Skip to main content

News about Scitation

In December 2016 Scitation will launch with a new design, enhanced navigation and a much improved user experience.

To ensure a smooth transition, from today, we are temporarily stopping new account registration and single article purchases. If you already have an account you can continue to use the site as normal.

For help or more information please visit our FAQs.

banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
1.F. Schweitzer, Brownian Agents and Active Particles (Springer, Berlin, 2003).
2.Janus Particle Synthesis, Self-Assembly and Applications, edited by S. Jiang and S. Granick (RSC Publishing, Cambridge, 2012);
2.A. Walther and A. H. E. Müller, Chem. Rev. 113, 5194 (2013).
3.J. Elgeti, R. G. Winkler, and G. Gompper, Rep. Progr. Phys. 78, 056601 (2015).
4.Y. Hong, D. Velegol, N. Chaturvedi, and A. Sen, Phys. Chem. Chem. Phys. 12, 1423 (2010).
5.J. R. Howse, R. A. L. Jones, A. J. Ryan, T. Gough, R. Vafabakhsh, and R. Golestanian, Phys. Rev. Lett. 99, 048102 (2007).
6.H. R. Jiang, N. Yoshinaga, and M. Sano, Phys. Rev. Lett. 105, 268302 (2010).
7.F. Peruani and L. G. Morelli, Phys. Rev. Lett. 99, 010602 (2007).
8.R. Golestanian, T. B. Liverpool, and A. Adjari, Phys. Rev. Lett. 94, 220801 (2005).
9.R. Golestanian, Phys. Rev. Lett. 102, 188305 (2009).
10.P. K. Ghosh, V. R. Misko, F. Marchesoni, and F. Nori, Phys. Rev. Lett. 110, 268301 (2013).
11.H. Risken, The Fokker–Planck Equation (Springer, Berlin, 1986).
12.For a minireview see, X. Ao, P. K. Ghosh, Y. Li, G. Schmid, P. Hänggi, and F. Marchesoni, Eur. Phys. J.: Spec. Top. 223, 3227 (2014).
13.See, e.g., G. Volpe, I. Buttinoni, D. Vogt, H.-J. Kümmerer, and C. Bechinger, Soft Matter 7, 8810 (2011).
14.F. Lugli, E. Brini, and F. Zerbetto, J. Phys. Chem. C 116, 592 (2012).
15.M. Mijalkov and G. Volpe, Soft Matter 9, 6376 (2013).
16.F. Kümmel, B. ten Hagen, R. Wittkowski, I. Buttinoni, R. Eichhorn, G. Volpe, H. Löwen, and C. Bechinger, Phys. Rev. Lett. 110, 198302 (2013).
17.P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations (Springer, 1992).
18.G. Costantini and F. Marchesoni, EPL 48, 491 (1999).
19.D. Takagi, A. B. Braunschweig, J. Zhang, and M. J. Shelley, Phys. Rev. Lett. 110, 038301 (2013).

Data & Media loading...


Article metrics loading...



A self-propelled artificial microswimmer is often modeled as a ballistic Brownian particle moving with constant speed aligned along one of its axis, but changing direction due to random collisions with the environment. Similarly to thermal noise, its angular randomization is described as a memoryless stochastic process. Here, we speculate that finite-time correlations in the orientational dynamics can affect the swimmer’s diffusivity. To this purpose, we propose and solve two alternative models. In the first one, we simply assume that the environmental fluctuations governing the swimmer’s propulsion are exponentially correlated in time, whereas in the second one, we account for possible damped fluctuations of the propulsion velocity around the swimmer’s axis. The corresponding swimmer’s diffusion constants are predicted to get, respectively, enhanced or suppressed upon increasing the model memory time. Possible consequences of this effect on the interpretation of the experimental data are discussed.


Full text loading...


Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd