March 30, 2012
 |
| The Center for Polymer Microfabrication is designing processes for manufacturing microfluidic chips. Pictured here is a chip fabricated by the center's tailor-made production machines. Photo: Melinda Hale |
In the not-too-distant future, plastic chips the size of flash cards may quickly and accurately diagnose diseases such as AIDS and cancer, as well as detect toxins and pathogens in the environment. Such lab-on-a-chip technology — known as microfluidics — works by flowing fluid such as blood through microscopic channels etched into a polymer’s surface. Scientists have devised ways to manipulate the flow at micro- and nanoscales to detect certain molecules or markers that signal disease.
Microfluidic devices have the potential to be fast, cheap and portable diagnostic tools. But for the most part, the technology hasn’t yet made it to the marketplace. While scientists have made successful prototypes in the laboratory, microfluidic devices — particularly for clinical use — have yet to be manufactured on a wider scale.
MIT's David Hardt is working to move microfluidics from the lab to the factory. Hardt heads the Center for Polymer Microfabrication — a multidisciplinary research group funded by the Singapore-MIT Alliance — which is designing manufacturing processes for microfluidics from the ground up. The group is analyzing the behavior of polymers under factory conditions, building new tools and machines to make polymer-based chips at production levels, and designing quality-control processes to check a chip’s integrity at submicron scales — all while minimizing the cost of manufacturing.
“These are devices that people want to make by the millions, for a few pennies each,” says Hardt, the Ralph E. and Eloise F. Cross Professor of Mechanical Engineering at MIT. “The material cost is close to zero, there’s not enough plastic here to send a bill for. So you have to get the manufacturing cost down.”
Micromachines
Hardt and his colleagues found that in making microfluidic chips, many research groups and startups have adopted equipment mainly from the semiconductor industry. Hardt says this equipment — such as nano-indenting and bonding machines — is incredibly expensive, and was never designed to work on polymer-based materials. Instead, Hardt's team looked for ways to design cheaper equipment that’s better suited to work with polymers.
The group focused on an imprinting technique called microembossing, in which a polymer is heated, then stamped with a pattern of tiny channels. In experiments with existing machines, the researchers discovered a flaw in the embossing process: When they tried to disengage the stamping tool from the cooled chip, much of the plastic ripped out with it.
To prevent embossing failures in a manufacturing setting, the team studied the interactions between the cooling polymer and the embossing tool, measuring the mechanical forces between the two. The researchers then used the measurements to build embossing machines specifically designed to minimize polymer “stickiness.” In experiments, the group found that the machines fabricated chips quickly and accurately, “at very low cost,” Hardt says. “In many cases it makes sense to build your own equipment for the task at hand,” he adds.
In addition to building microfluidic equipment, Hardt and his team are coming up with innovative quality-control techniques. Unlike automobile parts on an assembly line that can be quickly inspected with the naked eye, microfluidic chips carry tiny features, some of which can only be seen with a high-resolution microscope. Checking every feature on even one chip is a time-intensive exercise.
Hardt and his colleagues came up with a fast and reliable way to gauge the “health” of a chip’s production process. Instead of checking whether every channel on a chip has been embossed, the group added an extra feature — a tiny X — to the chip pattern. They designed the feature to be more difficult to emboss than the rest of the chip. Hardt says how sharply the X is stamped is a good indication of whether the rest of the chip has been rendered accurately.
Jumpstarting an industry
The group’s ultimate goal is to change how manufacturing is done. Typically, an industry builds up its production processes gradually, making adjustments and improvements over time. Hardt says the semiconductor industry is a prime example of manufacturing’s iterative process.
“Now what they do in manufacturing is impossibly difficult, but it’s been a series of small incremental improvements over years,” Hardt says. “We’re trying to jumpstart that and not wait until industry identifies all these problems when they’re trying to make a product.”
The group is now investigating ways to design a “self-correcting factory” in which products are automatically tested. If the product doesn’t work, Hardt envisions the manufacturing process changing in response, adjusting settings on machines to correct the process. For example, the team is looking for ways to evaluate how fluid flows through a manufactured chip. The point at which two fluids mix within a chip should be exactly the same in every chip produced. If that mixing point drifts from chip to chip, Hardt and his colleagues have developed algorithms that adjust equipment to correct the drift.
Holger Becker, co-founder of Microfluidic ChipShop, a lab-on-a-chip production company in Jena, Germany, says the center's research plays an important role in understanding the different processes involved in large-scale production of microfluidics.
"Most of the academic work in microfluidics concentrates on applications, and unfortunately only very few concentrate on the actual manufacturing technologies suited for industrialization," Becker says. "David Hardt's team takes a very holistic approach looking into all different process steps and the complete manufacturing process instead of individual technologies."
“We’re at the stage where we’d like industry to know what we’re doing,” Hardt says. “We’ve been sort of laboring in the vineyard for years, and now we have this base, and it could get to the point where we’re ahead of the group.”
Source: MIT
0 comments:
Post a Comment