Published papers

Liquid-phase exfoliation is a general route to obtain two-dimensional (2D) materials. Manipulation of colloidally dispersed nanosheets including monolayers, few-layers, and multi-layers is required for their applications. The exfoliated nanosheets are used in the assembled states with coating on substrates. However, the significant factors controlling the surface morphologies of the coatings are not fully elucidated in previous reports. In the present work, the morphologies are studied by changing the coating method and thickness of the nanosheets. The thinner nanosheets of graphene oxide and clay with the high aspect ratio provide homogenous flat films exhibiting noniridescent structural color by drop casting on a substrate. On the other hand, the water-repellent rough surfaces are formed by spraying the thicker titanate nanosheets with the low aspect ratio. The morphologies of the coatings can be controlled by the thickness and aspect ratio of the nanosheets. Based on these results, the superhydrophobic surface is designed by the two-step coatings; the primary flat coating covers a glass slide and secondary coating enhances the surface roughness. These insights can be applied to achieve tailored coating of 2D materials.

Imaging of invisible mechanical stresses is a significant challenge in a variety of fields. 2D distribution imaging of compression stresses applied by irregularly shaped 3D objects is not achieved using conventional sensing materials and devices. In the present work, such a compression-stress distribution in the range of 0.1 kPa–5 MPa is imaged using 3D transparent silicone rubber containing stimuli-responsive color-changing conjugated polymer, layered polydiacetylene (PDA). Compression-responsive capsules, liquid droplets surrounded by solid particles, collapse with compression on the rubber. The outflowed interior liquid containing polyethyleneimine (PEI) oligomer is diffused into the bulk rubber through the free volume space of the rubber matrix. PEI serves as a guest for intercalation into the interlayer space of the layered PDA, i.e. chemical stress, directing the blue-to-red color change. As the red-color intensity increases with increasing applied compression stress, the strength is colorimetrically quantified. The transparent 3D device enables 2D distribution imaging of the compression stresses applied by irregularly shaped 3D objects in the millimeter to the centimeter scales. The device design can be applied to achieve 2D stress-distribution imaging in various length scales and strength ranges.

Sensing devices are fabricated using stimuli-responsive materials. In general, the responsivity is controlled by designing molecules and materials based on professional experience. If predictors are constructed for the responsivity control, the number of experiments can be reduced without consumption of time, cost, and effort. However, such dynamic properties of functional polymer materials are not easily predicted because of the small data and complex structure–function relationship. How to prepare a dataset and train small data remain significant challenges. The present work shows construction and application of a prediction model for controlling thermoresponsive color-changing properties of layered polydiacetylenes (PDAs). The responsivity was changed by the intercalated guest molecules. The training dataset was prepared from a series of the photographs representing the color at each temperature. The prediction model of the thermoresponsivity, namely color-changing temperature, was constructed by combining machine learning and our chemical insight based on the small experimental data. The thermoresponsivity of the newly synthesized layered PDAs was predicted by the model. The modeling methods can be applied to predict various dynamic properties of functional polymer materials.

Here we demonstrate the prediction of the isotropization temperatures of nanostructured ionic liquid crystals (ILCs) by a machine learning method. ILCs, which self-assemble into dynamic and well-ordered nanostructures, have been extensively studied because they can be used as various functional materials including electrolytes, water treatment membranes, and stimuli-responsive materials. In general, machine learning is not easily applied to predict dynamic functions of such self-assembled materials because of the complex structure–function relationship and small data size. Our approach is to use the machine learning method combined with our experimental experiences and chemical insights as researchers. The training data of order–disorder transition (isotropic) temperatures of 116 wedge-shaped ILCs reported by our groups or other groups have been analyzed by this method, leading to the successful construction of the prediction model with a root mean squared error value of 23.7 °C. The model is straightforward, interpretable and reasonable from the viewpoint of chemistry. Moreover, the constructed model predicted the transition temperatures of 45 wedge-shaped ILCs in the test data. The model has sufficient accuracy to predict the transition temperatures of certain kinds of ILCs, which helps the molecular design of ILCs. The transition temperature of isotropization, which is the transition from an ordered liquid-crystalline state to a random isotropic state, is the key factor for ILCs to achieve excellent properties. It is important to predict the transition temperatures before starting experiments, but this has been challenging because complex factors determine the transition temperatures. The model constructed in the present study may accelerate the development of functional ILCs.

Two-dimensional (2D) anisotropic structures have numerous advantages for designing functional materials. Layered materials accommodate guests in their interlayer space, and exfoliated nanosheets are used as units to obtain organized architectures. If exfoliated nanosheets preserving their multilayered structures are prepared, both the intercalation and building-block capabilities can be used for functionalization. In the present work, a stimuli-responsive thin-film coating with noniridescent structural color was prepared using exfoliated nanosheets with multilayered structures. The precursor layered composite of a host transition-metal oxide and a guest organic molecule was exfoliated into surface-modified nanosheets. The resultant nanosheet was not a monolayer but comprised multilayers with an interlayer space. Coating the exfoliated nanosheets provided a thin film with uneven thickness and noniridescent structural color. As additional guest molecules were introduced into the interlayer space of the multilayers in the nanosheet, an increase in the film thickness induced changes in the structural color. Harmful biogenic amines and their mixtures were colorimetrically quantified using the prepared thin-film sensor. The thin-film coating of the multilayered nanosheets has potential for a variety of sensing applications.