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 Tp = 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 Tp in three regimes, first increasing by 10 times through Tp = 800 °C due to graphitization of disordered carbon at the CNT-catalyst interface, and then decreasing back to a weak interface through Tp = 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 Tp 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.

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

Capillary-mediated densification is an inexpensive and versatile approach to tune the application-specific properties and packing morphology of bulk nanofiber (NF) arrays, such as aligned carbon nanotubes. While NF length governs elasto-capillary self-assembly, the geometry of cellular patterns formed by capillary densified NFs cannot be precisely predicted by existing theories. This originates from the recently quantified orders of magnitude lower than expected NF array effective axial elastic modulus (E), and here we show via parametric experimentation and modeling that E determines the width, area, and wall thickness of the resulting cellular pattern. Both experiments and models show that further tuning of the cellular pattern is possible by altering the NF-substrate adhesion strength, which could enable the broad use of this facile approach to predictably pattern NF arrays for high value applications.

Synthesis of polymer bead nano-necklaces on aligned carbon nanotube scaffolds.

Here, we report the fabrication of aligned carbon nanotube (A-CNT)/conducting polymer (CP) heterostructures with both uniform conformal and periodic beaded polymer morphologies via oxidative chemical vapor deposition of poly(ethylenedioxythiophene). Periodic beaded CP morphologies are realized utilizing the Plateau-Rayleigh instability to transform the original uniform conformal film, yielding a beaded CP morphology with a >50% enhancement in specific surface area (SSA). Modeling indicates that this SSA increase originates from the internal volume of the A-CNTs becoming available for adsorption, and that these internal A-CNT surfaces, if they could be made accessible to electrolyte ions, could lead to >30% enhancement of specific gravimetric and volumetric capacitances of current state-of-the-art A-CNT/CP heterostructures.

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.

Fabrication and morphology tuning of graphene oxide nanoscrolls.

Here we report the synthesis of graphene oxide nanoscrolls (GONS) with tunable dimensions via low and high frequency ultrasound solution processing techniques. GONS can be visualized as a graphene oxide (GO) sheet rolled into a spiral-wound structure and represent an alternative to traditional carbon nano-morphologies. The scrolling process is initiated by the ultrasound treatment which provides the scrolling activation energy for the formation of GONS. The GO and GONS dimensions are observed to be a function of ultrasound frequency, power density, and irradiation time. Ultrasonication increases GO and GONS C-C bonding likely due to in situ thermal reduction at the cavitating bubble-water interface. The GO area and GONS length are governed by two mechanisms; rapid oxygen defect site cleavage and slow cavitation mediated scission. Structural characterization indicates that GONS with tube and cone geometries can be formed with both narrow and wide dimensions in an industrial-scale time window. This work paves the way for GONS implementation for a variety of applications such as adsorptive and capacitive processes.

Mechanics of aligned carbon nanotube polymer matrix nanocomposites simulated via stochastic three-dimensional morphology.

The promise of enhanced and tailored properties motivates the study of one-dimensional nanomaterials, especially aligned carbon nanotubes (A-CNTs), for the reinforcement of polymeric materials. While CNTs have remarkable theoretical properties, previous work on aligned CNT polymer matrix nanocomposites (A-PNCs) reported mechanical properties that are orders of magnitude lower than those predicted by rule of mixtures. This large difference primarily originates from the morphology of the CNTs, because the CNTs that comprise the A-PNCs have significant local curvature commonly referred to as waviness. Here we present a simulation framework capable of analyzing 10(5) wavy CNTs with realistic three-dimensional morphologies to quantify the impact of waviness on the effective elastic modulus contribution of wavy CNTs. The simulation results show that due to the low shear modulus of the reinforcing CNT 'fibers', and large ([Formula: see text]) compliance contribution of the shear deformation mode, waviness reduces the effective stiffness contribution of the A-CNTs by two to three orders of magnitude. Also, the mechanical property predictions resulting from the simulation framework outperform those previously reported using finite element analysis since representative descriptions of the morphology are required to accurately predict properties of the A-PNCs. Further work to quantify the morphology of A-PNCs in three-dimensions, simulate their full non-isotropic constitutive relations, and predict their failure mechanisms is planned.

Packing morphology of wavy nanofiber arrays.

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 array stiffness from stochastic three-dimensional morphology.

The landmark theoretical properties of low dimensional materials have driven more than a decade of research on carbon nanotubes (CNTs) and related nanostructures. While studies on isolated CNTs report behavior that aligns closely with theoretical predictions, studies on cm-scale aligned CNT arrays (>10(10) CNTs) oftentimes report properties that are orders of magnitude below those predicted by theory. Using simulated arrays comprised of up to 10(5) CNTs with realistic stochastic morphologies, we show that the CNT waviness, quantified via the waviness ratio (w), is responsible for more than three orders of magnitude reduction in the effective CNT stiffness. Also, by including information on the volume fraction scaling of the CNT waviness, the simulation shows that the observed non-linear enhancement of the array stiffness as a function of the CNT close packing originates from the shear and torsion deformation mechanisms that are governed by the low shear modulus (∼1 GPa) of the CNTs.

Aligned carbon nanotube film enables thermally induced state transformations in layered polymeric materials.

The energy losses and geometric constraints associated with conventional curing techniques of polymeric systems motivate the study of a highly scalable out-of-oven curing method using a nanostructured resistive heater comprised of aligned carbon nanotubes (A-CNT). The experimental results indicate that, when compared to conventional oven based techniques, the use of an "out-of-oven" A-CNT integrated heater leads to orders of magnitude reductions in the energy required to process polymeric layered structures such as composites. Integration of this technology into structural systems enables the in situ curing of large-scale polymeric systems at high efficiencies, while adding sensing and control capabilities.

Exohedral physisorption of ambient moisture scales non-monotonically with fiber proximity in aligned carbon nanotube arrays.

Here we present a study on the presence of physisorbed water on the surface of aligned carbon nanotubes (CNTs) in ambient conditions, where the wet CNT array mass can be more than 200% larger than that of dry CNTs, and modeling indicates that a water layer >5 nm thick can be present on the outer CNT surface. The experimentally observed nonlinear and non-monotonic dependence of the mass of adsorbed water on the CNT packing (volume fraction) originates from two competing modes. Physisorbed water cannot be neglected in the design and fabrication of materials and devices using nanowires/nanofibers, especially CNTs, and further experimental and ab initio studies on the influence of defects on the surface energies of CNTs, and nanowires/nanofibers in general, are necessary to understand the underlying physics and chemistry that govern this system.

Coordination number model to quantify packing morphology of aligned nanowire arrays.

The average inter-wire spacing in aligned nanowire systems strongly influences both the physical and transport properties of the bulk material. Because most studies assume that the nanowire coordination is constant, a model that provides an analytical relationship between the average inter-wire spacings and measurable physical properties, such as nanowire volume fraction, is necessary. Here we report a continuous coordination number model with an analytical relationship between the average nanowire coordination, diameter, and volume fraction. The model is applied to vertically aligned carbon nanotube (VACNT) and nanofiber (VACNF) arrays, and the effective nanowire coordination number is established from easily accessible measures, such as the nanowire spacing and diameter. VACNT analysis shows that the coordination number increases with increasing nanowire volume fraction, leading the measured inter-CNT spacing values to deviate by as much as 13% from the spacing values predicted by the typically assumed hexagonal packing. VACNF analysis suggests that, by predicting an inter-fiber spacing that is within 6% of the reported value, the continuous coordination model outperforms both square and hexagonal packing in real nanowire arrays. Using this model, the average inter-wire spacing of nanowire arrays can be predicted, thus allowing more precise morphology descriptions, and thereby supporting the development of more accurate structure-property models of bulk materials comprised of aligned nanowires.