Dr. Goon Mo Koo Profile

Dr. Goon Mo Koo

at Purdue Univ

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Proceedings Article | 18 April 2022 Presentation + Paper

The effect of inter-filler transport on AC piezoresistivity in CNF-modified epoxy nanocomposites

G. M. Koo, S. Ghazzawi, T. Tallman

Proceedings Volume 12046, 1204609 (2022) https://doi.org/10.1117/12.2612365

KEYWORDS: Resistance, Nanocomposites, Data modeling, Calibration, Structural health monitoring

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Nanofiller-modified composites have received immense attention for potential applications as sensor technology in civil, mechanical, and aerospace systems. Sensing in these materials is predicated on the piezoresistive effect–the material having deformation-dependent electrical conductivity. To date, work in this area has focused overwhelmingly on the relationship between direct current (DC) electrical transport and deformation. This is important because utilizing changes in alternating current (AC) transport as a metric of deformation has notable potential advantages such as enhanced data density (i.e. by relating both impedance and phase to deformation), higher sensitivity via electrodynamic principles, and reduced power requirements. Existent studies on AC piezoresistivity have focused mainly on macroscale equivalent circuit modeling that homogenizes the net input-output electrical response of the material corresponding to deformations. Therefore, in order to generate new basic knowledge in this field, this work presents a preliminary study into the effect of inter-filler transport on deformation-dependent impedance in piezoresistive nanocomposites. This is done by discretizing the nanofillers into a complex network of AC circuit elements, calculating the net frequency response of the network, modulating the tunneling resistance and inter-filler capacitance to replicate the effect of deformation, and then recalculating the net frequency response. The model was compared to experimental data for 1.0 wt.% carbon nanofiber (CNF)-modified epoxy subject to uniaxial stress. Results of this preliminary investigation show that both inter-filler tunneling resistance and capacitance play a strong role in the AC piezoresistive response of nanocomposites and modulation of these inter-filler transport parameters can qualitatively replicate experimental observations with good accuracy.

Proceedings Article | 22 April 2020 Presentation + Paper

An experimental exploration of deformation-dependent AC conductivity in carbon nanofiber-modified epoxy

G. M. Koo, T. Tallman

Proceedings Volume 11377, 113770F (2020) https://doi.org/10.1117/12.2557307

KEYWORDS: Manufacturing, Carbon, Structural health monitoring, Nanocomposites, Composites, Nondestructive evaluation, Polymers

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Conductive nanofiller-modified materials have received a significant amount of interest for use in self-sensing, nondestructive evaluation (NDE) and structural health monitoring (SHM) owing to their piezoresistive properties (i.e. having deformation-dependent electrical transport). To date, the majority of the work related to piezoresistivity has focused on the relation between direct current (DC) conductivity or resistivity and strains. However, DC-based methods of selfsensing have important limitations such as poor sensitivity to spatially distributed damage and high resistivity. Alternating current (AC)-based methods of electrical interrogation have potential to address these limitations. Unfortunately, much less work exists on the effect of strain on AC conductivity. Therefore, we herein explore the effect of strain on AC conductivity in piezoresistive polymer nanocomposites. Specifically, epoxy is modified with carbon nanofibers (CNFs) at 1 wt.% and tested under uniaxial loading as AC conductivity is measured as a function of interrogation frequency. The AC conductivity-frequency relation is then fit to a universal power law for a range of compressive and tensile strains such that power-law fitting constants can be expressed as a function of normal strain. The basic insights revealed from this work are an important step toward transitioning piezoresistive-based self-sensing from prevailing DC approaches to potentially much more powerful AC methods.

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