Lecture Date: January 14, 2013

1. Introduction to Analytical Chemistry Lecture Date: January 14, 2013

2. What is Analytical Chemistry? Analytical chemistry is the science of obtaining, processing, and communicating information about the composition and structure of matter. In other words, it is the art and science of determining what matter is and how much of it exists. Qualitative:provides information about the identity of an atomic, molecular or biomolecular species Quantitative:provides numerical information as to the relative amounts of species Definitions from www.acs.org

3. The Role of Analytical Chemistry They adapt proven methodologies to new materials/systems or to answer new questions about their composition. Analytical chemists work to improve existing techniques to meet the demands of for faster, cheaper, more sensitive chemical measurements Analytical chemists research to completely new types of measurements and are at the forefront of the utilization of major discoveries in fields as diverse as photonics and implantable microchip sensors. Analytical chemistry is applied to many branches of science Medicine Industry Environmental Food and Agriculture Forensics Archaeology Space science

4. History of Analytical Methods • Classical methods: • Separation via precipitation, extraction or distillation • Qualitative:recognized by color, boiling point, solubility, taste • Quantitative:gravimetric or titrimetric measurements • Instrumental Methods: • Separation via chromatography and electrophoresis • Qualitative and Quantitative: recognized by their interaction with radiation (spectroscopy), their mass (mass spectrometry), their electrical properties, or their interaction with environment (temperature, humidity)

5. Modern Instrumental Techniques Separation Techniques Gas chromatography High performance liquid chromatography Ion chromatography Super critical fluid chromatography Capillary electrophoresis Planar chromatography Mass Spectrometry Electron ionization MS Chemical ionization MS High resolution MS Gas chromatography MS Fast atom bombardment MS Liquid chromatography MS Laser MS Ambient ionization MS

6. Modern Instrumental Techniques Spectroscopic techniques Infrared spectrometry Raman spectrometry Nuclear magnetic resonance (NMR) X-ray spectrometry Atomic absorption spectrometry Inductively coupled plasma atomic emission spectrometry Inductively coupled plasma MS Atomic fluorescence spectrometry Ultraviolet/visible spectrometry (CD) Molecular fluorescence spectrometry Chemiluminescence spectrometry X-Ray Fluorescence spectrometry Electrochemical techniques Amperometry Voltammetry Potentiometry Conductiometry Microscopic and surface techniques Atomic force microscopy Scanning tunneling microscopy Auger electron spectrometry X-ray photon electron spectrometry

7. Major Steps in Solving an Analytical Problem 1. Understanding and defining the problem, by looking at the history of the material to be analyzed and background of the problem 2. Choosing your analytical technique(s) and running the experiments (or developing a new analytical technique) 3. Data analysis and interpretation, validation of results (if needed), and reporting of results

8. 1. Understanding and Defining the Problem • What is it that you want to know? • What accuracy is required? • Is there a time (or money) limit? • How much sample is available? • What is the concentration range of the analyte? • What components of the sample may cause an interference? • What are the physical and chemical properties of the sample matrix? • How many samples are to be analyzed?

9. History of sample and background of the problem • Background information can originate from many sources • The client and competitor’s products • Literature searches on related systems • Sample history: • How was the sample collected, transported, and stored? • How was it sampled? • If synthesized, by what synthetic route? • What was the source of the raw materials used to make the sample? • What analysis has already been performed?

10. 2. Choosing the Analytical Technique Consider the sample characteristics Choose an instrument (and ultimately a method) that can obtain the desired information Evaluate the performance characteristics of that instrument and method Does an entirely new technique need to be developed?

11. Technique Selection • Analysis type • Quantitative, Qualitative • Location of sample • bulk or surface • Physical state of sample • gas, liquid, solid, dissolved solid, dissolved gas • Amount of Sample • macro, micro, nano, … • Fate of sample • destructive, non destructive • Estimated purity of sample • pure, simple mixture, complex mixture • Analyte concentration • major or minor component, trace or ultra trace • Elemental information • total analysis, speciation, isotopic and mass analysis • Qualitative Molecular information • compounds present, polyatomic ionic species, functional group, structure, molecular weight, physical property

12. Comparing Two Analytical Techniques: High pressure Liquid Chromatography (HPLC) vs. Nuclear Magnetic Resonance (NMR) limited T,S (ion)

13. Obtain and store sample Pretreat and prepare sample Present/Report information in a understandable form Interpret Data (Qualitative) Components of an Analytical Method Extract data from sample Perform measurement(s)/ experiment and process raw data (if needed) After reviewing results might be necessary to modify and repeat procedure Compare results with standards Apply Statistics (Quantitative) Covert data into information Transform information into knowledge

14. 3. Analyzing Data and Reporting Results • Analytical data analysis takes many forms: statistics, chemometrics, simulations, empirical interpretation, etc… • Analytical results can be reported in • Peer-reviewed papers • Technical reports • Laboratory notebook records • Analytical results can be subject to extreme scrutiny and can be challenged by other experts

15. Basis Quantitative Analysis • Precision refers to the reproducibility of analytical results. When a result is precise, numerical results agree closely. Precision can be estimated by repeating the measurement n times (when possible). • Accuracy describes the correctness of a result by its closeness to an accepted or true value. Precise, not accurate Accurate, not precise Accurate and precise See pg. 967 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.

16. Basis Quantitative Analysis • Selectivity: the extent to which a technique or method can determine particular analytes under given conditions in mixtures or matrices, simple or complex, without interferences from other components. • Also referred to as “specificity” • Sensitivity: the ability of a technique or method discriminate between small differences in level of an analyte

17. Basis Quantitative Analysis Limit of linearity • Limit of detection (LOD): the lowest amount of an analyte that can be detected at a known confidence level • Signal-to-noise: ratio of the average signal to the average level of noise. • Limit of quantitation (LOQ): the range over which quantitative measurements can be made (usually the linear range), often defined by detector dynamic range Slope relates to sensitivity LOQ Detector response LOD Dynamic range Concentration • Linearity: the degree to which a response of an analytical detector to analyte concentration/mass approximates a linear function • Dynamic range: range between the LOQ and limit of linearity

18. Significant Figures • All nonzero digits are significant: • 1.234 g has 4 significant figures • Zeroes between nonzero digits are significant: • 1002 kg has 4 significant figures • Leading zeros to the left of the first nonzero digits are not significant; such zeroes merely indicate the position of the decimal point: • 0.001 oC has only 1 significant figure • Trailing zeroes that are also to the right of a decimal point in a number are significant: • 0.0230 mL has 3 significant figures • When a number ends in zeroes that are not to the right of a decimal point, the zeroes are not necessarily significant: • 190 miles may be 2 or 3 significant figures

19. Significant Figures • Addition and subtraction, the result is rounded off so that it has the same number of digits as the measurement having the fewest decimal places 100 (no decimal places) + 23.643 (3 places) = 123.643, which should be rounded to 124 (no places). • In multiplication and division, the result should be rounded off so as to have the same number of significant figures as in the input value with the least number of significant figures 3.0 (2 significant figures ) × 12.60 (4 significant figures) = 37.8000 which should be rounded to 38 (2 significant figures).

20. Scientific Notation and Prefixes 0.00000356 M 3.56 x 10-6 M 3.56 mM or …. ppm Prefixes for SI Units giga- G 109 mega- M 106 kilo- k 103 deci- d 10-1 centi- c 10-2 milli- m 10-3 micro- µ 10-6 nano- n 10-9 pico- p 10-12 femto- f 10-15 atto- a 10-18

21. Working with Numbers: Analytical Concentrations Molarity Moles of solute / L Parts per Million (ppm) cppm = mass of solute X 106 ppm mass of solution For dilute aqueous solutions whose densities are approximately 1.00 g/mL 1 ppm = 1 mg/L =1 µg/mL Parts per Billon (ppb) cppb = mass of solute X 109 ppb or 1 µg/L mass of solution

22. Basic Statistics • Mean (average) of a population: • Mean (average) of a sample:

23. The Standard Deviation • The standard deviation indicates the spread of data • The sample standard deviation (for a data set of limited size) is given by s: • Relative standard deviation (RSD) (%) See pg. 971-972 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.

24. The Gaussian Probability Distribution • If you take a large number of measurements, the values with be distributed around the expected value, or mean • The likelihood of a result will become lower the farther away the result is from the mean Response Measurement (e.g. spectral frequency) • Many physical phenomena studied in analytical chemistry result in measurements that can be modeled as Gaussian distributions See pg. 971-972 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.

25. Probability Distributions and Measurement Confidence Intervals Consider the following eight results : mean = 2.35 and std deviation = 0.635 The question is, what is the chance that the large value of 3.8 occurred by random chance assuming a Gaussian distribution?

26. Confidence Intervals – An example • N = 8, mean = 2.35, and s = 0.635 • For this example, choose a 95% confidence level. • Use Skoog et al. table A1-5 to obtain t (95% CI, dof =7) 2.35 ± (2.36*0.635)/Sqrt[8] 2.35 ± 0.53 • Result: 3.8 is outside the range of 2.35 ± 0.53. We can be 95% confident that the value of 3.8 is from a different system, etc…

27. Calibration Curves • Measure signal response vs. known analyte concentration • The data is plotted and fit to a function to obtain the equation of the “best” fit and the uncertainty in the fit. • Typically the best fit is linear • y = mx+b • response = slope [c] + intercept • m is related to the method sensitivity • Measure the sample response to determine the concentration • Matrix effects must be minimal Response Concentration

28. Linear Least Squares or Linear Regression • Method to minimize the residual of the experimental values and fitted line • Appendix 1 shows the how to do linear regression by hand • However, typically this is done with software

29. Correlation Coefficient (R2 ) • A fraction between 0.0 and 1.0 • Dimensionless – it has no units If R2 is near 1.0, the regression model fits the data much better than the null hypothesis If the regression model were not much better than the null hypothesis, R2 would be near zero Sum of residuals associated with a linear relationship SSreg Sum, of residuals associated with the null hypothesis – average of the y values SStot

30. Using a Calibration Curve What is the mole % of isooctane in the sample with a peak area of 2.65? What is the standard deviation? sr= stdev of regression line M= number of sample measurements N= number of samples for calibration y= average peak area of the calibration Sxx = sum of squares of the deviation for x

31. The Standard Addition Method • Known quantities of analyte are added to a sample of unknown concentration • Good for systems with significant matrix effects (“interferences”), where selectivity or specificity is lacking [X] = concentration of analyte [S] = concentration of standard I = signal i = initial, f = final [x]f

32. The Standard Addition Method: An Example • Example: atomic absorbance measurements of a Zn spectral line for determination of Zn2+ • Three standard additions are made to the actual sample, which is also analyzed (four samples) • Data analyzed by linear regression • A = 0.179[c] + 0.199 • (A = absorbance, [c] = concentration) • Solve for [X]f , where A = 0 • Zn2+ in sample =1.11 ppm Example from H. A. Strobel and W. R. Heineman, Chemical Instrumentation: A Systematic Approach, Wiley: New York, 1989, p. 393.

33. Non-Linear Fitting The Nernst equation is an equation that can be used (in conjunction with other information) to determine the equilibrium reduction potential of a half-cell in an electrochemical cell. Kohlrausch’s Law states that the conductivity of a dilute solution is the sum of independent values: the molar conductivity of the cations and the molar conductivity of the anion. The law is based on the independent migration of ions.

34. Simple Chemical Tests • While most of this class is focused on instrumental methods, a very large number of simple chemical tests have been developed over the past ~300 years • Examples: • Barium: solutions of barium salts yield a white precipitate with 2 N sulfuric acid. This precipitate is insoluble in hydrochloric acid and in nitric acid. Barium salts impart a yellowish-green color to a non-luminous flame that appears blue when viewed through green glass. • Phosphate: With silver nitrate TS, neutral solutions of orthophosphates yield a yellow precipitate that is soluble in 2 N nitric acid and in 6 N ammonium hydroxide. With ammonium molybdate TS, acidified solutions of orthophosphates yield a yellow precipitate that is soluble in 6 N ammonium hydroxide. Examples are from US Pharmacopeia and National Formulary USP/NF

35. A Colormetric Test for Mercury • A modern example of a “spot” test: a test for Hg2+ developed using DNA and relying on the formation of a thymidine-Hg2+-thymidine complex • LOD = 100 nM (20 ppb) in aqueous solution • Linearity from the high nanomolar to low micromolar range • Selective for Hg2+ and insensitive to Mg2+, Pb2+, Cd2+, Co2+, Zn2+, Ni2+, and other metal ions Angew. Chem. Int. Ed., DOI: 10.1002/anie.200700269 http://pubs.acs.org/cen/news/85/i19/8519news6.html

36. Further Reading • Optional Reading: • Skoog et al. Chapter 1 and Appendix 1 • Skoog et al. Chapters 6 and 7 (Spectroscopy)