Mathias A. Kiefer | Searcharxiv

arXiv2026

Incommensurate structural and magnetic modulations in potassium-rich cryptomelane, K$_x$Mn$_8$O$_{16}$ ($x\approx1.45$)

Cryptomelane is a hollandite-like material consisting of K$^+$ cations in an $α$-MnO$_2$ tunnel-like crystallographic motif. Cryptomelane with stoichiometry K$_x$Mn$_8$O$_{16}$ ($x\approx1.45$) has been synthesized and its magnetic properties investigated using variable-temperature magnetic susceptibility, heat capacity, and neutron powder diffraction. Three distinct transitions at $T_1=184$\,K, $T_2=54.5$\,K, and $T_3=24$\,K are observed. At $T_1$ there is a subtle tetragonal$\rightarrow$monoclinic transition associated with emergence of a set of non-magnetic superstructure peaks indexable to a $\vec{k}_\mathrm{struc}\approx0.74\vec{c^*}$ incommensurate modulation parallel to the $α$-MnO$_2$ tunnels. Our findings are consistent with a relation previously reported in titanate hollandites, that $x\approx2|\vec{k}_\mathrm{struc}|$. Magnetic Bragg peaks emerge below $T_2=54.5$\,K, and their positions indicate an incommensurate modulated magnetic structure. The model consistent with the data is a dual-$\vec{k}_\mathrm{mag}$ structure with a ferromagnetic $|\vec{k}_\mathrm{mag}|=0$ component and an incommensurate $\vec{k}_\mathrm{mag}\approx0.37\vec{c^*}$, with the latter most likely to be helical. The period of oscillation of the incommensurate magnetic component is in line with predictions based on a Heisenberg spin Hamiltonian [Mandal \textit{et al}. Phys. Rev. B 90, 104420 (2014)]. Below $T_3=24$\,K, there is a magnetic transition, which gives rise to a different set of magnetic Bragg peaks indicative of a highly complex magnetic structure.

cond-mat.mtrl-sci↗

arXiv2026

Automating the analysis of micron-scale synchrotron diffraction data on inhomogeneous polycrystalline samples: a solid oxide electrolysis cell case study

A novel approach to post mortem characterisation of electrochemical and photovoltaic devices is spatially-resolved diffraction using a hyper-focused, micron-width x-ray beam to examine the distribution of degradation products and strain, a technique called $μ$-XRD. Aside from the experimental difficulties associated with beam focusing and sample preparation, which are themselves non-trivial, the analysis of resulting data is complex and challenging, with full Rietveld analysis rarely attempted in literature. The difficulty lies in the size of the data, which may consist of hundreds or even thousands of diffraction patterns with very different crystallographic phase compositions depending on position within the device, and the difficulty in fitting the data due to the presence of many phases at the same position, including possible degradation products which may be difficult to index and assign to known phases. In this paper, we present a fully-automated open access Python routine for performing phase identification and Rietveld analysis on 2D datasets of diffraction pattern taken at micron-scale positions, measured over the cross-section of a chemically inhomogeneous device with polycrystalline phases. Solid oxide electrolyser cells are a promising technology for green hydrogen production which can utilise waste heat to split water at higher efficiencies than low-temperature electrolysis techniques such as polymer electrolyte membranes, but exhibit many degradation modes due to the high operating temperatures. We present a case study using our analysis protocol on an SOEC fragment encompassing the air electrode, cation diffusion barrier, electrolyte, and fuel electrode. With modification, this protocol could be applied to other devices such as all-solid-state batteries, wet-electrolyte battery electrodes, solid oxide fuel cells, photovoltaic devices, and metal-oxide pseudocapacitors.

cond-mat.mtrl-sci↗