The use of carbon nanotubes in ultrafast computers and other electronic devices has been stymied because batches of the material contain nanotubes with varying electronic properties. One nanotube is semiconducting, while the next is conducting. Now Northwestern University researchers have developed a reliable and potentially practical way to sort through this mess, segregating nanotubes into precisely the types needed for high-performance electronics. The advance could speed progress toward nanotube computers and has many nearer-term applications, including high-definition displays, devices for nanotoxicity testing, and solar cells.
The new process separates metallic and semiconducting nanotubes. It also segregates them by diameter (another important parameter for reliable computer chips) and eliminates contaminants, such as other forms of carbon. While the researchers expected to be able to sort nanotubes by diameter, the sorting by electronic type came as a surprise, says Mark Hersam, materials-science and engineering professor and one of the Northwestern researchers. “We didn’t believe it at first,” he says.
Carbon nanotubes are appealing candidates for eventually replacing silicon-based computing because of their small size and excellent electronic properties: some are semiconductors–perfect for transistors–and others are metallic conductors and could be useful as wires for connecting transistors. But getting the right electronic type “makes a big, big difference,” says Mildred Dresselhaus, professor of physics and electrical engineering at MIT. Placing metallic nanotubes where there should be semiconducting nanotubes would cause the chip to fail.
So although researchers have been able to painstakingly create logic circuits using carbon nanotubes (see “Carbon Nanotube Computers”), the methods employed to sort them are “all pretty tedious,” Dresselhaus says, and not something that could be scaled up for manufacturing chips with the millions of transistors needed to compete with today’s computers. In addition, past methods have failed to completely separate semiconducting and metallic nanotubes, says Richard Martel, chemistry professor at the University of Montreal. Martel calls the Northwestern researchers’ new approach, described this month in the new journal Nature Nanotechnology, “a breakthrough in the field.”
The researchers begin by adding surfactants to a batch of nanotubes. The surfactants latch on to the nanotubes, but differences in the nanotubes’ size and electronic properties cause the surfactants to assemble in different concentrations and arrangements, which in turn lead to measurable differences in density. These distinct densities can be sorted out using a well-known process called ultra-centrifugation, which involves spinning the materials at ultrafast speeds–up to 64,000 revolutions per minute.
The discovery came after the researchers noticed that the density changes depended on the type of surfactant they used, which led them to try combining multiple surfactants. They found that the right combinations could be used to exaggerate density disparities between nanotubes of different diameters, and also, surprisingly, disparities between semiconducting and metallic nanotubes. While they still have not identified the precise mechanism involved, the researchers believe it has to do with the marked difference in the ability of the semiconducting and metallic nanotube to be electrically polarized.
Andrew Rinzler, professor of physics at the University of Florida at Gainesville, says the method has produced “the best data I’ve seen so far.” The resulting batches are pure enough, he says, for high-performance electronics. Indeed, Hersam says, the method can produce batches of semiconducting or metallic nanotubes with better than 99 percent purity.
What’s more, the method has the potential to be scaled up for large-scale manufacturing. While the Northwestern researchers used lab-scale centrifuges capable of producing only milligrams of sorted nanotubes a day, Hersam says an industrial-scale centrifuge could purify 100,000 times as much. Large-scale manufacturing, he says, would involve running many of these centrifuges in parallel, twenty-four hours a day, with products removed and new batches added while the machines continue to spin.
Rinzler and Martel caution that the effectiveness of the method at a larger scale still needs to be demonstrated. Furthermore, Martel says, tests are needed to demonstrate that individual nanotubes are not damaged during the process. However, since surfactants don’t typically cause damage to materials such as carbon nanotubes, he doesn’t expect that this will be a problem.
Indeed, researchers are already making use of the new technique. In the Nature Nanotechnology paper, Hersam reports employing the method to make relatively simple transistors using thin-film meshes of semiconducting nanotubes. Such transistors could be useful for controlling pixels in computer monitors and flat-screen TVs.
At the same time, Martel is using the sorting technique to help produce transparent thin films made from a fine mesh of conductive nanotubes. These thin films could also be useful in some displays, replacing indium tin oxide electrodes, which are becoming more expensive as demand for them grows. Because of the superior conductivity of metallic carbon nanotubes, these films have the potential to improve the performance of organic solar cells, which also require transparent electrodes, Martel says.
The ultimate goal of computer chips that use single-nanotube transistors is years away–researchers still face the challenge of developing techniques for assembling millions of densely packed transistors and the connecting wires in intricate circuits. But the Northwestern advance could remove one of the basic obstacles to developing such nanotube-based electronics. “We find this very exciting,” says Martel, “because we can push forward with our work.”
This new data poisoning tool lets artists fight back against generative AI
The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models.
Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist
An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.
The Biggest Questions: What is death?
New neuroscience is challenging our understanding of the dying process—bringing opportunities for the living.
Driving companywide efficiencies with AI
Advanced AI and ML capabilities revolutionize how administrative and operations tasks are done.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.