journal article·2024-08

Multiscale exploration of Ti-Nb-Zr-based alloys for enhanced bioimplant performance

Daniela A. Damasceno, Camilo A.F. Salvador, Bruno F. Zornio, Caetano R. Miranda

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Abstract

Beta-titanium alloys (beta-Ti alloys) with low elastic modulus are metallic materials of great technological interest for high-performance bioimplants. This study employs a comprehensive multiscale approach to investigate alloys from the Ti-Nb-Zr-Sn system, exhibiting desired bioimplant properties such as low elastic modulus and high beta-phase stability. The multiscale strategy encompasses electronic structure calculations, integrating them with device simulations through a coupling of calculation of phase diagrams, density functional theory (DFT), machine learning (ML), and finite element analysis (FEA). Utilizing ML and DFT methodologies, we predict and analyze the elastic and electronic properties of the optimized ternary and quaternary alloys. DFT calculations point to elevated beta-phase stability compared to omega-phase, suggesting a potential formation of orthorhombic martensite in the Ti-22Zr-11 Nb-4Sn (at%) alloy. Incorporating small amounts of Sn changes the nature of the bonds, resulting in structural and electronic stabilization of the beta-phase. FEA further validates the mechanical performance of the proposed alloys, demonstrating their potential compared to the well-established Ti-Nb-Ta-Zr (TNZT) alloy, a reference in the field. Our findings underscore the effectiveness of multiscale methodologies in advancing the understanding of alloy design for bioimplant applications. We conclude that this multiscale strategy not only elucidates the compositions of interest but also serves as a catalyst for innovation and progress in the field of bioimplantation.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Titanium Alloys Microstructure and Properties · Orthopaedic implants and arthroplasty · Advanced materials and composites

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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