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There are two ways of expressing molar concentration: Molar analytical concentration is the total number of moles of a solute, regardless of its chemical state, 

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The analytical molarity describes how a solution of a given molarity can be prepared Equilibrium molarity is the molar concentration of a particular species in a

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example, we measure the quantity of heat produced during a chemical reac- tion in joules, (J), Table 2 1 Fundamental SI Units of Importance to Analytical Chemistry Both molarity and formality express concentration as moles of solute

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Solutions Manual to Analytical Chemistry 2 1 by David Harvey (Summer 2016) Copyright tion B to calculate the concentration of solution A; thus Solution A: 

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0.00.20.40.60.81.0

0.00.20.40

.60.81.0metal in excess ligand in excess X L absorbance stoichiometric mixtureIn

HInpH = pK

a,HIn indicator"s color transition rangeindicator is color of In indicator is color of H In pH

2.953.003.053.103.153.203.25Mass of Pennies (g)

Phase 2

Phase 1 (a)(b)

Analytical Chemistry 2.1

Solutions Manual

Production History

Print Version

Solutions Manual to Modern Analytical Chemistry by David Harvey

ISBN 0-697-39760-3

Copyright 2000 by McGraw-Hill Companies

Copyright transferred to David Harvey, February 15, 2008

Electronic Version

Solutions Manual to Analytical Chemistry 2.1 by David Harvey (Summer 2016)

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ous section.

Table of Contents

Chapter 1 ......................................................5 Chapter 2 ......................................................9 Chapter 3 ....................................................15 Chapter 4 ....................................................21 Chapter 5 ....................................................39 Chapter 6 ....................................................51 Chapter 7 ....................................................79 Chapter 8 ....................................................95 Chapter 9 ..................................................115 Chapter 10 ................................................157 Chapter 11 ................................................181 Chapter 12 ................................................201 Chapter 13 ................................................217 Chapter 14 ................................................227 Chapter 15 ................................................245 Appendix ...................................................249

Chapter 1 Introduction to Analytical Chemistry

Chapter 1

1. (a) A qualitative and a quantitative analysis is the best choice because we need to determine the identify of the possible contaminants and determine if their concentrations are greater than the expected back- ground levels. (b) A forged work of art often contains compounds that are not pres- ent in authentic materials or contains a distribution of compounds that do not match the authentic materials. Either a qualitative anal- ysis (to identify a compound that should not be present in authentic materials) or a quantitative analysis (to determine if the concentra- tions of compounds present do not match the distribution expected in authentic materials) is appropriate. (c) Because we are interested in detecting the presence of specic compounds known to be present in explosive materials, a qualitative analysis is the best choice. (d) A compound"s structure is one of its characteristic properties; a characterization analysis, therefore, is the best approach. (e) In searching for a new acid-base indicator we are seeking to im- prove the performance of an existing analytical method, which re- quires a fundamental analysis of the method"s properties. (f) A quantitative analysis is used to determine if an automobile emits too much carbon monoxide. 2. Answers to this problem will vary, but here is a list of important points that you might address: e goal of this research is to develop a fast, automated, and real-time instrumental method for determining a coee"s sensory prole that yields results similar to those from trained human sensory panels. One challenge the authors have to address is that a human sensory panelist reports results on a relative scale, typically 0-10, for charac- teristics that are somewhat arbitrary: What does it mean, for example, to say that a coee is bitter? An instrumental method, on the other hand, reports results on an absolute scale and for a clearly dened signal; in this case, the signal is a raw count of the number of ions with a particular mass-to-charge ratio. Much of the mathematical processing described by the authors is used to transform the instru- mental data into a relative form and to normalize the two sets of data to the same relative scale. e instrumental technique relies on gas chromatography equipped with a mass spectrometer as a detector. e specic details of the instrument are not important, but the characteristics the authors de- scribe—low fragmentation, high time resolution, broad linear dy-

See Chapter 12 for a discussion of gas

chromatography and for detection using a mass spectrometer.

Solutions Manual for Analytical Chemistry 2.1

namic range—are important. When a species enters a mass spectrom- eter it is ionized (the PTR—proton transfer reaction—in PTR-MS simply describes the method of ionization) and the individual ions, being unstable, may decompose into smaller ions. As a roasted coee has more than 1000 volatile components, many of which do not con- tribute to the sensory prole, the authors wish to limit the number of ions produced in the mass spectrometer. In addition, they want to ensure that the origin of each ion traces back to just a small number of volatile compounds so that the signal for each ion carries information about a small number of compounds. Table 1, for example, shows that the 16 ions monitored in this study trace back to just 32 unique volatile compounds, and that, on average, each ion traces back to 3-4 unique volatile compounds with a range of one to eight. e authors need high time resolution so that they can monitor the release of volatile species as a function of time, as seen in Figure 1, and so that they can report the maximum signal for each ion during the three-minute monitoring period. A rapid analysis also means they can monitor the production of coee in real time on the production line instead of relying on a lengthy o-line analysis completed by a sensory panel. is is advantageous when it comes to quality control where time is important. A broad linear dynamic range simply means there is a linear relation- ship between the measured signal and the concentration of the com- pounds contributing to that signal over a wide range of concentra- tions. e assumption of a linear relationship between signal and con- centration is important because a relative change in concentration has the same aect on the signal regardless of the original concentration. A broad range is important because it means the signal is sensitive to a very small concentration of a volatile compound and that the signal does not become saturated, or constant, at higher concentrations of the volatile compound; thus, the signal carries information about a much wider range of concentrations. To test their method, the authors divide their samples into two sets: a training set and a validation set. e authors use the training set to build a mathematical model that relates the normalized intensities of the 16 ions measured by the instrument to the eight normalized relative attributes evaluated by members of the sensory panel. e specic details of how they created the mathematical model are not important here, but the agreement between the panel"s sensory prole and that predicted using the instrumental method generally is very good (see Figure 3; note that the results for Espresso No. 5 and No.

11 show the least agreement).

Any attempt to create a model that relates one measurement (results from the sensory panel) to a second measurement (results from the

For a discussion of the relationship be-

tween signal and concentration, see Chap- ter 5.

For a discussion of quality control and

quality assurance, see Chapter 15.

Chapter 1 Introduction to Analytical Chemistry

instrumental analysis) is subject to a number of limitations, the most important of which is that the model works well for the data set used to build the model, but that it fails to work for other samples. To test the more general applicability of their model—what they refer to as a robust model—the authors use the model to evaluate the data in their validation set; the results, shown in Figure 4, suggest that the can apply their model both to coees of the same type, but harvested in a dierent year, and to coees of a dierent type.

See Chapter 14 for a discussion of robust-

ness and other ways to characterize an an- alytical method.

Solutions Manual for Analytical Chemistry 2.1

Chapter 2 Basic Tools of Analytical Chemistry

Chapter 2

1. (a) 3 signicant gures; (b) 3 signicant gures; (c) 5 signicant g-

ures; (d) 3 signicant gures; (e) 4 signicant gures; (f) 3 signicant gures For (d) and for (e), the zero in the tenths place is not a signicant digit as its presence is needed only to indicate the position of the decimal point. If you write these using scientic notation, they are 9.0310 -2 and 9.03010 -2 , with three and four signicant gures respectively. 2. (a) 0.894; (b) 0.893; (c) 0.894; (d) 0.900; (e) 0.0891 3. (a) 12.01; (b) 16.0; (c) 6.02210 23
mol -1 ; (d) 9.6510 4 C/mol 4. a. 4.591 0.2309 67.1 71.9 b. 313 - 273.15 39.85 39.8 Note that for (b) we retain an extra signicant gure beyond that sug- gested by our simple rules so that the uncertainty in the nal answer (1 part out of 398) is approximately the same as the most uncertain of our two measurements (1 part out of 313). Reporting the answer as

40, or 4.010

1 , as suggested by our simple rules, gives an uncertainty in the nal result of 1 part out of 40, which is substantially worse than either of our two measurements. c. 712 8.6 6123.2 6.110 3 d. 1.43/0.026 55.00 55 e. (8.314 298)/96 485 2.5710 -2 f. log(6.53 10 -5 ) -4.18509 = -4.185 Note that when we take the logarithm of a number, any digits before the decimal point provide information on the original number"s pow- er of 10; thus, the 4 in -4.185 is not counted as a signicant dig it. g. 10 -7.14

7.24410

-8 7.210 -8 Note that we take the antilog of a number, the digits before the dec- imal point provide information on the power of 10 for the resulting answer; thus, the 7 in -7.14 is not counted as a signicant digit. h. (6.51 10 -5 ) (8.14 10 -9 ) 5.2991410 -13

5.3010

-13 5. To nd the %w/w Ni, we rst subtract the mass of Co from point B from the combined mass of Ni and Co from point B, and then divide by the mass of sample; thus ..121374

0230600813

100

121374

014931230gsample

gNi gsample gNi%w/wNi#

In problem 4 we use a bold red font to

help us keep track of signicant gures.

For example, in (a) we mark the last sig-

nicant digit common to the numbers we are adding together, and in (e), where we are multiplying and dividing, we identify the number with the smallest number of signicant digits.

Solutions Manual for Analytical Chemistry 2.1

6. Using the atomic weights from Appendix 18, we nd that the formula weight for Ni(C 4 H 14 Nquotesdbs_dbs17.pdfusesText_23