Back

A Tough Biointerface in Human Knee Empowered by Dynamic Phase-transforming Minerals in Collagenous Matrix

Li, W.; Wang, X.; Mao, R.; Li, D.; Meng, H.; Zhang, R.; Fang, J.; Kang, Z.; Wu, B.; Ma, W.; Yao, X.; Xie, C.; Li, R.; Wang, J.; Chen, X.; Pan, X.; Chen, W.; Duan, W.; Gao, H.; Ouyang, H.

2024-08-06 bioengineering
10.1101/2024.08.03.606023 bioRxiv
Show abstract

Joining heterogeneous materials in engineered structures remains a daunting challenge because of stress concentration, often resulting in unexpected failures1,2. Studying the structures in organisms that evolved for centuries provides valuable insights that can be instrumental in addressing this mechanical challenge3-5. The human meniscus root-bone interface is a remarkable example known for its exceptional fatigue resistance, toughness and interfacial adhesion properties throughout its lifespan6-8. We studied the multiscale graded mineralization structure designs within the 30-micron soft-hard interface at the root-bone junction and examined its toughening mechanisms. This graded interface with interdigitated structures and exponential modulus increase exhibits a phase transition from amorphous calcium phosphate (ACP) to gradually matured hydroxyapatite (HAP) crystals, mediated by location-specific distributed biomolecules. In coordination with collagen fibril deformation and reorientation, ACP particles debond with collagen and slide to new positions which enable frictional energy dissipation, and HAP particles arrest cracks. The mineral in transforming phases work synergistically to provide interfacial toughening. The presented biointerface model exemplifies human musculoskeletal systems adaptations to mechanical requirements, offering a blueprint for developing tough interfaces in broad applications.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.