From avalanches to stochastic self-assembly in low-dimensional soft granular flows.

Soft granular materials, that is materials consisting of close-packed deformable grains separated by thin fluid films, are ubiquitous in industry and nature (concentrated emulsions, foams, tissues). Rheology and flow of such materials is intrinsically complex and involves phenomena characteristic of either fluid or solid phases depending on the dynamic state of the system. In general, soft granular flows rely on overcoming of the local energy barriers via rearrangements. The multi-dimensional complex and constantly evolving energy landscape leads to stochastic dynamics and overall unpredictable behavior such as avalanches. Here, we exploit droplet microfluidics to not only study the soft granular flows at the micro- and meso-scale--with droplets as the model ‘grains’--but also to precisely control the interplay of rearrangements versus arrest in such types of systems. In particular, we generate regular yet disordered quasi-1D structures in which the spatial droplet patterns reflect the history of the rearrangements. Due to granularity, the patterns are transcriptable as digital sequences resembling, e.g., the DNA code. We discuss how such patterns could be used to store information, e.g., about the content of the droplets for applications in microfluidic assays.