Porosimetry and packing morphology of vertically aligned carbon nanotube arrays via impedance spectroscopy

TitlePorosimetry and packing morphology of vertically aligned carbon nanotube arrays via impedance spectroscopy
Publication TypeJournal Article
Year of Publication2017
AuthorsMutha, HK, Lu, Y, Stein, IY, H. Cho, J, Suss, ME, Laoui, T, Thompson, CV, Wardle, BL, Wang, EN
Date Published2017/02/03/
ISBN Number0957-4484
Keywordsaligned nanowires, carbon nanotubes, electrochemical impedance spectroscopy, electrochemical capacitors, electrodes, fabrication, ion batteries, nanocomposites, nanowire arrays, porosimetry, power-density, semiconductor nanowires, sensitized solar-cells, supercapacitor, supercapacitors

Vertically aligned one-dimensional nanostructure arrays are promising in many applications such as electrochemical systems, solar cells, and electronics, taking advantage of high surface area per unit volume, nanometer length scale packing, and alignment leading to high conductivity. However, many devices need to optimize arrays for device performance by selecting an appropriate morphology. Developing a simple, non-invasive tool for understanding the role of pore volume distribution and interspacing would aid in the optimization of nanostructure morphologies in electrodes. In this work, we combined electrochemical impedance spectroscopy (EIS) with capacitance measurements and porous electrode theory to conduct in situ porosimetry of vertically aligned carbon nanotube (VA-CNT) forests non-destructively. We utilized the EIS measurements with a pore size distribution model to quantify the average and dispersion of inter-CNT spacing (G), stochastically, in carpets that were mechanically densified from 1.7 x 10(10) tubes cm(-2) to 4.5 x 10(11) tubes cm(-2). Our analysis predicts that the inter-CNT spacing ranges from over 100 +/- 50 nm in sparse carpets to sub 10 +/- 5 nm in packed carpets. Our results suggest that waviness of CNTs leads to variations in the inter-CNT spacing, which can be significant in sparse carpets. This methodology can be used to predict the performance of many nanostructured devices, including supercapacitors, batteries, solar cells, and semiconductor electronics.

Short TitleNanotechnology