Breathable, Antibacterial, and Biocompatible Collagen Fiber Network Decorated with Zwitterionic Silver Nanoparticles for Plantar Pressure Monitoring

Foot plantar pressure monitoring and gait analysis are of great significant in footwear design, sport biomechanics, injury prevention, and early warning of disease. Flexible and wearable smart insoles pave a feasible way for these application scenarios. However, the majority of the currently developed smart insoles are composed of synthetic polymers (e.g., plastics, rubbers, etc.), leading to inevitable problems associated with air permeability, hygiene condition, biocompatibility, and wearing comfort. Here, a new paradigm of a natural collagen fiber network (CFN) with soft and breathable features, which can be obtained by facilely treating animal hides via conventional leather pretreatment process, is selected as substrate material for constructing smart insoles due to its high permeability and porosity. Further, biocompatible zwitterionic silver nanoparticles (AgNPs) with both carboxybetaine and catechol groups on the interface were designed for firmly and uniformly immobilization onto the hierarchical micro-/nanoscale fibers of CFN through mussel-inspired catechol/amino chemistry, giving rise to both good antibacterial property and pressure sensing capability of the resultant material. The finally developed smart insole by using the AgNPs decorated CFN exhibits good capability for plantar pressure mapping and gait feature analysis. Especially, the smart insole will be very suitable for pressure monitoring and gait analysis of a diabetic foot with sensitive skin that requires a high biocompatible and antibacterial environment.

Automated summary

Foot plantar pressure monitoring and gait analysis are important in various fields. A new paradigm of a natural collagen fiber network (CFN) with soft and breathable features, combined with biocompatible zwitterionic silver nanoparticles (AgNPs), has been developed to create smart insoles for plantar pressure mapping and gait feature analysis.