Application of a Hessian-Based Image-Processing Method for Enhanced Visualization of Nanoscale Rubber Cross-Linked Network Structures from Electron Microscopy Images

Characterizing rubber cross-linking, nanoscale structures that significantly influence rubber’s mechanical properties, is crucial for advancing the design of rubber materials, and electron microscopy is essential for observing the cross-linked networks. However, reliance on manual markup to make the network visible introduces subjectivity and hinders the objective analysis of many samples. This study presents an image-processing method based on the Hessian matrix to enhance and extract cross-linking network structures. We confirmed that this method effectively enhanced network structures across a wide range of cross-linking levels. From parameters extracted from enhanced images, we derived an equation to predict the cross-link density and mechanical properties. This equation outperformed the use of image luminance alone, thus confirming the effectiveness of the image enhancement method in characterizing cross-linking. We successfully estimated nanoscale cross-linked network structure parameters directly from images previously measured indirectly. This versatile approach, applicable to various structures, is expected to significantly improve nanoscale structural analysis, thereby paving the way for the development of higher-performance materials.