Swarming is a frequently observed process in social insects and in herding mammals. The driving factor in each case differs from foraging in ants, tracking nectar source in scouts bees to evading predators in large herds of migrating animals.
The central point of interest in all such processes is the evolution of specific geometric patterns as a function of time. Decoding such naturally patterns and the low-level of information exchange between interacting agents within the swarm has inspired mathematical models for problem solving in social media to communication networks.
Recently, the research team at IBM Zurich Laboratories, have devised a strategy to observe dynamics information exchange and pattern formation at the nanoscale between high-speed organic molecules in a chaotic medium as liquids.
Through high-precision metrology experiments with real-space imaging capability, they have demonstrated that key information, that was previously unexplored on fault tolerance to terrain adaptiveness can now be converted to an algorithmic pathway, with deep implications in programmable self-assembly.
The major advance of this work is striking the right balance between image capture rates and nanometer scale spatial information content under standard laboratory conditions. This work was published in Scientific Reports, 2015.
Source: Capturing the Embryonic Stages of Self-Assembly – Design Rules for Molecular Computation. Peter N. Nirmalraj, Damien Thompson and Heike Riel. Scientific Reports, 2015, 5, 10116.