Packing morphology of wavy nanofiber arrays

Abstract

Existing theories for quantifying the morphology of nanofibers (NFs) in aligned arrays either neglect or assume a simple functional form for the curvature of the NFs, commonly known as the NF waviness. However, since such assumptions cannot adequately describe the waviness of real NFs, errors that can exceed 10% in the predicted inter-NF separation can result. Here we use a theoretical framework capable of simulating >10(5) NFs with stochastic three-dimensional morphologies to quantify NF waviness on an easily accessible measure of the morphology, the inter-NF spacing, for a range of NF volume fractions. The presented scaling of inter-NF spacing with waviness is then used to study the morphology evolution of aligned carbon nanotube (A-CNT) arrays during packing, showing that the effective two-dimensional coordination number of the A-CNTs increases much faster than previously reported during close packing, and that hexagonal close packing can successfully describe the packing morphology of the A-CNTs at volume fractions greater than 40 vol%.

Aligned carbon nanotube morphogenesis predicts physical properties of their polymer nanocomposites

“…w) and vice versa. 48 Using the 3D morphology data, we first consider the elastic modulus of the A-PNCs.…”

“…(B) Plot of tortuosity versus the analytical waviness ratio (w) for sinusoidal, helical and random helical formulations of waviness. 48 The shaded region represents the range of tortuosity (τ) values calculated from tomography data. (C) Elastic modulus (E) of A-PNCs in the longitudinal (||, parallel to CNT alignment) and transverse (⊥, perpendicular to CNT alignment) directions, and the respective model predictions, as a function of the in situ CNT volume fraction V f and accounting for morphology evolution.…”

“…Recent work on three-dimensional morphological quantification 45,46 and modeling of CNS reinforced polymers 47,48 has shown that a significant amount of stochastically-varying local curvature is exhibited by the CNSs present in the polymer matrix. 45 These works also show that such structural randomness leads to the formation of a large number of van der Waals dominated junctions that may contribute to the mechanical and transport properties of such architectures.…”

Influence of Waviness on the Elastic Properties of Aligned Carbon Nanotube Polymer Matrix Nanocomposites

“…These oversimplifications of the CNT morphology lead to large over-predictions of the stiffness contribution of the CNTs to elastic modulus of the A-PNC, and a truly three-dimensional description of the CNT morphology that accounts the stochastic (random) nature of the CNT waviness (See Figure 1b for illustration) and the evolution of w with CNT packing proximity is necessary for more representative mechanical property prediction. 27 In this work, a previously reported simulation framework capable of simulating 10 5 CNTs with stochastic waviness, 27,31,60 and representative scaling of w with with V f , is used to evaluate the scaling of the intrinsic CNT elastic modulus (E cnt ) with w. 27,31 This is achieved by studying the contribution of (axial) stretching, (radial) shear, and bending on the deformation of CNTs in the A-PNC using an analysis similar to applied to study wavy CNTs, 31 which originates from early work on the mechanical behavior of carbon nanocoils. 61 In conjunction with recent experimental data on an exemplary A-PNC system, 26 these results are used to compare the predicted scaling of the A-PNC elastic modulusp (E pnc ) with V f for the current scheme with the results of a previous finite element analysis (FEA) to show that more complete descriptions of the CNT morphology can lead to more accurate material property prediction.…”

Morphology control of aligned carbon nanotube pins formed via patterned capillary densification

“…growth has previously been achieved by patterning catalyst layers via photo-and electron-beam-lithography [51,52] and by using metallic layers as catalyst deactivators [53][54][55], but these processes are time-consuming and cost-prohibitive at large scales. Additionally, they often produce fragile CNT arrays with synthesis-induced structural variations [56][57][58] and a low density per unit area [59,60], which collectively yield properties that can be orders of magnitude below theoretical values [61][62][63]. To overcome these limitations, scalable processing techniques, such as simple catalyst patterning via mechanical scribing [26,64,65] (figure 1(a)), post-CNTgrowth O 2 plasma treatment to increase structural uniformity [18], and capillary densification (figure 1(b)) are attractive methods to create high-density, morphology-controlled bulk nanostructured materials provided that models can guide the processing towards these desired structures.…”