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Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation
Magnesium (Mg) and its alloys can degrade gradually up to complete dissolution in the physiological environment. This property makes these biomaterials appealing for different biomedical applications, such as bone implants. In order to qualify Mg and its alloys for bone implant applications, there i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6316266/ https://www.ncbi.nlm.nih.gov/pubmed/30501102 http://dx.doi.org/10.3390/bioengineering5040105 |
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author | Amerinatanzi, Amirhesam Mehrabi, Reza Ibrahim, Hamdy Dehghan, Amir Shayesteh Moghaddam, Narges Elahinia, Mohammad |
author_facet | Amerinatanzi, Amirhesam Mehrabi, Reza Ibrahim, Hamdy Dehghan, Amir Shayesteh Moghaddam, Narges Elahinia, Mohammad |
author_sort | Amerinatanzi, Amirhesam |
collection | PubMed |
description | Magnesium (Mg) and its alloys can degrade gradually up to complete dissolution in the physiological environment. This property makes these biomaterials appealing for different biomedical applications, such as bone implants. In order to qualify Mg and its alloys for bone implant applications, there is a need to precisely model their degradation (corrosion) behavior in the physiological environment. Therefore, the primary objective develop a model that can be used to predict the corrosion behavior of Mg-based alloys in vitro, while capturing the effect of pitting corrosion. To this end, a customized FORTRAN user material subroutine (or VUMAT) that is compatible with the finite element (FE) solver Abaqus/Explicit (Dassault Systèmes, Waltham, MA, USA) was developed. Using the developed subroutine, a continuum damage mechanism (CDM) FE model was developed to phenomenologically estimate the corrosion rate of a biocompatible Mg–Zn–Ca alloy. In addition, the mass loss immersion test was conducted to measure mass loss over time by submerging Mg–Zn–Ca coupons in a glass reactor filled with simulated body fluid (SBF) solution at pH 7.4 and 37 °C. Then, response surface methodology (RSM) was applied to calibrate the corrosion FE model parameters (i.e., Gamma (γ), Psi (ψ), Beta (β), and kinetic parameter (K(u))). The optimum values for γ, ψ, β and K(u) were found to be 2.74898, 2.60477, 5.1, and 0.1005, respectively. Finally, given the good fit between FE predictions and experimental data, it was concluded that the numerical framework precisely captures the effect of corrosion on the mass loss over time. |
format | Online Article Text |
id | pubmed-6316266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63162662019-01-10 Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation Amerinatanzi, Amirhesam Mehrabi, Reza Ibrahim, Hamdy Dehghan, Amir Shayesteh Moghaddam, Narges Elahinia, Mohammad Bioengineering (Basel) Article Magnesium (Mg) and its alloys can degrade gradually up to complete dissolution in the physiological environment. This property makes these biomaterials appealing for different biomedical applications, such as bone implants. In order to qualify Mg and its alloys for bone implant applications, there is a need to precisely model their degradation (corrosion) behavior in the physiological environment. Therefore, the primary objective develop a model that can be used to predict the corrosion behavior of Mg-based alloys in vitro, while capturing the effect of pitting corrosion. To this end, a customized FORTRAN user material subroutine (or VUMAT) that is compatible with the finite element (FE) solver Abaqus/Explicit (Dassault Systèmes, Waltham, MA, USA) was developed. Using the developed subroutine, a continuum damage mechanism (CDM) FE model was developed to phenomenologically estimate the corrosion rate of a biocompatible Mg–Zn–Ca alloy. In addition, the mass loss immersion test was conducted to measure mass loss over time by submerging Mg–Zn–Ca coupons in a glass reactor filled with simulated body fluid (SBF) solution at pH 7.4 and 37 °C. Then, response surface methodology (RSM) was applied to calibrate the corrosion FE model parameters (i.e., Gamma (γ), Psi (ψ), Beta (β), and kinetic parameter (K(u))). The optimum values for γ, ψ, β and K(u) were found to be 2.74898, 2.60477, 5.1, and 0.1005, respectively. Finally, given the good fit between FE predictions and experimental data, it was concluded that the numerical framework precisely captures the effect of corrosion on the mass loss over time. MDPI 2018-11-29 /pmc/articles/PMC6316266/ /pubmed/30501102 http://dx.doi.org/10.3390/bioengineering5040105 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Amerinatanzi, Amirhesam Mehrabi, Reza Ibrahim, Hamdy Dehghan, Amir Shayesteh Moghaddam, Narges Elahinia, Mohammad Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation |
title | Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation |
title_full | Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation |
title_fullStr | Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation |
title_full_unstemmed | Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation |
title_short | Predicting the Biodegradation of Magnesium Alloy Implants: Modeling, Parameter Identification, and Validation |
title_sort | predicting the biodegradation of magnesium alloy implants: modeling, parameter identification, and validation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6316266/ https://www.ncbi.nlm.nih.gov/pubmed/30501102 http://dx.doi.org/10.3390/bioengineering5040105 |
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