Tools of the Trade
Laboratory Notebook
Objectives of a Good Lab Notebook
(a) State what was done
(b) State what was observed
(c) Be easily understandable to someone
else
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-inventor’s lab notebook was a primary piece of evidence to support Medichem’s 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
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)

l1
l2
Double-pan balance
m1
m2
m
m
m1l1 = m2l2
(a) balance beam suspended on a
sharp knife edge
(b) Standard weights are added to
the second pan to balance
sample weight
(c) Weight of sample is equal to the
total weight of standards
Tools of the Trade
Weight Measurements
Single-pan balance
(a)
(b)
(c)
(d)
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.
Tools of the Trade
Weight Measurements
Electronic balance
(a) Uses electromagnetic force to return the pan to original position
(b) 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
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

Container’s weight is set automatically to read “0”
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
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
Office dust
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.
balsa

Different displacement of
ice and balsa wood in water
ice
Correction for buoyancy to give true mass of sample
m' ( 1 
m
(1
da
)
dw
da
)
d
m = true mass of sample
m’ = mass read from balance
d = density of sample
da = density of air (0.0012 g/ml at 1 atm & 25oC)
dw = density of calibration weights (~ 8.0 g/ml)
Tools of the Trade
Weight Measurements
3.) Errors in Weighing: Sources
Example: The densities (g/ml) of several substances are:
acetic acid 1.05
lithium 0.53
lead 11.4
CCl4 1.59
mercury 13.5
iridium 22.5
Sulfur 2.07
PbO2 9.4
From the following figure:
predict which substance will have the smallest percentage buoyancy
correction and which will have the greatest.
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
d a  0.46468 (
B  0.3783V
)
T
B = Barometer pressure (torr)
V = vapor pressure of water in the air (torr)
T = air temperature (K)
Tools of the Trade
Volume Measurements
1.) Burets
(i) Purpose: used to deliver multiple aliquots of a liquid in known volumes
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
(ii) Correct use of buret

Read buret at the bottom of a concave meniscus
Meniscus at 9.68 mL
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
(iv) Consistently read all levels versus a given position on the nearest mark
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.
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
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



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
- adhere to surface
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
Transfer Pipet
- similar to volumetric flask
Volume (mL) Tolerance (mL)
- transfers a single volume  fill to calibration mark
- last drop does not drain out of the pipet  do not blow out
0.5
±0.006
- more accurate than measuring pipet
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

Measuring pipet
- calibrated similar to buret
- use to delivery a variable volume
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
100% of pipet volume
Accuracy (%)
Precision (%)
Accuracy (%)
Precision (%)
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
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
Pipet volume (mL)
Adjustable pipets
Fixed pipets
Disposable tip
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
> 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
- remove bubbles
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
(iv) dry sample
Rubber policeman
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
(i)
(ii)
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.
(i) Contains a drying agent to absorb water from the atmosphere
(ii) Airtight seal
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
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
Both numbers have 4 significant figures
0.006302 = 6.302 x10-3
Zeros are simple place holders
2.) Zeros are counted as significant figures only if:
(i) occur between other digits in the number
9502.7 or 0.9907
Both zeros are significant figures
(ii) occur at the end of number and to the right of the decimal point
177.930
zero is a significant figure
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
6.52 ± 0.12 should be 6.5 ± 0.1
2 significant figures
But
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
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
1.25 x 105
2.48 x 104
+ 1.235 x 104


12.5
x 104
2.48 x 104
+ 1.235 x 104
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
2.48
+ 1.235
16.215
x
x
x
x
104
104
104
104
1 decimal point
=
16.2 x 104
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
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
34.60
)
2.4287
14.05
3 significant figures
4 significant figures
Experimental Error & Data Handling
Significant Figures
7.) Logarithms and Antilogarithms
(i) the logarithm of a number “a” is the value “b”, where:
a = 10b
or
Log(a) = b
(ii) example:
The logarithm of 100 is 2, since:
100 = 102
(iii) The antilogarithm of “b” is “a”
a = 10b
(iv) the logarithm of “a” is expressed in two parts
Log(339) = 2.530
character
mantissa
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
4 sig. fig.
4 sig. fig.
(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
2 sig. fig.
2 sig. fig.
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
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
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
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
Experimental Error & Data Handling
Errors
3.) Accuracy and Precision
(i) Accuracy: refers to how close an answer is to the “true” value

Generally, don’t 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
Low accuracy, low precision
High accuracy, low precision
Low accuracy, high precision
High accuracy, high precision
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
Absolute Uncertaint y
Relative Uncertaint y 
Measured Value
(v) Example:
For a buret reading of 12.35 ± 0.02 mL, the relative uncertainty is:
(Make sure units cancel)
0.02 mL
Relative Uncertaint y(%) 
(100)  0.16%  0.2%
12.35 mL
1 sig. fig.
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:
Abs . Uncert .Answer 

Abs . Uncert .
Abs . Uncert .Answer 
2


value 2
2
value1
Value
1.76
+ 1.89
- 0.59
Answer: 3.06
Example:
   Abs . Uncert .
Abs. Uncert.
(± 0.03)
(± 0.02)
(± 0.02)
0.032  0.022  0.022
 0.04
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):
0.04
Re l . Uncert .(%) 
( 100 )  1.3%  1%
3.06

1 sig. fig.
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
Round-off errors
Experimental Error & Data Handling
Errors
5.) Propagation of Uncertainty
(i) Multiplication and Division

The relative uncertainties are used for all numbers in the calculation
Re l . Uncert .Answer 

Re l . Uncert .
  Re l . Uncert .
2
value1
2


value 2
Example:
1.76  0.03   1.89  0.02 
 5.64
0.59  0.02 
Re l . Uncert . 
3 sig. fig.
 0.03 ( 100 ),  0.02  ( 100 ) ,  0.02  ( 100 )
1.76
1.89
0.59
Re l . Uncert .  1.7%,  1.1% ,  3.4%
Re l . Uncert .Answer 
1.7 2  1.12  3.4 2
 4.0%  4%
1 sig. fig.
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:
Relative Uncertaint y(%) 
Absolute Uncertaint y
( 100 )
Calculated Value
Rearrange:
Absolute Uncertaint y 
Relative Uncertaint y(%)
(calculate d value)
( 100 )
(iii) Example (using the previous example):
 4.0% 
Absolute Uncertaint y  ( 5.64 )
  0.23  0.2
 100 
1 sig. fig.
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:
1.76  0.03   0.59 0.02   0.619 ? 
1.89 0.02 
3 sig. fig.
First operation: differences in brackets
1.76  0.03  0.59 0.02   1.17  0.036 
0.036 
0.032  0.02 2
3 sig. fig.
1 sig. fig., but carry two sig.
fig. through calculation
Experimental Error & Data Handling
Errors
5.) Propagation of Uncertainty
(v) Example:
Second operation: Division
1.17  0.036  1.17  3.1%

 0.61% 3%




1.89  0.02
1.89  1.1%
3.3%  3% 
3.1%  1.1%
2
Convert to relative
uncertainty
3 sig. fig.
2
1 sig. fig.
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)
Result & uncertainty match
in decimal place
But:
12.532 (±0.064)  too many significant figures
12.53 (±0.06)  reduce to 1 sig. fig. in uncertainty
same reduction in results
The first digit in the answer with any uncertainty associated
with it should be the last significant figure in the number.
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.
23.97
 2.596966414
9.23
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 =