X-ray fluorescence technology (XRF) provides one of the simplest, most accurate and most economic analytical methods for the determination of the chemical composition of many types of materials. It is non-destructive and reliable, requires no, or very little, sample preparation and is suitable for solid, liquid and powdered samples. It can be used for a wide range of elements, from sodium (11) to uranium (92), and provides detection limits at the sub-ppm level; it can also measure concentrations of up to 100% easily and simultaneously.
The physics and examples of this technique can be review in:Complete introduction
Samples were provided by geology deparment toward analysis to compare the pottery from an area in Pueblo Indians to Chihuahua dessert communities.
3 samples labeled as:
- 12363 (H)
- 15018 (J)
Portable XRF Analyzer
Each sample was located carefully onto analysis area in the Portable XRF analyzer. Data was obtained and using the fit parameters (listed below) in the PyMCA softwere. Plots were obtained to find the material composition.
1. Load your data.
2. Select S# 2.1 on left top window; this opens the window on the right with the calibrate button.
3. Select "Internal(from Source or PyMCA)" on the calibration menu.
4. Click on Calibrate > Compute.
5. Click on Search on the MCA Calibration window that opens. (Sensitivity = 3 for our particular samples used)
6. Click on the Ag peak -see graph below.
7. Select Ag(47) from the Element menu and KL3(0.54112) from the Line menu, click OK.
8. Click OK to go back to PyMCA Main Window, the energy axis should then be calibrated.
Plots were obtained by PyMCA software for each of the samples:
Main materials were identified, resulting in the following results: (Fe was take out of the analysis, assuming the cover of the portable analyser gave that signal)
Main materials concentrations per sample:
Table of elements (major peaks) over background:
Comparison of concentration in an element vs element basis:
Samples mainly elements identified were Sr, Pd, Zr, Ag, Zn, Rd, and W.
Element vs element plots suggest that samples 17153 (R) and (15019(J) have more similitudes between each other and not that much with sample 12363(H).
Fabian Almeida / Jorge Rodriguez