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CH915: Elemental Analysis

CH915: Elemental Analysis. Module leader: Dr. Claudia Blindauer Lecturers: Dr. Claudia Blindauer Dr. John Fenlon (Statistics) Dr. Andrew Mead (Warwick HRI) Lab classes: Dr. Abraha Habtemariam Book recommendations, e.g.: D.C. Harris: Quantitative Chemical Analysis

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CH915: Elemental Analysis

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  1. CH915: Elemental Analysis • Module leader: Dr. Claudia Blindauer • Lecturers: • Dr. Claudia Blindauer • Dr. John Fenlon (Statistics) • Dr. Andrew Mead (Warwick HRI) • Lab classes: • Dr. Abraha Habtemariam • Book recommendations, e.g.: • D.C. Harris: Quantitative Chemical Analysis • Vogel’s textbook of quantitative chemical analysis • For the entire course: Skoog, Holler, Nieman: Principles of Instrumental Analysis

  2. Aims of the module • Introduce the Analytical Process • Introduce concepts for quantitative analysis • Including Statistics for Data Analysis • Enable professional data analysis • Introduce important methods for elemental analysis of liquid and solid samples • Enable selection of the best possible method for a given analysis problem • Enable to design experiments

  3. Module Overview • 5 sessions on chemical aspects of quantitative and elemental analysis (C. Blindauer, see handout) • 4 lab classes (A. Habtemariam) • 8 sessions on understanding data and statistical aspects of quantitative analysis (J. Fenlon, A. Mead, J. Lynn) – together with MAOC and Systems Biology students

  4. What is elemental analysis and where is it applied ?

  5. What is Elemental Analysis ? • Determine the elemental composition of material • Qualitative • Quantitative • CHNX: Combustion analysis for verification of compound identity • Other elements

  6. Elemental Analysis is applied in: • Materials Sciences • Metallurgy, glass, ceramics, cements, superconductors, microelectronics… • Geosciences • geochemistry, mineralogy, geochronology… • Environmental Sciences • Biological Systems and Medicine • In Industry: • Quality control: Establish that produced material conforms in terms of composition and purity • Process control • Food safety incl. packaging • Forensics: • Determine composition of soil, fibres, plastic, paint etc to establish origin • Trace analysis of Firearms Projectile Lead (FBI procedure)

  7. Elemental Analysis – Method overview • Classical methods: • Qualitative Inorganic Analysis (Fresenius, Treadwell) • Quantitative: Gravimetry,Titrimetry, Colorimetry… • Instrumental trace analysis in solution • Spectroscopic methods: AAS, ICP-AES/OES • Mass spectrometry: ICP-MS • Electrochemical methods ( CH914) • Instrumental methods for solid materials • X-ray methods (also spectroscopic) • Mass spectrometry methods: SIMS and many other • NB: Most instrumental methods are based on physics, not chemistry of element

  8. Solid state methods Analysis in liquid state Select method Acquire/define sample Acquire/define sample Process sample Process sample No Soluble? Chemical dissolution Yes The analytical process General considerations and steps Measurable property? Yes No Eliminate interferences Change chemical form Measure X Calculate result Determine error

  9. Method selection - considerations • Destructive/non-destructive ? • Non-destructive methods of analysis • X-ray fluorescence, emission, etc. • Destructive methods of analysis • Combustion analyses • Volumetric, gravimetric, electroanalytical analyses • Atomic absorbance (AA) and inductively coupled plasma (ICP) spectroscopy • Mass spectrometry • Expected analyte concentrations and performance characteristics of method must match • Sample must be compatible with required processing and measurement

  10. Quantitative Analysis - Principles • Define sample amount (mass or volume) • Measure quantity proportional to analyte concentration • Measured property must vary in a defined way: calibration with known standards necessary • Analysis must be specific: Interferences must be known and if possible be eliminated • Accuracy: Proximity of measured value to accepted (or "true") value: must be determined • Precision: Closeness of measured values to one another: must be defined and reported

  11. Performance characteristic of quantitative analytical methods For definitions see: http://www.nmschembio.org.uk/GenericArticle.aspx?m=98&amid=445 • Accuracy • Bias • Recovery • Precision • Reproducibility and Repeatability • Detection capability • Sensitivity • Limit of Detection (LoD) • Limit of Quantitation (LoQ) • Selectivity and Specificity • Linearity • Working Range • Robustness/Ruggedness All these characteristics are intimately linked to the experimental error

  12. Experimental error • Systematic error: • Sources: • Instrumental • Method • Personal • Can be discovered and corrected • Standard reference materials • Blanks • Controls, e.g. spiked samples • Handle error by proper standardisation/calibration or application of a correction factor Systematic errors impact on Bias

  13. Experimental error • Random error: • Always present, can't be corrected • Consequence of uncertainty of measurements • electrical noise from instrument, causing fluctuations in reading • uncertainties in measurements of mass and volume • Ultimate limitation in quantitation • Must be aware of error and deal with it • Repeated measurements Random errors impact on Precision, Reproducibility, Repeatability, LOD and LOQ Both systematic and random errors affect accuracy

  14. Reporting quantitative data • Errors can be defined via: • Standard deviation (SD) • Variance • Relative std. deviation • Coefficient of variation • All quantitative data must be reported with error – SD and RSD most common • Propagation of errors must be considered

  15. Sampling errors: dealing with heterogeneity • “Real” samples are usually heterogeneous • Examples: Foodstuffs, soils, water samples… • Random sampling: • Sample fractions selected randomly • Composite sampling: • Samples taken at regular intervals and mixed Lot Sampling Representative bulk sample Sample preparation Homogeneouslab sample Aliquots

  16. Sampling error • Overall error is composed of the errors introduced by the analytical procedure (including sample preparation and actual measurement(s)) and the sampling error: • SDo = overall standard deviation, SDa = sd of analytical procedure, SDs = SD of sampling procedure • If SDa << SDs or SDs << SDa, there is little point in trying to reduce the smaller one • Eg. If sa = 5% and ss = 10%, then so = 11%. Using a more expensive and time consuming method whose sa = 1% will only reduce so to 10% SDo2 = SDa2 + SDs2

  17. Summary • Elemental Analysis is important in a range of sectors • The analytical process consists of many steps • Meaningful analysis must consider all steps together • Meaningful experimental design requires understanding data • Awareness of performance characteristics of methods • Awareness of statistics

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