
( ) x = =
1.12
= 0.22

20. 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  0.12 mL
percent relative error = %e1 = e1* x1
e.g. 0.12*100/43.27 = 0.28%

21. 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
 et

22. Multplication & Division

23. Combined Example

24. Chapter 4: Statistics

25. Gaussian Distribution:
Fig 4.2

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

27. Standard Deviation and Probability

28. Confidence Intervals

29. 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

31. 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 95% confidence interval of the mean.
ave = 12.55, std dev = 0.465
N = 5, t = (N-1)
 = ± (0.465)(2.776)/sqrt(5)
= ± 0.58

33. Rejection of Data - the Grubbs Test
A way to statistically reject an “outlier”
Compare to Gcrit from a table at a given confidence interval.
Reject if Gexp > Gcrit

34. Sidney: 10.2, 10.8, 11.6
Cheryl: 9.9, 9.4, 7.8
Tien: 10.0, 9.2, 11.3
Dick: 9.5, 10.6, 11.3

35. Linear Least Squares (Excel’s “Trendline”) - finding the best fit to a straight line