New Gold Compound To Be Used In Replacement Knee and Hip Joints
Scientists discover that mixing gold with titanium makes a compound that is four times stronger than titanium alone and could soon be used as the leading material in artificial knee and hip replacements.
Scientists discover that mixing gold with titanium makes a compound that is four times stronger than titanium alone and could soon be used as the leading material in artificial knee and hip replacements. A new study led by Emila Morosan, professor of physics and astronomy, of chemistry, and of materials science and nanoengineering at Rice University, reveals how the team was able to develop this pure, ultrahard compound.
The compound itself is easy to make. When this particular gold-titanium compound was made the team tried to grind it into powder for X-ray purposes, but the material was too hard for them to do it, even using a diamond coated mortar and pestle. Follow-up tests were carried out to see just how hard the substance was. During these tests, the team also tested previous compositions made of titanium and gold that were used as comparison tools in the original study.
Results from the study showed that by making the titanium-3-gold compound at high temperatures produces a crystalline form of the beta version of the alloy which is four times harder than titanium. At low temperature, another cubic structure was formed that was the alpha form of the titanium-3-gold and was about the same hardness as titanium. They also measured the hardness of the beta form of the crystal and believed that titanium-3-gold would be comparable to titanium biomedical implants currently used. With the promising results of the study, we may soon see this new compound as a replacement to all biomedical implants in the future.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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