In silico methods to study biodegradable implants have recently received increasing attention due to their potential in reducing experimental time and cost. An important application case for in silico methods are magn...In silico methods to study biodegradable implants have recently received increasing attention due to their potential in reducing experimental time and cost. An important application case for in silico methods are magnesium(Mg)-based biodegradable implants, as they represent a powerful alternative to traditional materials used for temporary orthopaedic applications. Controlling Mg alloy degradation is critical to designing an implant that supports the bone healing process. To simulate different aspects of this biodegradation process, several mathematical models have been proposed with the ultimate aim of replacing laboratory experiments with computational modeling. In this review, we provide a comprehensive and critical discussion of the published models and their performance with respect to capturing the complexity of the biodegradation process. This complexity is presented initially. Additionally, the present review discusses the different approaches of optimizing and quantifying the different sources of errors and uncertainties within the proposed models.展开更多
Magnesium alloys are highly attractive for the use as temporary implant materials, due to their high biocompatibility and biodegradability.However, the prediction of the degradation rate of the implants is difficult, ...Magnesium alloys are highly attractive for the use as temporary implant materials, due to their high biocompatibility and biodegradability.However, the prediction of the degradation rate of the implants is difficult, therefore, a large number of experiments are required. Computational modelling can aid in enabling the predictability, if sufficiently accurate models can be established. This work presents a generalized model of the degradation of pure magnesium in simulated body fluid over the course of 28 days considering uncertainty aspects. The model includes the computation of the metallic material thinning and is calibrated using the mean degradation depth of several experimental datasets simultaneously. Additionally, the formation and precipitation of relevant degradation products on the sample surface is modelled, based on the ionic composition of simulated body fluid. The computed mean degradation depth is in good agreement with the experimental data(NRMSE=0.07). However, the quality of the depth profile curves of the determined elemental weight percentage of the degradation products differs between elements(such as NRMSE=0.40 for phosphorus vs. NRMSE=1.03 for magnesium). This indicates that the implementation of precipitate formation may need further developments. The sensitivity analysis showed that the model parameters are correlated and which is related to the complexity and the high computational costs of the model. Overall, the model provides a correlating fit to the experimental data of pure Mg samples of different geometries degrading in simulated body fluid with reliable error estimation.展开更多
基金funding from the Helmholtz-Incubator project Uncertainty Quantification。
文摘In silico methods to study biodegradable implants have recently received increasing attention due to their potential in reducing experimental time and cost. An important application case for in silico methods are magnesium(Mg)-based biodegradable implants, as they represent a powerful alternative to traditional materials used for temporary orthopaedic applications. Controlling Mg alloy degradation is critical to designing an implant that supports the bone healing process. To simulate different aspects of this biodegradation process, several mathematical models have been proposed with the ultimate aim of replacing laboratory experiments with computational modeling. In this review, we provide a comprehensive and critical discussion of the published models and their performance with respect to capturing the complexity of the biodegradation process. This complexity is presented initially. Additionally, the present review discusses the different approaches of optimizing and quantifying the different sources of errors and uncertainties within the proposed models.
基金funding from the Helmholtz-Incubator project Uncertainty Quantification.
文摘Magnesium alloys are highly attractive for the use as temporary implant materials, due to their high biocompatibility and biodegradability.However, the prediction of the degradation rate of the implants is difficult, therefore, a large number of experiments are required. Computational modelling can aid in enabling the predictability, if sufficiently accurate models can be established. This work presents a generalized model of the degradation of pure magnesium in simulated body fluid over the course of 28 days considering uncertainty aspects. The model includes the computation of the metallic material thinning and is calibrated using the mean degradation depth of several experimental datasets simultaneously. Additionally, the formation and precipitation of relevant degradation products on the sample surface is modelled, based on the ionic composition of simulated body fluid. The computed mean degradation depth is in good agreement with the experimental data(NRMSE=0.07). However, the quality of the depth profile curves of the determined elemental weight percentage of the degradation products differs between elements(such as NRMSE=0.40 for phosphorus vs. NRMSE=1.03 for magnesium). This indicates that the implementation of precipitate formation may need further developments. The sensitivity analysis showed that the model parameters are correlated and which is related to the complexity and the high computational costs of the model. Overall, the model provides a correlating fit to the experimental data of pure Mg samples of different geometries degrading in simulated body fluid with reliable error estimation.
