Analytical Chemistry Analytical by pptfiles


									Analytical Chemistry

 The branch of chemistry that deals with
  the separations, identification and
  determination of components in a

 It also traditionally includes coverage of
  chemical equilibrium and stastistical
  treatment of data.
Analytical Chemistry

 Analytical chemistry can be broken down into
  two general areas of analysis :
 1. Qualitative analysis – attempting to identify
  what materials are present in a sample.
 2. Quantitative analysis – determining how
  much of a material is present in a sample.
 Example : GC/MS – this method includes both
  a separation tool(GC) and a spectral
  method(MSpectrometry). The combination is a
  very powerful method.
Type of methods

 The are many approaches can and have
  been taken.
 1. gravimetry – methods based on a
  measured weight.
 2. titrimetry - methods based on a
  measured volume.
 3. electrochemical – approaches that
  rely on measurement of potential,
  current, resistance, charge, etc
Type of methods

 4. spectral methods – interaction of an
  analyte with electromagnetic radiation.
 5. Chromatography – separation of a
  material due to its interaction with two
  different phases.
 6. chemometrics – the statistical
  treatment of data.
Quantitative analysis

 We need to review the general steps that
  are taken for any quantitative methods.
 These steps are taken to ensure an
  accurate and reliable answer.
 What type of information do we need?
Quantitative analysis

 Complete analysis – the goal is to
  determine the amount of each
  component in a sample.
 ultimate analysis – the amount of each
  element present without regard to
  actual composition
 partial analysis – determining one or a
  limited number of species sample. This
  is the most common approach.
Quantitative analysis

 Example :
    Iron in an ore sample
    Electrolyte level in blood
    Presence of lead in a water sample
    Concrete strength
Basic steps in an analysis

 technique to be used
 sampling and sample preparation
 proper application of the method
 data analysis and reporting
Factor to consider

 1. accuracy and sensitivity
 2. cost
 3. number of sample to be assayed
 4. number of components in a sample

   The approach we taken, must produce
    the result you require in a timely, cost
    effect manner – primarily determined
    by the type of sample you have.

 Must be representative.
 Steps must be taken to ensure that your
  results reflect average composition.
 Example – determination of iron in an
     - Minerals and ores are
       heterogeneous. To assay single
       sample may not yield results for
       an entire sample lot.

 Proper sample selection and preparation
  can help minimize this problem.
Sample selection

 Require some knowledge as to sample
  source and history.
 One common approach is to select
  several random samples for analysis.
    -Powder the samples
    -Blend the powders
    -Select a fraction for assay
Sample preparation

 One must then convert the sample to a
  suitable form for the method of analysis.
 Based on the method, this may include :
     Drying to ensure an accurate weight.
     Sample dissolution.
     Elimination or masking of potential
     Conversion of analyte to a single or
      measurable form.
Samples replicates

 All methods have errors associated with
 Using multiple samples and replicates
  helps track and identify this error.
 Multiply samples
 - Identically prepared from another
 - used to verify if your sampling was
Replicate samples

 splits of the same sample.
 Helps track and identify errors in

 For most methods, we measure a
  response that is proportional to the
  concentration of our analyte.
    Gravimetric – weight of a precipitate.
    Titration - volume of a titrant.
    Spectrophotometric – light absorbed.
    Chromatographic – peak area.

 Once your response has been obtained, the
    final steps is to calculate your results.
   This will include
        Application of your standard
        Estimation of error based on
        Reporting in a standard, usable

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