five

DataSheet1_Stress quantification in a composite matrix via mechanophores.docx

收藏
NIAID Data Ecosystem2026-05-01 收录
下载链接:
https://figshare.com/articles/dataset/DataSheet1_Stress_quantification_in_a_composite_matrix_via_mechanophores_docx/22580743
下载链接
链接失效反馈
官方服务:
资源简介:
Stress concentrations in polymer matrix composites occur due to non-uniform loadings which develop near the interface between the matrix and reinforcement in a stressed composite. Methods to better understand the evolution of this stress concentration are required for the development of advanced composites. Mechanophores, which are stress responsive molecules, can be embedded into the polymer matrix and used to quantify the local stresses in a loaded composite. In this work, single particle model composites were fabricated by combining functionalized glass particles embedded into a silicone/mechanophore matrix. Confocal microscopy was then used to measure the mechanophore activation in situ during mechanical loading. The fluorescence intensity was correlated to maximum principal stress values obtained from a finite element analysis (FEA) model of the system utilizing an Ogden hyperelastic model to represent the elastomer. By calibrating stress to fluorescence intensity spatially, quantitative stress measurements can be obtained directly from fluorescent images. To validate this technique, calibrated stress values for a two-particle composite system were compared to a FEA model and good agreement was found. Further experiments were performed on silicone matrix composites containing short cylindrical particles oriented with their major axis parallel or perpendicular to the stretching direction. To demonstrate the versatility of the single particle intensity/stress calibration approach, maximum principal stress values were mapped on the fluorescence images of the cylindrical experiments. This technique has potential to quantify stress concentrations quickly and accurately in new composite designs without the use of FEA models or differential image correlation.
创建时间:
2023-04-10
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作