Skip to Navigation Skip to Page Content

MAE assistant professor working to fill gaps in carbon nanotube forest understanding

A scanning electron micrograph of a carbon nanotube forest (left) and a numerically simulated CNT forest (right).

A scanning electron micrograph of a carbon nanotube forest (left) and a numerically simulated CNT forest (right). Matt Maschmann, assistant professor of mechanical and aerospace engineering, will have a paper in the upcoming issue of Carbon outlining his findings on the characterization of carbon nanotube forest formation and their resulting mechanical properties. Photo courtesy of Matt Maschmann

There’s a gap in the greater scientific community’s knowledge of how carbon nanotube arrays form, and Matt Maschmann is working on filling it.

Maschmann, an assistant professor of mechanical and aerospace engineering at the University of Missouri, will publish an article in the upcoming edition of the journal Carbon titled “Integrated simulation of active carbon nanotube forest growth and mechanical compression.” The paper outlines his findings on the characterization of carbon nanotube forest formation and their resulting mechanical properties.

Matt Maschmann mug shot

Matt Maschmann’s paper uses 2D modeling to map — based on a variety of parameters — how nanotubes grow into particular types of forests before attempting to test the resulting properties of said forests.

Carbon nanotubes are extremely tiny ­— invisible to the naked eye — tubular graphite structures that grow through a process called chemical vapor deposition (CVD). It is a high-temperature catalytic process conducted in a furnace, with carbon structures forming atop a substrate, often silicon. As they grow in relatively dense populations, mechanical forces combine them into clusters known as forests or arrays.

“The structural morphology that they form in the end dictates their properties,” Maschmann said. “If they’re very dense and well aligned, you’re going to have a rigid material and probably very conductive. If they’re very disorganized, they’re going to be soft and have an entirely different set of properties.”

But there’s a gap in understanding when it comes to these structures. Most models examining carbon nanotube forest properties currently take preconceived nanotube-like networks and test what happens when you compress them or check such things as their thermal properties or conductivity. However, these models don’t take into account the process by which that particular forest was formed. In other words, the models test the properties of a forest without accounting for how the forest grew and why it grew in the way it did. Understanding, and ultimately controlling the synthesis process could help engineers create nanotube forests with desired mechanical, thermal, and electrical properties.

“The complex interactions between growing carbon nanotubes in forests are currently poorly understood,” Maschmann explained.

Maschmann’s paper potentially is the first step toward filling that gap. It uses 2D modeling to map — based on a variety of parameters — how nanotubes grow into particular types of forests before attempting to test the resulting properties of said forests.

“The idea is you have a population of, essentially, beams that grow from, in this case of this paper, a flat substrate. And each nanotube has properties assigned to it based on some distributed parameter space,” Maschmann said.

“One nanotube could have a different growth rate from a neighbor, and it could be oriented at a different angle from the substrate. When neighboring nanotubes are in close proximity, an attractive force called the van der Waals force brings the nanotubes into contact and tends to locally pin them together. This is the same force that allows insects and geckos to walk on vertical surfaces. Overall, I chose the most simplistic kind of parameter distributions and inputs I could use — very obvious things to see what the outputs would be.”

The model Maschmann developed predicts that the larger the standard deviation of the growth rates in the nanotube population, the more “wavy” the forest appears. And it also predicts the greater the density of the carbon nanotubes, the greater their alignment, reproducing outcomes Maschmann said have been seen experimentally.

The model tests the forests’ mechanical properties by compressing them after their synthesis. The mechanical behavior of the simulated forests is quite similar to those observed experimentally.

“The advantage of this approach is that one can map how synthesis parameters influence structural morphology and then how structure influences forest performance metrics in one comprehensive simulation,” Maschmann said. “I am very encouraged that the model successfully predicts major synthesis and mechanical behaviors that have been experimentally observed.”

Maschmann’s research group currently is extending the model to three-dimensional space and adding functionality that will allow for the prediction of thermal and electrical properties. “It is my hope that this modeling construct will allow us to design carbon nanotube forests with advantageous properties for sensors, interfacial materials, composites, and more,” he said.

Further model validation is planned using experiments conducted inside of a scanning electron microscope.

Maschmann has worked with carbon nanotubes for a number of years. His applied carbon nanotube research has looked at their role in electromechanical sensors and as electrical transistors. Read more here: engineering.missouri.edu/2014/08/mae-assistant-professor-maschmann-earns-award-from-oak-ridge-national-lab