Engineering topics

Jørgensen, Mads Ry Vogel

Publications and source records attributed to Jørgensen, Mads Ry Vogel.

4 records

DOE OSTI2023

Local structural mechanism for enhanced energy storage properties in heterovalent doped NaNbO 3 ceramics

In recent years, there is a growing interest for new lead-free oxides with reversible antiferroelectric (AFE)-ferroelectric (FE) phase transition for high-power energy-storage applications. NaNbO 3 -based ceramics are particularly attractive due to their easy synthesis and cost-effectiveness. In order to stabilize reversible AFE-FE phase transition, NaNbO 3 is doped with a combination of heterovalent substitutions, although the underlying structural mechanism for the same is poorly understood. Here, we investigated local and average structures of Ca/Zr doped NaNbO 3 using neutron total scattering. Here, we show that Ca/Zr doping increases the average AFE phase (Pbma) fraction, however, the material remains as a composite of both FE (P2 1 ma) and AFE regions. Analysis of local structure suggests that increase in the long-range AFE phase results from more extensive twinning of local FE regions, due to introduced charge disorder. We propose that enhanced energy-storage properties of Ca/Zr-doped NaNbO 3 arises from localized twin boundary motion between the defect-induced pinning centers.

25 ENERGY STORAGE↗

DOE OSTI2023

Scaling behavior of order parameters for the hybrid improper ferroelectric (Ca, Sr) 3 Ti 2 O 7

Here, we show that in contrast to the conventional view of a mean-field Landau-type behavior, the oxygen octahedral tilt ($\textit{R}$) and polarization (P) in the $A_3B_2\text{O}_7$ Ruddlesden-Popper hybrid improper ferroelectric (Ca,Sr) 3 Ti 2 O 7 exhibits the scaling behavior of a 2D Ising class near the para-to-ferroelectric transition temperature ($T_c$). We also show that $\textit{P}$ exhibits an additional transition from a 2D Ising to a 3D mean-field critical behavior at a temperature $T_{\text{d}} < T_{\text{c}}$, which is driven by enhanced interlayer dipolar correlations due to an increase in the ferroelectrically active $\textit{B}$-site centered dipoles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2023

Pressure-induced charge-transfer and structural transition in hexagonal multiferroic HoMnO 3

The structural properties of the hexagonal multiferroic ℎ-HoMnO 3 under high pressure have been explored using synchrotron x-ray diffraction and x-ray absorption spectroscopy in diamond anvil cells. The structure was found to undergo a pressure-induced phase transition at ~24 GPa to a rhombohedrally distorted superstructure, which is isostructural to the oxygen-loaded h-RMnO 3+δ (R = Y,Dy,Ho,Er; δ ≈ 0.28) phases found in the same systems. The driving force behind the phase transition is the highly compressible ab plane which facilitates a gradual charge disproportionation of Mn(III) with pressure. We speculate this stabilizes the spin-liquid phase due to ferromagnetic coupling between neighboring Mn(II)/Mn(IV) and Mn(III). In addition, we demonstrate that the structural behavior is highly susceptible to nonhydrostatic conditions and the choice of pressure medium should be carefully made.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.

36 MATERIALS SCIENCE↗