Micro-Fluidics

Researchers Look to Micro-fluidics as Answer for Smaller Devices

Dr. Dutta and Dr. Liu examining a glass slide used in microfluidics.
Dr. Dutta and Dr. Liu examining a glass slide used in microfluidics.

By Alyssa Patrick, Voiland College of Engineering and Architecture Intern

A recent NSF grant is helping School of Mechanical and Materials Engineering researchers develop models that could allow for more in-home medical testing and smaller mobile devices with more capabilities.

With support from a three-year, $431,882 National Science Foundation grant, Professors Prashanta Dutta and Jin Liu are leading an effort to develop models for molecular assembly.

Dutta is working in micro-fluidics, a multidisciplinary research area that operates at a microscopic level. His research group is involved in design and development of tiny devices for medical diagnostics and explosive detection. These devices are generally made on glass sides roughly 2 inches by 2 inches in size. The slides contain numerous micro and nanostructures that are only visible through a microscope.

“This is considered top-down manufacturing; start with a piece of glass that we then etch, creating a path that the biofluid such as blood, sweat, or saliva can move through when an electric field is applied,” Dutta said.

A similar kind of technology is used in mobile devices to store data. As these devices get smaller and yet hold more information, the transistors that process that information must also get smaller — even smaller than the width of a human hair.

“The only way to create a path small enough for all of those transistors is to build a chip from the bottom up, molecule by molecule,” Dutta said.

The researchers’ models aim to predict how and under what conditions molecules can be mobilized to make nano-chips used in medical diagnostics and mobile devices. The models are based on multiscale, multi-physics phenomena, considering both how the molecules interact with each other and their mobilization on a surface. By creating a model that can test both of these scales, they eliminate other variables, making future experiments more effective.