J-Integral Experimental Reduction Reveals Fracture Toughness Improvements in Thin-Ply Carbon Fiber Laminates with Aligned Carbon Nanotube Interlaminar Reinforcement

The Mode I, Mode II, and mixed-mode interlaminar failure behavior of a thin-ply (54 gsm) carbon fiber-epoxy laminated composite reinforced by 20 μm tall z-direction-aligned carbon nanotubes (CNTs), comprising ∼50 billion CNT fibers per cm2, is analyzed following J-integral-based data reduction methods. The inclusion of aligned CNTs in the ply interfaces provides enhanced crack resistance, resulting in sustained crack deflection from the reinforced interlaminar region to the intralaminar region of the adjacent plies, i.e., the CNTs drive the crack from the interlaminar region into the plies. The CNTs do not appreciably increase the interlaminar thickness or laminate weight and preserve the intralaminar microfiber morphology. Improvements of 34 and 62% on the Mode I and Mode II initiation fracture toughness, respectively, are observed. This type of interlaminar nanoreinforcement effectively drives crack propagation from the interface to within the ply where the crack propagates parallel to the interlaminar region, providing new insight into previously reported strength and fatigue performance increases. These findings extend to industries where lightweight and durable materials are critical for improving the structural efficiency.

Selectively Tuning the Substrate Adhesion Strength of Aligned Carbon Nanotube Arrays via Thermal Postgrowth Processing

The excellent intrinsic properties of aligned nanofibers, such as carbon nanotubes (CNTs), and their ability to be easily formed into multifunctional 3D architectures motivate their use for a variety of commercial applications, such as batteries, chemical sensors for environmental monitoring, and energy harvesting devices. While controlling nanofiber adhesion to the growth substrate is essential for bulk-scale manufacturing and device performance, experimental approaches and models to date have not addressed tuning the CNT array-substrate adhesion strength with thermal processing conditions. In this work, facile "one-pot" thermal postgrowth processing (at temperatures T_p = 700-950 °C) is used to study CNT-substrate pull-off strength for millimeter-tall aligned CNT arrays. CNT array pull-off from the flat growth substrate (Fe/Al₂O₃/SiO₂/Si wafers) via tensile testing shows that the array fails progressively, similar to the response of brittle microfiber bundles in tension. The pull-off strength evolves nonmonotonically with T_p in three regimes, first increasing by 10 times through T_p = 800 °C due to graphitization of disordered carbon at the CNT-catalyst interface, and then decreasing back to a weak interface through T_p = 950 °C due to diffusion of the Fe catalyst into the substrate, Al₂O₃ crystallization, and substrate cracking. Failure is observed to occur at the CNT-catalyst interface below 750 °C, and the CNTs themselves break during pull-off after higher T_p processing, leaving residual CNTs on the substrate. Morphological and chemical analyses indicate that the Fe catalyst remains on the substrate after pull-off in all regimes. This work provides new insights into the interfacial interactions responsible for nanofiber-substrate adhesion and allows tuning to increase or decrease array strength for applications such as advanced sensors, energy devices, and nanoelectromechanical systems (NEMS).

Substrate adhesion evolves non-monotonically with processing time in millimeter-scale aligned carbon nanotube arrays

The advantageous intrinsic and scale-dependent properties of aligned nanofibers (NFs) and their assembly into 3D architectures motivate their use as dry adhesives and shape-engineerable materials. While controlling NF-substrate adhesion is critical for scaled manufacturing and application-specific performance, current understanding of how this property evolves with processing conditions is limited. In this report, we introduce substrate adhesion predictive capabilities by using an exemplary array of NFs, aligned carbon nanotubes (CNTs), studied as a function of their processing. Substrate adhesion is found to scale non-monotonically with process time in a hydrocarbon environment and is investigated via the tensile pull-off of mm-scale CNT arrays from their growth substrate. CNT synthesis follows two regimes: Mode I ('Growth') and Mode II ('Post-Growth'), separated by growth termination. Within 10 minutes of post-growth, experiments and modeling indicate an order-of-magnitude increase in CNT array-substrate adhesion strength (∼40 to 285 kPa) and effective elastic array modulus (∼6 to 47 MPa), and a two-orders-of-magnitude increase in the single CNT-substrate adhesion force (∼0.190 to 12.3 nN) and work of adhesion (∼0.07 to 1.5 J m^-2), where the iron catalyst is found to remain on the substrate. Growth number decay in Mode I and carbon accumulation in Mode II contribute to the mechanical response, which may imply a change in the deformation mechanism. Predictive capabilities of the model are assessed for previously studied NF arrays, suggesting that the current framework can enable the future design and manufacture of high-value NF array applications.

Aligned carbon nanotube morphogenesis predicts physical properties of their polymer nanocomposites

Carbon nanostructure (CNS) based polymer nanocomposites (PNCs) are of interest due to the superior properties of the CNS themselves, scale effects, and the ability to transfer these properties anisotropically to the bulk material. However, measurements of physical properties of such materials are not in agreement with theoretical predictions. Recently, the ability to characterize the 3D morphology of such PNCs at the nanoscale has been significantly improved, with rich, quantitative data extracted from tomographic transmission electron microscopy (TEM). In this work, we use new, nanoscale quantitative 3D morphological information and stochastic modeling to re-interpret experimental measurements of continuous aligned carbon nanotube (A-CNT) PNC properties as a function of A-CNT packing/volume fraction. The 3D tortuosity calculated from tomographic reconstructions and its evolution with volume fraction is used to develop a novel definition of waviness that incorporates the stochastic nature of CNT growth. The importance of using randomly wavy CNTs to model these materials is validated by agreement between simulated and previously-measured PNC elastic moduli. Secondary morphological descriptors such as CNT-CNT junction density and inter-junction distances are measured for transport property predictions. The scaling of the junction density with CNT volume fraction is observed to be non-linear, and this non-linearity is identified as the primary reason behind the previously unexplained scaling of aligned-CNT PNC longitudinal thermal conductivity. By contrast, the measured electrical conductivity scales linearly with volume fraction as it is relatively insensitive to junction density beyond percolation. This result verifies prior hypotheses that electrical conduction in such fully percolated and continuous CNT systems is dominated by the bulk resistivity of the CNTs themselves. This combination of electron tomographic data and stochastic simulations is a powerful method for establishing a predictive capability for nanocomposite structure-property relations, making it an essential aid in understanding and tailoring the next-generation of advanced composites.

Electrochemical Impedance Imaging via the Distribution of Diffusion Times

We develop a mathematical framework to analyze electrochemical impedance spectra in terms of a distribution of diffusion times (DDT) for a parallel array of random finite-length Warburg (diffusion) or Gerischer (reaction-diffusion) circuit elements. A robust DDT inversion method is presented based on complex nonlinear least squares regression with Tikhonov regularization and illustrated for three cases of nanostructured electrodes for energy conversion: (i) a carbon nanotube supercapacitor, (ii) a silicon nanowire Li-ion battery, and (iii) a porous-carbon vanadium flow battery. The results demonstrate the feasibility of nondestructive "impedance imaging" to infer microstructural statistics of random, heterogeneous materials.

Process-morphology scaling relations quantify self-organization in capillary densified nanofiber arrays

Model-informed experiments reveal that cellular pattern formation in capillary-densified aligned carbon nanotube arrays is governed not only by their height, but also by substrate adhesion strength.

Systematic periodicity in waviness of vertically aligned carbon nanotubes explained by helical buckling

A hypothesis is proposed in this work to account for the geometry of individual vertically aligned carbon nanotubes (VACNTs) that not only justifies the directionality of their growth, but also explains the origin of the waviness frequently reported for these nanotube forests. Such waviness has fundamental effects on the transport/conduction properties of VACNTs, either through or along them, regarding phenomena such as mass, stress, heat and electricity. Despite the general opinion about randomness of carbon nanotubes (CNTs) tortuosity, we demonstrate here that rules of helical buckling of tubular strings is applicable to VACNTs, based on which a regular 3D helical geometry is proposed for VACNTs, with a 2D sine wave shape side-profile. In this framework, gradual increase of the total free surface energy by growth of CNTs ensues their partial cohesion, driven by van der Waals interactions, to reduce the excess surface energy. On the other hand, their cohesion is accompanied by their deformation and loss of straightness, which in turn, translates to buildup of an elastic strain energy in the system. The balance of the two energies along with the spatial constraints on each CNT at its contact points with neighboring CNTs, is manifested in its helical buckling, that is systematically influenced by nanostructural characteristics of VACNTs, such as their diameter, wall thickness and inter-CNT spacing.

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

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 (Γ), stochastically, in carpets that were mechanically densified from [Formula: see text] tubes cm-2 to [Formula: see text] 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.