Atomistic Studies and Machine Learning-Based Optimization of an Electrohydrodynamic Nano-Printing System

Electrohydrodynamic (EHD) nanoprinting is an emerging nanofabrication technology with applications in metamaterials patterning, quantum dot LED display devices, transparent electronics, and more. During EHD nanoprinting, fluid in the form of droplets or jets with sizes in tens of nanometers are ejected from a patterning nozzle under the influence of an electric field and patterned into features over a substrate, making the method particularly suitable for high-resolution nanopatterning. To design better nanofabrication systems with improved resolution, ejection stability, and patterning speed, it is important to understand the ejection mechanism of fluids from a nanoscale nozzle under the action of electric fields. To date, most modeling studies have utilized continuum approaches and have investigated the ejection of microscale droplets/jets from macroscopic nozzles with sizes ranging from hundreds of microns to millimeter ranges. However, when the size of nozzles approaches the nanoscale regime (below 100 nm), modeling fluid flows with continuum models becomes challenging as thermal fluctuations play a major role in the breakup of jets/droplets in the nanoscale regime, and the continuum model may not predict proper breakup of the jet into droplets. To model electric field actuated deformation of fluids from nanoscale nozzles, modeling techniques are required that can incorporate thermal fluctuation of constituent fluid particles, simulate slip flow at wall boundaries, and explicitly model charge dynamics. Many-body Dissipative Particle Dynamics (MDPD) is one particle-based technique that can explicitly model charge dynamics, incorporate thermal fluctuations of particles, and accurately model slip flows at walls in a nanoscale regime. Here, we present an MDPD model for studying the electric field deformation of fluids at the nanoscale regime and examine the ejection of fluids from nanoscale nozzles for printing applications. The MDPD model is later utilized within an active learning genetic algorithm framework to optimize the design and operation of an electrohydrodynamic nanoprinting system. The modeling/optimization tools presented here will enable a better understanding of the electrohydrodynamic nanoprinting process and advance the development of prototypes for nanoprinting applications.