Self-Sensing Composites via an Embedded 3D-Printed PVDF-MoS₂ Nanosensor for Structural Health Monitoring.

Area of Science:

• Materials Science
• Composite Materials
• Nanotechnology

Background:

• Carbon fiber (CF)-reinforced epoxy composites are vital in vehicles, necessitating early damage detection for safety.
• Existing methods for damage detection in composites can be limited.
• Developing integrated self-sensing capabilities is crucial for enhanced structural health monitoring.

Purpose of the Study:

• To develop a self-sensing epoxy/CF composite with embedded PVDF-MoS2 nanosensors.
• To optimize an embedded 3D printing method for reliable sensor integration.
• To demonstrate real-time structural health monitoring capabilities.

Main Methods:

• Developed a self-sensing composite by embedding PVDF-MoS2 nanosensors using embedded 3D printing.
• Tailored epoxy rheological properties by analyzing curing kinetics and establishing a quantitative relationship between curing temperature, conversion rate, and yield modulus.
• Identified critical processing windows (epoxy yield modulus 180-294 Pa, conversion rate <10%) for sensor filament support.

Main Results:

• Successfully embedded complex PVDF-MoS2 sensors within the epoxy matrix with minimal deformation using the 3D printing method.
• Embedded sensors showed stable signals under constant loads.
• Sensors detected crack formation (17-35% higher voltage) and catastrophic failure (1 order of magnitude higher voltage) in real time.

Conclusions:

• The embedded 3D printing method is scalable and adaptable for industrial applications.
• PVDF-MoS2 nanocomposite sensors enable real-time structural health monitoring in epoxy-CF composites.
• This technology enhances structural reliability by enabling early defect detection and reducing failure risks.