Yu Jiajian, Chen Zhiwei, Xu Xiaoli, Su Xinjie, Liang Shuai, Wang Yanchao, Hong Junqing, Zhang Shaofeng
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, China.
Materials (Basel). 2024 Dec 4;17(23):5926. doi: 10.3390/ma17235926.
Understanding the enhancing mechanisms of graphene oxide (GO) on the pore structure characteristics in the interfacial transition zone (ITZ) plays a crucial role in cemented waste rock backfill (CWRB) nanoreinforcement. In the present work, an innovative method based on metal intrusion techniques, backscattered electron (BSE) images, and deep learning is proposed to analyze the micro/nanoscale characteristics of microstructures in the GO-enhanced ITZ. The results showed that the addition of GO reduced the interpore connectivity and the porosity at different pore throats by 53.5-53.8%. GO promotes hydration reaction in the ITZ region; reduces pore circularity, solidity, and aspect ratio; enhances the mechanical strength of CWRB; and reduces transport performance to form a dense microstructure in the ITZ. Deep learning-based analyses were then proposed to classify and recognize BSE image features, with a high average recognition accuracy of 95.8%. After that, the deep Taylor decomposition (DTD) algorithm successfully located the enhanced features of graphene oxide modification in the ITZ. The calculation and verification of the typical pore optimization area of the location show that the optimization efficiency reaches 9.6-9.8%. This study not only demonstrated the deepening of the enhancement effect of GO on the pore structure in cement composites and provided new insights for the structural modification application of GO but also revealed the application prospect of GO in the strengthening of CWRB composites and solid waste recycling.
了解氧化石墨烯（GO）对界面过渡区（ITZ）孔隙结构特征的增强机制在胶结废石回填（CWRB）纳米增强中起着至关重要的作用。在本工作中，提出了一种基于金属侵入技术、背散射电子（BSE）图像和深度学习的创新方法，以分析GO增强ITZ中微观结构的微/纳米尺度特征。结果表明，添加GO使不同孔径喉道处的孔隙连通性和孔隙率降低了53.5 - 53.8%。GO促进了ITZ区域的水化反应；降低了孔隙的圆形度、紧实度和纵横比；增强了CWRB的机械强度；并降低了传输性能，从而在ITZ中形成致密的微观结构。然后提出基于深度学习的分析方法对BSE图像特征进行分类和识别，平均识别准确率高达95.8%。之后，深度泰勒分解（DTD）算法成功定位了ITZ中氧化石墨烯改性的增强特征。对该位置典型孔隙优化区域的计算和验证表明，优化效率达到9.6 - 9.8%。本研究不仅证明了GO对水泥基复合材料孔隙结构增强效果的深化，为GO的结构改性应用提供了新的见解，还揭示了GO在增强CWRB复合材料及固体废物回收方面的应用前景。
