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Thermophoresis refers to the directed motion of colloidal particles in the presence of a temperature gradient, which can occur towards cold (thermophobic colloids) or to the warm areas (thermophilic colloids) [1]. Together with the colloid drift, the temperature gradient also induces a flow of the surrounding solvent [2]. This flow is responsible for example of the long-ranged hydrodynamic attraction between colloidal particles near a heated boundary wall [3]. Thermophoretic flows are also induced in the proximity of walls that experience a tangential temperature gradient which can be used to generate diverse flow patterns in microfluidic environments. The thermophoretic effect can also be exploited to build micromachines. Asymmetric microgears locally heated in a cooled surrounding solvent can be shown to rotate spontaneously and unidirectionally [4]. The resulting temperature gradient along the edges of the gear teeth translates into a directed thermophoretic force, which will exert a net torque on the gear. Different devices are microscale turbines which can rotate in the presence of an external temperature gradient [5]. This microturbine can be constructed assembling anisotropic blades in a chiral manner and it is based in the anisotropic thermophoretic effect. Self-propelled motion can be induced for example in the cases of Janus or dimers colloidal particles with asymmetric properties [6,7]. In these cases, one half of the particle can be heated to a fixed temperature producing a radially symmetric temperature gradient. The thermophoretic properties of the other half produce then a propulsion against or towards the heated part, such that the asymmetric microparticle becomes a microswimmer. These self-propelled particles can have properties of puller, pushers or neutral swimmers [8]. Interestingly, similar effects can be found by exploiting the diffusiophoretic effect which rely on concentration gradients [9]. I will summarize recent work performed by means of a mesoscopic simulation technique known as multiparticle collision dynamics simulations (MPC) [10,11].
1. Luesebrink, D., Yang, M. and Ripoll, M., J. Phys.: Condens. Matter 24, 284132, 2012. 2. Yang, M. and Ripoll, M., Soft Matter 9, 4661, 2013. 3. Weinert F. M. and Braun D., Phys. Rev. Lett., 101, 168301, 2008. 4. Yang, M. and Ripoll, M., Soft Matter 10, 1006, 2014. 5. Yang, M., Liu R., Ripoll, M., and Chen K., Nanoscale 6, 13550, 2014. 6. Jiang, H. R., Yoshinaga, N., and Sano, M., Phys. Rev. Lett., 105, 268302, (2010). 7. Yang, M. and Ripoll, M., Phys. Rev. E, 84, 061401, (2011). 8. Yang, M., Wysocki, A., and Ripoll, M., Soft Matter 10, 6208, 2014. 9. Yang, M., Ripoll, M., and Chen K., J. Chem. Phys., 142, 054902, 2015. 10. Malevanets, A. and Kapral, R., J. Chem. Phys. 110, 8605–8613, 1999. 11. Lu ̈sebrink, D. and Ripoll, M., J. Chem. Phys. 136, 084106, 2012. |