activity
20222024
most citedCorrelation between ordering and shear thinning in confined liquids

8 citations · 20 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.soft2024

Novel stabilization mechanisms for concentrated emulsions with tunable morphology via amphiphilic polymer-grafted nanoparticles

Kojiro Suzuki, Yusei Kobayashi, Takashi Yamazaki +2

This study explores the stabilization mechanisms of concentrated emulsions with tunable morphology using amphiphilic polymer-grafted nanoparticles (PGNPs). We employ coarse-grained…

cond-mat.soft2024★ 4 cited

Structure and dynamics of amphiphilic patchy cubes in a nanoslit under shear

Takahiro Ikeda, Yusei Kobayashi, Masashi Yamakawa

Patchy nanocubes are intriguing materials with simple shapes and space-filling and multidirectional bonding properties. Previous studies have revealed various mesoscopic structures…

cond-mat.soft2024★ 7 cited

Thermal transport of confined water molecules in quasi-one-dimensional nanotubes

Shun Imamura, Yusei Kobayashi, Eiji Yamamoto

Dimensions and molecular structure play pivotal roles in the principle of heat conduction. The dimensional characteristics of solution within nanoscale systems depend on the degree…

cond-mat.soft2023

Structure Formation of Amphiphilic Nanocubes at Rest and Under Shear

Takahiro Yokoyama, Yusei Kobayashi, Noriyoshi Arai +1

We investigate the self-assembly of amphiphilic nanocubes under rest and shear using molecular dynamics (MD) simulations and kinetic Monte Carlo (KMC) calculations. These particles…

cond-mat.soft2022★ 8 cited

Correlation between ordering and shear thinning in confined liquids

Yusei Kobayashi, Noriyoshi Arai, Kenji Yasuoka

Despite the extensive research that has been conducted for decades on the behavior of confined liquids, detailed knowledge of this phenomenon, particularly in the mixed/boundary lu…

physics.chem-ph2022★ 1 cited

Prediction of transport property via machine learning molecular movements

Ikki Yasuda, Yusei Kobayashi, Katsuhiro Endo +5

Molecular dynamics (MD) simulations are increasingly being combined with machine learning (ML) to predict material properties. The molecular configurations obtained from MD are rep…