arXiv subjects

Karel Tesař

Publications and source records attributed to Karel Tesař.

2 recordsLinked to original sources

arXiv2026

Dilute Zn alloying in biodegradable Mg wires: microstructure, mechanical performance, and degradation behavior

Dilute Mg-Zn wires are of great interest for biodegradable small-bone fixation, as magnesium degradation can support bone-related processes, while low zinc additions may provide biological benefits without compromising biocompatibility. In this work, the influence of Zn content below the room-temperature solubility limit was assessed in Mg-Zn wires intended for resorbable implant applications. Mg-0.4Zn, Mg-0.6Zn, Mg-0.8Zn, and Mg-1.5Zn alloys were processed by single-step direct hot extrusion into thin wires and characterized by correlative microstructural analysis, tensile testing, bending experiments, and in vitro degradation. All compositions achieved a recrystallized fine equiaxed grain size of 5.0-5.9 um and exhibited ultimate tensile strengths of 246-256 MPa with elongations of 23-28 %. In these thin wires, Zn content had only a limited effect on grain size, tensile properties, and bending behavior, although lower-Zn alloys showed a pronounced sharp yield point. Bending was governed mainly by extrusion texture and preserved reversible plasticity through twinning and detwinning. Simulated body fluid caused rapid localized degradation and loss of mechanical integrity within 7 days, while the biologically more relevant DMEM-based medium better reflected the expected in vivo response. Together, these findings support dilute Mg-Zn wires as a simple material platform for the development of future resorbable bone fixation devices.

cond-mat.mtrl-sci↗

arXiv2026

Accelerating the Design of Resorbable Magnesium Alloys: A Machine Learning Approach to Property Prediction

Resorbable magnesium (Mg) alloys are promising candidates for temporary medical devices due to their biodegradability and favorable mechanical properties. To accelerate the design of diluted Mg alloys for implants, we developed a data-driven framework to elucidate the complex relationships between composition, processing, and mechanical properties. The framework screens mechanical properties within biocompatible compositional limits, treating degradation as a design constraint rather than an explicit prediction target. Using a dataset of 410 samples, we trained six different machine learning (ML) models to predict yield strength, ultimate tensile strength, and elongation. Among them, ensemble models, particularly CatBoost, demonstrated high predictive accuracy (R2, YS = 0.950, UTS = 0.916 and El = 0.903). SHapley Additive exPlanation analysis revealed that thermomechanical processing conditions and alloying elements such as Zn, Mn and Gd are the most influential factors governing mechanical behavior in diluted Mg alloys. Validation on the experimental dataset confirmed the models' robustness and generalization capability in capturing process-property relationships. The optimized CatBoost model was further employed to generate predictive property maps visualizing the strength-ductility trade-off as a function of Zn-Mn composition. This work establishes a validated ML framework for rapid in silico screening and targeted design of next-generation resorbable Mg alloys.

cond-mat.mtrl-sci↗