Abstract：
Piezoelectric fiber yarns produced by electrospinning offer a versatile platform for intelligent devices, demonstrating mechanical durability and the ability to convert mechanical strain into electric signals. While conventional methods involve twisting a single poly(vinylidene fluoride-co-trifluoroethylene)(P(VDF-TrFE)) fiber mat to create yarns, by limiting control over the mechanical properties, an approach inspired by composite laminate design principles is proposed for strengthening. By stacking multiple electrospun mats in various sequences and twisting them into yarns, the mechanical properties of P(VDF-TrFE) yarn structures are efficiently optimized. By leveraging a multi-objective Bayesian optimization-based machine learning algorithm without imposing specific stacking restrictions, an optimal stacking sequence is determined that simultaneously enhances the ultimate tensile strength (UTS) and failure strain by considering the orientation angles of each aligned fiber mat as discrete design variables. The conditions on the Pareto front that achieve a balanced improvement in both the UTS and failure strain are identified. Additionally, applying corona poling induces extra dipole polarization in the yarn state, successfully fabricating mechanically robust and high-performance piezoelectric P(VDF-TrFE) yarns. Ultimately, the mechanically strengthened piezoelectric yarns demonstrate superior capabilities in self-powered sensing applications, particularly in challenging environments and sports scenarios, substantiating their potential for real-time signal detection.
摘要：
通过静电纺丝生产的压电纤维纱线为智能设备提供了一个通用的平台，证明机械耐久性和将机械应变转换为电信号的能力。虽然常规方法涉及扭曲单个聚(偏二氟乙烯-共-三氟乙烯)(P(VDF-TrFE))纤维垫以产生纱线,通过限制对机械性能的控制，提出了一种由复合层合板设计原理启发的方法来加强。通过按不同顺序堆叠多个电纺垫并将它们扭曲成纱线，P(VDF-TrFE)纱线结构的力学性能得到有效优化。通过利用基于多目标贝叶斯优化的机器学习算法，而不施加特定的堆叠限制，通过将每个对齐纤维垫的取向角视为离散设计变量，确定了同时增强极限拉伸强度（UTS）和破坏应变的最佳堆叠顺序。确定了在UTS和破坏应变方面实现平衡改善的Pareto前沿条件。此外,施加电晕极化会在纱线状态下引起额外的偶极极化，成功制造机械坚固和高性能的压电P（VDF-TrFE）纱线。最终,机械强化的压电纱线在自供电传感应用中表现出卓越的能力，特别是在具有挑战性的环境和运动场景中，证实其实时信号检测的潜力。
