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Analytical Chemistry

Analytical Chemistry. Lecture #1: Easy and old-school stuff Dr. Khoi Nguyen. Sprin g 2011. What is Analytical Chemistry?. Inorganic Chemistry. Analytical Chemistry. Physical Chemistry. Organic Chemistry. Chemistry. What is Analytical Chemistry?.

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Analytical Chemistry

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  1. Analytical Chemistry Lecture #1: Easy and old-school stuff Dr. Khoi Nguyen Spring 2011

  2. What is Analytical Chemistry? Inorganic Chemistry Analytical Chemistry Physical Chemistry Organic Chemistry Chemistry

  3. What is Analytical Chemistry? • Analytical Chemistry provides the methods and tools needed for insight into our material world… for answering four basic questions about matters: • What? • Where? • How much? • What arrangement, structure or form?

  4. Analytical Chemistry Environmental quality medicine industry forensics Archaeology Space science food Analytical Chemistry

  5. How analytical chemistry originate? • Archimedes weighing gold wreaths. • By the alchemists. • Robert Boyle  the term “analyst.” • Antoine Lavoisier is considered the father of analytical chemistry.

  6. The evolution of Analytical Chemistry • Gravimetry • Titrimetry • First analytical textbook: “Anleitung zur Quantitaven Chemischen Analyse” – by Karl Fresenius 1845. • Second analytical textbook: “Die wissenschaflichen Grundagen der analytischen Chemie” by Wilhem Ostwald in 1894. • Analytical Chemistry has been growing fast in the 20th century. In 1927, methods as electroanalysis, conductimetric titrations, colorimetric methods had been added to textbooks of this field.

  7. Chemical Analysis Define the Problem Select a sound method Obtain a Representative Sample Sample Preparation Chemical Separation Measurement Data analysis

  8. Defining a problem • What exactly do you want answers for? • Would the findings be scientifically useful? • Is the problem expensive? • What’s the degree of accuracy?

  9. Method selection • Sample type • Sample size • Sample preparation • Sensitivity • Accuracy/Precision • Instrumentation • Experience/Expertise • Cost • Speed

  10. Obtain a Representative Sample • Sample type/size/homogenerity • Sampling statistics/errors • Sample statistical distribution

  11. Sample Preparation • Solid/liquid/gas? • Soluble? • Ash/digest? • Chemical separation needed? • Chemical concentration needed? • Need to alter the analyte for easier detection? • Need to compromise the conditions?

  12. Measurement • Calibration • Validation/controls/blanks • Number of replicates

  13. Data Analysis • Statistical analysis • Report results with the appropriate level of confidence

  14. Rules of thumb • 1. never handle objects to be weighed with bare hands (use clean paper, tongs, tweezers…) • 2. do not weigh objects when it’s hotter or colder than room temperature. • 3. always use weighing boats and keep the pan clean at all times. • 4. Close the balance case door while tarring and carrying out the reading. • 5. when moving the balance or loading/unloading the samples and weights, engage the beam/pan arrests (with mechanical balances)

  15. How to choose weighing boats/dishes/bottles? • Are you weighing a liquid or solid sample? • How heavy is your object being weighed? • Is your sample hygroscopic or non-hygroscopic? • Is your sample reactive to certain materials?

  16. Rough weighing - Accurate weighing • Accurate weighings require meticulous procedures and of course and GOOD balance. • Rough weighings are usually followed by some sort of titrations or adjustments.

  17. Operating principle of an analytical balance • Fig 2.2 and explanation

  18. Sources of errors • Zero-point drift (due to temperature, humidity, static) • Variation in air density ( affecting the buoyancy) • On going chemical/physical processes during the course of weighing • Samples being hygroscopic • Different elevation of the locations

  19. http://www.surveymonkey.com/s/9QC7Q3Z

  20. Measuring volumes

  21. Volumetric toys

  22. Or just simply this

  23. Glass Pipets: are used for medium volumes • Transfer/volumetric pipets: • Are used to measureANDtransfer a volume from one place to another. • the interior of these pipets may not be uniform. • Measuring/graduate pipets: • The interiors of these pipets are uniform. • Are often used for measuring volumes (duh!) These pipets can be blowout pipets or not.

  24. Volumetric flasks • Are used to measure large volumes.

  25. Mirco-pipets, micro-syringe: for tiny amounts

  26. Burets: used for titrations • Looks like a graduate measuring pipet • Used with a clamp stand and a stop-cock

  27. Rules of thumb • Always measure volumes at room temperature (why?) • Think of possible chemical processes may occur between your liquids and the volumetric toys. (why?) • Think of the appropriate means according to the desired amounts. • Perform the calibration, if needed.

  28. Calibration? • Wvac = weight in vacuum (g) • Wair = observed weight in air (g) • Do = density of object • Dw = density of weights • 0.0012: density of air

  29. Examples

  30. Qualitative - Quantitative • Qualitative: answers the questions of: • Is there A, B, C? • Is it good or bad? • How do things look/smell? • Quantitative: answers the questions of: • How much of A, B, C are there? • How much is good, how much is too much, how much is too little.

  31. Accuracy and precision

  32. Errors/Uncertainty in chemical analyses • Determinate (systematic) errors: errors that follow a predictable pattern; therefore, can be corrected. • Instrumental errors • Operative errors • Errors come from the methodology implemented • Indeterminate (random) errors: these are accidental. These can also be eliminated with mathematical tools.

  33. Systematic errors • Systematic errors tend to produce inaccurate results by introducing a common shift into measured values. This shift can be an offset or a percentage change. • For example, if your wooden meter stick had the first mm cut off, there would be an offset in all of your measurements. If, on the other hand, the humidity in the room had caused the meter stick to expand by 1%, there would be a percentage error in all of your measurements.

  34. Incorrect calibration of the equipment can cause systematic error; reduce by equipment recalibration. • Systematic errors might also be caused by not correctly accounting for some phenomena in your model and might be corrected by adopting a more sophisticated model. • The effects of systematic errors on an experiment should be estimated. • If important, systematic errors should be reported separately from the random errors in the experimental results.

  35. Systematic errors may have no effect on the slope of your data. • However, it leads to an incorrect value for the intercept. • Such systematic errors may or may not be important in an experiment, depending on whether the slope or the intercept (or both) provide critical information. Question: In what kinds of experiments, systematic errors could lead to an incorrect value for the slope?

  36. Random errors • Many sources of random errors: such as equipment limitations, reading uncertainties, and statistical fluctuations. • Examples: the uncertainties in reading scale divisions of an analog voltmeter or a ruler. • Repeated measurements may help. However, random errors can never be completely eliminated.

  37. Errors • The error analysis is generally more tedious than the calculation of the numbers being measured. • However, measurements can be quite meaningless without knowledge of their associated errors. • Why?

  38. Errors If you are told that Sue is 162 cm tall and Beth is 165 cm tall you might conclude that Beth is taller than Sue. But if you then learn that the measurements had errors of ±5 cm, you should realize that you can’t determine who is taller.  For every measurement, you MUST record the uncertainty in the measured quantity.

  39. Internal errors • For repeated measurements of the same quantity, statistical analysis can be used to study the uncertainties in our measurements. This type of analysis yields internal errors, • i.e., the uncertainties determined from the data themselves without requiring further estimates. • mean, the standard deviation and the standard error (error of the mean)

  40. Mean (Average) Standard deviation (σ) is defined as:

  41. Sample error: • Sample error: or

  42. Two variables x and y? • If we measure x and y N times and we want to compare the measured x and y with the relationship y=f(x).

  43. In many cases, the quantity that we wish to determine is derived from several measured quantities.

  44. Addition or subtraction

  45. Multiplication/division Constant*parameter If then If then

  46. Uncertainty in a power If then

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