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
ALL Measurements have an Associated Error
Essential to understand instrument limitations
-
Use proper procedures to minimize source of errors
Have to accept a certain level of instrumental errors
Only counting can lack an error
Buret
Balance
Transfer Pipet
Volumetric Flask
Vol. (mL)
Error (mL)
Vol. (mL)
Error (mL)
1
± 0.02
0.5
±0.006
± 0.01
2
± 0.02
1
±0.006
10
± 0.02
5
± 0.02
2
±0.006
25
± 0.03
10
± 0.02
3
±0.01
50
± 0.05
25
± 0.03
4
±0.01
100
± 0.10
50
± 0.05
5
±0.01
0.010
100
± 0.08
10
±0.02
5
0.010
200
± 0.10
15
±0.03
0.034
2
0.010
250
± 0.12
20
±0.03
0.034
1
0.010
500
± 0.20
25
±0.03
1000
± 0.30
50
±0.05
2000
± 0.50
100
±0.08
Grams
error
mg
error
500
1.2
500
0.010
200
0.5
200
0.010
100
0.25
100
0.010
50
0.12
50
0.010
20
0.074
20
0.010
10
0.050
10
5
0.034
2
1
Vol. (ml)
Error (mL)
5
Tools of the Trade
Weight Measurements
1.) 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
2.) 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
- important to dry sample before weighing

Sample not at room temperature
- avoid convection air currents (push/lift pan)

Adsorption of water, etc. from air by sample
Office dust


Vibrations or wind currents around balance
Non-level balance
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

ice
Different displacement of
ice and balsa wood in water
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
Volume Measurements
1.) Errors in volumes: Source
(i) Always measure volume at bottom of a concave meniscus
- always fill all volumetric flasks or transfer pipettes to calibration line
(ii) always read at the same eye level as the liquid
Eye level
15.46 mL
View from above
15.31 mL 1% error
(iii) Don’t force out last drop from pipette!
(iv) Remove air bubbles
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 =