title: Magnesium degradation as determined by artificial neural networks.
abstract: Magnesium degradation under physiological conditions is a highly complex process in which 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.
authors.affiliation: Institute of Materials Research, Helmholtz-Zentrum Geesthacht, Max-Planck-Strasse 1, 21502 Geesthacht, Germany. Electronic address: regine.willumeit@hzg.de.
authors.forename: Regine
authors.initials: R
authors.lastname: Willumeit
authors.forename: Frank
authors.initials: F
authors.lastname: Feyerabend
authors.forename: Norbert
authors.initials: N
authors.lastname: Huber
mesh_terms: D002245:Carbon Dioxide / Q000737:chemistry; D003343:Corrosion; D055664:Electrochemical Techniques; D008274:Magnesium / Q000737:chemistry*; D016571:Neural Networks, Computer; D010100:Oxygen / Q000737:chemistry; D010313:Partial Pressure; D012965:Sodium Chloride / Q000737:chemistry
chemical_list: D002245:Carbon Dioxide; D012965:Sodium Chloride; D008274:Magnesium; D010100:Oxygen
keywords: Artificial neural networks; Cell culture conditions; Implants; In vitro; Magnesium degradation