Objective
Magnesium (Mg) alloys, with their excellent mechanical properties, outstanding biocompatibility, and gradual degradation within the body, are demonstrating vast potential for applications in the medical field. Their greatest advantage lies in their ability to degrade in sync with the bodys healing process, eliminating the need for a second surgery to remove traditional implants. However, in chloride-rich physiological environments, Mg alloy implants are prone to rapid corrosion, leading to early failure and posing risks to patient health. To address this challenge, the DREAM project integrates cutting-edge research on Mg alloys, convolutional neural networks (CNNs), density functional theory (DFT) calculations, and pH-responsive materials, with the goal of developing intelligent biomedical Mg alloys with controlled degradation rates. First, the project will establish a comprehensive database of Mg-Zn-Ca-RE (rare earth) alloy corrosion and mechanical properties to support the accurate prediction of alloy performance using CNN models. By incorporating DFT calculations, the project will gain deeper insights into the corrosion and mechanical behaviours of Mg alloys, further enhancing the prediction accuracy of the CNN models. Ultimately, the optimized alloys will be combined with pH-responsive materials to achieve intelligent degradation control, ensuring the degradation rate aligns with the bodys healing process. Through this innovation, Mg alloys are expected to see a 30-40% improvement in corrosion resistance, a 50% reduction in experimental time, and a significant decrease in harmful waste production. The DREAM project will play a crucial role in the advancement of green medical technologies while driving innovation in biomedical materials.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesdatabases
- medical and health sciencesclinical medicinesurgery
- medical and health sciencesmedical biotechnologyimplants
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
SO17 1BJ Southampton
United Kingdom