Graphene-based phononic crystal lenses: Machine learning-assisted analysis and design.

The advance of artificial intelligence and graphene-based composites brings new vitality into the conventional design of acoustic lenses which suffers from high computation cost and difficulties in achieving precise desired refractive indices. This paper presents an efficient and accurate design methodology for graphene-based gradient-index phononic crystal (GGPC) lenses by combing theoretical formulations and machine learning methods. The GGPC lenses consist of two-dimensional phononic crystals possessing square unit cells with graphene-based composite inclusions.

Effects of Reorientation of Graphene Platelets (GPLs) on Young's Modulus of Polymer Nanocomposites under Uni-Axial Stretching.

The orientation of reinforcement fillers in composites plays a vital role in their mechanical properties. This paper employs the Mori⁻Tanaka micromechanics model, incorporating the effect of stretching-induced reorientation of graphene platelets (GPL), to predict Young's modulus of GPL/polymer nanocomposites. Subjected to uni-axial stretching, dispersion of GPLs is described by an orientation distribution function (ODF) in terms of a stretching strain and two Euler angles.