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The mechanical performance of large-area chemical vapor deposition (CVD) graphene membranes is often compromised by coexisting lattice defects and surface contamination. However, the underlying synergistic mechanisms remain elusive, particularly regarding how defects mediate contaminant nucleation and how their coupling degrades the mechanical properties of graphene. Here, combining in situ spherical aberration-corrected transmission electron microscopy with controlled contaminant removal and redeposition experiments, we provide atomic-scale experimental validation for the causal correlation between graphene point defects and amorphous carbon contamination on CVD-grown graphene. We further achieve real-time, site-resolved visualization of active carbon species migration and defect-trapping dynamics. Multiscale simulations uncover a defect-mediated kinetic-trapping mechanism: active carbon species migrate rapidly over intact graphene yet become captured and immobilized at defects, with the calculated diffusion coefficient dropping from ∼14.4 to 1.65 nm2 ns–1. Moreover, nanoindentation tests confirm that clean graphene exhibits superior mechanical properties (stiffness: ∼305 N m–1 and fracture force: ∼1900 nN) compared to the contaminated counterpart (stiffness: ∼224 N m–1 and fracture force: ∼1077 nN). Continuum finite-element analysis indicates that the reduced stiffness of unclean graphene cannot be explained by surface contamination alone, supporting the presence of defect-induced softening beneath the contamination. Atomically resolved simulations further reveal that the mechanical response is jointly governed by defect type, contamination, and interfacial bonding. In particular, chemically bonded contamination can redistribute local stress and mitigate defect-induced stress concentration, thereby altering the failure process of defective graphene. Our study identifies point defects as both contamination traps and mechanical weak links, offering a unified strategy for high-performance graphene membrane design.
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Wang et al. (2026) studied this question.
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