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

机译:垂直对准碳纳米管阵列通过阻抗光谱法的波隆净测定和包装形态

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摘要

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.
机译:垂直对准的一维纳米结构阵列在许多应用中具有很多应用,例如电化学系统,太阳能电池和电子器件,利用每单位体积,纳米长度缩放包装和对准导致高导电性的高表面积。然而,许多设备需要通过选择适当的形态来优化用于设备性能的阵列。制定简单的非侵入性工具,用于理解孔体积分布和间隙的作用,有助于优化电极中的纳米结构形态。在这项工作中,我们将电化学阻抗光谱(EIS)与电容测量和多孔电极理论相结合,以防止垂直对准的碳纳米管(VA-CNT)森林的原位孔隙测量法。我们利用具有孔径分布模型的EIS测量,以量化CNT间距(G),随机地毯的平均和分散,所述地毯从1.7×10(10)管CM(-2)到4.5 x 10(11)管CM(-2)。我们的分析预测,CNT间​​距在稀疏地毯上超过100 +/- 50nm到填充地毯中的10 +/- 5 nm。我们的结果表明,CNT的波纹导致CNT间距的变化,这在稀疏地毯中可能很大。该方法可用于预测许多纳米结构装置的性能,包括超级电容器,电池,太阳能电池和半导体电子器件。

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