Dynamic Self-Organization and Catalysis: Periodic versus Random Driving Forces

Dynamic self-assembly is an emerging area of research where properly designed self-assembly elements can be used reversibly to trigger and control some tasks at the molecular level. The interactions between decorated nanoparticles, NPs, are experimentally modifiable by a variety of stimuli that can also vary in time periodically or randomly. In coarse-grained simulations, we activate a switch, either periodically or randomly, which assembles-disassembles clusters of NPs. We then introduce a single catalytic NP(C) covered with catalytic moieties, C, and leave all remaining NP(R)s decorated with reactive moieties, R. The catalytic reaction that converts R into products P depends on the encounter of C and R. Particle-based simulations are here used to study the catalytic activity and reaction yields of decorated nanoparticles that aggregate/disaggregate with the application of time-varying perturbations. Static aggregation is not catalytically efficient because it traps the catalyst. The application of random perturbations that vary in time in the form of colored noises improves the reaction yields and can provide opportunities for more efficient catalytic activity. The work can also allow us to understand how in Nature many biological processes are affected or driven by random/noisy fluctuations of the environment.