Amitabh Bhattacharya

An aptamer-functionalized chemomechanically modulated biomolecule catch-and-release system

The efficient extraction of (bio)molecules from fluid mixtures is vital for applications ranging from target characterization in (bio)chemistry to environmental analysis and biomedical diagnostics. Inspired by biological processes that seamlessly synchronize the capture, transport and release of biomolecules, we designed a robust chemomechanical sorting system capable of the concerted catch and release of target biomolecules from a solution mixture. The hybrid system is composed of target-specific, reversible binding sites attached to microscopic fins embedded in a responsive hydrogel that moves the cargo between two chemically distinct environments. To demonstrate the utility of the system, we focus on the effective separation of thrombin by synchronizing the pH-dependent binding strength of a thrombin-specific aptamer with volume changes of the pH-responsive hydrogel in a biphasic microfluidic regime, and show a non-destructive separation that has a quantitative sorting efficiency, as well as the systems stability and amenability to multiple solution recycling.

Chemically-mediated communication in self-oscillating, biomimetic cilia

A single biological cilium can sense minute chemical variations and transmit this information to neighboring cilia to produce a global response to the local change. Herein, we undertake the first computational study of self-oscillating, artificial cilia and show that this system can “communicate” to undergo a biomimetic, collective response to small-scale chemical changes. The cilia are formed from chemo-responsive gels undergoing the oscillatory Belousov–Zhabotinsky (BZ) reaction. The activator for the reaction, u, is generated within these BZ cilia and diffuses between the neighboring gels. We find that the spatial arrangement of the BZ cilia affects the local distribution of u, which in turn affects the dynamic behavior of the system. Consequently, two closely spaced cilia bend away from each other and the chemo-mechanical traveling waves within the gels propagate top down. By increasing the inter-cilia spacing, we dramatically alter the behavior of the system and uncover a distinctive form of chemotaxis: the tethered gels bend towards higher concentrations of u and hence, towards each other. This chemotaxis is particularly pronounced in an array of five cilia, where we observe a “bunching” of the cilia towards the highest concentration in u, accompanied by the synchronization of the chemo-mechanical waves. We also show that the cilial oscillations can be controlled remotely and non-invasively by light. By selectively illuminating certain cilia, we could “play” the array like a keyboard, causing a rhythmic variation in the heights of the gels. These attributes could be exploited in a range of microfluidic applications, where the controllable communication among the BZ cilia and self-oscillating surface topology can be harnessed to transport microscopic objects within the devices.

Theoretically based optimal large-eddy simulation

Large eddy simulation (LES), in which the large scales of turbulence are simulated while the effects of the small scales are modeled, is an attractive approach for predicting the behavior of turbulent flows. However, there are a number of modeling and formulation challenges that need to be addressed for LES to become a robust and reliable engineering analysis tool. Optimal LES is a LES modeling approach developed to address these challenges. It requires multipoint correlation data as input to the modeling, and to date these data have been obtained from direct numerical simulations (DNSs). If optimal LES is to be generally useful, this need for DNS statistical data must be overcome. In this paper, it is shown that the Kolmogorov inertial range theory, along with an assumption of small-scale isotropy, the application of the quasinormal approximation and a mild modeling assumption regarding the three-point third-order correlation are sufficient to determine all the correlation data required for optimal LES m...

Modeling the Transport of Nanoparticle-Filled Binary Fluids through Micropores

Understanding the transport of multicomponent fluids through porous medium is of great importance for a number of technological applications, ranging from ink jet printing and the production of textiles to enhanced oil recovery. The process of capillary filling is relatively well understood for a single-component fluid; much less attention, however, has been devoted to investigating capillary filling processes that involve multiphase fluids, and especially nanoparticle-filled fluids. Here, we examine the behavior of binary fluids containing nanoparticles that are driven by capillary forces to fill well-defined pores or microchannels. To carry out these studies, we use a hybrid computational approach that combines the lattice Boltzmann model for binary fluids with a Brownian dynamics model for the nanoparticles. This hybrid approach allows us to capture the interactions among the fluids, nanoparticles, and pore walls. We show that the nanoparticles can dynamically alter the interfacial tension between the two fluids and the contact angle at the pore walls; this, in turn, strongly affects the dynamics of the capillary filling. We demonstrate that by tailoring the wetting properties of the nanoparticles, one can effectively control the filling velocities. Our findings provide fundamental insights into the dynamics of this complex multicomponent system, as well as potential guidelines for a number of technological processes that involve capillary filling with nanoparticles in porous media.

Biomimetic chemical signaling across synthetic microcapsule arrays

Using theory and simulation, we design a system of interacting microcapsules that effectively act like a relay: receiving a chemical signal from one capsule and transmitting this signal to another, so that a “message” is propagated over macroscopic distances. We utilize two types of capsules, which are localized on an adhesive surface in solution. The “signaling” capsules release inducer molecules (IM), which trigger the “target” capsules to release nanoparticles. The released nanoparticles can bind to the surface and thus, create adhesion gradients, which propel the signaling capsules to shuttle between neighboring targets. Using simulations based on the lattice Boltzmann method, we first show how steady activation of one target can lead to periodic activation of the neighboring target. Using an approximate numerical model for the system, we also study the effect of a non-steady, periodic activation of the first column of target capsules on the propagation of the signal. We demonstrate that under certain conditions, the signal in the second column of target capsules reproduces the signal in the first target column. The latter result can potentially be utilized to transmit a chemical wave across long, linear arrays of synthetic microcapsules.

Size Selectivity in Artificial Cilia! Particle Interactions: Mimicking the Behavior of Suspension Feeders

Inspired by the ability of marine suspension feeders to selectively capture small particles by their hairlike cilia, we simulate the interaction between artificial cilia and microscopic particles of different sizes to determine if a purely synthetic system can display analogous size-selective behavior. Our computational approach specifically models the capture of particles suspended in the surrounding fluid by adhesive filaments, which are anchored by one end to a surface. Via this model, we show that this size selectivity can arise as a result of adhesive and hydrodynamic interactions in the system. The substantial reduction in the mobility of the large particles near surfaces leads to a failure in capturing large particles. Using a simple analytical model, we show that the balance of hydrodynamic and adhesive forces favors capture of particles below a critical size for a given cilia-particle interaction. Our findings provide guidelines for designing artificial cilia that can be used for sorting and transporting particles within microfluidic devices.

A filtered-wall formulation for large-eddy simulation of wall-bounded turbulence

Simulating high Reynolds number wall-bounded turbulence using large-eddy simulation (LES) requires modeling the subgrid force in the bulk of the flow and instantaneous viscous and pressure stresses at the wall. Here, LES of turbulent channel flow is conducted at Reτ=590 using a filtered-wall formulation, in which a buffer region with u=0 is attached adjacent to the wall, and the underlying velocity field defined over the extended domain is filtered using a nonlocal filter (in this case, a Fourier cutoff filter) in all directions. The instantaneous wall stress is computed by first prescribing a target velocity field for the filtered velocity inside the buffer and then minimizing the error between the actual and target velocity at every time step. The optimal LES (OLES) approach is used to model the subgrid force in terms of the resolved velocity field via linear stochastic estimation. The correlations required to carry out this stochastic estimation are computed from direct numerical simulation (DNS). Resu...

Representing anisotropy of two-point second-order turbulence velocity correlations using structure tensors

A locally homogeneous representation for the two-point, second-order turbulent velocity fluctuation Rij(x,r)=⟨ui′(x)uj′(x+r)⟩ is formulated in terms of three linearly independent structure tensors [Kassinos et al., J. Fluid Mech. 428, 213 (2001)]: Reynolds stress Bij, dimensionality Dij, and stropholysis Qijk∗. These structure tensors are single-point moments of the derivatives of vector stream functions that contain information about the directional and componential anisotropies of the correlation. The representation is a sum of several rotationally invariant component tensors. Each component tensor scales like a power law in r, while its variation in r/r depends linearly on the structure tensors. Continuity and self-consistency constraints reduce the number of degrees of freedom in the model to 17. A finite Re correction is introduced to the representation for separations of the order of Kolmogorov’s length scale. To evaluate our representation, we construct a model correlation by fitting the representa...

Designing Bioinspired Artificial Cilia to Regulate Particle-Surface Interactions.

Biological cilia play a critical role in a stunning array of vital functions, from enabling marine organisms to trap food and expel fouling agents to facilitating the effective transport of egg cells in mammals. Inspired by the performance of these microscopic, hair-like filaments, researchers are synthesizing artificial cilia for use in lab-on-a-chip devices. There have, however, been few attempts to harness the artificial cilia to regulate the movement of particulates in these devices. Here, we review recent computational studies on the interactions between actuated artificial cilia and microscopic particles, showing that these cilia are effective at transporting both rigid and deformable particles in microchannels. The findings also reveal that these beating filaments can be used to separate microparticles based on their size and stiffness. Importantly, these studies indicate that artificial cilia can be used to prevent fouling by a wide variety of agents because they can expel both passive particulates and active swimmers from the underlying surface. These results can help guide experimental efforts to fully exploit artificial cilia in controlling particle motion within fluid environments.

Stiffness-modulated motion of soft microscopic particles over active adhesive cilia

Micro-scale artificial cilia have the potential to selectively manipulate particles in the surrounding fluid in unprecedented ways. Computational modeling can provide effective guidelines for optimizing the performance of these filaments for various applications in microfluidic devices. To this end, we model an array of actuated cilia that interact with compliant particles via adhesive bonds. Simulations based on the Lattice Boltzmann Method (LBM) show that the cilia are able to transport the particle in the direction of the effective stroke via a combination of fluid advection and forces due to tacky cilia–particle bonds. These simulations are performed over a range of particle stiffness and cilia–particle adhesion. For low cilia–particle adhesion, the soft particles travel faster than the rigid ones. On the other hand, for high cilia–particle adhesion strength, this trend is reversed; rigid particles travel faster than the soft ones. This stiffness-based modulation of particle speed can be explained by the larger deformation of the softer particles, leading, in turn, to a larger number of particle–cilia bonds. Our results point towards a new and robust way to separate micro-particles based on their stiffness by using active artificial cilia.