Huisman Max, Huerre Axel, Saha Saikat, Crocker John C, Garbin Valeria
Department of Chemical Engineering, Delft University of Technology, Delft 2629 HZ, The Netherlands.
Laboratoire Matière et Systèmes Complexes, CNRS UMR 7057, Université Paris Cité, Paris, France.
Soft Matter. 2024 Nov 13;20(44):8888-8896. doi: 10.1039/d4sm00486h.
Brittle fracturing of materials is common in natural and industrial processes over a variety of length scales. Knowledge of individual particle dynamics is vital to obtain deeper insight into the atomistic processes governing crack propagation in such materials, yet it is challenging to obtain these details in experiments. We propose an experimental approach where isotropic dilational strain is applied to a densely packed monolayer of attractive colloidal microspheres, resulting in fracture. Using brightfield microscopy and particle tracking, we examine the microstructural evolution of the monolayer during fracturing. Furthermore, we propose and test a parameter termed Weakness that estimates the likelihood for particles to be on a crack line, based on a quantified representation of the microstructure in combination with a machine learning algorithm. Regions that are more prone to fracture exhibit an increased Weakness value, however the exact location of a crack depends on the nucleation site, which cannot be predicted . An analysis of the microstructural features that most contribute to increased Weakness values suggests that local density is more important than orientational order. Our methodology and results provide a basis for further research on microscopic processes during the fracturing process.
材料的脆性断裂在各种长度尺度的自然和工业过程中都很常见。了解单个粒子的动力学对于更深入地洞察控制此类材料中裂纹扩展的原子过程至关重要,但在实验中获取这些细节具有挑战性。我们提出了一种实验方法,即对紧密堆积的单层有吸引力的胶体微球施加各向同性膨胀应变,从而导致断裂。使用明场显微镜和粒子跟踪,我们研究了单层在断裂过程中的微观结构演变。此外,我们提出并测试了一个称为“弱点”的参数,该参数基于微观结构的量化表示并结合机器学习算法来估计粒子位于裂纹线上的可能性。更容易发生断裂的区域表现出更高的“弱点”值,然而裂纹的确切位置取决于成核位点,而成核位点无法预测。对导致“弱点”值增加的最主要微观结构特征的分析表明,局部密度比取向有序性更重要。我们的方法和结果为进一步研究断裂过程中的微观过程提供了基础。