Research

Controlling structured fluids with steerable bacteria

There's an amazing type of naturally-occuring bacteria that produces a line of magnetics along its body; the result is that these "magneto-tactic bacteria" acts like a compass needle and aligns with an external magnetic field. The feature was evolved to respond to earth's magnetic field, but we can use the same feature to control the direction of swimming by applying an additional field. In particular, how these bacteria swim in complex fluids like shear thinning polymer solutions helps us understand the microscopic forces they feel. By applying time varying magnetic fields (forcing them to turn rapidly or swim in circles), we aim to understand what changes between a purely Newtonian fluid like saltwater and a complex fluid where there may be complicated local changes to the viscosity bacteria enounter.

Dynamical systems — built from DNA

During development, living systems have to organize into precise spatial structures. Each cell has the same genome, yet cells take on different functions and position themselves during division and via flow. The trick to achieving this is a kind of reaction network in which cells signal and receive signals from other cells, changing the local levels of different proteins produced and leading the robust formation of a functional organism. We are developing a synthetic version of this reaction network where different DNA species (the equivalent of genes above) are copied into RNA (the equivalent of proteins above), which interact with the original DNA species to create tunable feedback loops. To achieve spatial organization, we tether the DNA species into a gel within a microfluidic device, as shown to the right (top). We can measure the RNA output by designing "reporter" molecules to fluorescence in the presence of a particular RNA species (right, bottom). With just a few species of DNA, we can achieve features like bistability and oscillation. Initial work here, further work in progress.

Sheared glassy materials: combining rheology and structure

Rheology is the study of how materials flow; rheometers are the instruments that make this possible by applying stress to a material and measuring the strain response (or vice versa). However, it is not always evident what structurally is going on in the material that gives rise to the measured behavior. A recently developed tool, called rheo-XPCS, combines rheology with measurements of structure using coherent x-rays (X-ray photon correlation spectroscopy, or XPCS). We are using this tool to tease out the relationship between measured behavior and the corresponding structural features in nanocolloidal glasses. While many features correlate one-to-one between the measured rheological and structural behavior, we find cases where XPCS reveals features not evident by rheological measurements alone. 

Models of memory for a simpler understanding

The ability of a disordered system to store memories like those described below (Memory in granular materials) has been challenging to understanding, in part because the structure of the material doesn't change in any obvious way. Instead, attempts have been made to connect the dynamical behavior of the system — the series of instabilities that the particles undergo during a shear cycle — to a simpler model system: spins that flip back and forth between their two states but with some hysteresis. In a series of papers, we explored the effect of interactions between these spins (here), highlighted how pairs of interacting spins can give rise to a neat "latching" behavior (here), and looked at how dynamics can allow for the emergence of long timescales where a single spin creeps from one state to the other (here).

Memory in granular materials

Granular materials like sand, coffee beans, or the somewhat simplified simulation of repulsive disks shown to the right exhibit a striking sensitivity to how the material is formed. Sand that has just been poured, for example, is not as dense as sand that has been tamped down or allowed to settle. Other memories are more subtle: a box of grains can remember the amplitude of deformation like compression or shear that has been applied, yet the structure of the grains is not changed in any obvious ways. This memory of past inputs is one way of understanding how these complex systems explore different configurations. Our particle simulations have explored questions like, what is the minimal form of this memory? And can we probe the particle rearrangements that make up the memory along different axes (see above for experiments!)? Relevant papers here and in preparation.

Motion of droplets on solid surfaces

Next time it rains, take a look at droplets as they collect on the window. They stick, coalesce, and slide down the surface — what determines when a droplet sticks and when it moves? Using ultrapure water on carefully prepared surfaces, we measured the dynamics of so-called "contact lines", or droplet edges where the solid, liquid, and air all meet. Surprisingly, the speed of contact lines subjected to the same procedure depended on how long the drop had been sitting on the surface: drops that had been sitting for a few minutes showed slower contact line motion. Additional experiments showed that this effect is due to changes across the whole footprint of the drop, suggesting that there may be microscopic or chemical changes to the droplet's interaction with the surface over time. See the full paper here.