A Multiscale Adhesion Model for Deposition Prediction in Laser Enhanced Nanoparticle Deposition Process

Abstract

Nanoparticle deposition processes have the potentials to achieve rapid printing of large areas without the use of toxic solvents. Laser processing has been applied as an auxiliary method to enhance the performance of the deposition processes. Yet, there is still a lack of effective process modeling tools to predict the quality of deposition. In this study, we propose a multiscale adhesion modeling framework to assess the deposition performance of a laser enhanced nanoparticle deposition process. A new analytical adhesion model is developed to efficiently predict the deposition effect from particle size, particle velocity, and laser power, with the considerations of thermal energy induced by laser irradiation and kinetic energy of particles. The elastic-plastic properties of nanoparticles, which are dependent on the temperature and size of the particles, are predicted by molecular dynamics simulations. The model predictions are validated experimentally.