Development of powder diffraction
analysis tools for a nanocrystalline
specimen:
An emphasis upon NiTi (Nitinol)
Erich Owens
Albion College
Stanford Linear Accelerator Center
August 16th, 2006
X-ray diffraction
Bragg’s law:
*
θ
θ
d
θ
Powder Diffraction Basics
Features of the Diffraction Image
Peak width
Crystallization of material
Peak intensity
Texture
Peak location
Lattice spacing (d)
Overview Signal Identification and Extraction
Data
Science e.g.
Features of the Diffraction Image
Signal versus Background
Data Analysis
Data Analysis
Data Analysis
Relevant data needed and how to get there
Each row (some chi value) to have a single peak fitted
(Gaussian/Doppler, Lorentzian, or Voigt [a convolution of
Gaussian and Lorentzian distribution]).
Interpretation and subtraction of background from relevant
signal
Stored data along each chi of fitted peak’s width, amplitude,
and location.
Fitting the Curves
Data Analysis
Residues
Developed Algorithm
-Minimizes user input in determining
signal from background
-Extracts needed peak qualities
Developed Algorithm (in action)
Pretty pictures of fitted data
Before
After
Overview (again) Refinement and Signal Extraction
Data
Science e.g.
Results – succesful data extraction
allows some science to be performed
Acknowledgements
Matthew Strasberg,
Cornell University
Apurva Mehta and Samuel Webb,
Stanford Linear Accelerator Center
SULI Program Coordinators at SLAC
Office of Science, Department of Energy