Integrating nanoscale fibres such as carbon nanotubes (CNTs) into commercial applications, from coatings for aircraft wings to heat sinks for mobile computing, requires them to be produced in large scale and at low cost. Chemical vapor deposition (CVD) is a promising approach to manufacture CNTs in the needed scales, but it produces CNTs that are too sparse and compliant for most applications.
Applying and evaporating a few drops of a liquid such as acetone to the CNTs is an easy, cost-effective method to more tightly pack them together and increase their stiffness, but until now, there was no way to forecast the geometry of these CNT cells.
MIT researchers have now developed a systematic method to predict the two-dimensional patterns CNT arrays form after they are packed together, or densified, by evaporating drops of either acetone or ethanol. CNT cell size and wall stiffness grow proportionally with cell height, they report in the Physical Chemistry Chemical Physics.
One way to think of this CNT behavior is to imagine how entangled fibres such as wet hair or spaghetti collectively reinforce each other. The larger this entangled region is, the higher its resistance to bending will be.
Similarly, longer CNTs can better reinforce one another in a cell wall. The researchers also find that CNT binding strength to the base on which they are produced, in this case, silicon, makes an important contribution to predicting the cellular patterns that these CNTs will form.
“These findings are directly applicable to industry because when you use CVD, you get nanotubes that have curvature, randomness, and are wavy, and there is a great need for a method that can easily mitigate these defects without breaking the bank,” says Itai Stein SM ’13, PhD ’16, who is a postdoc in the Department of Aeronautics and Astronautics.
Co-authors include materials science and engineering graduate student Ashley Kaiser, mechanical engineering postdoc Kehang Cui, and senior author Brian Wardle, professor of aeronautics and astronautics.
“From our previous work on aligned carbon nanotubes and their composites, we learned that more tightly packing the CNTs is a highly effective way to engineer their properties,” says Wardle.
“The challenging part is to develop a facile way of doing this at scales that are relevant to commercial aircraft (hundreds of meters), and the predictive capabilities that we developed here are a large step in that direction.”
Carbon nanotubes are highly desirable because of their thermal, electrical, and mechanical properties, which are directionally dependent. Earlier work in Wardle’s lab demonstrated that waviness reduces the stiffness of CNT arrays by as little as 100 times, and up to 100,000 times.
The technical term for this stiffness, or ability to bend without breaking, is elastic modulus. Carbon nanotubes are from 1,000 to 10,000 times longer than they are thick, so they deform principally along their length.
For an earlier paper published in the journal Applied Physics Letters, Stein and colleagues used nanoindentation techniques to measure stiffness of aligned carbon nanotube arrays and found their stiffness to be 1/1,000 to 1/10,000 times less than the theoretical stiffness of individual carbon nanotubes.
Stein, Wardle, and former visiting MIT graduate student Hülya Cebeci also developed a theoretical model explaining changes at different packing densities of the nanofibres.
The new work shows that CNTs compacted by the capillary forces from first wetting them with acetone or ethanol and then evaporating the liquid also produces CNTs that are hundreds to thousands of times less stiff than expected by theoretical values.
This capillary effect, known as elastocapillarity, is similar to a how a sponge often dries into a more compact shape after being wetted and then dried.
“Our findings all point to the fact that the CNT wall modulus is much lower than the normally assumed value for perfect CNTs because the underlying CNTs are not straight,” says Stein. “Our calculations show that the CNT wall is at least two orders of magnitude less stiff than we expect for straight CNTs, so we can conclude that the CNTs must be wavy.”
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Image credit: MIT.