How to Interpret XRF Data

Written by sean lancaster
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How to Interpret XRF Data
Geologic evaluation of terrain is aided by portable XRF instruments. (Jupiterimages/Photos.com/Getty Images)

Sophisticated chemical analysis instrumentation becomes available for field use quickly. As of 2011, X-ray fluorescence instruments are available in portable models, as well as laboratory-based units. Data obtained from these instruments is only useful if the data is interpretable. XRF is widely used in geologic analysis, recycling and environmental remediation efforts. The basics of interpreting XRF data involve the consideration of signals that arise from the sample, instrument artefacts and physical phenomena. The spectra of the XRF data allow a user to interpret the data qualitatively and quantitatively.

Skill level:
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Instructions

  1. 1

    Plot the XRF data in a graph of intensity versus energy. This allows the user to evaluate the data and quickly observe the largest percentage elements present in the sample. Each element that gives an XRF signal appears at a unique energy level and is characteristic of that element.

  2. 2

    Note that you will only plot intensities for lines that yield K and/or L lines. These lines refer to the movement of electrons between orbitals within the atom. Organic samples will not exhibit any lines because the energies given off are too low to transmit through air. Low atomic number elements only exhibit K lines because the energies of the L lines are also too low to detect. High atomic number elements only exhibit L lines because the energies of the K lines are too high for detection by the limited power of handheld devices. All other elements may give responses for both K and L lines.

  3. 3

    Measure the ratio of K(alpha) and K(beta) lines for elements to confirm that they are in a ratio of 5 to 1. This ratio may vary slightly but is typical for most elements. The separation of peaks within K or L lines is usually on the order of a few keV. The ratio for L(alpha) and L(beta) lines is typically 1 to 1.

  4. 4

    Use your knowledge of the sample and spectra to determine if there is overlapping of spectra from similar elements. The spectra of two elements that give responses in the same energy region may overlay each other or modify the intensity curve in that region.

  5. 5

    Take into consideration the resolution of your field analyzer. The lower resolution instruments can't resolve two neighbouring elements on the periodic table. The differences between the energy levels of these two elements can blur together with instruments that have low resolution.

  6. 6

    Eliminate signals that are instrument artefacts from the spectra. These signals relate to signals that arise from artefacts within the instrument design or may be due to the construction of that particular instrument. Back-scattering effects of the sample generally cause very broad peaks in a spectrum. These are typical of low-density samples.

  7. 7

    Locate and remove from consideration any instances of Rayleigh peaks. These are a low intensity group of peaks that often occur in dense samples. Most often these peaks appear on a particular instrument for all samples.

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