Degradation rate is an important property to evaluate bioabsorbable metallic material; however, values vary depending on the method of measurement. In this study, three different methods of measuring corrosion rate are compared. The degradable samples to analyze corrosion rates include pure magnesium (Mg), lab produced Mg–Zn–Ca alloy (47-7-2), Mg–Zn–Zr–RE (rare earth) alloys (60-13, 60-14), Mg–Zn–Ca–RE alloy (59B), and pure zinc (Zn). A eudiometer was used to measure hydrogen evolution from the reaction of degradable alloys in Hank’s Balanced Salt Solution (HBSS). Corrosion rates based on volume loss of tested alloys in 30 days were calculated using Micro-computed tomography (micro-CT). Final mass change due to corrosion and corrosion removal was measured with a scale. We observed that the corrosion rates indicated by hydrogen evolution were high initially, and slowed down sharply in the following measurements. The corrosion rates of tested alloys calculated by volume loss and mass loss from high to low are: 60–13 ≈ 60–14 ≈ 47–7–2 > 59B > Mg > Zn (p < 0.05). The results provide instruction to experimental methodology to measure corrosion rates of degradable alloys.

There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively.

Rare earth elements (REEs) have been long applied in magnesium alloys, among which the mischmetal-containing WE43 alloy has already got the CE mark approval for clinical application. A considerable amount of REEs (7 wt%) is needed in that multi-phased alloy to achieve a good combination of mechanical strength and corrosion resistance. However, the high complex RE addition accompanied with multiple second phases may bring the concern of biological hazards. Single-phased Mg-RE alloys with simpler compositions were proposed to improve the overall performance, i.e., “Simpler alloy, better performance”. The single-phased microstructure can be successfully obtained with typical high-solubility REEs (Ho, Er or Lu) through traditional smelting, casting and extrusion in a wide compositional range. A good corrosion resistance with a macroscopically uniform corrosion mode was guaranteed by the homogeneously single-phased microstructure. The bimodal-grained structure with plenty of sub-grain microstructures allow us to minimize the RE addition to <1 wt%, without losing mechanical properties. The single-phased Mg-RE alloys show comparable mechanical properties to the clinically-proven Mg-based implants. They exhibited similar in-vitro and in-vivo performances (without local or systematic toxicity in SD-rats) compared to a high purity magnesium. In addition, metal elements in our single-phased alloys can be gradually excreted through the urinary system and digestive system, showing no consistent accumulation of RE in main organs, i.e., less burden on organs. The novel concept in this study focuses on the simplification of Mg-RE based alloys for biomedical purpose, and other biodegradable metals with single-phased microstructures are expected to be explored.