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

• 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

Transfer Pipet Transfer Pipet
Vol. (mL) Error (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
Volumetric Flask Volumetric Flask
Vol. (mL) Error (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
Buret Buret
Vol. (ml) Error (mL)
5 0.01
10 0.02
25 0.03
50 0.05
100 0.10
Balance Balance Balance Balance
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 0.010
5 0.034 5 0.010
2 0.034 2 0.010
1 0.034 1 0.010
4
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

5
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
6
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
• m mass read from balance

7
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 (iii) Dont force out
last drop from pipette! (iv) Remove air
bubbles
8
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
9
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
10
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
11
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
12
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
13
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
14
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
15
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.
16
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
17
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
18
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
19
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
20
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

21
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.
22
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
23
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
24
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.
25
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.
26
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
27
Experimental Error Data Handling

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

Convert to relative uncertainty
3 sig. fig.
1 sig. fig.
28
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.
29
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.
30
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