Scalable processing of granular crystals by high-frequency oscillation

High-frequency vibration proves a fundamental step towards scalable growth in colloidal crystals. Various experimentally-replicable mechanisms are hereby devised and exerted on randomly generated silicoid particles to explore geometric dynamic effects the induced crystal at equilibrium state. The Discrete Element Method is employed, with inter-particle contact idealized by linear mass–spring–dashpot system Coulomb sliding friction. With vertical (Z) direction determined gravity, comprise vertical, circular, angular oscillation, latter realized plane normal gravity. Characterization of quantified hybrid machine-learning algorithm developed upon direct correlation between coordinate data, crystallinity grain-surface parameters. Parametric analysis carried out terms parameters (frequency, amplitude duration) square-prismatic box aspect ratio. Results manifest optimum values ratio, frequency, mostly saturating after 2 minutes vibration.