Multi-Objective Bayesian Optimization for Laminate-Inspired Mechanically Reinforced Piezoelectric Self-Powered...

Area of Science:

• Materials Science
• Nanotechnology
• Polymer Science

Background:

• Electrospun piezoelectric fiber yarns, like poly(vinylidene fluoride-co-trifluoroethylene) (P(VDF-TrFE)), are key for intelligent devices.
• Conventional yarn fabrication limits control over mechanical properties.

Purpose of the Study:

• To enhance the mechanical robustness and piezoelectric performance of P(VDF-TrFE) fiber yarns.
• To optimize yarn structure using composite laminate principles and machine learning.

Main Methods:

• Stacking multiple electrospun P(VDF-TrFE) mats in varied sequences and twisting them into yarns.
• Utilizing a multi-objective Bayesian optimization algorithm to determine optimal stacking sequences.
• Applying corona poling to enhance dipole polarization in the yarn state.

Main Results:

• An optimal stacking sequence was identified, simultaneously improving ultimate tensile strength (UTS) and failure strain.
• The optimized yarns exhibited enhanced mechanical properties and piezoelectric performance.
• Mechanically robust piezoelectric yarns were successfully fabricated.

Conclusions:

• The proposed composite laminate-inspired approach significantly strengthens piezoelectric yarns.
• These enhanced yarns show great potential for self-powered sensing in challenging environments and sports.
• The study demonstrates a pathway for high-performance piezoelectric yarn development.