Truskett Research Group

Interfaces and the structure & dynamics of complex fluids

McKetta Department of Chemical Engineering, The University of Texas at Austin, 1 University Station C0400, Austin, Texas 78712

Thomas Truskett | People | Papers | Google Scholar Profile

News Highlights

Tom Truskett

Department Chair, Paul D. & Betty Robertson Meek Centennial Professor, and

Bill L. Stanley Leadership Chair

McKetta Department of Chemical Engineering

ttruskett at utexas dot edu

Current Research Focus

	*Concentrated Dispersions of Therapeutic Proteins for Subcutaneous Delivery* Current Sponsor: National Science Foundation Protein-based drugs represent some of the most promising therapies for a wide range of diseases, including cancer. Subcutaneous injection is the preferred method of delivery, but its usefulness is currently limited by unwanted outcomes such as protein aggregation and gelation that occur for high doses. Previous attempts to address these problems by modifying the amino-acid sequence of potential therapeutics have been expensive and often unsuccessful. We explore a new method for creating highly concentrated, low-viscosity dispersions of stable protein nanoclusters that are not only of great fundamental interest but also could provide a basis for an unconventional means for solving major challenges in the protein-based therapeutics. The goal of the proposal is to explore and develop a fundamental understanding of how the protein nanoclusters form, why they stabilize the folded state of the proteins, and the impact of the clusters on the physical properties of dispersions.
	*How Nanoscale Confinement Affects the Liquid State* Current Sponsor: The Welch Foundation Molecules of a liquid respond to the presence of an interface by adopting a spatially inhomogeneous average structure. The nature of this structure has important implications for thermodynamic properties as well as for single-molecule and collective relaxation processes of the liquid. While classical density functional theories can often make quantitatively reliable predictions about how a particular substrate will modify the equilibrium behavior of a liquid it contacts, the consequences for the liquid-state molecular dynamics in the interfacial region remain challenging to forecast even for basic model systems. Moreover, despite the fact that time- and space-resolved single-molecule displacement data are now becoming more widely accessible from both computer simulations and experiments (e.g., confocal microscopy of confined colloidal suspensions), a theoretical framework for quantitatively analyzing how local aspects of molecular structuring and dynamics of inhomogeneous liquids are linked to one another has been slow to emerge. We are leveraging new theoretical methods - coarse master equation approaches, advanced Monte Carlo sampling techniques, and efficient molecular dynamics algorithms - to study (i) how the nature of the substrate affects molecular relaxation processes near the liquid-substrate interface and (ii) the inverse problem of how best to modify the chemistry of the substrate in order to tune local relaxation processes of the liquid.

	Inverse Methods for Tuning Dynamics of Complex Fluids Current Sponsor: National Science Foundation The aim of this work is to develop strategy for systematically predicting how the effective interactions between suspended particles in complex fluids should be modified in order to achieve desired changes in their dynamic properties. Recent advances in materials chemistry and colloid science have provided experimentalists with a variety of tools for both measuring and tuning the effective interactions between nanoparticles or colloids suspended in solvent. To help take full advantage of these developments, new theoretical inverse optimization methods of statistical mechanics have been introduced to predict which effective interactions will cause a system to realize a targeted structural or thermodynamic property under equilibrium conditions. Unfortunately, similar progress on optimizing interactions for dynamic properties has been elusive, largely due to the lack of an accurate first principles theory for dynamics (e.g., comparable in reliability and generality to statistical mechanical theories for static properties of the liquid state). However, in work from our recent NSF grant (CBET 0448721), we demonstrated that there are basic static properties related to the excess entropy of both simple and complex fluid systems that strongly correlate, over a very wide range of system parameters, with the corresponding transport coefficients. The hypothesis of our work in this area is that, as a consequence of these correlations, one can indirectly tune the dynamics of complex fluid systems by applying inverse optimization techniques to the associated “dynamically relevant” static quantities.

	Computational Design of Nanomaterials by Pattern Replication Current Sponsor: National Aeronautics and Space Administration Nanometer-scale components are required for disk drives and memory storage devices, light-emitting diodes, solar energy devices, and microarrays for genomics, proteomics, and tissue engineering applications. Lithographic methods are commonly used to manufacture these components, however a key challenge that they face is creating features smaller than 20nm in a way that is reliable, cost-effective and rapid enough for industrial-scale manufacturing processes. The aim of this project is to use computational tools taken from inverse statistical mechanics to allow for a "bottom up" design of nanomaterials. The focus is on use of substrates pre-patterned on the micron scale to direct the self-assembly of much smaller nanoparticles into technologically useful structures that would be difficult to create otherwise.

	Equilibrium dispersant behavior relevant to deep-sea water conditions Current Sponsor: Gulf of Mexico Research Initiative The behaviors of oil dispersants in water at low temperature and high pressure help determine the fate of oil-water droplets in the deep-sea column and the formation of undersea oil-water “plumes”. These phenomena have received considerable attention lately in the search for practical ways to reduce negative environmental effects of deepwater oil releases. Rational design of surfactant or nanoparticle dispersant systems for such applications is challenging for a few reasons. First, most available information on thermodynamic and transport properties of aqueous-phase dispersant systems comes from experiments or simulations at near-ambient conditions (moderate temperatures and pressures and low salt concentration). Furthermore, experiments and computer simulations of simplified lattice-based models that have focused on low temperature and high pressure report qualitatively different micellization and surfactant aggregation behavior under these conditions, owing to entropy-enthalpy compensation effects with subtle molecular-scale origins. In other words, even qualitative predictions based on extrapolated behavior from higher temperature and lower pressure are not generally reliable for these systems. We are using molecular simulation and liquid-state theory to gain insights into these systems, focusing on the low-temperature, high-pressure, and high-salt-concentration behavior.

	Multiscale Modeling of Protein Solutions Current Sponsor: The David and Lucile Packard Foundation Our group has also developed a new multi-scale approach for modeling the effective interactions between proteins in solution. These interactions are calculated by considering the statistical mechanics of collapsible heteropolymers in aqueous solvent. The goal of this project is to build an effective computational strategy for predicting how protein sequence and solution parameters affect both (i) the stability of protein molecules in concentrated or interfacial environments and (ii) the competition between crystallization, aggregation, and gelation in protein mixtures. We have analyzed the behavior of protein solutions by integrating our effective interactions into both statistical mechanical theory and Transition Matrix Monte Carlo simulations.

News Highlights

Truskett and collaborators awarded $600,000 National Science Foundation grant to study concentrated protein solutions for subcutaneous delivery

National Science Foundation

September 2012

Pond et al. BD-MD scaling [Soft Matter 7, 9859 (2011)] shown to describe dynamics simulations of complex (dusty) plasmas.

Physics of Plasmas 19, 034503 (2012)

Link to the Pond et al. Soft Matter paper

March 2012

Soft Matter highlights Carmer et al. work on designing tracer particles with targeted dynamics

Soft Matter blog, RSC publshing

Link to the Carmer et al. Soft Matter paper

March 2012

Material witness: Cluster control

Philip Ball, Nature Materials 11, 185 (2012); doi:10.1038/nmat3264