Posted in | Nanotechnology

New Tool to Optimize CNT Electrodes for Energy Storage, Water Desalination Applications




In a series of tests, the researchers confirmed that their adapted model can reproduce key measurements taken on CNT samples under varying conditions. They're now using their approach to determine detailed parameters of their samples — including the spacing between the nanotubes — and to optimize the design of CNT electrodes for a device that rapidly desalinates brackish water.


One way to achieve those goals is by immersing electrodes into the electrolyte or the saltwater and then imposing a voltage on the system. The electric field that's created causes the charged particles to cling to the electrode surfaces. When the voltage is cut, the particles immediately let go.

"Whether salt or other charged particles, it's all about adsorption and desorption," says Heena Mutha PhD '17, a senior member of technical staff at the Charles Stark Draper Laboratory. "So the electrodes in your device should have lots of surface area as well as open pathways that allow the electrolyte or saltwater carrying the particles to travel in and out easily."

One way to increase the surface area is by using CNTs. In a conventional porous material, such as activated charcoal, interior pores provide extensive surface area, but they're irregular in size and shape, so accessing them can be difficult. In contrast, a CNT "forest" is made up of aligned pillars that provide the needed surfaces and straight pathways, so the electrolyte or saltwater can easily reach them.


Designing for device performance

"Much work has been devoted to determining how CNT morphology affects electrode performance in various applications," says Evelyn Wang, the Gail E. Kendall Professor of Mechanical Engineering. "But an underlying question is, 'How can we characterize these promising electrode materials in a quantitative way, so as to investigate the role played by such details as the nanometer-scale interspacing?'"


For the past two years, Wang and Mutha have been working on a better option. "We wanted to develop a nondestructive method that combines simple electrochemical experiments with a mathematical model that would let us 'back calculate' the interspacing in a CNT forest," Mutha says. "Then we could estimate the porosity of the CNT forest — without destroying it."

Adapting the conventional model

One widely used method for studying porous electrodes is electrochemical impedance spectroscopy (EIS). It involves pulsing voltage across electrodes in an electrochemical cell at a set time interval (frequency) while monitoring "impedance," a measure that depends on the available storage space and resistance to flow. Impedance measurements at different frequencies is called the "frequency response."

The classic model describing porous media uses that frequency response to calculate how much open space there is in a porous material. "So we should be able to use [the model] to calculate the space between the carbon nanotubes in a CNT electrode," Mutha says.


Using the model

To demonstrate her approach, Mutha first fabricated a series of laboratory samples and then measured their frequency response. In collaboration with Yuan "Jenny" Lu '15, a materials science and engineering graduate, she deposited thin layers of aligned CNTs onto silicon wafers inside a furnace and then used water vapor to separate the CNTs from the silicon, producing free-standing forests of nanotubes. To vary the CNT spacing, she used a technique developed by MIT collaborators in the Department of Aeronautics and Astronautics, Professor Brian Wardle and postdoc associate Itai Stein PhD '16. Using a custom plastic device, she mechanically squeezed her samples from four sides, thereby packing the nanotubes together more tightly and increasing the volume fraction — that is, the fraction of the total volume occupied by the solid CNTs.



As Mutha explains, those trends are typical of EIS analyses. "At high frequencies, the voltage changes so quickly that — because of resistance in the CNT forest — it doesn't penetrate the depth of the entire electrode material, so the response comes only from the surface or partway in," she says. "But eventually the frequency is low enough that there's time between pulses for the voltage to penetrate and for the whole sample to respond."


The most striking feature of Mutha's results is the gradual transition from the high-frequency to the low-frequency regime. Calculations from a model based on uniform spacing — the usual assumption — show a sharp transition from partial to complete electrode response. Because Mutha's model incorporates subtle variations in spacing, the transition is gradual rather than abrupt. Her experimental measurements and model results both exhibit that behavior, suggesting that the modified model is more accurate.


Comparing approaches

Work in collaboration with Wardle and Stein has validated the two groups' differing approaches to determining CNT morphology. In their studies, Wardle and Stein use an approach similar to Monte Carlo modeling, which is a statistical technique that involves simulating the behavior of an uncertain system thousands of times under varying assumptions to produce a range of plausible outcomes, some more likely than others. For this application, they assumed a random distribution of "seeds" for carbon nanotubes, simulated their growth, and then calculated characteristics, such as inter-CNT spacing with an associated variability. Along with other factors, they assigned some degree of waviness to the individual CNTs to test the impact on the calculated spacing.


To investigate the role played by waviness, Mutha compared the variabilities in her results with those in Stein's results from simulations assuming different degrees of waviness. At high volume fractions, the EIS variabilities were closest to those from the simulations assuming little or no waviness. But at low volume fractions, the closest match came from simulations assuming high waviness.

Based on those findings, Mutha concludes that waviness should be considered when performing EIS analyses — at least in some cases. "To accurately predict the performance of devices with sparse CNT electrodes, we may need to model the electrode as having a broad distribution of interspacings due to the waviness of the CNTs," she says. "At higher volume fractions, waviness effects may be negligible, and the system can be modeled as simple pillars."



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