Graphene is an ideal reinforcement material for metal-matrix composites owing to its exceptional mechanical properties. However, as a 2D layered material, graphene shows highly anisotropic behavior, which greatly affects the mechanical properties of graphene-based composites. In this study, the interaction between an edge dislocation (b = 1/2 (111)) and a pair of graphene nanosheets (GNSs) in GNS reinforced iron matrix composite (GNS/Fe) was investigated using molecular dynamic simulations under simple shearing conditions. We studied the cases wherein the GNS pair was parallel to the (1 $\overline{1}$ 0), (11 $\overline{2}$ ), and (111) planes, respectively. The results showed that the GNS reinforcement can effectively hinder dislocation motion, which improves the yield strength. The interaction between the edge dislocation and the GNS pair parallel to the (11 $\overline{2}$ ) plane showed the strongest effect of blocking dislocations among the three cases, resulting in increases in the shear modulus and yield stress of 107% and 1400%, respectively. This remarkable enhancement was attributed to the Orowan “by-passing” strengthening mechanism, whereas cross-slip of dislocation segments was observed during looping around GNSs. Our results might contribute to the development of high-strength iron matrix composites.

The adhesion feature of graphene on metal substrates is important in graphene synthesis, transfer and applications, as well as for graphene-reinforced metal matrix composites. We investigate the adhesion energy of graphene nanosheets (GNs) on iron substrate using molecular dynamic (MD) simulations. Two Fe–C potentials are examined as Lennard–Jones (LJ) pair potential and embedded-atom method (EAM) potential. For LJ potential, the adhesion energies of monolayer GN are 0.47, 0.62, 0.70 and 0.74 J/m² on the iron {110}, {111}, {112} and {100} surfaces, respectively, compared to the values of 26.83, 24.87, 25.13 and 25.01 J/m² from EAM potential. When the number of GN layers increases from one to three, the adhesion energy from EAM potential increases. Such a trend is not captured by LJ potential. The iron {110} surface is the most adhesive surface for monolayer, bilayer and trilayer GNs from EAM potential. The results suggest that the LJ potential describes a weak bond of Fe–C, opposed to a hybrid chemical and strong bond from EAM potential. The average vertical distances between monolayer GN and four iron surfaces are 2.0–2.2 Å from LJ potential and 1.3–1.4 Å from EAM potential. These separations are nearly unchanged with an increasing number of layers. The ABA-stacked GN is likely to form on lower-index {110} and {100} surfaces, while the ABC-stacked GN is preferred on higher-index {111} surface. Our insights of the graphene adhesion mechanics might be beneficial in graphene growing, surface engineering and enhancement of iron using graphene sheets.

Stress concentration around nanosized defects such as cavities always leads to plastic deformation and failure of solids. We investigate the effects of depth, size, and shape of a lotus-type nanocavity on onset plasticity of single crystal Al during nanoindentation on a (001) surface using a quasicontinuum method. The results show that the presence of a nanocavity can greatly affect the contact stiffness (S_c) and yield stress (σ_y) of the matrix during nanoindentation. For a circular cavity, the S_c and σ_y gradually increase with the cavity depth. A critical depth can be identified, over which the S_c and σ_y are insensitive to the cavity depth and it is firstly observed that the nucleated dislocations extend into the matrix and form a y-shaped structure. Moreover, the critical depth varies approximately linearly with the indenter size, regarding the same cavity. The S_c almost linearly decreases with the cavity diameter, while the σ_y is slightly affected. For an ellipsoidal cavity, the S_c and σ_y increase with the aspect ratio (AR), while they are less affected when the AR is over 1. Our results shed light in the mechanical behavior of metals with cavities and could also be helpful in designing porous materials and structures.

Particle size has a significant effect on mechanical properties of particle reinforced metal matrix composites (PRMMCs). Here, the effect of particle size on mechanical properties of SiCp/Fe composite has been studied using a finite element (FE) model incorporated with the Taylor-based nonlocal theory (TNT) of plasticity, where the tested particle size is 5, 10, 16, 21, 28 and 40μm, respectively. The results indicate that the TNT-FE model overcomes the shortage of the traditional FE model, which cannot deal with the intrinsic size effect. With the particles of 20 vol.%, the simulated flow stress of the iron matrix composite reinforced by 16μm SiC particles is the highest and then follows the order of 28 > 5 > 10 > 21 > 40μm particle reinforced ones, close to the tendency of experimental results. A large area of compressive stress zone is observed in the matrix near the particles in the composites reinforced by 16 and 28μm particles, which implies that the matrix can be well protected during loading, so that the load-bearing capability is better. The purpose is to optimize mechanical property of SiCp/Fe composite by adjusting particle size and eventually to design PRMMCs from microstructural control.