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Direct numerical simulation of the self-propelled Janus particle: use of grid-refined fluctuating lattice Boltzmann method

  • Li Chen
  • Chenyu Mo
  • Lihong Wang
  • Haihang CuiEmail author
Research Paper
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Part of the following topical collections:
  1. 2018 International Conference of Microfluidics, Nanofluidics and Lab-on-a-Chip, Beijing, China

Abstract

The study of the microscopic propelling mechanism of anisotropic Janus particles often involves the analysis of motion at multi-time scale. In this paper, an efficient method is developed for directly simulating the motion of non-equilibrium particles to resolve the issue of requiring large computational resources to simulate the complex multi-time scale motion of Janus particles. We combined fluctuating lattice Boltzmann method and grid-refinement technology to simulate spherical and cylindrical Janus particles, and compared their motion characteristics with experimental results at multi-time scales, which proved that the method is efficient and feasible. This simulation method can also be applied to more complex active particle motion systems.

Keywords

Self-propelled motion Brownian motion Janus particle Anisotropy particle Fluctuating lattice Boltzmann method Grid-refined technique 

Notes

Acknowledgements

This study is supported by the National Natural Science Foundation of China (11602187 and 11447133), the Natural Science Basic Research Plan in Shaanxi Province of China (2016JQ1008 and 2018JM1029).

References

  1. Aidun CK, Clausen JR (2010) Lattice-Boltzmann method for complex flows. Annu Rev Fluid Mech 42(1):439–472MathSciNetzbMATHGoogle Scholar
  2. Aidun CK, Lu Y (1995) Lattice Boltzmann simulation of solid particles suspended in fluid. J Stat Phys 81(1):49–61zbMATHGoogle Scholar
  3. And JFB, Bossis G (1988) Stokesian dynamics. Annu Rev Fluid Mech 20(1):111–157Google Scholar
  4. Bechinger C, Di Leonardo R et al (2016) Active particles in complex and crowded environments. Rev Mod Phys 88(4):045006MathSciNetGoogle Scholar
  5. Bialk E, Speck T et al (2015) Active colloidal suspensions: clustering and phase behavior. J Non-Cryst Solids 407:367–375Google Scholar
  6. Brady JF (2011) Particle motion driven by solute gradients with application to autonomous motion: continuum and colloidal perspectives. J Fluid Mech 667:216–259MathSciNetzbMATHGoogle Scholar
  7. Chen S, Chen H et al (1991) Lattice Boltzmann model for simulation of magnetohydrodynamics. Phys Rev Lett 67(27):3776–3779Google Scholar
  8. Chen L, Yu Y et al (2014) A comparative study of lattice Boltzmann methods using bounce-back schemes and immersed boundary ones for flow acoustic problems. Int J Numer Meth Fluids 74(6):439–467MathSciNetGoogle Scholar
  9. Chen L, Zhu H et al (2017) A study of the Brownian motion of the non-spherical microparticles on fluctuating lattice Boltzmann method. Microfluid Nanofluid 21(3):54Google Scholar
  10. Córdova-Figueroa UM, Brady JF (2008) Osmotic propulsion: the osmotic motor. Phys Rev Lett 100(15):158303Google Scholar
  11. Córdovafigueroa UM, Brady JF et al (2013) Osmotic propulsion of colloidal particles via constant surface flux. Soft Matter 9(28):6382–6390Google Scholar
  12. Cui HH, Tan XJ et al (2015) Experiment and numerical study on the characteristics of self-propellant Janus microspheres near the wall. Acta Phys Sin 64(13):134705Google Scholar
  13. Dabiri GA, Sanger JM et al (1990) Listeria monocytogenes moves rapidly through the host-cell cytoplasm by inducing directional actin assembly. Proc Natl Acad Sci 87(16):6068–6072Google Scholar
  14. Eitel-Amor G, Meinke M et al (2013) A lattice-Boltzmann method with hierarchically refined meshes. Comput Fluids 75(Supplement C):127–139zbMATHGoogle Scholar
  15. Elgeti J, Winkler RG et al (2015) Physics of microswimmers—single particle motion and collective behavior: a review. Rep Prog Phys 78(5):056601MathSciNetGoogle Scholar
  16. Ermak DL, Mccammon JA (1978) Brownian dynamics with hydrodynamic interactions. Journal of Chemical Physics 69(69):1352–1360Google Scholar
  17. Fakhari A, Geier M et al (2016) A mass-conserving lattice Boltzmann method with dynamic grid refinement for immiscible two-phase flows. J Comput Phys 315:434–457MathSciNetzbMATHGoogle Scholar
  18. Fei LI, Zhang HY et al (2014) Study on fractional Brownian motion of self-propelled Janus microspheres. Appl Math Mech 35(6):663–673Google Scholar
  19. Felix K, Borge TH et al (2013) Circular motion of asymmetric self-propelling particles. Phys Rev Lett 110(19):198302Google Scholar
  20. Feng ZG, Michaelides EE (2004) The immersed boundary-lattice Boltzmann method for solving fluid-particles interaction problems. J Comput Phys 195(2):602–628zbMATHGoogle Scholar
  21. Geier M, Greiner A et al (2009) Bubble functions for the lattice Boltzmann method and their application to grid refinement. Eur Phys J Spec Top 171(1):173–179Google Scholar
  22. Gendre F, Ricot D et al (2017) Grid refinement for aeroacoustics in the lattice Boltzmann method: a directional splitting approach. Phys Rev E 96(2–1):023311Google Scholar
  23. Ghosh PK, Misko VR et al (2013) Self-propelled Janus particles in a ratchet: numerical simulations. Phys Rev Lett 110(26):268301Google Scholar
  24. Granick S, Jiang S et al (2009) Janus particles. Phys Today 62:68–69Google Scholar
  25. Hasert M, Masilamani K et al (2014) Complex fluid simulations with the parallel tree-based Lattice Boltzmann solver Musubi. J Comput Sci 5(5):784–794MathSciNetGoogle Scholar
  26. Henry S, Anurag T et al (2013) Active ciliated surfaces expel model swimmers. Langmuir 29(41):12770–12776Google Scholar
  27. Howse JR, Jones RA et al (2007) Self-motile colloidal particles: from directed propulsion to random walk. Phys Rev Lett 99(4):048102Google Scholar
  28. Ke H, Ye S et al (2010) Motion analysis of self-propelled Pt–silica particles in hydrogen peroxide solutions. The Journal of Physical Chemistry A 114(17):5462–5467Google Scholar
  29. Kline TR, Paxton WF et al (2005) Catalytic nanomotors: remote-controlled autonomous movement of striped metallic nanorods. Cheminform 117(5):744–746Google Scholar
  30. Koelman J (1991) A simple lattice Boltzmann scheme for Navier–Stokes fluid flow. EPL (Europhysics Letters) 15(6):603Google Scholar
  31. Ladd AJ (1993) Short-time motion of colloidal particles: numerical simulation via a fluctuating lattice-Boltzmann equation. Phys Rev Lett 70(9):1339Google Scholar
  32. Ladd AJC (1994) Numerical simulations of particulate suspensions via a discretized Boltzmann equation. Part 1. Theoretical foundation. J Fluid Mech 271:285–309MathSciNetzbMATHGoogle Scholar
  33. Lallemand P, Luo LS (2003) Lattice Boltzmann method for moving boundaries. J Comput Phys 184(2):406–421MathSciNetzbMATHGoogle Scholar
  34. Lee KJ, Yoon J et al (2011) Recent advances with anisotropic particles. Curr Opin Colloid Interface Sci 16(3):195–202Google Scholar
  35. Liu Z, Song A et al (2014) Parallel algorithms for multi-grid lattice Boltzmann method. J Comput Appl 34(11):3065–3068+3072Google Scholar
  36. Maestre MANG, Fantoni R et al (2013) Janus fluid with fixed patch orientations: theory and simulations. J Chem Phys 138(9):094904Google Scholar
  37. Nie D, Lin J (2009) A fluctuating lattice-Boltzmann model for direct numerical simulation of particle Brownian motion. Particuology 7(6):501–506Google Scholar
  38. Orozco J, García-Gradilla V et al (2012) Artificial enzyme-powered microfish for water-quality testing. ACS Nano 7(1):818–824Google Scholar
  39. Patra D, Sengupta S et al (2013) Intelligent, self-powered, drug delivery systems. Nanoscale 5(4):1273–1283Google Scholar
  40. Paxton WF, Kistler KC et al (2004) Catalytic Nanomotors: autonomous Movement of Striped Nanorods. J Am Chem Soc 126(41):13424Google Scholar
  41. Peskin CS (1977) Numerical analysis of blood flow in the heart. J Comput Phys 25(3):220–252MathSciNetzbMATHGoogle Scholar
  42. Pethig R (2010) Dielectrophoresis: status of the theory, technology, and applications. Biomicrofluidics 4(2):39901Google Scholar
  43. Qian YH, D’Humieres D et al (1992) Lattice BGK models for Navier–Stokes equation. EPL (Europhys Lett) 17:479–484zbMATHGoogle Scholar
  44. Ronald P (2010) Review article-dielectrophoresis: status of the theory, technology, and applications. Biomicrofluidics 4(3):39901Google Scholar
  45. Rosenthal G (2012) Theory and computer simulations of amphiphilic Janus particles. Universitatsbibliothek der Technischen Universitat, BerlinGoogle Scholar
  46. Ruckner G, Kapral R (2007) Chemically powered nanodimers. Phys Rev Lett 98(15):150603Google Scholar
  47. Satoh A (2012) On the method of activating Brownian motion for application of the lattice Boltzmann method to magnetic particle dispersions. Mol Phys 110(1):1–15MathSciNetGoogle Scholar
  48. Sch Nherr M, Kucher K et al (2011) Multi-thread implementations of the lattice Boltzmann method on non-uniform grids for CPUs and GPUs. Comput Math Appl 61(12):3730–3743Google Scholar
  49. Sciortino F, Giacometti A et al (2010) A numerical study of one-patch colloidal particles: from square-well to Janus. Phys Chem Chem Phys 12(38):11869–11877Google Scholar
  50. Shen Z, Würger A et al (2018) Hydrodynamic interaction of a self-propelling particle with a wall : comparison between an active Janus particle and a squirmer model. Eur Phys J E 41(3):39Google Scholar
  51. Solovev AA, Xi W et al (2012) Self-propelled nanotools. ACS Nano 6(2):1751–1756Google Scholar
  52. Soto R, Golestanian R (2015) Self-assembly of active colloidal molecules with dynamic function. Phys Rev E 91(5):052304Google Scholar
  53. Ten Hagen B, van Teeffelen S et al (2011) Brownian motion of a self-propelled particle. J Phys: Condens Matter 23(19):194119Google Scholar
  54. Ten HB, Wittkowski R et al (2015) Can the self-propulsion of anisotropic microswimmers be described by using forces and torques? J Phys: Condens Matter 27(19):194110–194119Google Scholar
  55. Vutukuri HR, Bet B et al (2017) Rational design and dynamics of self-propelled colloidal bead chains: from rotators to flagella. Sci Rep 7(1):16758Google Scholar
  56. Walther A, Müller AH (2013) Janus particles: synthesis, self-assembly, physical properties, and applications. ACS Publ 113:5194–5261Google Scholar
  57. Wang L, Chen L et al (2018) Efficient propulsion and hovering of bubble-driven hollow micromotors underneath an air–liquid interface. Langmuir 34(35):10426–10433Google Scholar
  58. Wu M, Zheng X et al (2014) Experiment research on the effective diffusion coefficient of Janus particles. Chin J Hydrodyn 29(3):274–281MathSciNetGoogle Scholar
  59. Zheng X, Ten Hagen B et al (2013) Non-Gaussian statistics for the motion of self-propelled Janus particles: experiment versus theory. Phys Rev E 88(3):032304Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Li Chen
    • 1
    • 2
  • Chenyu Mo
    • 1
  • Lihong Wang
    • 1
  • Haihang Cui
    • 1
    • 2
    Email author
  1. 1.School of Building Services Science and EngineeringXi’an University of Architecture and TechnologyXi’anPeople’s Republic of China
  2. 2.Institue of Mechanics and TechnologyXi’an University of Architecture and TechnologyXi’anPeople’s Republic of China

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