GIDInd: an automated indexing software for grazing‐incidence X‐ray diffraction data. Issue 4 (30th July 2021)

• Record Type: Journal Article
• Authors: Kainz, Manuel Peter; Legenstein, Lukas; Holzer, Valentin; Hofer, Sebastian; Kaltenegger, Martin; Resel, Roland; Simbrunner, Josef
• Is Part Of: Journal of applied crystallography. Volume 54:Issue 4(2021)
• Page Start: 1256
• Page End: 1267
• Publication Date: 2021-07-30
• Subjects: grazing‐incidence X‐ray diffraction -- indexing -- crystallographic unit cells -- software; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576721006609
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : The GIDInd software package is a MATLAB‐based application for automated indexing of grazing‐incidence X‐ray diffraction data. Abstract : Grazing‐incidence X‐ray diffraction (GIXD) is a widely used technique for the crystallographic characterization of thin films. The identification of a specific phase or the discovery of an unknown polymorph always requires indexing of the associated diffraction pattern. However, despite the importance of this procedure, only a few approaches have been developed so far. Recently, an advanced mathematical framework for indexing of these specific diffraction patterns has been developed. Here, the successful implementation of this framework in the form of an automated indexing software, named GIDInd, is introduced. GIDInd is based on the assumption of a triclinic unit cell with six lattice constants and a distinct contact plane parallel to the substrate surface. Two approaches are chosen: (i) using only diffraction peaks of the GIXD pattern and (ii) combining the GIXD pattern with a specular diffraction peak. In the first approach the six unknown lattice parameters have to be determined by a single fitting procedure, while in the second approach two successive fitting procedures are used with three unknown parameters each. The output unit cells are reduced cells according to approved crystallographic conventions. Unit‐cell solutions are additionally numerically optimized. The computational toolkit is compiled in the form of a MATLAB executable and presented within a user‐friendly graphical user interface. The program is demonstrated by application on two independent examples of thin organic films.