arXiv subjects

Katarzyna Mulewska

Publications and source records attributed to Katarzyna Mulewska.

3 recordsLinked to original sources

arXiv2025

Nanoscale structure formation in nickel-aluminum alloys synthesized far from equilibrium

The present study reports on the structure formation in thin epitaxial nickel-aluminum films (Ni1-xAlx; Al atomic fraction x up to x=0.24) grown on MgO(001) substrates by magnetron sputtering. Experimental and computational data demonstrate that for x<0.11, the films exhibit the face-centered cubic random solid-solution Ni1-xAlx structure (γ). Whereas in the range x=0.11-0.24 the phase coexists with the ordered L12 structure (γ' phase). The two phases are homogenously intermixed forming a coherent and strained nano-solution, which exhibits a single lattice parameter that expands as the Al content increases. Isothermal annealing of films containing x=0.14 of Al, coupled with structural and nano-mechanical characterization, reveal that the nano-solution retains its overall integrity for temperatures up to 673 K, while the film hardness increases from 5.5 GPa (as deposited films) to 6 GPa. Further increase of the annealing temperature to 873 K and 1073 K causes the nano-solution to dissolve into distinct γ and γ' phase domains and the hardness to decrease down to values of 4 GPa. These findings confirm the metastable nature of the as-deposited thin Ni1-xAlx alloy films and underpin the effectiveness of high supersaturation/undercooling for creating non-equilibrium phases and self-organized nanostructures upon synthesis of multicomponent materials.

cond-mat.mtrl-sci↗

arXiv2023

Multi-component low and high entropy metallic coatings synthesized by pulsed magnetron sputtering

This paper presents the findings of the synthesis of multicomponent (Al, W, Ni, Ti, Nb) alloy coatings from mosaic targets. For the study, a pulsed magnetron sputtering method was employed under different plasma generation conditions: modulation frequency (10 Hz and 1000 Hz), and power (600 W and 1000 W). The processes achieved two types of alloy coatings, high entropy and classical alloys. After the deposition processes, scanning electron microscopy, X-ray diffraction, and energy-dispersive X-ray spectroscopy techniques were employed to find the morphology, thickness, and chemical and phase compositions of the coatings. Nanohardness and its related parameters, namely H3.Er2, H.E, and 1.Er2H ratios, were measured. An annealing treatment was performed to estimate the stability range for the selected coatings. The results indicated the formation of as-deposited coatings exhibiting an amorphous structure as a single-phase solid solution. The process parameters had an influence on the resulting morphology-a dense and homogenous as well as a columnar morphology, was obtained. The study compared the properties of high-entropy alloy (HEA) coatings and classical alloy coatings concerning their structure and chemical and phase composition. It was found that the change of frequency modulation and the post-annealing process contributed to the increase in the hardness of the material in the case of HEA coatings.

cond-mat.mtrl-sci↗

arXiv2022

Prediction of steel nanohardness by using graph neural networks on surface polycrystallinity maps

As a bulk mechanical property, nanoscale hardness in polycrystalline metals is strongly dependent on microstructural features that are believed to be heavily influenced from complex features of polycrystallinity -- namely, individual grain orientations and neighboring grain properties. We train a graph neural network (GNN) model, with each grain center location being a graph node, to assess the predictability of micromechanical responses of nano-indented low-carbon 310S stainless steel (alloyed with Ni and Cr) surfaces, solely based on surface polycrystallinity, captured by electron backscatter diffraction maps. The grain size distribution ranges between $1-100~μ$m, with mean grain size at $18~μ$m. The GNN model is trained on a set of nanomechanical load-displacement curves, obtained from nanoindentation tests and is subsequently used to make predictions of nano-hardness at various depths, with sole input being the grain locations and orientations. Model training is based on a sub-standard set of $\sim10^2$ hardness measurements, leading to an overall satisfactory performance. We explore model performance and its dependence on various structural/topological grain-level descriptors, such as the grain size and number of nearest neighbors. Analogous GNN model frameworks may be utilized for quick, inexpensive hardness estimates, for guidance to detailed nanoindentation experiments, akin to cartography tool developments in the world exploration era.

cond-mat.mtrl-sci↗