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All seminars will be held in the Lindy Boggs Center room 242 from 2:00 pm – 3:00 pm unless noted otherwise. For more information please call (504) 314-2914 or email email@example.com.
Sponsored through generous gifts from members of The Chemical and Biomolecular Engineering Board of Advisors.
A better understanding of how molecules interact in aqueous solutions has ramifications across the biosphere, lithosphere, atmosphere, and hydrosphere. For example, aqueous solutions of dissolved organic molecules and salts are central to all of biology and biochemistry. Unsurprisingly, documented studies of how organic solutes, dissolved salts, and water interact with each other arguably go back to at least the late 18th Century with Franz Hoffmeister’s seminal work on protein solubility. However, to date no comprehensive atomistic model of the interactions between this trinity of solute, salt, and water has been forthcoming.
One major facet of our research is building up an atomistic viewpoint of aqueous supramolecular chemistry, and in doing so building systems that engender unusual phenomena; for example, water-based, yoctoliter reaction vessels. This presentation will focus on our recent studies examining the aqueous supramolecular interactions involving deep-cavity cavitands and small ions, and how these interactions control their bulk properties. Relatedly, the presentation will also summarize how understanding aqueous supramolecular chemistry can lead to novel supramolecular containers that function as yoctoliter reaction vessels, and tools for bringing about novel separation protocols.
Polymeric materials, due to their adaptability and array of functionality, touch almost every aspect of our daily lives. This seminar will focus on two areas in which new behavior was engineered into “old” materials. In the first part, I will discuss my lab’s efforts to design new polymers for water treatment membranes. Membrane-based water purification techniques are the current state of the art, but face limitations including thermodynamically limited transport, high material and operation costs, the perm-selectivity tradeoff, and fouling-prone or chlorine-sensitive membrane materials. Through the addition of charged sites, specifically zwitterions, to poly(arylene ether sulfone)s, the hydrophilicity, water permeability, and fouling resistance were all improved while maintaining constant salt rejection.
In the second part, I will discuss a new paradigm for studying molecular-level behavior in nanocomposites. These multifunctional materials enable combinations of tunable optical properties, smart sensing, conductivity, and excellent thermomechanical properties. Currently, mechanoresponsive polymers use a limited subset of active backbone chemistries to yield changes in optical properties. My lab has developed a strategy to yield mechanoresponsive fluorescence by adding quantum dots and fluorescently labeled carbon nanotubes. Pronounced changes in fluorescence emerge following plastic deformation, indicating a transduction of mechanical force into fluorescence. Thus, the force activation of fluorescence for quenching pairs can serve as a general strategy to develop new nanocomposite matrices that impart desirable functionalities, including damage sensing and robust mechanical strength.
Polyimides are at the forefront of advanced membrane materials for CO2 capture and gas purification processes. Recently, “ionic polyimides” (i-PIs) have been reported as a new class of condensation polymers which combine structural components of both ionic liquids (ILs) and polyimides through covalent linkages. In this work, the CO2 separation characteristics of ionic polyimides are modeled using molecular dynamics simulations in combination with grand canonical Monte Carlo calculations. The performance of neat i-PI systems is evaluated, as well as composite structures containing both i-PIs and various ionic liquids (ILs). The i-PI+IL composites are based on combinations of 1-n-butyl-3-methylimidazolium ([C4mim+]) cations with three common molecular anions: (bis(trifluoromethylsulfonyl)imide ([Tf2N-]), tetrafluoroborate ([BF4-]), and hexafluorophosphate ([PF6-]). It is found that 50 mol% IL inclusion can increase CO2/CH4 selectivity by 16% in [BF4-]-based materials and by 36% in [PF6-]-based materials from mixtures of 5% CO2 / 95% CH4. While the [BF4-]-based system shows higher CO2/CH4 selectivity, the [Tf2N-]-based system shows higher CO2/N2 gas selectivity. A comprehensive structural analysis (fractional free volume (FFV), pore size distribution, surface area, etc.) is used to highlight the underlying differences among the different i-PI+IL systems that lead to the different adsorption properties.
13C metabolic flux analysis (MFA) is the gold standard approach for quantifying rates of biochemical reactions in living cells. It has been widely applied to debottleneck the metabolism of industrial host organisms, but it is now being increasingly used to investigate metabolic disease mechanisms both in cellular and in vivo models. Over the past decade, my lab has focused on establishing novel 13C MFA tools and approaches that enable us to probe entirely new aspects of metabolism previously inaccessible to measurement. In particular, we have developed a publicly available software package called INCA that automates the computational workflow of MFA. I will discuss several ongoing studies where my lab has leveraged INCA to (i) identify targets for metabolic engineering of host cell factories and (ii) investigate metabolic disease mechanisms using 2H/13C MFA to simultaneously assess gluconeogenesis, citric acid cycle, and anaplerotic fluxes in conscious, unstressed mice. These studies have established 13C flux analysis and the INCA software package as a comprehensive platform to map carbon fluxes in microbial and mammalian cell cultures, as well as whole animals.
We use models in science and engineering extensively. We use them to make predictions about the behavior of systems, to optimize designs, and to understand why systems behave the way they do. Most of our models are built from physical principles, and the parameters in the models are usually determined from measured data. That data is often expensive to gather, but the model is then cheap to evaluate. The accuracy of these models depends both on the depth of understanding we have, and the quality of the data, and when we hit the limit of our understanding it is difficult to make better models. Machine learning offers a path forward to build models that are not necessarily based on physics, but which more accurately predict outputs.
We are interested in building models that allow us to perform molecular simulations that require many (hundreds of thousands) of calculations. These are not practical with quantum chemical calculations, which are too expensive to run at this scale. Existing molecular force fields are efficient enough for this, however, they lack the accuracy required to obtain meaningful results. I will present how we are using machine learning in conjunction with quantum chemical calculations to develop efficient models that can be used to simulate effects such as segregation, diffusion, etc., which can only be probed using simulation methods such as Monte Carlo and molecular dynamics.
Machine learning has more to offer science and engineering than just model development. I will also discuss some aspects of how machine learning works, particularly the role that automatic differentiation has in machine learning. This has implications for many types of scientific programming, and may enable new ways to think about science and engineering problem solving.
Poly(N-isopropylacrylamide) (PNIPAM) is a well-known thermo-sensitive polymer that exhibits a low critical solution temperature (LCST) at around 305 K in aqueous solutions. The coil-to-globule transition of PNIPAM can be induced by a small temperature variation (1~2 K) accompanied by abrupt conformational changes. The LCST behavior of PNIPAM has been attracting research interests for several decades because of its implication in a number of living phenomena, especially on protein folding and DNA packing. However, the coil-to-globule transition of PNIPAM has been greatly affected by some additives, such as surfactants and inorganic salts. Therefore, the researches on the effects of the additives on the coil-to-globule transition of PNIPAM have gained great realistic significance. In this work, with the applications of laser light scattering (LLS), viscometry as well as high-resolution nuclear magnetic resonance (NMR), the effects of the anion surfactant sodium n-dodecyl sulfate (SDS) on the coil-to-globule transition of PNIPAM under various temperatures and surfactant concentrations have been systematically studied. Besides, the effects of eight inorganic salts on the coil-to-globule transition of this polymer have been also explored. Several interesting results have been drawn.
The multiscale self-assembly of atoms, molecules, and particles is the origin of all physical mesoscopic matter. The spatial organization, symmetry, and physical properties of the assembled structures are determined by thermodynamic characteristics of their building blocks. Colloidal particles are emerging as models for understanding governing principles of directed-assembly and non-equilibrium response of advanced materials. Here, I will present the concept of using external field driven interactions to direct the assembly and spatial migration of colloids. First, I will present the principle of using magnetostatic interactions to direct surface patterning using sessile drop drying. In droplets of magnetite nanoparticles, magnetic establish a microconvection from droplet edge to center. This magnetostatic convection is used to assemble secondary nonmagnetic particles in droplets, allowing for the assembly of four distinct kinetically stable states, and enabling a new route for surface patterning. Second, I will introduce the concept of directing spatial motion and non-equilibrium behavior of metal-dielectric patchy colloids using external electric field. The electric field drives a local force imbalance around the particle, resulting into its direction motion. I will demonstrate that the particle’s velocity, chirality, and its 3D trajectory can be programed by engineering the patchy particle/cluster size and shape. I will show that the coupling of translation and rotational component of the energy enables programming helical motion in spherical colloids, and provides an alternative mode of navigating through complex cross-linked matrices. This approach introduces a new method of engineering the assembly and self-propulsion of microparticles, which could lead to the development of advanced micro-motors and miniature robots capable of navigating through complex biological environments.
Sponsored through generous gifts from members of The Chemical and Biomolecular Engineering Board of Advisors.
Rafael Verduzco | Rice University
Nanette Boyle | Colorado School of Mines
Christopher G. Arges | Louisiana State University
Kristala Jones Prather | Massachusetts Institute of Technology