Analytical Chemistry
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Transcript of Analytical Chemistry
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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
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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)
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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
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Chapter 1:Chemical Measurements
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Chemical Concentrations
liter
moles(M)Molarity
L
mg
grams 1000
mg
grams10
grams10
grams10
gram 1 ppm
3
-3
6
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Dilution Equation
Concentrated HCl is 12.1 M. How many milliliters should be diluted to 500 mL to make 0.100 M HCl?
M1V1 = M2V2
(12.1 M)(x mL) = (0.100 M)(500 mL)
x = 4.13 M
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Chapter 3:Math Toolkit
accuracy = closeness to the true or accepted value
precision = reproducibility of the measurement
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Significant Figures
Digits in a measurement which are known with certainty, plus a last digit which is estimated
beaker graduated cylinder buret
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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)
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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
6077063.56542
ans = 63.5
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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
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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 EVEN
158.5 = 158157.5 = 157
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Wait until the END of a calculation in order to Wait until the END of a calculation in order to avoid a “rounding error”avoid a “rounding error”
(1.23(1.2355 - 1.0 - 1.022) x 15.23) x 15.2399 = 2.923438 = = 2.923438 = 1.11.122
1.231.2355-1.0-1.022
0.20.21515 = 0.2 = 0.222
? sig figs? sig figs 5 sig figs5 sig figs
3 sig figs3 sig figs
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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%
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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 0.15129.31 et
......eeee
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2
3
2
2
2
1t
2
3
2
2
2
1
2
t
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Multplication & Division
......ee%ee
......eeee
2
3
2
2
2
1t
2
3
2
2
2
1
2
t
%%
%%%
0.02)( 0.59
0.02)( 1.89 x 0.03)( 1.76
4.0%
1.7
0.59
100*0.02
1.89
100*0.02
1.76
100*0.03e
222
t
222 )4.3()1.1(
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Combined Example
0.35)( 2.57
0.020)( 0.25 0.10)( 1.10
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Chapter 4:Statistics
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Gaussian Distribution:
Fig 4.2
22 2/)(exp2
1);;(
ii xxP
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N
xN
ii
1
2)(
1
)(1
2_
N
xx
s
N
ii
Standard Deviation – measure of the spread of the data (reproducibility)
Infinite population Finite population
Mean – measure of the central tendency or average of the data (accuracy)
N
iixN
1
1lim
Infinite population
N
iixN
x1
_ 1
Finite population
N
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Standard Deviation and Probability
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Confidence Intervals
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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
N
ts
N
ts
N
ts xμ
_
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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 = 2.776 (N-1)
= 12.55 ± (0.465)(2.776)/sqrt(5)
= 12.55 ± 0.58
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Rejection of Data - the "Q" Test
A way to reject data which is outside the parent population.
value smallest-value largest
neighbor nearestvalue lequestionabQexp
Compare to Qcrit from a table at a given confidence interval.
Reject if Qexp > Qcrit
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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%?
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Linear Least Squares- finding the best fit to a straight line
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