文献类型：期刊文献

英文题名：Tensile Mechanical Properties and Edge Defect-Driven Degradation in Bilayer Graphene

作者：Su, Ting[1,2];Rong, Chao[1,2];Yan, Yabin[1,2,3];Xuan, Fuzhen[1,2,3]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Intelligent Sensing & Detect, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Key Lab Pressure Syst & Safety, Minist Educ, Shanghai 200237, Peoples R China

年份：2025

卷号：17

期号：34

起止页码：48680

外文期刊名：ACS APPLIED MATERIALS & INTERFACES

收录：;EI(收录号：20253619092248);WOS:【SCI-EXPANDED(收录号:WOS:001552001800001)】；

基金：Y.Y. thanks the support of National Natural Science Foundation of China (Grant No. 52275149) and Scientific Research Innovation Capability Support Project for Young Faculty (Grant No. ZYGXQNJSKYCXNLZCXM-D5). F.Z.X. thanks the support of Science Fund for Creative Research Groups of the National Natural Science Foundation of China (Grant No. 52321002) and National Natural Science Foundation of China (Grant No. 51835003).

语种：英文

外文关键词：bilayer graphene; in situ tensile experiments; edge defects; mechanical properties; moleculardynamics; machine learning

摘要：Bilayer graphene attracts significant attention due to its unique electronic structure and excellent physical properties, with its mechanical performance being crucial for understanding deformation mechanisms and assessing application reliability. The mechanical properties of bilayer graphene measured by atomic force microscopy currently exhibit considerable scatter and show clear deviations from theoretical predictions. In situ tensile testing is widely regarded as a more reliable and authoritative approach for evaluating the mechanical properties of two-dimensional materials. Accordingly, the Young's modulus of bilayer graphene is measured to be 873.80 +/- 12.68 GPa using a push-to-pull device inside a scanning electron microscope, which is close to the theoretical value. Moreover, the integration of bilayer graphene into device architectures requires micro/nanoscale patterning and shaping, which inevitably introduces edge defects. However, it remains experimentally challenging to precisely control the concentration of these edge defects. To address these limitations, a combined approach of molecular dynamics simulations and machine learning was employed to systematically uncover the effects of edge defects on the mechanical behavior of bilayer graphene. This study provides a theoretical foundation for a deeper understanding and optimization of the mechanical behavior of bilayer graphene, thereby laying important groundwork for its application in microelectronic devices.