Keywords :
Degradable metals; Finite element method; In silico medicine; Magnesium corrosion; Reaction–diffusion models; Biodegradable magnesiums; Biodegradable metals; Computational model; Degradation process; Element method; Implant design; Magnesium corrosions; Reaction-diffusion models; Chemistry (all); Chemical Engineering (all); Materials Science (all); Computer Science - Computational Engineering; Finance; and Science; Physics - Materials Science; General Materials Science; General Chemical Engineering; General Chemistry
Abstract :
[en] Despite the advantages of using biodegradable metals in implant design, their uncontrolled degradation and release remain a challenge in practical applications. A validated computational model of the degradation process can facilitate tuning implant biodegradation properties. In this study, a mathematical model of the chemistry of magnesium biodegradation was developed and implemented in a 3D computational model. The parameters were calibrated by Bayesian optimization using dedicated experimental data. The model was validated by comparing the predicted and experimentally obtained pH change in saline and buffered solutions, showing maximum 5% of difference, demonstrating the model's validity to be used for practical cases.
Funding text :
This research is financially supported by the Prosperos project, funded by the Interreg VA Flanders – The Netherlands program, CCI grant no. 2014TC16RFCB046 and by the Fund for Scientific Research Flanders (FWO) , grant G085018N . LG acknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation programme, ERC CoG 772418. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government – department EWI.This research is financially supported by the Prosperos project, funded by the Interreg VA Flanders – The Netherlands program, CCI grant no. 2014TC16RFCB046 and by the Fund for Scientific Research Flanders (FWO), grant G085018N. LG acknowledges support from the European Research Council under the European Union's Horizon 2020 research and innovation programme, ERC CoG 772418. The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government– department EWI.
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