Atomic-Scale Mapping and Quantification of Local Ruddlesden-Popper Phase Variations

Area of Science:

• Materials Science
• Crystallography
• Solid-State Chemistry

Background:

• Ruddlesden-Popper (A_n-1B_nO_3n+1) compounds exhibit tunable properties influenced by structural phase (n).
• Minor variations in stoichiometry can lead to local structural changes due to similar formation energies of different n-phases.

Purpose of the Study:

• To present a Python analysis platform for detecting, measuring, and quantifying n-phases in layered materials.
• To enable detailed analysis of local structural variations and intergrowth occurrences.

Main Methods:

• Utilizing atomic-resolution scanning transmission electron microscopy (STEM) images.
• Employing image phase analysis to identify and quantify Ruddlesden-Popper faults.
• Developing a semiautomated technique accounting for projection thickness, field of view, and sampling rates.

Main Results:

• Successful detection and quantification of different n-phases within STEM images.
• Identification of horizontal Ruddlesden-Popper faults and their spatial distribution.
• Method provides real-space mapping of layer variations for quantitative analysis.

Conclusions:

• The developed Python platform offers a robust method for analyzing structural variations in Ruddlesden-Popper materials.
• This technique facilitates the quantification of intergrowth occurrence and distribution in layered materials.
• The approach is adaptable for a broad range of layered materials analysis.