Liquid Crystalline Behaviors of Single-Walled Carbon Nanotubes in an Aqueous Sodium Cholate Dispersion.

Controlling the alignment of single-walled carbon nanotubes (SWCNTs) on the macroscopic scale is critical for practical applications because SWCNTs are extremely anisotropic materials. One efficient technique is to create an effective SWCNT dispersion, which shows a liquid crystal (LC) phase. A strong acid treatment can realize SWCNT liquid crystalline dispersions. However, strong acids pose a substantial safety risk, which renders the process unfit for mass production. Herein, an isolated SWCNT dispersion displaying an LC behavior is prepared using sodium cholate without an acid treatment, and its phase transition behaviors are systematically investigated across the isotropic to biphasic to nematic phases. As the SWCNT concentration increases, the dispersion undergoes an isotropic-to-nematic phase transition in which the spindle-shaped LC droplets, or the so-called tactoids, and the Schlieren textures can be observed in the intermediate biphasic state and the nematic phase, respectively. The arrangements of SWCNTs in the tactoids and the Schlieren structures are directly investigated by polarized optical microscopy. The clear LC behaviors of the CNT dispersion suggest that the CNT orientations can be controlled by the normal surfactant-assisted method, which is a crucial advantage for the liquid-phase processing of CNT fibers and films.

N2 Gas Adsorption Sites of Single-Walled Carbon Nanotube Bundles: Identifying Interstitial Channels at Very Low Relative Pressure.

Utilizing the nanoscale space created by carbon nanotubes (CNTs) is of importance for applications like energy storage devices, sensors, and functional materials. Gas adsorption is a versatile, quantitative characterization method to analyze nanoscale pore sizes and volumes. Here, we inspected N2 adsorption to the nanospace formed by the bundles of single-walled CNTs with an average nanotube diameter of ca. 2.0 nm and its distributions of 0.7-4.1 nm. Based on comparisons among the as-grown, purified (opened), and heat-treated (closed) CNTs with similar geometric bundle structures, we found that the interstitial channels emerged from a very low relative pressure of approximately 10-8 by removing the impurities from the CNT bundles, which is the first empirical demonstration. These findings can not only be utilized to understand the structures of CNT films, fibers, and bulks but also applied to porous materials science.

Near-Infrared Hyperspectral Absorption/Scattering Imaging Method Using Multiple Ground Plates for Evaluating Polymer Composites.

This paper proposes a nondestructive method of evaluating polymer composites using near-infrared (NIR) diffuse reflection spectroscopy with multiple ground plates. Wavelength-dependent absorption and reduced scattering coefficients were acquired to evaluate the chemical structure and the concentration of the substances from absorption and to determine the size and the dispersity of filler in the polymer domain from scattering. NIR spectra of the sample were measured on multiple ground plates, namely, "ground-plate-dependent" diffuse reflection spectra. The effects of the external reflection on the ground-plate-dependent diffuse reflection spectra were subsequently removed. The internal reflection coefficient was calculated based on the difference between the diffuse reflectances of the neat resin and ground plates without prior information on the incident angle of light and the refractive index of sample. The external reflection coefficient was evaluated by the gap of diffuse reflectances between the sample and a white ground plate. After the corrections of reflections, the spectra were fitted by a physical model of light propagation based on the two-flux theory to acquire the absorption and the reduced scattering coefficients. The calculated absorption coefficients indicated a good linear relationship with particle concentration. The calculated reduced scattering coefficients agreed with the theoretical values by Mie scattering theory. It was demonstrated that the proposed method achieved the simultaneous evaluation of particulate-filler concentrations and sizes in polymer composites.

Tabular Two-Dimensional Correlation Analysis for Multifaceted Characterization Data.

We propose tabular two-dimensional correlation spectroscopy analysis for extracting features from multifaceted characterization data, essential for understanding material properties. This method visualizes similarities and phase lags in structural parameter changes through heatmaps, combining hierarchical clustering and asynchronous correlations. We applied the proposed method to data sets of carbon nanotube (CNT) films annealed at various temperatures and revealed the complexity of their hierarchical structures, which include elements such as voids, bundles, and amorphous carbon. Our analysis addresses the challenge of attempting to understand the sequence of structural changes, especially in multifaceted characterization data where 11 structural parameters derived from eight characterization methods interact with complex behavior. The results show how phase lags (asynchronous changes from stimuli), and parameter similarities can illuminate the sequence of structural changes in materials, providing insights into phenomena such as the removal of amorphous carbon and graphitization in annealed CNTs. This approach is beneficial even with limited data and holds promise for a wide range of material analyses, demonstrating its potential in elucidating complex material behaviors and properties.

A Comprehensive and Versatile Multimodal Deep-Learning Approach for Predicting Diverse Properties of Advanced Materials.

A multimodal deep-learning (MDL) framework is presented for predicting physical properties of a ten-dimensional acrylic polymer composite material by merging physical attributes and chemical data. The MDL model comprises four modules, including three generative deep-learning models for material structure characterization and a fourth model for property prediction. The approach handles an 18-dimensional complexity, with ten compositional inputs and eight property outputs, successfully predicting 913 680 property data points across 114 210 composition conditions. This level of complexity is unprecedented in computational materials science, particularly for materials with undefined structures. A framework is proposed to analyze the high-dimensional information space for inverse material design, demonstrating flexibility and adaptability to various materials and scales, provided sufficient data are available. This study advances future research on different materials and the development of more sophisticated models, drawing the authors closer to the ultimate goal of predicting all properties of all materials.

A Microwave-Assisted, Solvent-Free Approach for the Versatile Functionalization of Carbon Nanotubes.

While the functionalization of carbon nanotubes (CNTs) has attracted extensive interest for a wide range of applications, a facial and versatile strategy remains in demand. Here, we report a microwave-assisted, solvent-free approach to directly functionalize CNTs both in raw form and in arbitrary macroscopic assemblies. Rapid microwave irradiation was applied to generate active sites on the CNTs while not inducing excessive damage to the graphitic network, and a gas-phase deposition afforded controllable grafting for thorough or regioselective functionalization. Using methyl methacrylate (MMA) as a model functional group and a CNT sponge as a model assembly, homogeneous grafting was exhibited by the increased robust hydrophobicity (contact angle increase from 30 to 140°) and improved structural stability (compressive modulus increased by 135%). Therefore, when our MMA-functionalized CNTs served as a solar absorber for saline distillation, high operating stability with a superior water evaporation rate of ∼2.6 kg m-2 h-1 was observed. Finally, to highlight the efficacy and versatility of this functionalization approach, we fabricated asymmetrically hydrophobic CNT sponges by regioselective functionalization to serve as a moisture-driven generator, which demonstrated a stable open-circuit voltage of 0.6 mV. This versatile, solvent-free approach can complement conventional solution-based techniques in the design and fabrication of multifunctional nanocarbon-based materials.

Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning.

Synthetic trade-offs exist in the synthesis of single-walled carbon nanotube (SWCNT) forests, as growing certain desired properties can often come at the expense of other desirable characteristics such as the case of crystallinity and growth efficiency. Simultaneously achieving mutually exclusive properties in the growth of SWCNT forests is a significant accomplishment, as it requires overcoming these trade-offs and balancing competing mechanisms. To address this, we trained a machine-learning regression model with a set of 585 "real" experimental synthesis data, which were taken using an automatic synthesis reactor. Subsequently, 16000 exploratory "virtual" experiments were performed by our trained model to examine potential routes toward addressing the current crystallinity-height trade-off limitation, and suggestions on growth conditions were predicted. Importantly, additional validation using "real" experimental syntheses showed good agreement with the predictions as well as a 48% increase in growth efficiency while maintaining the high crystallinity (G/D-ratio). This highlighted the effectiveness and accuracy of the predictive capability of our machine-learning model, which achieved improved results in less than 50 validation tests. Furthermore, the trained model revealed the surprising importance of the nature of the carbon feedstock, particularly the reactivity and concentration, as a route for overcoming the trade-off between the SWCNT crystallinity and growth efficiency. These results of the high-efficiency synthesis of highly crystalline SWCNT forests represent a significant advance in overcoming synthetic trade-off barriers for complex multivariable systems.

Comprehensive Characterization of Structural, Electrical, and Mechanical Properties of Carbon Nanotube Yarns Produced by Various Spinning Methods.

A comprehensive characterization of various carbon nanotube (CNT) yarns provides insight for producing high-performance CNT yarns as well as a useful guide to select the proper yarn for a specific application. Herein we systematically investigate the correlations between the physical properties of six CNT yarns produced by three spinning methods, and their structures and the properties of the constituent CNTs. The electrical conductivity increases in all yarns regardless of the spinning method as the effective length of the constituent CNTs and the density of the yarns increase. On the other hand, the tensile strength shows a much stronger dependence on the packing density of the yarns than the CNT effective length, indicating the relative importance of the interfacial interaction. The contribution of each physical parameter to the yarn properties are quantitatively analyzed by partial least square regression.

Novel Approaches to In-Situ ATR-FTIR Spectroscopy and Spectroscopic Imaging for Real-Time Simultaneous Monitoring Curing Reaction and Diffusion of the Curing Agent at Rubber Nanocomposite Surface.

Here, we propose a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy method for simultaneously monitoring the curing reaction and the diffusion behavior of curing agents at the surface of rubber in real-time. The proposed scheme was demonstrated by fluorine rubber (FKM) and FKM/carbon nanotube (CNT) nanocomposites with a target curing agent of triallyl-isocyanurate (TAIC). The broadening and the evolution of the C=O stretching of TAIC were quantitatively analyzed to characterize the reaction and the diffusion. Changes in the width of the C=O stretching indicated the reaction rate at the surface was even faster than that of the bulk as measured by a curemeter. The diffusion coefficient of the curing agent in the course of heating was newly calculated by the initial increase in the absorbance and our model based on Fickian diffusion. The diffusion coefficients of TAIC during curing were evaluated, and its temperature and filler dependency were identified. Cross-sectional ATR-FTIR imaging and in situ ATR-FTIR imaging measurements supported the hypothesis of the unidirectional diffusion of the curing agent towards the heated surface. It was shown that our method of in situ ATR-FTIR can monitor the degrees of cure and the diffusion coefficients of curing agents simultaneously, which cannot be achieved by conventional methods, e.g., rheological measurements.

Near-Infrared Spectroscopic Evaluation of the Water Content of Molded Polylactide under the Effect of Crystallization.

During melt processing, the moisture inside polylactide (PLA) easily induces hydrolysis, which deteriorates the mechanical and thermal properties of the product. The state of dryness of resin pellets must be monitored to prevent PLA hydrolysis. In this study, near-infrared (NIR) spectroscopy was applied to measure water content in PLA. In addition, the shape of the NIR spectrum is also affected by crystallization, which could lead to a reduction in the accuracy of evaluating the water content. The objective of this research is to construct a robust model for estimating the water content with varying dispersive extents of crystallization. Two methods for estimating water content measured during a drying process were conducted: the integration of absorbance and partial least squares (PLS) regression were conducted to estimate the water contents in PLA considering the effect of crystallization. The slope of the calibration line of the water content obtained from integrating absorbance varied between PLA with different crystallinities. This is due to the overlap between the NIR band of water and that of PLA crystal in the range of 5100-5400 cm-1. We found that the shape of the NIR spectrum was changed by crystallization, and the crystallinity, compared to the thickness of lamellae, was the dominant factor determining such a change of NIR spectra. The PLS model of water content constructed from only amorphous PLA showed large error of estimation in crystallized PLA. In contrast, the PLS model constructed from both amorphous and crystallized PLA estimated the water contents with lower errors. This was because latent variables obtained from both amorphous and crystallized PLA cancelled the effect of crystallization on NIR spectra.