One of the more interesting recent discoveries has been the ability to design nano/microparticles which catalytically harness the chemical energy in their environments to move autonomously. These "nanomotors" can be directed by externally applied magnetic fields, or optical and chemical gradients. Our group has now developed two systems in which chemical secretions from the translating micro/nanomotors initiate long-range, collective interactions among the particles via self-diffusiophoresis. Herein, we discuss two different approaches to model the complex emergent behavior of these particles, the first being a qualitative probability-based model with wide applicability, and the second being a more quantitative Brownian dynamics simulation specific to the self-diffusiophoretic phenomenon.
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