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Martin Hasenburger

Publications and source records attributed to Martin Hasenburger.

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Direct nanoscale observation of melting and solute redistribution in a hypoeutectic Al-Cu alloy with $\it{in\ situ}$ STEM

Melting and solidification of eutectic systems are classical topics in physical metallurgy, yet the mechanisms at nanoscale are less investigated, due to experimental limitations in spatiotemporal resolution. The advent of $\it{in\ situ}$ STEM heating with MEMS technology has recently enabled investigation of eutectic behavior as a function of temperature, time and electrical resistivity. Using this methodology, we investigate the evolution of a nanocrystalline hypoeutectic Al-Cu alloy. Melting initiated in the hotter central region and propagated outward, with grain boundaries acting as preferred sites for eutectic liquid formation via Cu enrichment. The Al$_2$Cu phase melted prior to complete matrix melting. Liquid-state Cu redistribution over a distance of 258 $\mu$m - several orders of magnitude beyond solid-state diffusion limits - resulted in Al-rich rim accumulations and Cu enrichment at the outermost edge of the observed chip region. These observations are discussed in the context of classical predictions for melting of eutectic systems.

cond-mat.mtrl-sci

Unlocking nanoscale microstructural detail in aluminium alloys through differential phase contrast segmentation in STEM

Differential phase contrast (DPC) imaging in scanning transmission electron microscopy (STEM) maps projected electric fields through the phase sensitivity of segmented low-angle detectors. Although typically applied to atomic-resolution imaging at low beam currents, STEM-DPC is here demonstrated as a rapid micro- and nanoscale image-segmentation tool for materials characterization in advanced aluminium alloys. Decomposition of false-colour DPC micrographs in hue-saturation-value space enables simultaneous identification and quantification of nanoclusters, GP zones, intermediate precipitate phases, dislocation cores, and associated strain fields within a single field of view. The method is demonstrated across multiple alloy systems, including clustering and strain-field mapping in a deformed AlMgZn(Cu) crossover alloy, precipitate identification in a paint-baked automotive sheet alloy, phase-variant segmentation in overaged AA7075-T7, and nanopore and nanoparticle detection in an anodic coating on AA2024-T3. Coupling DPC with neural-network segmentation further enables automated grain-boundary delineation and quantification in nanocrystalline aluminium thin films. Combined with STEM-EDX, DPC-based segmentation enables correlative microstructural analysis, establishing DPC as a rapid complement to techniques such as SPED and 4D-STEM.

cond-mat.mtrl-sci