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

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

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

What is elemental analysis and where is it applied ?

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

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)

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

The analytical process General considerations Solid state methods Select method Analysis in liquid state 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

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

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

Performance characteristic of quantitative analytical methods 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 For definitions see: http://www.nmschembio.org.uk/ GenericArticle.aspx?m=98&amid=445 All these characteristics are intimately linked to the experimental error

Experimental error Systematic error: Systematic errors impact on Bias 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

Both systematic and random errors affect accuracy 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

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

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 Homogeneous lab sample Aliquots

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

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