1 / 32

Analytical Chemistry

Analytical Chemistry. Definition: the science of extraction, identification, and quantitation of an unknown sample. Example Applications: Human Genome Project Lab-on-a-Chip (microfluidics) and Nanotechnology Environmental Analysis Forensic Science. Course Philosophy.

caspar
Télécharger la présentation

Analytical Chemistry

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Analytical Chemistry • Definition: the science of extraction, identification, and quantitation of an unknown sample. • Example Applications: • Human Genome Project • Lab-on-a-Chip (microfluidics) and Nanotechnology • Environmental Analysis • Forensic Science

  2. Course Philosophy • develop good lab habits and technique • background in classical “wet chemical” methods (titrations, gravimetric analysis, electrochemical techniques) • Quantitation using instrumentation (UV-Vis, AAS, GC)

  3. Analyses you will perform • Basic statistical exercises • %purity of an acidic sample • %purity of iron ore • %Cl in seawater • Water hardness determination • UV-Vis: Amount of caffeine and sodium benzoate in a soft drink • AAS: Composition of a metal alloy • GC: Gas phase quantitation titrations

  4. Chapter 1:Chemical Measurements

  5. Chemical Concentrations

  6. Dilution Equation Concentrated HCl is 12.1 M. How many milliliters should be diluted to 500 mL to make 0.100 M HCl? M1V1 = M2V2 (12.1 M)(x mL) = (0.100 M)(500 mL) x = 4.13 M

  7. Chapter 3:Math Toolkit accuracy = closeness to the true or accepted value precision = reproducibility of the measurement

  8. Significant Figures • Digits in a measurement which are known with certainty, plus a last digit which is estimated beaker graduated cylinder buret

  9. Rules for Determining How Many Significant Figures There are in a Number • All nonzero digits are significant (4.006, 12.012, 10.070) • Interior zeros are significant (4.006, 12.012, 10.070) • Trailing zeros FOLLOWING a decimal point are significant (10.070) • Trailing zeros PRECEEDING an assumed decimal point may or may not be significant • Leading zeros are not significant. They simply locate the decimal point (0.00002)

  10. ans = 63.5 Reporting the Correct # of Sig Fig’s • Multiplication/Division 12.154 5.23 36462 24308 60770 Rule: Round off to the fewest number of sig figs originally present 63.56542

  11. Reporting the Correct # of Sig Fig’s • Addition/Subtraction 15.02 9,986.0 3.518 10004.538 Rule: Round off to the least certain decimal place

  12. Rounding Off Rules • digit to be dropped > 5, round UP158.7 = 159 • digit to be dropped < 5, round DOWN158.4 = 158 • digit to be dropped = 5, round UP if result is EVEN158.5 = 158157.5 = 157

  13. ? sig figs 5 sig figs 3 sig figs 1.235-1.02 0.215 = 0.22 Wait until the END of a calculation in order to avoid a “rounding error” (1.235 - 1.02) x 15.239 = 2.923438 = 1.12

  14. Propagation of Errors A way to keep track of the error in a calculation based on the errors of the variables used in the calculation error in variable x1 = e1 = "standard deviation" (see Ch 4) e.g. 43.27  0.12 mL percent relative error = %e1 = e1*100 x1 e.g. 0.12*100/43.27 = 0.28%

  15. Addition & Subtraction Suppose you're adding three volumes together and you want to know what the total error (et) is: 43.27  0.12 42.98 0.22 43.06 0.15 129.31  et

  16. Multplication & Division

  17. Combined Example

  18. Chapter 4:Statistics

  19. Gaussian Distribution: Fig 4.2

  20. Mean – measure of the central tendency or average of the data (accuracy) N   Infinite population Finite population Standard Deviation – measure of the spread of the data (reproducibility) Infinite population Finite population

  21. Standard Deviation and Probability

  22. Confidence Intervals

  23. Confidence Interval of the Mean The range that the true mean lies within at a given confidence interval True mean “” lies within this range x

  24. Example - Calculating Confidence Intervals • In replicate analyses, the carbohydrate content of a glycoprotein is found to be 12.6, 11.9, 13.0, 12.7, and 12.5 g of carbohydrate per 100 g of protein. Find the 95% confidence interval of the mean. ave = 12.55, std dev = 0.465 N = 5, t = 2.776 (N-1)  = 12.55 ± (0.465)(2.776)/sqrt(5) = 12.55 ± 0.58

  25. Rejection of Data - the "Q" Test A way to reject data which is outside the parent population. Compare to Qcrit from a table at a given confidence interval. Reject if Qexp > Qcrit

  26. Example: Analysis of a calcite sample yielded CaO percentages of 55.95, 56.00, 56.04, 56.08, and 56.23. Can the last value be rejected at a confidence interval of 90%?

  27. Linear Least Squares- finding the best fit to a straight line

More Related