Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals

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

Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals

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

Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals

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

Unexpected Efficient Synthesis of Millimeter-Scale Single-Wall Carbon Nanotube Forests Using a Sputtered MgO Catalyst Underlayer Enabled by a Simple Treatment Process

An unexpected 5000% increase in growth efficiency and high (95%) single-wall selectivity synthesis of vertically aligned carbon nanotubes (CNTs) was shown from Fe catalysts supported on a sputtered MgO underlayer from a simple underlayer treatment, i.e., annealing treatment. In this way, millimeter-scale single-wall carbon nanotube “forests” could be synthesized in a 10 min time, which has never been previously reported for MgO catalyst underlayer or any underlayer besides Al₂O₃. This level of efficiency and characterized SWCNT properties were similar to those grown using Al₂O₃ underlayers. Spectroscopic and microscopic analyses revealed that the treatment improved stability of the catalyst nanoparticle array by the suppressing catalyst subsurface diffusion and retaining the metallic state of the surface Fe atoms. Taken together, these results reveal a new route in achieving highly efficient SWCNT synthesis

Tailoring Temperature Invariant Viscoelasticity of Carbon Nanotube Material

Using carbon nanotubes (CNTs) as building blocks, we fabricated a viscoelastic material. In contrast to existing conventional materials where the stiffness (storage modulus) increases when the viscosity (damping ratio) decreases, both of these two aspects could be simultaneously improved for the viscoelastic CNT material. This allows fabricating both strong and highly viscous materials. This unique phenomenon was explained by a zipping and unzipping of carbon nanotubes at contacts as the origin of viscoelasticity

Alignment Control of Carbon Nanotube Forest from Random to Nearly Perfectly Aligned by Utilizing the Crowding Effect

Alignment represents an important structural parameter of carbon nanotubes (CNTs) owing to their exceptionally high aspect ratio, one-dimensional property. In this paper, we demonstrate a general approach to control the alignment of few-walled CNT forests from nearly random to nearly ideally aligned by tailoring the density of active catalysts at the catalyst formation stage, which can be experimentally achieved by controlling the CNT forest mass density. Experimentally, we found that the catalyst density and the degree of alignment were inseparably linked because of a crowding effect from neighboring CNTs, that is, the increasing confinement of CNTs with increased density. Therefore, the CNT density governed the degree of alignment, which increased monotonically with the density. This relationship, in turn, allowed the precise control of the alignment through control of the mass density. To understand this behavior further, we developed a simple, first-order model based on the flexural modulus of the CNTs that could quantitatively describe the relationship between the degree of alignment (HOF) and carbon nanotube spacing (crowding effect) of any type of CNTs

Mutual Exclusivity in the Synthesis of High Crystallinity and High Yield Single-Walled Carbon Nanotubes

We report the mutually exclusive relationship between carbon nanotube (CNT) yield and crystallinity. Growth conditions were optimized for CNT growth yield and crystallinity through sequential tuning of three input variables: growth enhancer level, growth temperature, and carbon feedstock level. This optimization revealed that, regardless of the variety of carbon feedstock and growth enhancer, the optimum conditions for yield and crystallinity differed significantly with yield/crystallinity, preferring lower/higher growth temperatures and higher/lower carbon feedstock levels. This mutual exclusivity stemmed from the inherent limiting mechanisms for each property

Ion Diffusion and Electrochemical Capacitance in Aligned and Packed Single-Walled Carbon Nanotubes

Direct measurement of ion diffusion in aligned, densified single-walled carbon nanotube electrodes showed that the diffusion coefficient for transport of ions (KSCN in acetonitrile) parallel to the alignment direction of the nanotubes was close to the theoretical limit of perfectly straight pores, achieving a value 20 times larger than that of activated carbon electrodes (1 × 10−5 vs 5 × 10−7 cm2/s). In contrast, the diffusion coefficient for ion transport perpendicular to the alignment direction was an order of magnitude smaller (8 × 10−7 cm2/s). As an example of the ramifications of this anisotropic diffusion phenomenon, the difference in performance of the aligned carbon nanotubes as electrochemical-capacitor electrodes was evaluated. At low discharge rates, the performances of the two orientations were identical, but as the discharge rate was increased, a more rapid decline in capacitance was observed for the perpendicular orientation (66 vs 14% decline in capacitance when the discharge current was increased from 0.01 to 1 A/g). Furthermore, the maximum power rating of the perpendicular electrode was lower than that of the parallel electrode (1.85 vs 3 kW/kg during operation at 1 V)

Gas Dwell Time Control for Rapid and Long Lifetime Growth of Single-Walled Carbon Nanotube Forests

The heat history (i.e., “dwell time”) of the carbon source gas was demonstrated as a vital parameter for very rapid single-walled carbon nanotube (SWNT) forest growth with long lifetime. When the dwell time was raised to 7 s from the 4 s used for standard growth, the growth rate increased to 620 μm/min: a benchmark for SWNT forest growth on substrates. Importantly, the increase in growth rate was achieved without decreasing either the growth lifetime or the quality of the SWNTs. We interpret that the conversion rate of the carbon feedstock into CNTs was selectively increased (versus catalyst deactivation) by delivering a thermally decomposed carbon source with the optimum thermal history to the catalyst site

A Hydrogen-Free Approach for Activating an Fe Catalyst Using Trace Amounts of Noble Metals and Confinement into Nanoparticles

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