Background Treating septic arthritis of the hip with coexisting advanced degenerative disease is challenging.

Homologous series are layered phases that can have a range of stoichiometries depending on an index n. Examples of perovskite-related homologous series include (ABO3)nAO Ruddlesden–Popper phases and (Bi2O2) (An−1BnO3n+1) Aurivillius phases. It is challenging to precisely control n because other members of the homologous series have similar stoichiometry and a phase with the desired n is degenerate in energy with syntactic intergrowths among similar n values; this challenge is amplified as n increases. To improve the ability to synthesize a targeted phase with precise control of the atomic layering, we apply the x-ray diffraction (XRD) approach developed for superlattices of III–V semiconductors to measure minute deviations from the ideal structure so that they can be quantitatively eradicated in subsequent films. We demonstrate the precision of this approach by improving the growth of known Ruddlesden–Popper phases and ultimately, by synthesizing an unprecedented n = 20 Ruddlesden–Popper phase, (ATiO3)20AO where the A-site occupancy is Ba0.6Sr0.4. We demonstrate the generality of this method by applying it to Aurivillius phases and the Bi2Sr2Can–1CunO2n+4 series of high-temperature superconducting phases.

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The Ruddlesden–Popper (A n+1B n O3n+1) compounds are highly tunable materials whose functional properties can be dramatically impacted by their structural phase n. The negligible differences in formation energies for different n can produce local structural variations arising from small stoichiometric deviations. Here, we present a Python analysis platform to detect, measure, and quantify the presence of different n-phases based on atomic-resolution scanning transmission electron microscopy (STEM) images. We employ image phase analysis to identify horizontal Ruddlesden–Popper faults within the lattice images and quantify the local structure. Our semiautomated technique considers effects of finite projection thickness, limited fields of view, and lateral sampling rates. This method retains real-space distribution of layer variations allowing for spatial mapping of local n-phases to enable quantification of intergrowth occurrence and qualitative description of their distribution suitable for a wide range of layered materials.