graphene

The physical properties of graphene nanoribbons (GNRs) are closely related to their morphology; meanwhile GNRs can easily slide on surfaces (e.g., superlubricity), which may largely affect the configuration and hence the properties. However, the morphological evolution of GNRs during sliding remain elusive. We explore the intriguing tail swing behavior of GNRs under various sliding configurations on Au substrate. Two distinct modes of tail swing emerge, characterized by regular and irregular swings, depending on the GNR width and initial position relative to the substrate.

We investigate the mechanics and stability of a nanoscale origami crease via a combination of equilibrium and nonequilibrium statistical mechanics. We identify an entropic torque on nanoscale origami creases, and find stability properties have a nontrivial dependence on bending stiffness, radii of curvature of its creases, ambient temperature, its thickness, and its interfacial energy.

Advanced Electrical Conductors (CNT, Graphene, and Metal-based) -- US-based candidates with work authorization only.

In our latest article, “Uncovering stress fields and defects distributions in graphene using deep neural networks”: https://doi.org/10.1007/s10704-023-00704-z , we showed that conditional generative adversarial networks (cGANs) could transform complex deformation fields into stress fields by eliminating the need to evaluate elasticity distributions and develop complex nonlinear constitutive relations.

Dear iMechanicians, I would like to share our recent work on a drop confined by two adhesive graphene sheets (as illustrated below). 

Jiao, S., & Liu, M.* (2021). Snap-through in Graphene Nanochannels: With Application to Fluidic Control. ACS Appl. Mater. Interfaces, 13, 1158–1168.

Abstract: Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction.

The extraordinary mechanical properties of graphene were measured on very small or supported samples. In our new paper published in Nature Communications, by developing a protocol for sample transfer, shaping and straining, we report the outstanding elastic properties and stretchability of free-standing single-crystalline monolayer graphene under in situ tensile tests.