2022 Fiscal Year Final Research Report
Metallo-Dielectric Janus Particles as Building Blocks for Designer Active Materials
| Project/Area Number |
20K03786
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 13010:Mathematical physics and fundamental theory of condensed matter physics-related
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| Research Institution | Kyoto University |
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Principal Investigator |
Molina John 京都大学, 工学研究科, 助教 (20727581)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | Electro-Hydrodynamics / Active Particles / Electrophoresis / Janus Particles / Quincke Rollers / Machine Learning / Multi-Scale Simulations / Flow Inference |
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| Outline of Final Research Achievements |
The purpose of this work is to understand the mechanisms behind the rich single- and many-particle dynamics of active colloids. For this, we have developed a computational method that accounts for the electro-hydrodynamics and rigid-body dynamics of colloidal particles under AC/DC fields. We succeeded in reproducing the dynamics of metallo-dielectric Janus particles, including the velocity dependence on the electric field strength, as well as the velocity reversal at high frequencies, which are accompanied by strong field gradients near the particle surface. We have further investigated the collective dynamics of dielectric particles under DC fields, to reveal how the interplay between the hydrodynamics and electrostatics accounts for the different phases observed experimentally. Finally, we have developed Machine-Learning methods capable of inferring constitutive relations for complex flows, as well as solutions to Stokes flow problems.
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| Free Research Field |
Computational Soft Matter Physics
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| Academic Significance and Societal Importance of the Research Achievements |
We have developed physical models that can be used to understand the rich dynamics of active colloidal particles, which can be used as building blocks for novel materials. We have also developed Machine-Learning methods that can significantly enhance our ability to predict complex flows.
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