It is essentially important for understanding of viscoelastic properties of polymers to use “model polymer” with well-defined molecular structure. In order to synthesize the model polymers, one of the precise polymerization techniques, living anionic polymerization, was used for all the polymers in this study. Preparation, characterization, and viscoelastic properties of several model polymers such as highly-purified ring polymers, ring-based polymers, ring block copolymers, and a series of poly(4-n-alkylstyrene)s are introduced and the molecular architecture-rheology relationships for the model polymers are investigated and discussed.

We have developed schemes for synthesizing polystyrene-b-polyisoprene-b-polystyrene (SIS) with non-covalent bonding groups via the post-polymerization chemical modification that is an industrially-friendly scheme. SIS was first reacted with maleic anhydride in a diluent, followed by reacting with alkylamine. By neutralizing the carboxy group with a metal alkoxide, ionically-modified SIS (i-SIS) was also prepared. i-SIS with a sodium cation (i-SIS(Na)) or a barium cation (i-SIS(Ba)) exhibited excellent tensile toughness of 480 and 230 MJ m^–3 , respectively. Drop weight impact tests also revealed that i-SIS(Ba) exhibited higher impact resistance than that of typical high-strength glass fiber-reinforced plastic. In the future, by achieving optimization of production of i-SIS in an industrial scale, i-SIS can be used for production of lightweight automobiles, resulting in contribution to achieving net-zero carbon and sustainable society.

Polymer networks, distinguished by their crosslinking structures—which can be either stable or transient—display prominent rheological properties. Nevertheless, the inability to control these network structures obstructs a fundamental understanding of the molecular mechanisms and their subsequent applications. Recently, I have conceptualized model transient networks formed of star-shaped polymers, utilizing reversible interactions. In this review, I will elucidate our most recent studies, emphasizing the relationships between the viscoelasticity and the microscopic molecular dynamics, encompassing diffusivity and binding/dissociation kinetics. Moreover, I will highlight a medical application of this knowledge: a carrier material meticulously designed for the adeno-associated virus vector.

We develop an improved multi-scale simulation method for polymer melt spinning processes by replacing the microscopic simulator with a machine-learned constitutive relation (MLCR). For the MLCR method, the estimation of stress responses for shear deformations has previously been validated. In this study, we apply this method to uniaxial elongational deformations, as a necessary step towards predicting multi-deformation mode flows. Applied to the KGCG model, the MLCR method has a higher computational efficiency and a degree of accuracy, we expect to enhance the accuracy of the predictions for the KGCG model, by using more training data to learn the constitutive relations. The high computational efficiency and a degree of accuracy of the MLCR method has helped us to simulate complex calculation conditions more efficiently, e.g., varying the apparent Reynolds numbers. The MLCR method enables us to analyze the quantitative characteristics of the flows of the KGCG model for a polymer melt spinning application more efficiently. It is an important step to be able to handle industrially relevant applications of melt spinning, e.g., the analysis of flow-induced crystallization.