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Shriyar Tariq

Publications and source records attributed to Shriyar Tariq.

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TetMaG-Guided Design and Operando Electron Holography Validation of Current-Induced Domain-Wall Motion in 3D Curved and Cornered Fe Nanobridges

Three-dimensional (3D) magnetic nanostructures offer new opportunities for controlling domain-wall (DW) configurations beyond the limitations of planar systems, providing promising architectures. However, the realization of reliable 3D magnetic devices requires precise control of geometry-dependent DW behaviour and quantitative experimental validation of the resulting magnetic states. Here, we combine TetMaG micromagnetic simulations, focused electron beam induced deposition (FEBID), and off-axis electron holography to investigate the influence of curvature and corner geometries on DW behaviour in 3D magnetic nanobridges. TetMaG simulations predict fundamentally different magnetic properties for curved and cornered geometries. Cornered nanobridges act as preferential DW pinning sites, stabilizing localized magnetic configurations and enabling controlled switching between neighbouring pinning positions. While curved nanobridges promote gradual magnetization rotation, reduced pinning, and smoother DW motion. These optimized geometries were fabricated with high structural fidelity using FEBID and subsequently characterized by quantitative electron holography. Electron holography measurements revealed magnetic induction maps that matched the simulated magnetization configurations, providing direct experimental validation of the TetMaG predictions. Curved and cornered geometries exhibited distinct DW characteristics governed by their local structural features, demonstrating the critical role of geometry in tailoring magnetic behaviour in 3D systems. Operando current-biasing experiments further revealed current-induced DW motion, including the displacement of a tail-to-tail DW into a head-to-tail configuration within corner structures.

cond-mat.mes-hall

High Throughput Analysis of Nanobeam Electron Diffraction Datasets using Unsupervised Clustering

If disk detection is applied to nanobeam electron diffraction datasets, then the results are effectively a list of vectors describing the position of every diffraction peak in real and reciprocal space. This is the natural territory for the application of clustering algorithms, and they are shown to be highly effective at decomposing such datasets and automating imaging and analysis. Examples are shown in both polycrystalline and single crystal (with precipitates) systems. Additionally, automated separation of amorphous or deeply nanocrystalline components is also found to be possible allowing composite images of both amorphous and crystalline components in partially crystallised samples to be easily and automatically generated. These advances promise to increase throughput in atomic structure analysis with nanobeam diffraction, and also make finding minor components much easier. They can also serve as a preliminary step towards more detailed crystallographic or crystal size/shape distribution analysis.

cond-mat.mtrl-sci