Authors: E.P. Furlani
Affilation: Eastman Kodak Company, United States
Pages: 521 - 524
Keywords: MEMS drop ejector, electrostatic drop ejection, squeeze film analysis, drop on demand
MEMS are finding increasing use for applications that require the controlled generation and delivery of picoliter-sized droplets. Common applications include biomedical and biochemical microdispensing and most notably, ink jet printing. Micro-droplets can be produced as needed by generating a short-lived pressure pulse within a microfluidic chamber. The pressure profile can be tuned to eject a discrete volume of fluid through an orifice, with the ejected fluid evolving into a desired droplet. The most common methods used for producing the required ejection pressure include piezoelectric actuation or the generation of a thermally induced vapor bubble. In this presentation, we discuss an alternative method of drop generation that is based on electrostatic actuation. Specifically, we study a MEMS drop ejector that consists of a microfluidic chamber, an orifice plate, and an electrostatically driven piston positioned a few microns beneath the orifice. The piston is supported by cantilevered flexural members that act as restoring springs. To eject a drop, a potential difference is applied between the orifice plate and the piston, and this produces an electrostatic force that moves the piston towards the nozzle. As the piston moves it generates a pressure distribution in the gap region above it that acts to eject the drop. A MEMS drop ejector based on this principle has been fabricated and characterized at Sandia National Laboratories. We discuss the basic operating physics of this device, and simulate its performance using a semi-numerical lumped-element model. Specifically, we integrate the equation of motion of the piston. We use the model to study key performance parameters such as piston displacement, pressure generation and fluid refill. We also estimate drop volume and velocity by integrating the momentum of the ejected fluid as it leaves the orifice, and we compare these predictions with measured data.
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