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To meet the need for more adaptable and expedient approaches in research and manufacturing, we present a continuous autonomous system that leverages real-time, in situ characterization and an active-learning-based decision-making processor. This system was applied to a plastic film forming process to demonstrate its capability in autonomously determining process conditions for specified film dimensions without human intervention. Application of the system to nine film dimensions (width and thickness) highlighted its ability to explore the search space and identify appropriate and stable process conditions, with an average of 11 characterization-adjustment iterations and a processing time of 19 minutes per width, thickness combination. The system successfully avoided common pitfalls, such as repetitive over-correction, and demonstrated high accuracy, with R 2 values of 0.87 and 0.90 for film width and thickness, respectively. Moreover, the active learning algorithm enabled the system to begin exploration with zero training data, effectively addressing the complex and interdependent relationships between control factors (material supply rate, applied force, material viscosity) in the continuous plastic forming process. Given that the core concept of this autonomous process can, in principle, be transferred to other continuous material processing systems, these results have implications for accelerating progress in both research and industry.

Achieving the thermal conductivity required for efficient heat management in semiconductors and other devices requires the integration of thermally conductive ceramic fillers at concentrations of 60 vol% or higher. However, an increased filler content often negatively affects the mechanical properties of the composite matrix, limiting its practical applicability. To address this issue, in this paper, we present a new strategy to reduce the required ceramic filler content: the use of a thermally conductive ceramic composite filler with carbon nanotubes (CNTs) grown on aluminum nitride (AlN). We combined catalyst coating technology with vacuum filtration to ensure that the catalyst was uniformly applied to micrometer-sized AlN particles, followed by the efficient and uniform synthesis of CNTs using a water-assisted process in a vertical furnace. By carefully controlling the number of vacuum filtration cycles and the growth time of the CNTs, we achieved precise control over the number and length of the CNT layers, thereby adjusting the properties of the composite to the intended specifications. When AlN/CNT hybrid fillers are incorporated into silicone rubber, while maintaining the mechanical properties of rubber, the thermal diffusivity achieved at reduced filler levels exceeds that of composites using AlN-only or simultaneous AlN and CNTs formulations. This demonstrates the critical influence of CNTs on AlN surfaces. Our study represents a significant advancement in the design of thermally conductive materials, with potential implications for a wide range of applications.

Dispersion represents a central processing method in the organization of nanomaterials; however, the strong interparticle interaction represents a significant obstacle to fabricating homogeneous and stable dispersions. While dispersants can greatly assist in overcoming this obstacle, the appropriate type is dependent on such factors as nanomaterial, solvent, experimental conditions, etc., and there is no general guide to assist in the selection from the vast number of possibilities. We report a strategy and successful demonstration of the machine-learning-based "Dispersant Explorer", which surveys and identifies suitable dispersants from open databases. Through the combined use of experimental and molecular descriptors derived from SMILES databases, the model showed exceptional predictive accuracy in surveying about ∼1000 chemical compounds and identifying those that could be applied as dispersants. Furthermore, fabrication of transparent conducting films using the predicted and previously unknown dispersant exhibited the highest sheet resistance and transmittance compared with those of other reported undoped films. This result highlights that, in addition to opening new avenues for novel dispersant discovery, machine learning has a potential to elucidate the chemical structures essential for optimal dispersion performance to assist in the advancement of the complex topic of nanomaterial processing.

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.

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.

Extreme environments represent numerous harsh environmental conditions, such as temperature, pressure, corrosion, and radiation. The tolerance of applications in extreme environments exemplifies significant challenges to both materials and their structures. Given the superior mechanical strength, electrical conductivity, thermal stability, and chemical stability of nanocarbon materials, such as carbon nanotubes (CNTs) and graphene, they are widely investigated as base materials for extreme environmental applications and have shown numerous breakthroughs in the fields of wide-temperature structural-material construction, low-temperature energy storage, underwater sensing, and electronics operated at high temperatures. Here, the critical aspects of structural design and fabrication of nanocarbon materials for extreme environments are reviewed, including a description of the underlying mechanism supporting the performance of nanocarbon materials against extreme environments, the principles of structural design of nanocarbon materials for the optimization of extreme environmental performances, and the fabrication processes developed for the realization of specific extreme environmental applications. Finally, perspectives on how CNTs and graphene can further contribute to the development of extreme environmental applications are presented., (© 2022 Wiley-VCH GmbH.)

Metallic iron (Fe) represents an exceptionally active catalyst, as shown in its use in the Haber-Bosch process to dissociate nitrogen molecules; however, the ease of corrosion by oxidation limits its usage. Hence, in most applications using metallic Fe catalysts, hydrogen is a necessary reactant. We report a novel hydrogen-free approach to fabricating reduced, highly active, and corrosion-resistive Fe-based catalysts using trace levels of noble metals (NMs) such as Ir, Rh, and Pt confined in the nanoparticle (NP). X-ray photoelectron spectroscopy (XPS) revealed that as little as ∼0.3 atom % was sufficient to induce the reduction of Fe. Extensive XPS analysis showed that the reduced NM atoms segregated to the NP surface and reduced the surrounding Fe atoms. We demonstrated the catalytic activity of the nanoparticles by the efficient synthesis of submillimeter tall, vertically aligned, and mainly double-walled carbon nanotube arrays using a completely hydrogen-free chemical vapor deposition process.

Carbon nanotubes have a helical structure wherein the chirality determines whether they are metallic or semiconducting. Using in situ transmission electron microscopy, we applied heating and mechanical strain to alter the local chirality and thereby control the electronic properties of individual single-wall carbon nanotubes. A transition trend toward a larger chiral angle region was observed and explained in terms of orientation-dependent dislocation formation energy. A controlled metal-to-semiconductor transition was realized to create nanotube transistors with a semiconducting nanotube channel covalently bonded between a metallic nanotube source and drain. Additionally, quantum transport at room temperature was demonstrated for the fabricated nanotube transistors with a channel length as short as 2.8 nanometers.

We present a study quantitatively demonstrating that the method of synthesis (gas phase, fixed bed, non-fixed bed) represents a determining factor in the level of crystallinity in growing single wall carbon nanotubes (SWCNTs). Using far infrared spectroscopy, the "effective length" (associated with the level of crystallinity) was estimated for CNTs grown using various synthetic methods (lab-produced and supplemented by commercially purchased SWCNTs) as a metric for crystallinity (i.e., defect density). Analysis of the observed "effective lengths" showed that the SWCNTs fell into two general groups: long and short (high and low crystallinity) synthesized by gas-phase methods and all other supported catalyst methods, respectively. Importantly, the "long" group exhibited effective lengths in the range of 700-2200 nm, which was greater than double that of the typical values representing the "short" group (110-490 nm). These results highlight the significant difference in crystallinity. We interpret that the difference in the crystallinity stemmed from stress concentration at the nanotube-catalyst interface during the growth process, which originated from various sources of mismatch in growth rates (e.g., vertically aligned array) as well as impact stress from contact with other substrates during fluidization or rotation. These results are consistent with well-accepted belief, but now are demonstrated quantitatively.