Efficient Syntheses of 2D Materials from Soft Layered Composites Guided by Yield Prediction Model: Potential of Experiment‐Oriented Materials Informatics

Machine-learning-assisted prediction of the size of microgels prepared by aqueous precipitation polymerization

The size of soft colloids (microgels) is essential; however, control over their size has typically been established empirically. Herein, we report a linear-regression model that can predict microgel size using a machine learning method, sparse modeling for small data, which enables the determination of the synthesis conditions for target-sized microgels.

Morphology and size control of an amorphous conjugated polymer network containing quinone and pyrrole moieties via precipitation polymerization

Morphology and size control of insoluble and infusible conjugated polymers are significant for their applications. Development of a precipitation polymerization route without using a surface stabilizer is preferred to control the reaction, morphology, and size. In the present work, precipitation polymerization for an amorphous conjugated polymer network, a new type of polymerized structure containing functional units, was studied for the size and morphology control in the solution phase at low temperature. The random copolymerization of benzoquinone (BQ) and pyrrole (Py) monomers formed microspheres of the BQ-Py network polymers as the precipitates in the solution phase. The particle diameter was controlled in the range of 70 nm and 1 μm by changing the pH of the solution and concentration of the monomers. The resultant nanoparticles were applied to a metal-free electrocatalyst for the hydrogen evolution reaction (HER). The catalytic activity of the BQ-Py nanoparticles was higher than that of the bulk micrometer-sized particles. The results imply that the morphology and size of amorphous conjugated polymer networks can be controlled by precipitation polymerization.

Selective Syntheses of Thick and Thin Nanosheets Based on Correlation between Thickness and Lateral-Size Distribution

Exfoliation of layered materials, a typical route to obtain 2D materials, is not easily controlled because of the unpredictable downsizing processes. In particular, the thickness control remains as a complex challenge. Here, we found a correlation between the thickness and lateral size distribution of the exfoliated nanosheets, such as transition metal oxides and graphene oxide. The layered composites of the host metal oxides and interlayer organic guests are delaminated into the surface-modified nanosheets in organic dispersion media. The exfoliation behavior varies by combination of the hosts, guests, and dispersion media. Here, we found that the thick and thin nanosheets were obtained on the monodispersed and polydispersed conditions, respectively. The selective syntheses of the thick and thin nanosheets were achieved using a prediction model of the lateral size distribution. The correlation between the thickness and lateral size distribution can be applied to thickness-selective syntheses of 2D materials.

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Surface-modified nanosheets are obtained by exfoliation of layered composites

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Thickness of 2D materials has a correlation with the lateral size distribution

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Thick and thin nanosheets are selectively synthesized under the predicted conditions

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A prediction model of lateral size distribution is applied to the selective syntheses

Nanoarchitectonics for conductive polymers using solid and vapor phases

Conductive polymers have been extensively studied as functional organic materials due to their broad range of applications. Conductive polymers, such as polypyrrole, polythiophene, and their derivatives, are typically obtained as coatings and precipitates in the solution phase. Nanoarchitectonics for conductive polymers requires new methods including syntheses and morphology control. For example, nanoarchitectonics is achieved by liquid-phase syntheses with the assistance of templates, such as macromolecules and porous materials. This minireview summarizes the other new synthetic methods using the solid and vapor phases for nanoarchitectonics. In general, the monomers and related species are supplied from the solution phase. Our group has studied polymerization of heteroaromatic monomers using the solid and vapor phases. The surface and inside of solid crystals were used for the polymerization with the diffusion of the heteroaromatic monomer vapor. Our nanoarchitectonics affords to form homogeneous coatings, hierarchical structures, composites, and copolymers for energy-related applications. The concepts using solid and vapor phases can be applied to nanoarchitectonics for not only conductive polymers but also other polymers toward a variety of applications.

Yield-prediction models for efficient exfoliation of soft layered materials into nanosheets

Yield-prediction models were studied for efficient exfoliation of soft layered materials stacked via van der Waals interactions with the assistance of machine learning on small experimental data. High-yield exfoliation of graphite and layered organic polymer was achieved under the conditions guided by the models in a limited number of experiments.

Lateral-size control of exfoliated transition-metal-oxide 2D materials by machine learning on small data

A wide variety of nanosheets including monolayers and few-layers have attracted much interest as two-dimensional (2D) materials for the unique anisotropic structures and properties. On the other hand, one of the significant remaining and challenging issues is the lateral-size control. Since 2D materials are generally synthesized by the exfoliation of layered materials, the lateral size is not easily controlled in the breaking-down processes. The experimental factors have not been found for the control and prediction. In the present work, lateral sizes of the exfoliated transition-metal-oxide nanosheets were predicted and controlled by the assistance of machine learning. Layered composites of host inorganic layers and guest organic molecules were exfoliated into nanosheets in organic dispersion media. The lateral size of the nanosheets was estimated by dynamic light scattering (DLS), instead of microscopy methods, to achieve high-throughput analyses. Factors governing the lateral size are explored on the small experimental data by the assistance of sparse modeling, a method of machine learning. The eight physicochemical parameters of the organic guests and dispersion media were extracted by sparse modeling for the construction of the size-prediction model. The size-prediction model accelerated the selective syntheses of the nanosheets with large and small lateral sizes in a limited number of experiments. The results indicate that the prediction model is a guideline to find suitable exfoliation conditions for size control. Size-selective syntheses of a variety of 2D materials can be achieved by similar methods using sparse modeling on small experimental data. Moreover, sparse modeling is applicable to control the design and exploration of other materials and their processing based on small data.

Intercalation and flexibility chemistries of soft layered materials

Layered materials, alternate stackings of two or more components, are found in a wide range of scales. Chemists can design and synthesize layered structures containing functional units. The soft-type layered materials exhibit characteristic dynamic functions originating from two-dimensional (2D) anisotropy and structure flexibility. This feature article focuses on "intercalation" and "flexibility" as two new perspectives for designing soft layered materials. Intercalation of guests is a characteristic approach for design of layered structures. Flexibility is an important factor to control the dynamic functions of the layered structures. As a model case, the intercalation-induced tunable stimuli-responsive color-change properties of layered polydiacetylene (PDA) are introduced to study the impact of the intercalation and flexibility on the dynamic functions. Recently, layered materials have drastically expanded the research area from conventional rigid inorganic compounds to new self-assembled nanostructures consisting of organic components, such as polymers, metal-organic frameworks, and covalent-organic frameworks. These new layered architectures have potentials for exhibiting dynamic functions originating from the structure flexibility beyond the static properties originating from classical intercalation and host-guest chemistries. Therefore, intercalation and flexibility chemistries of soft layered materials are regarded as new perspectives for design of advanced dynamic functional materials.