ch217 fundamentals of analytical chemistry n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
CH217 Fundamentals of Analytical Chemistry PowerPoint Presentation
Download Presentation
CH217 Fundamentals of Analytical Chemistry

Loading in 2 Seconds...

play fullscreen
1 / 83
abedi

CH217 Fundamentals of Analytical Chemistry - PowerPoint PPT Presentation

248 Views
Download Presentation
CH217 Fundamentals of 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. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. CH217Fundamentals of Analytical Chemistry Module Leader: Dr. Alison Willows

  2. Assessment • Practicals 60% • Practical 1: online quiz during lab session • Practicals 2 & 3: electronic reports, see lab scripts • End of module examination 40% • In addition you are also required to: • Complete the guided study (not assessed) • Attend all the labs • Attend at least 80% lectures/workshops

  3. Studentcentral • Module content and assignments are available through studentcentral • You will be required to submit your coursework electronically via studentcentral • The guided study will be an electronic test on studentcentral • Feedback on assessments will also be electronic Please familiarise yourself with studentcentral!

  4. Recommended reading • The module descriptor tells you what you should know by the end of this module • The information given in lectures and on studentcentral is only a guideline to aid your study • Please refer to the module learning handbook and studentcentral for a list of recommended books and other useful resources. • You will not achieve a good grade in this module without doing additional reading outside of the lectures

  5. Principles of Analytical design DTI's Valid Analytical Measurement programme The six principles of good analytical practice • Analytical measurements should be made to satisfy an agreed requirement. • Analytical measurements should be made using methods and equipment which have been tested to ensure they are fit for purpose. • Staff making analytical measurements should be both qualified and competent to undertake the task. • There should be a regular independent assessment of the technical performance of a laboratory • Analytical measurements made in one location should be consistent with those elsewhere. • Organisations making analytical measurements should have well defined quality control and quality assurance procedures.

  6. Role of analytical chemistry in science Do I need analytical chemistry? Analytical chemistry might: • enable you to pass your course • help you to understand other modules • be useful in your career • be interesting • help with your final year project • change your life!

  7. What is analytical chemistry? Dictionary definitions • Analytical (adj) examining or tending to examine things very carefully • Chemistry(noun) 1.(the part of science which studies) the basic characteristics of substances and the different ways in which they react or combine with other substances. 2. INFORMAL understanding and attraction between two people Cambridge Advanced Learner's dictionary • Analytical chemistry encompasses any type of test that provides information on the amount or identification of the chemical composition of a sample. • This breaks down into two main areas of analysis: qualitativeandquantitative

  8. Qualitative vs.. Quantitative • Qualitativeanalyses give a positive/negative or yes/no answer. This tells us whether a substance (the analyte) is present but doesn't tell us how much is there. A qualitative analysis may also identify substances in a sample • Quantitativeanalyses tell us how much of a substance is in the sample.

  9. When and where is analytical chemistry used? • Food industry - wine production; contaminants; process lines • Medical- blood analysis; imaging; • Pharmaceutical- drug analysis • Environmental- water, gas & soil analysis • Engineering - materials characterisation • Crime - forensics (CSI) • Sport & leisure - pool chlorination; drugs tests • Research

  10. Analytical Process • Formulating the question • Selecting analytical procedures • Conducting the analysis • Sampling • Sample preparation • calibration of method • Sample analysis • Collection and processing of data and calculation of errors

  11. Analytical Process, cont. • Method validation • Reporting and interpretation (results & discussion) • Drawing conclusions (answering the question!)

  12. Method selection Valid Analytical Measurement (VAM) A result is fit for purpose when its uncertainty maximises its expected utility (cost, usually) • reducing uncertainty generally increases the cost of analysis • most users have tight budgets • uncertainty in measurement should be as large as can be tolerated to keep costs down • other factors can affect fitness for purpose • sensitivity of technique • sample throughput • accuracy and precision that is obtainable • sample type and preparation

  13. VAM, cont • Ultimately, the results are fit for purpose if they meet the specific needs of the customer, the customer is confident in the results and they represent value for money.

  14. Valid Analytical Measurement (VAM) • Goldmine A sampling and analysis game for Minitab can be found here http://www.rsc.org/Membership/Networking/InterestGroups/Analytical/AMC/Software/goldmine.asp

  15. Comparing techniques statistically The F test and Student's t test • F test -Is there a significant difference between the precision of two methods? i.e. are the standard deviations of the two methods significantly different? • Student’s t test - used to decide if two sets of results are "the same" or to compare a set of results with a known value. • You will have learnt these tests in your QS modules, please refresh your memory if you are unsure how to perform it. • You will be expected to be able to compare a set of results with a known value, compare two sets of matched results and compare two sets of unmatched results, please see me if you can not do this • Further information and worked examples are available on the CH217 studentcentral website

  16. Samples - sampling strategy • Probably the most important stage in any analysis. • If the sample taken is not representative of the original material everything you do next is worthless.

  17. Sample nomenclature • lot - quantity of material which is assumed to represent a single population for sampling purposes • batch - quantity of material known (or assumed) to have been produced under uniform conditions • increments - portions of material obtained using a sampling device from lot/batch • primary/gross sample - combination of increments • composite/aggregate sample - combination of primary samples • laboratory sample - portion of material delivered to lab for analysis • test (analytical) portion - material actually submitted for analysis

  18. Sampling - stages Horwitz. Pure and Applied Chemistry, 1990, 62, 1193-1208.

  19. Obtaining a representative sample • Usually the lot is not homogeneous but may be • randomly heterogeneous (different compositions occur on a small scale and randomly) or • segregated heterogeneous (large patches of different compositions) • A representative sample will not reflect the composition of the target exactly but will be adequate enough to be 'fit for purpose'. There will always be a degree of uncertainty from sampling.

  20. Sampling - n numbers How many replicate samples do we need to analyse? • Often in biology you will come across n=6 for all analyses. so where does this come from? Confidence limits - met in QS modules • Rearrange to make n the subject • Use the acceptable error and confidence level (to find t) to calculate n.

  21. Sampling - n numbers Worked Example • The concentration of lead in the bloodstream was measured for a sample of children from a large school near a busy main road. A preliminary sampling of 50 children gave a mean concentration of 10.12 ng ml-1 and standard deviation of 0.64 ng ml-1. How big does the sample need to be to give an error of less than ±0.1 ng ml-1 with 95% confidence? • For 95% confidence t = 1.96 (n = ∞) • So 160 children would need to be tested

  22. sample preparation Preparing samples for analysis • Depends on the form required for analysis Samples may require • Moisture control • Grinding • Dissolving • Ashing • Fusion • Extraction • Preconcentration/dilution • Derivatisation or a combination of several of these • Instruments such as microwave ovens, sonicating baths, pressure vessels (digestion bombs) and extraction cartridges may also be used. • Please see recommended reading for further details on these preparation techniques (ch28 Harris)

  23. solid phase extraction • Analyte is removed from sample by passing a solution over a solid. • Analyte is adsorbed, or absorbed by the solid and the remaining liquid can be discarded • Analyte is eluted by use of a stronger solvent

  24. solid phase extraction

  25. Sample storage To keep samples reflective we must prevent contamination & decomposition Problems & Solutions • Dirty containers - ensure adequate washing; use disposable containers • Type of Container - Avoid “ion-exchange” and adsorption of analyte • Light- use brown/foil-covered bottles • Air may oxidise sample - store under vacuum, or in a protective atmosphere • Moisture- keep tightly sealed • Evaporation- keep tightly sealed • Heat/cold - store in fridge/temperature controlled room The measures chosen will depend on the analyte and its sample matrix

  26. Calibration Analytical methods, particularly those using instruments, frequently require calibration procedures These are to establish: • the response to known quantities of analyte (standards) within the range used • the reliability/drift of the method • limits beyond which detection/quantitation is unreliable Calibration normally involves: • measurement of samples of known concentrations • measurement of a relevant range of concentrations • a range in which the response is linear • graphical treatment of results • modified calculation of errors

  27. External Standard • Simplest and most common form of calibration. • Prepare samples containing known quantities of analyte over a relevant range including blanks • Controls for sample preparation/matrix should be used, matched to the unknown samples • Carry out and record measurements • Plot quantity/concentration of analyte vs. response • Linear regression with least squares analysis is used to determine response (expressed as y = bx+a) • Repeat as and when appropriate (when it is likely that an unacceptable drift will have occurred)

  28. External Standard Advantages • May only need one calibration plot (of 5-10 samples) for 10’s to 100’s of unknown samples • Can be easily automated • Simple statistics will provide estimates of uncertainty for the method Disadvantages • Requires care to match conditions and matrix to that of the unknown samples • Does not control for sudden changes in method performance

  29. External standard You will have done this in more detail in BY131 You should be able to use linear regression to calculate the line of best fit and the errors in the calibration lineto calculate the concentration of the analyte and its errorfrom this information (see sec 5.4, 5.5, 5.6 in Miller & Miller) The ability to do this is assumed in this module.

  30. Internal Standard • Useful for methods which are not very reproducible; e.g. Gas chromatography uses very small volumes (<1 ml) - difficult to measure accurately • The instrument responses to mixtures of known amounts of analyte and of a different compound (internal standard) are measured, and response factor determined • A known amount of internal standard is added to the unknown sample. • Signals from the analyte and from the internal standard are measured • Response factor allows determination of analyte concentration

  31. Internal Standard Advantages • Can control for loss during sample preparation • Controls for unexpected changes in method performance Disadvantages • Requires suitable reference standard • The two compounds (standard and analyte) must be quantifiable independently and have linear responses over a range of concentrations • Must account for dilution steps in calculations

  32. Internal Standard-Worked Example • Measurement of caffeine concentration by HPLC, using theophyline as an internal standard. Standard solutions containing a range of known amounts of both caffeine and theophyline are prepared. These are subjected to HPLC and the relative instrument response (area under each peak) is determined, and response factor determined. Caffeine Theophyline Absorbance

  33. Internal Standard-Worked Example • Response Factor • In reality there would be some variation and multiple calibration samples would be used to determine precision of response factor • A 10ml of a 1mg.L-1 internal standard is added to 10ml of an unknown sample . Instrument signals measured: Analyte: 30,000, Internal Standard: 27,000

  34. Internal Standard-Worked Example • Response factor allows determination of analyte concentration in sample: •  Original concentration = 1.39 x 20/10 = 2.78mg.L-1

  35. Standard addition Frequently used where matrix effects and interferents are prevalent e.g. atomic absorption/emission • Prepare samples containing equal volumes of unknown analyte concentration • “Spike” each sample with known, different amounts of standard (same analyte, including a range from 0 to ~5x expected unknown concentration) • Dilute all samples to the same volume • Carry out and record measurements • Plot quantity/concentration of known analyte added vs.. response • Linear regression with least squares analysis is used to determine response (expressed as y = bx+a) • Concentration of unknown = - (x-intercept) = a/b • Repeat for each unknown sample

  36. Standard addition Advantages • Controls for matrix effects • Controls for unexpected changes in method performance Disadvantages • Requires several measurements for each unknown • May use more unknown sample than other methods • Must be careful to account for dilution steps in calculations

  37. Standard addition - Worked Example Measurement of Copper concentration by atomic absorption spectrometry • Five 10ml solutions of unknown (approx. 2mg.L-1) copper concentration were prepared and to these was added:0, 2, 4, 6 and 8 cm3 of 10mg.L-1 standard analyte solution in water (one volume to each flask).All samples diluted to 25cm3 with water and mixed well. The solutions were then measured using AAS and the results recorded

  38. Calculate concentration of copper added to solution, using c1V1 = c2V2 • i.e. 2 cm3 added: 10 x 2/1000 = c2 x 25/1000 c2 = 0.8 mg.L-1 etc • Plot quantity/concentration of known analyte added vs. response, and plot line using linear regression with least square analysis (expressed as y = bx+a)

  39. Conc. of unknown in samples = - (x-intercept) = a/b • = 0.813mg.L-1 • NB: 10cm3 aliquots of the original solution were diluted to 25cm3 in the samples, so concentration of original solution = 0.813 x 25/10 = 2.0325 ~ 2.03mg.L-1

  40. validation • Standards • Performance parameters • Errors in Analysis • Record Keeping

  41. “How long is a piece of string?” • The results from any analytical measurement depends upon and is traceable to the measurement standards used in the process. These include standards for mass, volume and amount of a chemical species. • Equipment is usually periodically calibrated using standards that can be traced back to an International Primary Standard.

  42. Example • An analytical balance will be calibrated periodically using calibrated weights. • These weights are regularly checked against a set of weights held at a reference laboratory. • The reference laboratory's weights will be checked periodically against the national standard kilogram (held at the National Physical Laboratory, NPL). • This national standard kilogram is occasionally compared to the international standard kilogram. • Each stage introduces a measurement uncertainty which has to be taken into account. This means that the standards used in a laboratory will always have a greater uncertainty associated with them than those from the reference laboratories.

  43. Standard solutions • Standard solutions can be used to help with calibration and to compare results against to establish the accuracy of a technique. • The two main grades of standard are: • Primary • Secondary • Certified Reference Materials (CRM) - specially prepared samples containing an analyte at a pre-determined concentration .

  44. Primary standards • Primary standards are highly purified compounds that are used, directly or indirectly, to establish the concentration of standard solutions. • Primary standards should meet the following requirements: • High purity • Stability toward air • Absence of hydrate water so composition does not change with variations in humidity • Ready availability at reasonable cost • Reasonable solubility in titration medium • Reasonably large molar mass so that relative error associated with weighing the standard is minimised

  45. Secondary standards • There are few compounds that meet these criteria. So • often a less pure compound has to be used: • secondary standard • The ideal standard solution should: • Be sufficiently stable that its concentration needs to be determined only once • React rapidly with the analyte • React more or less completely with the analyte for good end points • Undergo selective reaction with simple balanced equation • Few reagents meet all of these requirements

  46. Performance parameters • Accuracy – measure of agreement between a single analytical result and the true value • Precision – measure of agreement between observed values obtained by repeated application of the same analytical procedure • Selectivity – measure of the discriminating power of an analytical procedure in differentiating between the analyte and other components in the test sample • Sensitivity – the change of the measured signal as a result of one unit change in the content of the analyte (calculated from the calibration line) • Limit of Detection – calculated amount of analyte in the sample which corresponds to 3 times the sd of the blank sample • Limit of Quantitation – minimum content of the analyte that can be quantitatively determined with reasonable statistical confidence. Equivalent to 6 time the sd of the blank sample

  47. Linearity– a measure of the linearity of the calibration • Range – concentration range to which the technique is applicable • Ruggedness – insensibility of the method for variations during execution • Standard deviation and relative standard deviation (RSD) – measures of the spread in the observed values as a result of random errors • Repeatability– expected maximum difference between two results of identical test samples obtained under identical conditions • Within-lab reproducibility – expected maximum difference between two results obtained by repeated application of the analytical procedure to an identical test sample under different conditions (e.g. different operator, different days) but in the same laboratory • Between-lab reproducibility - expected maximum difference between two results obtained by repeated application of the analytical procedure to an identical test sample in different laboratories (e.g. different operators, different instrumentation in different labs on different days using same method

  48. Errors in Analysis • The key to any successful analysis is ensuring that it will “answer the question” • No analysis can be absolutely error-free • All analyses must be designed to produce acceptable levels of errors and uncertainty • The best way to minimise errors is by careful experimental design

  49. types of error • Three main types of error • Gross: So serious the experiment must be abandoned. e.g. dropping a key sample, instrumental breakdown • Random: When an experiment is repeated as exactly as possible, the replicate results will differ due to random errors. Estimates of random errors gives the precision or reproducibility of the analysis. • Systematic: An experimental method gives a reproducible under- or overestimate of the real result. Total of all systematic errors gives the bias of an analysis.