Magnesium degradation as determined by artificial neural networks


Magnesium degradation under physiological conditions is a highly complex process where temperature, the use of cell culture growth medium and the presence of CO2, O2 and proteins can influence the corrosion rate and the composition of the resulting corrosion layer. Due to the complexity of this process, it is almost impossible to predict the parameters that are most important and whether some parameters have a synergistic effect on the corrosion rate. Artificial Neural Networks are a mathematical tool that can be used to approximate and analyse non-linear problems with multiple inputs. In this work, we present the first analysis of corrosion data obtained using this method, which reveals that CO2 and the composition of the buffer system play a crucial role in the corrosion of magnesium, whereas O2, proteins and temperature play a less prominent role.
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