Islam Md Nurul, Smith Zane, Rupom Rifat Hasan, Rijal Rajan, Demchuk Zoriana, Dahotre Narendra, Wu H Felix, Advincula Rigoberto C, Choi Wonbong, Jiang Yijie
School of Aerospace & Mechanical Engineering, The University of Oklahoma, Norman, Oklahoma 73019, United States.
Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
ACS Appl Mater Interfaces. 2025 Jun 18;17(24):36026-36033. doi: 10.1021/acsami.5c05683. Epub 2025 Jun 3.
Carbon fiber (CF)-reinforced epoxy composites are widely used in vehicle applications, where early damage detection is crucial for reliability and safety. To address this need, we developed a self-sensing epoxy/CF composite by embedding a PVDF-MoS nanosensor via an embedded 3D printing method. By harnessing the intrinsic curing kinetics of epoxy, we tailored its rheological properties to optimize the embedded printing process, enabling precise and reliable support for sensor filaments without compromising the composite's structural and functional integrity. Through comprehensive rheological and kinetic analysis, we established a quantitative relationship among curing temperature, conversion rate, and resulting yield modulus─defining a narrow processing window essential for successful sensor integration. Specifically, we identified that an epoxy yield modulus range of 180-294 Pa and a conversion rate below 10% are critical to support the PVDF-MoS filament architecture. This embedded 3D printing method produces complex and multimaterial PVDF-MoS sensors within an epoxy matrix with minimal deformation and reduced postprocessing, which is scalable and adaptable for industrial applications. Under cyclic loading, the embedded sensors exhibited stable signals under constant loads and increased voltage signals in response to crack formation (17-35% higher) and catastrophic failure (1 order of magnitude higher), effectively capturing structural changes in real time. This study demonstrates the potential of PVDF-MoS nanocomposite sensor materials for real-time structural health monitoring in epoxy-CF composite systems, enabling early detection of defects and stress anomalies, significantly reducing the risk of unexpected failures, and enhancing structural reliability.
碳纤维（CF）增强环氧树脂复合材料广泛应用于车辆领域，早期损伤检测对于可靠性和安全性至关重要。为满足这一需求，我们通过嵌入式3D打印方法嵌入聚偏氟乙烯-二硫化钼（PVDF-MoS）纳米传感器，开发了一种自传感环氧树脂/CF复合材料。通过利用环氧树脂的固有固化动力学，我们调整了其流变性能以优化嵌入式打印过程，从而在不损害复合材料结构和功能完整性的情况下，为传感器细丝提供精确可靠的支撑。通过全面的流变学和动力学分析，我们建立了固化温度、转化率和所得屈服模量之间的定量关系，确定了成功集成传感器所需的狭窄加工窗口。具体而言，我们发现环氧树脂屈服模量范围为180 - 294 Pa且转化率低于10%对于支撑PVDF-MoS细丝结构至关重要。这种嵌入式3D打印方法在环氧树脂基体中制造出复杂的多材料PVDF-MoS传感器，变形最小且后处理减少，具有可扩展性且适用于工业应用。在循环加载下，嵌入式传感器在恒定载荷下表现出稳定信号，并且在裂纹形成时（高17 - 35%）和灾难性失效时（高1个数量级）电压信号增加，有效实时捕捉结构变化。本研究证明了PVDF-MoS纳米复合传感器材料在环氧树脂-CF复合系统实时结构健康监测中的潜力，能够早期检测缺陷和应力异常，显著降低意外失效风险，并提高结构可靠性。
