Research

Research

Our research is organized around three programs. Chemical-gradient transport is the established core of the lab. Alongside it, we investigate how freezing fronts reorganize soft materials and how nonlinear fluidic networks store information. Across these areas, we combine controlled experiments, quantitative imaging, numerical simulations, and theory to connect microscopic mechanisms with behavior at the scales of pores, materials, and networks.

Chemical-gradient transport in complex media

Whenever fluids of different composition meet, they create chemical gradients. In a soil pore, a gradient may form when fresh water displaces salty water; in a biofilm, it may be generated as microorganisms consume nutrients. As dissolved molecules spread, they can set much larger objects in motion, drive fluid along nearby surfaces, and guide swimming bacteria. We ask when these local effects become strong enough to change transport through an entire material.

Colloids: when gradients move particles and drive flow

When the concentration of a dissolved substance varies in space, a colloidal particle can migrate relative to the surrounding liquid. This process is called diffusiophoresis. The same gradient can drive liquid along a nearby wall through diffusioosmosis. Whether these motions reinforce or oppose one another depends on interactions among the dissolved substance, particle, and surface.

In a porous material, particles encounter fast channels, slow regions, and dead-end pores. Gradient-driven motion can carry particles across streamlines, exchanging them between these regions. A drift much weaker than the background flow can therefore strongly affect how far particles spread, how long they remain trapped, and how readily they can be removed.

We use microfluidic experiments, simulations, and theory to connect this pore-scale motion with transport across an entire material. Our experiments have shown that salinity gradients can reshape colloidal transit and dispersion through disordered pore networks. Solute spreading can sustain gradients that help release particles from dead-end pores, while wall-driven diffusioosmotic flow can even reverse the direction in which particles focus inside a confined channel. Our goal is to build predictive models of dispersion, retention, and delivery in environments where conventional descriptions based on flow and diffusion are incomplete.

Selected papers

Biofilms: delivery through a living barrier

When bacteria form a biofilm, they become embedded in a dense polymeric matrix. This matrix impedes incoming particles, while microbial activity and exchange with the surrounding fluid generate chemical gradients within and around the community. A biofilm is therefore neither a passive porous material nor a uniform barrier: its evolving structure helps determine what can enter it and how far that material can penetrate.

We study whether chemical gradients can improve the delivery of colloidal probes and carriers into biofilms. By growing biofilms to different densities and tracking particles of different sizes, we found that gradient-driven penetration decreases systematically as the biofilm becomes denser. Beyond a sufficiently dense regime, the biofilm matrix strongly suppresses the transport enhancement provided by diffusiophoresis. These results help identify when chemical gradients can assist delivery and when the structure of the biofilm becomes the controlling barrier.

Selected paper

Microbes: navigating gradients that do not stand still

Bacteria can sense chemical gradients and alter how they swim, a behavior known as chemotaxis. Chemotaxis is often studied in smooth, steady nutrient fields. In soils and other crowded habitats, however, flow and geometry continually create, stretch, fragment, and erase chemical signals.

Building on earlier work showing that flow disorder can reshape bacterial dispersion in porous media, we now ask how microorganisms navigate these changing landscapes. How do transient chemical signals affect the time bacteria spend in slow or sheltered regions? When does chemotaxis enhance spreading, and when does it promote retention? How does navigation by individual cells become population-scale transport through a heterogeneous environment?

Selected paper

Freezing-mediated transport and organization

Freezing is not simply a change from liquid to solid. As an ice front advances, it can reject, concentrate, trap, or rearrange dissolved substances and suspended particles. In porous and particulate materials, these microscopic interactions can reorganize the material and change the pathways available to fluids during freezing and after thawing.

We are developing controlled experiments and models to determine how freezing rate, temperature gradients, confinement, and particle–interface interactions govern these outcomes. We ask when particles are engulfed by ice or pushed ahead of it, how solutes and particles become organized between growing ice structures, and which changes persist through repeated freeze–thaw cycles.

These questions arise in seasonally frozen soils and permafrost regions, frost damage to materials and infrastructure, the cryopreservation of cells and tissues, and freeze-casting processes used to manufacture porous materials. Our goal is to connect microscale interactions at a moving phase boundary with the evolving structure and transport properties of frozen and thawed materials.

Memory in nonlinear fluidic networks

Can a fluid network remember the flows that have passed through it? In our microfluidic devices, flow bends an elastic element inside a channel. This deformation changes the channel’s hydraulic resistance, redistributing the flow and changing the load on the element. The resulting feedback can produce two stable configurations with different switching thresholds. Because its present state depends on how it was driven in the past, each element acts as a fluidic hysteron.

When many such elements are connected, switching one changes the pressure and flow experienced by the others. We study how these interactions generate collective switching and avalanches, how information about previous inputs is stored and retrieved, and when a network returns to an earlier state as the driving is reversed.

We are now asking how geometry, network architecture, and repeated inputs can be used to control this physical memory, and whether repeated experience can change a network’s future response, a minimal form of learning. These principles could enable soft machines and lab-on-chip devices that sense, store, and process information without electronic controllers.

Representative work

For a complete list of papers, visit our Publications page.