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Chemical and Physical Properties: Practical Session
 

Chemical and Physical Properties: Practical Session

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Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.

Lecture materials for the Introductory Chemistry course for Forensic Scientists, University of Lincoln, UK. See http://forensicchemistry.lincoln.ac.uk/ for more details.

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    Chemical and Physical Properties: Practical Session Chemical and Physical Properties: Practical Session Presentation Transcript

    • This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License Lab 6: Saliva Practical Beer-Lambert Law University of Lincoln presentation
    • This session….
      • Overview of the practical…
      • Statistical analysis….
      • Take a look at an example control chart…
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • The Practical
      • Determine the thiocyanate (SCN - ) in a sample of your saliva using a colourimetric method of analysis
      • Calibration curve to determine the [SCN - ] of the unknowns
      • This was ALL completed in the practical class
      • Some of your absorbance values may have been higher than the absorbance values of your top standards… is this a problem????
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License Types of data QUALITATIVE Non numerical i.e what is present? QUANTITATIVE Numerical: i.e. How much is present?
    • Beer-Lambert Law
        • Beers Law states that absorbance is proportional to concentration over a certain concentration range
        • A =  cl
        • A = absorbance
        •  = molar extinction coefficient (M -1 cm -1 or mol -1 L cm -1 )
        • c = concentration (M or mol L -1 )
        • l = path length (cm) (width of cuvette)
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Beer-Lambert Law
      • Beer’s law is valid at low concentrations, but breaks down at higher concentrations
      • For linearity, A < 1
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License 1
    • Beer-Lambert Law
      • If your unknown has a higher concentration than your highest standard, you have to ASSUME that linearity still holds ( NOT GOOD for quantitative analysis)
      • Unknowns should ideally fall within the standard range
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License 1
    • Quantitative Analysis
      • A < 1
        • If A > 1:
          • Dilute the sample
          • Use a narrower cuvette
            • (cuvettes are usually 1 mm, 1 cm or 10 cm)
      • Plot the data (A v C) to produce a calibration ‘curve’
      • Obtain equation of straight line (y=mx) from line of ‘best fit’
      • Use equation to calculate the concentration of the unknown(s)
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Quantitative Analysis This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Statistical Analysis This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License Mean The mean provides us with a typical value which is representative of a distribution
    • Normal Distribution This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Mean and Standard Deviation This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License MEAN
    • Standard Deviation
      • Measures the variation of the samples:
        • Population std (  )
        • Sample std (s)
      •  = √(  (x i – µ ) 2 /n)
      • s =√(  (x i – µ ) 2 /(n-1))
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    •  or s?
      • In forensic analysis, the rule of thumb is:
        • If n > 15 use 
        • If n < 15 use s
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Absolute Error and Error %
      • Absolute Error
        • Experimental value – True Value
      • Error %
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Confidence limits This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License 1  = 68% 2  = 95% 2.5  = 98% 3  = 99.7%
    • Control Data
      • Work out the mean and standard deviation of the control data
        • Use only 1 value per group
          • Which std is it?  or s?
      • This will tell us how precise your work is in the lab
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Control Data
      • Calculate the Absolute Error and the Error %
        • True value of [SCN – ] in the control = 2.0 x 10 –3 M
      • This will tell us how accurately you work, and hence how good your calibration is!!!
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Control Data
      • Plot a Control Chart for the control data
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License 2.5  2 
    • Significance
      • Divide the data into six groups:
        • Smokers
        • Non-smokers
        • Male
        • Female
        • Meat-eaters
        • Rabbits
      • Work out the mean and std for each group (  or s?)
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Significance
      • Plot the values on a bar chart
      • Add error bars (y-axis)
        • at the 95% confidence limit – 2.0 
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Significance This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • Identifying Significance
      • In the most simplistic terms:
        • If there is no overlap of error bars between two groups, you can be fairly sure the difference in means is significant
      This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License
    • This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales License Acknowledgements
      • JISC
      • HEA
      • Centre for Educational Research and Development
      • School of natural and applied sciences
      • School of Journalism
      • SirenFM
      • http://tango.freedesktop.org