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Nikola Koutna

Publications and source records attributed to Nikola Koutna.

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Interface-Controlled Defect Engineering in TiN/TaN Superlattices for Enhanced Hardness and Fracture Toughness

TiNTaN superlattice coatings were designed to investigate how atomic-scale interface chemistry and defect-stabilized TaN layers govern hardness and fracture toughness. Guided by first-principles predictions identifying TaN-based layers as more damage tolerant than TiN, coherent superlattices with a bilayer period of 6 nm were synthesized by reactive magnetron sputtering and interfacially doped with C, B, or Si. Structural and chemical analyses reveal coherent fcc architectures with well-defined interfaces. Si segregates preferentially to the interfaces while incorporating into both TiN and TaN, whereas C and B predominantly diffuse into the TaN layers, modifying coherency strain, bonding, and defect populations. Consequently, hardness increases from 34 GPa for the undoped superlattice to 41 GPa for the Si-doped architecture, whereas fracture toughness increases from 2.8 to 4.0 MPam0.5 for the B-doped superlattice. First-principles calculations show that vacancy-stabilized TaxNy enhances elastic compliance and elastic contrast rather than intrinsic toughness, while the additional toughening induced by B indicates localized defect-assisted energy dissipation at chemically engineered interfaces. Thus, Si maximizes interface strengthening, whereas B provides the most favourable hardness-toughness balance while preserving high hardness, 38 GPa. These findings establish interface chemistry as an additional design parameter for tailoring the mechanical performance of ceramic nitride superlattices.

cond-mat.mtrl-sci

Machine-Learning Potentials Predict Orientation- and Mode-Dependent Fracture in Refractory Diborides

Fracture toughness ($K_\mathrm{Ic}$) and fracture strength ($σ_\mathrm{f}$) are key criteria in the selection and design of reliable ceramics. However, their experimental characterization remains challenging -- especially for ceramic thin films, where size and interfacial effects hinder accurate and reproducible measurements. Here, machine-learning interatomic potentials (MLIPs) trained on \textit{ab initio} datasets of single crystal models deformed up to fracture are used to characterize transgranular cleavage in pre-cracked ceramic diboride TMB$_2$ (TM = Ti, Zr, Hf) lattices through stress intensity factor ($K$)-controlled loading. Mode-I simulations performed across distinct crack geometries show that fracture is primarily driven by straight crack extension along the original plane. The corresponding macroscale fracture-initiation properties ($K_\mathrm{Ic} \approx 1.7$-2.9 MPa$\cdot\sqrt{\text{m}}$, $σ_\mathrm{f} \approx 1.6$-2.4 GPa) are extrapolated using established scaling laws. Considering TiB$_2$ as a representative system, additional simulations explore loading conditions ranging from pure Mode-I (opening) to Mode-II (sliding). TiB$_2$ models containing prismatic cracks exhibit their lowest fracture resistance under mixed-mode conditions, where the crack deflects onto pyramidal planes--as confirmed by nanoindentation tests on TiB$_2$(0001) thin films. This study establishes $K$-controlled, MLIP-based simulations as predictive tools for orientation- and mode-dependent fracture in ceramics. The approach is readily extendable to finite temperatures for evaluating fracture behavior under conditions relevant to refractory applications.

cond-mat.mtrl-sci