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I'm glad to say that our latest paper in the Corrosion Science is now available online.🙂
"Computational modeling of degradation process of biodegradable magnesium biomaterials" doi.org/10.1016/j.cors…
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Biodegradable materials find important applications in designing supportive medical implants, but despite the advantages, assessing the uncontrolled degradation and release remain a challenge in practical use-cases.
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Combining the insights obtained through several years of experimental research with computational (in silico) modeling approaches enables us to save lots of resources by studying the biodegradation of medical devices virtually prior to conducting any in vitro/vivo tests.💪
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To this end, we developed a physicochemical model by deriving a mathematical description of the chemistry of magnesium biodegradation and implementing it in a parallel 3D computational model, capable of simulating the degradation of any desired implant/scaffold shape.
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The model was validated by comparing the predicted and experimentally obtained hydrogen evolution and change of pH during the in vitro corrosion tests in an immersion setup, showing a good agreement between the results.😎
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The model captures dissolution of the metallic part, reduction of water and oxygen, changes in pH, formation of a protective film on the surface of material, the effect of different ions in the medium, and morphology changes of material block during the degradation process
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A glance at the technicalities? Math model comprises of reaction-diffusion equations, level set formalism was employed to track the moving corrosion front, equations were solved using finite element method, and Bayesian optimization was used to calibrate the model.
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In the end, I want to appreciate my supervisor, @LiesbetGeris, for all the great support she provided me with. 🥰
I would also like to thank the collaborators, awesome chemists, Sviatlana and Di for all the things they taught me about the biodegradation process.
(1/6)
I'm very happy that we (finally) got an acceptance for our submitted paper in the Journal of Open Source Education, JOSE🙂.
"An open source crash course on parameter estimation of computational models using a Bayesian optimization approach" doi.org/10.21105/jose.…
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Parameter estimation is a crucial aspect of model development in science and engineering. In the proposed educational module, we have a look at the #Bayesian optimization processes in general and model calibration (parameter estimation) in particular.
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For demonstration purposes, we implement a model parameter estimation process for a fitting problem step by step in Python such that the readers can adapt it to their own models and use-cases.