Artificial neural network for cytocompatibility and antibacterial enhancement induced by femtosecond laser micro/nano structures

Abstract

Abstract The failure of orthopedic and dental implants is mainly caused by biomaterial-associated infections and poor osseointegration. Surface modification of biomedical materials plays a significant role in enhancing osseointegration and anti-bacterial infection. In this work, a non-linear relationship between the micro/nano surface structures and the femtosecond laser processing parameters was successfully established based on an artificial neural network. Then a controllable functional surface with silver nanoparticles (AgNPs) to was produced to improve the cytocompatibility and antibacterial properties of biomedical titanium alloy. The surface topography, wettability, and Ag+ release were carefully investigated. The effects of these characteristics on antibacterial activity and cytocompatibilty were also evaluated. Results show that the prepared surface is hydrophobic, which can prevent the burst release of Ag+ in the initial stage. The prepared surface also shows both good cytocompatibility toward the murine calvarial preosteoblasts MC3T3-E1 cells (derived from Mus musculus (mouse) calvaria) and good antibacterial effects against Gram-negative (E. coli) and Gram-positive (S. aureus) bacteria, which is caused by the combined effect of appropriate micro/nano-structured feature and reasonable Ag+ release rate. We do not only clarify the antibacterial mechanism but also demonstrate the possibility of balancing the antibacterial and osteointegration-promoting properties by micro/nano-structures. The reported method offers an effective strategy for the patterned surface modification of implants. Graphical Abstract

Keywords

• Femtosecond laser microprocessing
• Hierarchical micro/nano-structure
• Antibacterial activity
• Cytocompatibility
• GA-BP neural network