Tools of the Trade

1
Tools of the Trade
Laboratory Notebook

• Objectives of a Good Lab Notebook
• State what was done
• State what was observed
• Be easily understandable to someone else

2
Tools of the Trade

• Laboratory Notebook
• Bad Laboratory Practice (A Recent Legal Case)
• Medichem Pharmaceuticals v. Rolabo Pharmaceuticals

Two Patents describe a method for making the
antihistamine drug Loratidine (Claritin) - US
sales of 2.7 billion - the two patents are
essentially identical - Medichem sued to
invalidate Rolabo patent and claimed priority -
Medichem had to prove it used the method to make
loratidine before Rolabo did A co-inventors lab
notebook was a primary piece of evidence to
support Medichems claim - documented analysis
of a sample claimed to be made using the patented
method - NMR spectral data confirmed the
production of loratidine The evidence was not
enough to support Medichem's claim of reduction
to practice - NMR data do not show the process
by which loratidine was made - lab books were
not witnessed
Rolabo Pharmaceuticals won the case (and the
rights to make Loratidine) because of problems
with a Lab Notebook!!
Nature Reviews Drug Discovery (2006) 5, 180
3
Tools of the Trade

• Weight Measurements
• 1.) Analytical Balance (principal of operation)
• (i) sample on balance pushes the pan down with a
  force equal to m x g
• M is mass of object
• g is acceleration of gravity
• (ii) balance pan with equal and opposing
  mass
• Mechanical standard masses
• Electronic opposing electromagnetic force
• (iii) tare mass of empty vessel (pan)

• Double-pan balance
• balance beam suspended on a sharp knife edge
• Standard weights are added to the second pan to
  balance sample weight
• Weight of sample is equal to the total weight of
  standards

4
Tools of the Trade
Weight Measurements

• Single-pan balance
• balance beam suspended on a sharp knife edge
• Sample pan is balanced by counterweights on right
• Knob adjusted to remove weights from a bar above
  the pan
• Pan is moved back to its original position and
  the removed
• weights equals the mass of the sample.

5
Tools of the Trade
Weight Measurements

• Electronic balance
• Uses electromagnetic force to return the pan to
  original position
• Electric current required to generate the force
  is proportional to sample mass

Determines amount of deflection of pan due to
mass of sample
Increase in current causes magnetic field that
raises pan
6
Tools of the Trade

• Weight Measurements
• 2.) Methods of Weighing
• (i) Basic operational rules
• Chemicals should never be placed directly on the
  weighing pan
• - corrode and damage the pan may affect
  accuracy
• - not able to recover all of the sample
• Balance should be in arrested position when
  load/unload pan
• Half-arrested position when dialing weights
• - dull knife edge and decrease balance
  sensitivity ? accuracy
• (ii) Weight by difference
• Useful for samples that change weight upon
  exposure to the atmosphere
• - hygroscopic samples (readily absorb water
  from the air)
• Weight of sample ( weight of sample weight of
  container) weight of container
• (iii) Taring
• Done on many modern electronic balances
• Container is set on balance before sample is
  added

7
Tools of the Trade

• Weight Measurements
• 3.) Errors in Weighing Sources
• (i) Any factor that will change the apparent
  mass of the sample
• Dirty or moist sample container
• - also may contaminate sample
• Sample not at room temperature
• - avoid convection air currents (push/lift pan)
• Adsorption of water, etc. from air by sample
• Vibrations or wind currents around balance
• Non-level balance

Office dust
Tolerance (mg) Tolerance (mg) Tolerance (mg) Tolerance (mg)
Grams Class 1 Class 2 Milligrams Class 1 Class 2
500 1.2 2.5 500 0.010 0.025
200 0.5 1.0 200 0.010 0.025
100 0.25 0.50 100 0.010 0.025
50 0.12 0.25 50 0.010 0.014
20 0.074 0.10 20 0.010 0.014
10 0.050 0.074 10 0.010 0.014
5 0.034 0.054 5 0.010 0.014
2 0.034 0.054 2 0.010 0.014
1 0.034 0.054 1 0.010 0.014
8
Tools of the Trade

• Weight Measurements
• 3.) Errors in Weighing Sources
• (i) Any factor that will change the apparent
  mass of the sample
• Buoyancy errors failure to correct for weight
  difference due to displacement of air by the
  sample.
• Correction for buoyancy to give true mass of
  sample
• m true mass of sample

balsa
Different displacement of ice and balsa wood in
water
ice
9
Tools of the Trade
Weight Measurements 3.) Errors in Weighing
Sources Example The densities (g/ml) of several
substances are acetic acid 1.05 CCl4
1.59 Sulfur 2.07 lithium 0.53 mercury
13.5 PbO2 9.4 lead 11.4 iridium 22.5 From
the following figure predict
which substance will have the smallest percentage
buoyancy correction and which will have the
greatest.
10
Tools of the Trade

• Weight Measurements
• 3.) Errors in Weighing Sources
• (i) Any factor that will change the apparent
  mass of the sample
• Density of air changes with temperature and
  pressure
• To get da under non standard conditions
• B Barometer pressure (torr)
• V vapor pressure of water in the air (torr)
• T air temperature (K)

11
Tools of the Trade

• Volume Measurements
• 1.) Burets
• (i) Purpose used to deliver multiple aliquots
  of a liquid in known volumes
• (ii) Correct use of buret
• Read buret at the bottom of a concave meniscus

Buret volume (ml) Smallest graduation (ml) Tolerance (mL)
5 0.01 0.01
10 0.05 or 0.02 0.02
25 0.1 0.03
50 0.1 0.05
100 0.2 0.10
Meniscus at 9.68 mL
12
Tools of the Trade

• Volume Measurements
• 1.) Burets
• (iii) always read the buret at the same eyelevel
  as the liquid
• Avoids parallax errors

eyelevel
View from above
15.46 mL
15.31 mL 1 error
13
Tools of the Trade
Volume Measurements 1.) Burets (v) Estimate
the buret reading to the nearest 1/10 of a
division (vi) expel all air bubbles
from the stopcock prior to use (vii)
rinse the buret with a solution 2-3x before
filling the buret for a titration (viii)
Near the end of a titration, volume of 1 drop or
less per delivery should be
used with the buret.
14
Tools of the Trade
Volume Measurements 2.) Volumetric Flasks (i)
Purpose used to prepare a solution of a single
known volume (ii) Types of volumetric
flasks
Flask Capacity (mL) Tolerance (mL)
1 0.02
2 0.02
5 0.02
10 0.02
25 0.03
50 0.05
100 0.08
200 0.10
250 0.12
500 0.20
1000 0.30
2000 0.50
15
Tools of the Trade

• Volume Measurements
• 2.) Volumetric Flasks
• (iii) Correct use of volumetric flask
• Add reagent or solution to flask and dissolve in
  volume of solvent less than the total capacity of
  the flask
• Slowly add more solvent until the meniscus bottom
  is level with the calibration line.
• Stopper the flask and mix solution by inversion
  (40 or more times)
• (for later use) Remix by inverting the flask if
  the solution has been sitting unused for more
  than several hours
• Glass adsorbs trace amount of chemicals?clean
  using acid wash

stopper
16
Tools of the Trade

• Volume Measurements
• 3.) Pipets and Syringes
• (i) Use to deliver a given volume of liquid
• (ii) Types of pipets
• Transfer pipet
• - similar to volumetric flask
• - transfers a single volume ? fill to
  calibration mark
• - last drop does not drain out of the pipet ?
  do not blow out
• - more accurate than measuring pipet
• Measuring pipet
• - calibrated similar to buret
• - use to delivery a variable volume

Transfer Pipet Transfer Pipet
Volume (mL) Tolerance (mL)
0.5 0.006
1 0.006
2 0.006
3 0.01
4 0.01
5 0.01
10 0.02
15 0.03
20 0.03
25 0.03
50 0.05
100 0.08
17
Tools of the Trade

• Volume Measurements
• 3.) Pipets and Syringes
• (ii) Types of pipets
• Micropipet
• - deliver volumes of 1 to 1000 mL (fixed
  variable)
• - uses disposable polypropylene tip
• - stable in most aqueous and organic solvents
  (not chloroform)
• - need periodic calibration

10 of pipet volume 10 of pipet volume 100 of pipet volume 100 of pipet volume
Pipet volume (mL) Accuracy () Precision () Accuracy () Precision ()
Adjustable pipets
0.2-2 8 4 1.2 0.6
1-10 2.5 1.2 0.8 0.4
2.5-25 4.5 1.5 0.8 0.2
10-100 1.8 0.7 0.6 0.15
30-300 1.2 0.4 0.4 0.15
100-1000 1.6 0.5 0.3 0.12
Fixed pipets
10 0.88 0.4
25 0.88 0.3
100 0.5 0.2
500 0.4 0.18
1000 0.33 0.12
Disposable tip
18
Tools of the Trade

• Volume Measurements
• 3.) Pipets and Syringes
• (ii) Types of pipets
• Syringes
• - deliver volumes of 1 to 500 mL
• - accuracy precision 0.5-1
• - steel needle permits piercing stopper to
  transfer liquid under controlled atmosphere
• gt attacked by strong acid and contaminate
  solution with iron
• (iii) Correct use of pipets and syringes
• Use a bulb, never your mouth, for drawing
  solutions into pipets.
• Rinse pipets and syringes before using

19
Tools of the Trade

• Filtration
• 1.) Mechanical separation of a liquid from the
  undissolved particles floating in it.
• 2.) Purpose used in gravimetric analysis for
  analysis of a substance by the mass of a
  precipitate it produces
• (i) Solid collected in paper or fritted-glass
  filters
• 3.) Process
• (i) pour slurry of precipitate down a glass
  rod to
• prevent splattering.
• (ii) dislodge solid from beaker/rod with
  rubber
• policeman
• (iii) use wash liquid (squirt bottle) to
  transfer particles to
• filter paper

Rubber policeman
20
Tools of the Trade

• Drying
• 1.) Purpose (i) to remove moisture from
  reagents or samples
• (ii) to convert sample to a more readily
  analyzable form
• 2.) Oven Drying commonly used for reagent or
  sample preparation
• Typically 110 oC for H2O removal
• Use loose covers to prevent contamination from
  dust
• 3.) Dessicator used to cool and store reagent
  or sample over long periods of time.

21
Experimental Error Data Handling
Introduction 1.) There is error or uncertainty
associated with every measurement. (i) except
simple counting 2.) To evaluate the validity of
a measurement, it is necessary to evaluate its
error or uncertainty
You can read the name of the boat on the left
picture, which is lost in the right picture.
Can you read the tire manufacturer?
Same Picture Different Levels of Resolution
22
Experimental Error Data Handling
Significant Figures 1.) Definition The minimum
number of digits needed to write a given value
(in scientific notation) without loss of
accuracy. (i) Examples 142.7 1.427 x
102 0.006302 6.302 x10-3 2.) Zeros are
counted as significant figures only if (i)
occur between other digits in the
number 9502.7 or 0.9907 (ii) occur at the
end of number and to the right of the decimal
point 177.930
Both numbers have 4 significant figures
Zeros are simple place holders
Both zeros are significant figures
zero is a significant figure
23
Experimental Error Data Handling
Significant Figures 3.) The last significant
figure in any number is the first digit with any
uncertainty (i) the minimum uncertainty is 1
unit in the last significant figure (ii) if the
uncertainty in the last significant figure is
10 units, then one less
significant figure should be used. (iii)
Example 9.34 0.02 3 significant
figures But 6.52 0.12 should be 6.5
0.1 2 significant figures 4.) Whenever
taking a reading from an instrument, apparatus,
graph, etc. always estimate the result to the
nearest tenth of a division (i) avoids losing
any significant figures in the reading process
7.45 cm
24
Experimental Error Data Handling

• Significant Figures
• 5.) Addition and Subtraction
• (i) use the following procedure
• Express all numbers using the same exponent
• Align all numbers with respect to the decimal
  point
• Add or subtract using all given digits
• Round off the answer so that it has the same
  number of digits to the right of the decimal as
  the number with the fewest decimal places

12.5 x 104 2.48 x 104 1.235 x 104
1.25 x 105 2.48 x 104 1.235 x 104
1 decimal point
12.5 x 104 2.48 x 104 1.235 x 104
16.215 x 104 16.2 x 104
25
Experimental Error Data Handling

• Significant Figures
• 5.) Addition and Subtraction
• (i) use the following procedure
• Round off the answer to the nearest digit in the
  least significant figure.
• Consider all digits beyond the least significant
  figure when rounding.
• If a number is exactly half-way between two
  digits, round to the nearest even digit.
• - minimizes round-off errors
• Examples

3 sig. fig. 12.534 ? 12.5 4 sig.
fig. 11.126 ? 11.13 4 sig. fig. 101.250
? 101.2 3 sig. fig. 93.350 ? 93.4
26
Experimental Error Data Handling

• Significant Figures
• 6.) Multiplication and Division
• (i) use the following procedure
• Express the answers in the same number of
  significant figures as the number of digits in
  the number used in the calculation which had the
  fewest significant figures.
• Examples

3.261 x 10-5 x 1.78 5.80 x 10-5
3 significant figures
34.60 ) 2.4287 14.05
4 significant figures
27
Experimental Error Data Handling
Significant Figures 7.) Logarithms and
Antilogarithms (i) the logarithm of a number a
is the value b, where (ii)
example (iii) The antilogarithm of b is
a (iv) the logarithm of a is expressed in
two parts
a 10b or Log(a) b
The logarithm of 100 is 2, since 100 102
a 10b
Log(339) 2.530
mantissa
character
28
Experimental Error Data Handling
Significant Figures 7.) Logarithms and
Antilogarithms (v) when taking the logarithm of
a number, the number of significant figures
in the resulting mantissa should be the
same as the total number of
significant figures in the original number
a (vi) Example Log(5.403 x 10-8)
-7.2674 (vii) when taking the antilogarithm
of a number, the number of significant
figures in the result should be the same as
the total number of significant
figures in the mantissa of the original logarithm
b (viii) Example Antilog(-3.42) 3.8 x
10-4
4 sig. fig.
4 sig. fig.
2 sig. fig.
2 sig. fig.
29
Experimental Error Data Handling
Significant Figures 8.) Graphs (i) use graph
paper with enough rulings to accurately graph the
results (ii) plan the graph
coordinates so that the data is spread over as
much of the graph as
possible (iii) in reading graphs, estimate
values to the nearest 1/10 of a division on the
graph
30
Experimental Error Data Handling
Significant Figures 8.) Graphs (ii) plan the
graph coordinates so that the data is spread over
as much of the graph as
possible (iii) in reading graphs,
estimate values to the nearest 1/10 of a division
on the graph
31
Experimental Error Data Handling
Errors 1.) Systematic (or Determinate)
Error (i) An error caused consistently in all
results due to inappropriate methods or
experimental techniques. (ii) Results in all
measurements exhibiting a definite difference
from the true value. (iii)
This type of error can, in principal, be
discovered and corrected.
Buret incorrectly calibrated
32
Experimental Error Data Handling
Errors 2.) Random (or Indeterminate) Error (i)
An error caused by random variations in the
measurement of a physical
quantity. (ii) Results in a scatter of results
centered on the true value for repeated
measurements on a single sample. (iii) This
type of error is always present and can never be
totally eliminated
True value
Random Error
Systematic Error
33
Experimental Error Data Handling

• Errors
• 3.) Accuracy and Precision
• (i) Accuracy refers to how close an answer is
  to the true value
• Generally, dont know true value
• Accuracy is related to systematic error
• (ii) Precision refers to how the results of a
  single measurement compares from one trial to the
  next
• Reproducibility
• Precision is related to random error

34
Experimental Error Data Handling
Errors 4.) Absolute and Relative
Uncertainty (i) Both measures of the precision
associated with a given measurement. (ii)
Absolute uncertainty margin of uncertainty
associated with a measurement (iii)
Example If a buret is calibrated to read
within 0.02 mL, the absolute uncertainty for
measuring 12.35 mL is Absolute Uncertainty
12.35 0.02 mL (iv) Relative uncertainty
compares the size of the absolute uncertainty
with the size of its associated
measurement (v) Example For a buret
reading of 12.35 0.02 mL, the relative
uncertainty is
(Make sure units cancel)
1 sig. fig.
35
Experimental Error Data Handling

• Errors
• 5.) Propagation of Uncertainty
• (i) The absolute or relative uncertainty of a
  calculated result can be estimated
• using the absolute or relative
  uncertainties of the values used to obtain that
• result.
• (ii) Addition and Subtraction
• The absolute uncertainty of a number calculated
  by addition or subtraction is obtained by using
  the absolute uncertainties of numbers used in the
  calculations as follows
• Example

• Value Abs. Uncert.
• 1.76 ( 0.03)
• 1.89 ( 0.02)
• 0.59 ( 0.02)
• 3.06

Answer
36
Experimental Error Data Handling

• Errors
• 5.) Propagation of Uncertainty
• (iii) Once the absolute uncertainty of the
  answer has been determined, its
• relative uncertainty can also be calculated,
  as described previously.
• Example (using the previous example)
• Note To avoid round-off error, keep one digit
  beyond the last significant figure in all
  calculations.
• - drop only when the final answer is obtained

1 sig. fig.
Round-off errors
37
Experimental Error Data Handling

• Errors
• 5.) Propagation of Uncertainty
• (i) Multiplication and Division
• The relative uncertainties are used for all
  numbers in the calculation
• Example

3 sig. fig.
1 sig. fig.
38
Experimental Error Data Handling

• Errors
• 5.) Propagation of Uncertainty
• (ii) Once the relative uncertainty of the answer
  has been obtained, the absolute uncertainty can
  also be calculated
• (iii) Example (using the previous example)

Rearrange
1 sig. fig.
39
Experimental Error Data Handling

• Errors
• 5.) Propagation of Uncertainty
• (iv) For calculations involving Both
  additions/subtractions and
• multiplication/divisions
• Treat calculation as a series of individual steps
• Calculate the answer and its uncertainty for each
  step
• Use the answers and its uncertainty for the next
  calculation, etc.
• Continue until the final result is obtained
• (v) Example
• First operation differences in brackets

3 sig. fig.
3 sig. fig.
1 sig. fig., but carry two sig. fig. through
calculation
40
Experimental Error Data Handling

• Errors
• 5.) Propagation of Uncertainty
• (v) Example
• Second operation Division

Convert to relative uncertainty
3 sig. fig.
1 sig. fig.
41
Experimental Error Data Handling
Errors 5.) Propagation of Uncertainty (vi)
Uncertainty of a result should be consistent with
the number of significant figures
used to express the result. (vii)
Example 1.019 (0.002) 28.42
(0.05) But 12.532 (0.064) ? too many
significant figures 12.53 (0.06) ? reduce to
1 sig. fig. in uncertainty same
reduction in results
Result uncertainty match in decimal place
The first digit in the answer with any
uncertainty associated with it should be the last
significant figure in the number.
42
Experimental Error Data Handling
Errors 5.) Common Mistake (vi) Number of
Significant Figures is Not the number shown on
your calculator.
Not 10 sig. fig.
43
Experimental Error Data Handling
Errors Example Find the absolute and percent
relative uncertainty and express the answer with
a reasonable number of significant figures
4.97 0.05 1.86 0.01/21.1 0.2