1. 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

2. Laboratory Notebook Bad Laboratory Practice ( A Recent Legal Case) Medichem Pharmaceuticals v. Rolabo Pharmaceuticals Tools of the Trade 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

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 Balance Gramserrormgerror 5001.25000.010 2000.52000.010 1000.251000.010 500.12500.010 200.074200.010 100.050100.010 50.03450.010 20.03420.010 10.03410.010 Buret Vol. (ml)Error (mL) 5± 0.01 10± 0.02 25± 0.03 50± 0.05 100± 0.10 Volumetric Flask Vol. (mL)Error (mL) 1± 0.02 2 5 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 Transfer Pipet Vol. (mL)Error (mL) 0.5±0.006 1 2 3±0.01 4 5 10±0.02 15±0.03 20±0.03 25±0.03 50±0.05 100±0.08

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  Container’s weight is set automatically to read “0”

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  Vibrations or wind currents around balance  Non-level balance 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 d = density of sample d a = density of air (0.0012 g/ml at 1 atm & 25 o C) d w = density of calibration weights (~ 8.0 g/ml) Different displacement of ice and balsa wood in water ice balsa

7. 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) Don’t force out last drop from pipette! (iv) Remove air bubbles Tools of the Trade 15.46 mL 15.31 mL 1% error Eye level View from above

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 Same Picture Different Levels of Resolution 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?

9. Experimental Error & Data Handling Both numbers have 4 significant figures Zeros are simple place holders Both zeros are significant figures zero is a significant figure 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 10 2 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

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 ± 0.023 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. 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 Experimental Error & Data Handling 1 decimal point 12.5 x 10 4 2.48 x 10 4 + 1.235 x 10 4 16.215 x 10 4 = 16.2 x 10 4 12.5 x 10 4 2.48 x 10 4 + 1.235 x 10 4 1.25 x 10 5 2.48 x 10 4 + 1.235 x 10 4

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. 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: Experimental Error & Data Handling 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 = 10 b or Log(a) = b The logarithm of 100 is 2, since: 100 = 10 2 a = 10 b Log(339) = 2.530 character mantissa

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. 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 Random Error Systematic Error True value

20. 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 Low accuracy, high precision High accuracy, low precision High accuracy, high precision

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

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. 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 =