Lecture Date: January 14, 2013

Introduction to Analytical Chemistry

What is Analytical Chemistry?

Qualitative: provides information about the identity of an atomic, molecular or biomolecular species

Quantitative: provides numerical information as to the relative amounts of species

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.

Definitions from www.acs.org

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

MedicineIndustry

EnvironmentalFood and Agriculture

ForensicsArchaeology

Space science

The Role of Analytical Chemistry

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)

Modern Instrumental Techniques

Separation TechniquesGas chromatographyHigh performance liquid chromatographyIon chromatographySuper critical fluid chromatographyCapillary electrophoresisPlanar chromatography

Mass SpectrometryElectron ionization MSChemical ionization MSHigh resolution MSGas chromatography MSFast atom bombardment MSLiquid chromatography MSLaser MSAmbient ionization MS

Spectroscopic techniquesInfrared spectrometryRaman spectrometryNuclear magnetic resonance (NMR)X-ray spectrometryAtomic absorption spectrometryInductively coupled plasma atomic emission spectrometryInductively coupled plasma MSAtomic fluorescence spectrometryUltraviolet/visible spectrometry (CD)Molecular fluorescence spectrometryChemiluminescence spectrometryX-Ray Fluorescence spectrometry

Electrochemical techniquesAmperometryVoltammetryPotentiometryConductiometry

Microscopic and surface techniquesAtomic force microscopyScanning tunneling microscopyAuger electron spectrometryX-ray photon electron spectrometry

Modern Instrumental Techniques

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

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?

History of sample and backgroundof 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?

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?

Analysis typeQuantitative, Qualitative

Location of samplebulk or surface

Physical state of samplegas, liquid, solid, dissolved solid, dissolved gas

Amount of Samplemacro, micro, nano, …

Fate of sampledestructive, non destructive

Estimated purity of samplepure, simple mixture, complex mixture

Analyte concentrationmajor or minor component, trace or ultra trace

Elemental informationtotal analysis, speciation, isotopic and mass analysis

Qualitative Molecular informationcompounds present, polyatomic ionic species,

functional group, structure, molecular weight, physical property

Technique Selection

Comparing Two Analytical Techniques: High pressure Liquid Chromatography (HPLC)

vs. Nuclear Magnetic Resonance (NMR)

HPLC NMRLocation of sample

bulk or surface B B

Physical state of samplegas, liquid, solid, dissolved solid, dissolved gas L,Ds L,S,Ds

Amount of Samplemacro, micro Ma, Mi Ma, Mi

Estimated purity of samplepure, simple mixture, complex mixture Sm,M P,Sm

Fate of sampledestructive, non destructive N,D N

Elemental informationtotal analysis, speciation, isotopic and mass analysis

Molecular informationCompounds present, Polyatomic ionic species, Cp,Io,St Cp,Fn,StFunctional group, Structural, MW, Physical prop

Analysis typeQuantitative, Qualitative Ql,Qt Ql,Qt

T,S (ion) limited

Components of an Analytical Method

Perform measurement(s)/experiment and process raw

data (if needed)

Compare results with standards

Pretreat and prepare sample

Obtain and store sample

ApplyStatistics (Quantitative)

Interpret Data (Qualitative)

Present/Report information in a understandable form

Extract data from sample

Covert data into information

Transform information into

knowledge

After reviewing results might be necessary to modify and repeat procedure

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

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.

See pg. 967 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.

Precise, not accurate Accurate, not precise Accurate and precise

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

Basis Quantitative Analysis

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

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

Concentration

Det

ecto

r re

spon

se

LOQ

LOD

Limit of linearity

Slope relates to sensitivity

Dynamic range

Basis Quantitative Analysis

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

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).

Prefixes for SI Unitsgiga- 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

Scientific Notation and Prefixes

0.00000356 M3.56 x 106 M

3.56 Mor …. ppm

MolarityMoles 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

Working with Numbers: Analytical Concentrations

Basic Statistics

Mean (average) of a population:

Mean (average) of a sample:

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.

The Gaussian Probability Distribution

See pg. 971-972 of Skoog et al., Principles of Instrumental Analysis, Thomson Brooks/Cole, New York, 2007.

100

200

300

400

500

600

Res

pons

e

Measurement (e.g. spectral frequency)

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

Many physical phenomena studied in analytical chemistry result in measurements that can be modeled as Gaussian distributions

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?

Probability Distributions and Measurement Confidence Intervals

2.1 2.3 2.6 2.1 1.9 2.2 1.8 3.8

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…

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

Res

pons

e

Concentration

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

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 relationshipSSreg

Sum, of residuals associated with the null hypothesis – average of the y valuesSStot

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?

mol% 14.10925.2

2567.065.20925.2

2567.0

y

m

byx

mol% 076.0145.10925.2

5/51.1265.2

5

1

1

1

0925.2

1442.0

11

2

2

2

2

xx

cr

Sm

yy

NMm

sstdev

sr= stdev of regression lineM= number of sample measurementsN= number of samples for calibrationy= average peak area of the calibrationSxx = sum of squares of the deviation for x

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 standardI = signali = initial, f = final

[x]f

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 regressionA = 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.

Sample Added [Zn2+] ppm Absorbance1 0.0 0.1962 0.5 0.2893 1.0 0.3834 2.0 0.555

Slope: 0.179Intercept: 0.199

0

0.1

0.2

0.3

0.4

0.5

0.6

0.0 0.5 1.0 1.5 2.0 2.5Zinc Concentration (ppm)

Ab

sorb

ance

Non-Linear Fitting

Function Form Example

Logarithmic S=a+blnC Nernst Equation

Exponential S=aebC Healy's model for immunoassay

Power S=a+bCn Kohlrausch’s Law

Polynomial S=a+bC2+ cC3...Immunometric assays

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.

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

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.200700269http://pubs.acs.org/cen/news/85/i19/8519news6.html

Further Reading

Optional Reading:

Skoog et al. Chapter 1 and Appendix 1

Skoog et al. Chapters 6 and 7 (Spectroscopy)