1. Development of powder diffraction analysis tools for a nanocrystalline specimen: An emphasis upon NiTi (Nitinol) Erich Owens Albion College Stanford Linear Accelerator Center August 16 th, 2006

2. X-ray diffraction Bragg’s law: θ θ θ d *

3. Powder Diffraction Basics

4. Features of the Diffraction Image Peak width  Crystallization of material Peak intensity  Texture Peak location  Lattice spacing (d)

5. Overview Signal Identification and Extraction Data Science e.g.

6. Features of the Diffraction Image

7. Signal versus Background

8. Data Analysis

11.  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. Relevant data needed and how to get there

12. Fitting the Curves

13. Data Analysis

14. Residues

15. Developed Algorithm -Minimizes user input in determining signal from background -Extracts needed peak qualities

16. Developed Algorithm (in action)

17. Pretty pictures of fitted data Before After

18. Overview (again) Refinement and Signal Extraction Data Science e.g.

19. Results – succesful data extraction allows some science to be performed

20. 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