Abstract
Catalytic activity of the colloids and chemotactic response to gradients of the chemicals in the solution leads to effective interaction between catalytic colloids. In this paper, we simulate mixtures of active and passive colloids via a Brownian dynamics algorithm. These particles interact via phoretic interactions, which are determined by two independent parameters, surface activity and surface mobility. We find rich dynamic structures by tuning passive colloids’ surface mobility, size, and area fractions, which include schools of active colloids with exclusion zone, yolk/shell cluster, and stable active–passive alloys to motile clusters. Dynamical cluster can also be formed due to the nonreciprocity of the phoretic interaction. Increasing the size ratio of passive colloids to active colloids favors the phase separation of active and passive colloids, resulting in yolk/shell structure. Increasing the area fraction of active colloids tends to transfer from dynamical clusters into stable alloys. The simulated binary active colloid systems exhibit intriguing nonequilibrium phenomena that mimic the dynamic organizations of active/passive systems.
Highlights
IntroductionActive matter can harvest energy from the environment for mechanical motion, such as bacterial suspension (Ishikawa and Hota, 2006; Cates et al, 2010; McCarter, 2010; Marchetti et al, 2013; Singh et al, 2017), fish schools, and animal flocks (Ballerini et al, 2008; Moussaïd et al, 2009)
We can see that the passive colloids will have no effect on the active colloids, while the active colloids will attract(μ~p < 0)/repel(μ~p > 0) the passive colloids, which means that the interactions between the active and passive colloids are nonreciprocal
We have presented a systematic investigation for mixtures of chemically active and passive colloids via a diffusiophoresis model, which captures collective behavior observed experimentally and reveals some intriguing new selforganizations
Summary
Active matter can harvest energy from the environment for mechanical motion, such as bacterial suspension (Ishikawa and Hota, 2006; Cates et al, 2010; McCarter, 2010; Marchetti et al, 2013; Singh et al, 2017), fish schools, and animal flocks (Ballerini et al, 2008; Moussaïd et al, 2009). These biological systems have inspired the design of artificial swimmers experimentally with different sources of energy, including chemical (Howse et al, 2007), electromagnetic (Creppy et al, 2016), acoustic (Ahmed et al, 2014), magnetic (Wang M. et al, 2013; Tierno, 2014; Wang et al, 2021), or thermal (Jiang et al, 2010) energy.
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