Modern Experimental Chemistry

273 Pages·1971·7.664 MB·English

by  George W. Latimer, Jr. (Auth.)

MODERN
EXPERIMENTAL
CHEMISTRY
GEORGE W. LATIMER, JR.
PPG Industries
RONALD 0. RAGSDALE
University of Utah
Academic Press New York and London
COPYRIGHT © 1971, BY ACADEMIC PRESS, INC.
ALL RIGHTS RESERVED
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PREFACE
What should a student learn from the introductory chemistry laboratory?
The answer, of course, is not easy particularly since many chemistry de
partments are up-grading these laboratories by the introduction of better in
strumentation. However, we believe, regardless of the changes in chemistry
instruction, that a student:
1. Should have the responsibility of obtaining an original answer.
2. Should, from his data, be able (with some limited coaching and reading)
to draw a definite set of conclusions.
3. Should learn how to use chemical handbooks and have an introduction
to the chemical literature.
4. Should learn to exchange information critically and knowledgeably.
There are a number of approaches to the problem, but the one we favor is
to design experiments which require the best and most careful techniques
(these may not always be quantitative, however) and to require that the
student deal with an unknown.
The use of unknowns has a number of advantages over the traditional
approach:
1. Each student is on his own. Our experience has been that this responsi
bility greatly stimulates his interest.
2. References to the literature can be and should be made available to the
student without fear he will "dry-lab."
3. Since the "right" answer is unknown, any and all discussions among the
student, his neighbors, and teaching assistants are encouraged.
Such an approach also has its problems; suitable standards must be pre
pared and students' results evaluated. A teacher's supplement, available from
the publisher, contains not only directions on the preparation and acquisition
of standards (many analyzed unknowns which are commercially available
work very well in these experiments), but also gives data based on experience
which will permit a ready evaluation of the student's grade.
In this manual, the student does not simply determine the molecular weight
of a compound by depression of the freezing point; but with that information,
appropriate analytical data for the compound and with a given physical
property he is asked to identify the compound. The techniques of qualita
tive analysis are included to reinforce previous experiments on ionic equilib
ria and to teach descriptive chemistry. For many experiments additional
reading assignments are noted. These will provide the interested student
further information and the teacher can use them, as we have, as aids to
familiarize the student with the literature. All readings are in easily acces
sible journals.
This manual takes into consideration that many freshman laboratories have
introduced instrumentation such as single-pan analytical balances, pH
meters and colorimeters, but in most experiments larger samples will permit
use of less expensive equipment with little loss in accuracy.
vii
viii PREFACE
Safety is often simply a subject to which lip-service is paid. We do not
agree. For that reason, general safety is discussed first; in addition, we have
included comments at appropriate places in the manual. Nevertheless, since
we cannot foresee all problems which will arise, we strongly urge that
safety precautions be exercised in all experiments.
This manual contains enough experiments for classes which have 6 hours
of lab (two 3-hour meetings) per week to last two semesters.
SAFETY IN THE LABORATORY
CHEMISTRY CAN BE DANGEROUS! Note we did not say chemistry is
dangerous. There is a world of difference between these two statements;
driving can also be dangerous, but whether it is or not depends not only on
the condition of the car, but on the state of mind of its driver and the traffic.
Every attempt has been made to provide experiments which are fail-safe;
but they are not proof against carelessness or improper and unauthorized
experimentation. The best guarantee for your well-being is to come to the
laboratory well-prepared. (This, of course, implies the revolutionary concept
that you read and plan your work before the laboratory.)
Many schools will have specific safety rules of their own. Familiarize
yourself thoroughly with them. While any rules we can give you cannot
cover every contingency, the following should be scrupulously observed:
1. Learn the locations of the exits, the safety shower, fire extinguishers,
and fire blankets.
2. Wear safety glasses at all times when in the laboratory, not just at the
bench. Many errors can be remedied, but a ruined eye cannot be re
paired and the $600 exemption for blindness will hardly pay the food
for a lead dog. Do not wear contact lenses into the laboratory even with
safety goggles over them since fumes can seep between the lens and
the eyeball and badly irritate the eye. If chemicals do get into the eye,
flood immediately with water, or, preferably with sterile isotonic
solution while holding the lid well open. YELL for the instructor.
3. Do not taste chemicals unless specifically and explicitly told to do so.
4. Since it is very easy to suck liquids into your mouth and since the
vapors of many liquids are very toxic, never pipette by mouth; use a
rubber bulb or regular pipette bulb for pipetting. (Instructions for use
of the bulb are given in the section on volumetric manipulations.)
5. Remember you are not working alone in the laboratory. If you heat
chemicals in a test tube, do not point the opening of the tube toward
your unsuspecting partners or they may retaliate in kind.
SAFETY IN THE LABORATORY
χ
6. Dispense ammonia, volatile and/or flammable liquids, and poisonous
gases in the hoods.
7. When mixing solutions, add one reagent to another with constant
stirring. When diluting acid, always do what you ought'er, ADD THE
ACID TO THE WATER.
8. Before heating solutions, either add boiling beads or insert a stirring
rod to prevent bumping of the liquid.
9. When inserting glass tubing or thermometers into stoppers, lubricate
the tubing and the walls of the bore with glycerol or water. Wrap the
rod in a towel and grasp the tubing as close to the end being inserted
as possible. Slide the glass into the rubber stopper with a twisting
motion; do not push. Remove the excess lubricant.
10. Do not look directly into containers which are boiling or in which
reactions are taking place. Do not sniff fumes directly; waft the fumes
toward you with your hand.
SAFETY IN THE LABORATORY xi
11. Any and all accidents must be reported to the laboratory instructor.
Flush acids, bases, or other corrosive materials from the skin or clothes
immediately with large amounts of water.
12. Police your laboratory area during and after the laboratory period.
Wipe up spillages immediately. Do not throw water-insoluble mate
rials, e.g., glass, matches, filter paper, in the sink; containers are
provided. Thoroughly wash your hands before leaving.
13. Double-check the labels on chemicals before using. Discard, do not
return any unused chemical to the bottle; return runs the risk of contam
ination and others who use the chemical may get unsatisfactory results.
14. Do not use a beaker as a drinking glass even though it may be one
which you are absolutely sure has never been used for laboratory
work. Do not eat and do not smoke in the laboratory.
EXPERIMENT
1 THE EVALUATION OF DATA
Shown in Table 1-1 are three sets of analytical data obtained by different
people on the same sample.
TABLE 1-1
Determination of Copper in a Geological Sample
Analyst Percent copper found
A 0.56, 0.55, 0.59, 0.58
Β 0.53, 0.61, 0.49, 0.65
C 0.68, 0.67, 0.66, 0.53
Of these 12 results which is the true value? The truth is that we do not
know; any one of them might be correct. How might we choose? We could
make our selection on the basis of someone's authority, on what the majority
of our colleagues suggest, or on what custom demands, but any selection of
this type could not be scientific. There are only two statements which can
be made about the sample which have some basis in fact, i.e.,
1. The sample does not contain more than 100% Cu, nor less than 0% Cu.
(Such a statement may seem ridiculously obvious, but it is the only ab­
solutely certain statement that can be made about the Cu content.)
2. It appears that the percent Cu lies somewhere between 0.49 and 0.68.
(This statement, however, must be made with caution because another
determination might give an entirely different result.)
In the absence of any idea of what the true answer is, how can the true
answer be estimated?
We could sum all the values, divide by the number of values, and use
the mean (average) as the best estimate of the true value. Our estimate
would then be
(0.56+ 0.55... 0.53) or 0.59%
12
We might assume that the best estimate of the true value is that value which
appears most often. This term is known as the mode and in this set of data
it is 0.53%.
1
2 EXPERIMENT 1
We could rank the data beginning with the highest value down to the
lowest and select, as our estimate, a value such that half the values lie above
the selected value and half below. This value is known as the median. In
this set of data the median is 0.585%. (In computing the median, all data,
including identical values, are counted individually. If there is an even
number of items the number is halfway between the two middle terms.)
Conventionally, the average is used as the best estimate of data. (For
small sets of information, the median is still better, but in reporting results
in this class the average will be used.)
Simple use of the average in estimating the true value is not very informa­
tive unless we have some idea how close to the "true" value we might be.
(Remember, we do not know what the true value is!) There are other prob­
lems, also, with the use of the average. If you look at the results again —not
now as an aggregate, but as reported by the analysts — certain problems are
apparent. The average values for the sets reported by A and Β are the same.
Which set do you prefer? Most people, including these authors, would select
A's results as best simply because they are closer together. Since we do not
know the true value and since we are not aware of gross errors, it is quite
natural to equate closely agreeing values as being close to the true value.
Such an assumption is completely incorrect! If, for example, you were given
a ruler which had been mismarked and you repeatedly measured the same
item, your results would be close together, but not correct. In summary, there
are two ways in which data are judged:
1. In terms of how close the average is to the true value. (This is called
the accuracy.)
2. In terms of the internal consistency of the values which indicates how
closely the results can be reproduced. (This is called the precision.)
C's values offer another problem. Three results are close together, but the
fourth is considerably different. Did C goof? In scientific terms, was C sub­
ject to a determinate error? Determinate or systematic errors are constant
in magnitude and direction. They affect the accuracy. Such errors can be
detected and eliminated. The use of an incorrect molecular weight in a
calculation would be an example. If a determinate error is not involved,
then might the value 0.53 arise simply by indeterminate or random (chance)
errors? These errors vary in magnitude and direction and they affect the
precision. Such errors cannot be detected nor eliminated. Table 1-2 lists
TABLE 1-2
Examples of Determinate and Indeterminate Errors
Determinate/Systematic Indeterminate/Random
Use of incorrect molecular weight Estimating the final figure in a buret reading
in calculations
Placing a standard solution, e.g., Determining when an indicator changes color
1 Μ NaOH in a wet container
Absorption by the caustic of C0 Changes in temperature during the measurement
2
from the atmosphere of a temperature-dependent property
THE EVALUATION OF DATA
3
some examples of the types of errors which may be encountered in this and
other experiments. Perhaps (although the thought may be horrifying) 0.53 is
really a better estimate of the true value than the average of C's other results.
(Note that the value 0.53 is closer to the overall average than the average
of C's three other values.) If the value of 0.53 is a mistake, it could and should
be discarded. However, there must be a consistent way in which we deter­
mine whether a difference this large could be expected by chance in a series
of results. Otherwise, we may be in the most unscientific position of selecting
only the results that we like. If 0.53 can be rejected, the average of C's values
is quite different from those obtained by A and B. To answer the questions
posed, we obviously need a way to decide whether differences from the
average arise from random errors or whether they arise from systematic
errors (mistakes). More simply stated, are large or small errors more common?
To decide whether large or small errors are more common, let us turn our
attention for a moment to a somewhat different problem. What is the average
height of the American male adult? If we measured every American male
adult and plotted the number of people of a given height versus a height
interval, i.e., arbitrarily all people from 4 ft 6 in. to 5 ft 6 in. would be regis­
tered as being 5 ft tall, the graph (actually it is called a histogram) might look
like the one shown in Fig. 1-1. The graph tells quite a bit. First, we can get
1 =
ο ^3
d ^
5 6
Height in feet
FIG. 1-1. Plot of the heights of all the male adults in America.
some idea what the average height is. Second, we note that the number of
American males 3 ft or 8 ft tall is very small. In another way, though, the
histogram is disappointing. Since the height interval is rather large, our esti­
mate of the average height from this graph is rather crude; we could improve
the estimate by decreasing the interval, i.e., instead of plotting in 1 ft inter­
vals, we could plot heights in 1 in. intervals. The smaller we make the inter­
val the more accurate will be our estimate of the average. If the interval is
made very small, our graph approaches a smooth curve which can be des­
cribed mathematically by the equation:
e-(x - μ)2Ι2σ*
y =
The curves in Fig. 1-2 are described by this equation. These curves, known as
normal distributions, are found from experimentally observed facts to des­
cribe the behavior of many sets of experimental phenomena. In the above