380715 Molecular Engineering of Crystallization in Pursuit of More Potent Functional Materials, Cleaner Sources of Energy and Better Climate
Crystallization is a versatile phenomena in nature. For millennia, it has been widely used for extracting salt and other minerals, and is perhaps the oldest of the traditional chemical engineering separation processes. The importance of crystallization is however not confined to purification only. Crystallization is a vital component of many important natural phenomena, such as precipitation. It can also be employed for making functional materials, as many of these materials have crystalline, quasi-crystalline, or liquid-crystalline long-range order, and their functional properties usually emanate from their underlying long-range order. For instance, optical band gaps can only be realized in certain crystalline and quasi-crystalline arrangements of atoms, molecules or colloidal building blocks. Consequently, studying crystallization processes with the aim of discovering new crystallization routes, or engineering the existing crystallization processes, is of broad scientific and technological interest. Molecular engineering of crystallization processes is however not possible without having a thorough understanding of the kinetics and the mechanisms of the underlying processes. Computer simulations are invaluable tools in this endeavor since the existing experimental techniques lack the necessary spatiotemporal resolution for studying molecular mechanism(s) of crystallization. Typically, crystallization proceeds through a nucleation and growth mechanism, with the nucleation stage being the rate-limiting step. Historically, several advanced simulation techniques have been developed for studying rare event phenomena such as nucleation. However, there are various technical difficulties in applying these generic algorithms to crystallization processes.
One such difficulty arises in studying crystallization in systems in which structural relaxation is slow. In such circumstances, developing more efficient computer software can assist in expanding the range of accessible timescales. However, the existing advanced simulation techniques must also be modified to cope with the issue of correlated configurations/trajectories. A notable example is the supercooled water. Water transforms into hexagonal ice at ambient pressures. However, its crystallization can be suppressed for temperatures as low as 40 K below its melting point. Therefore, the ability to predict the extent and the rate of crystallization is key in understanding the behavior of supercooled aqueous systems. For instance, the fraction of icy droplets in a cloud is a very important parameter to many climatological models, and determines the light-absorbing properties and the precipitation propensity of a cloud, but the extent of freezing in atmospheric droplets and aerosols can not be determined from thermodynamics only. However, computational studies of ice nucleation are very challenging and the problem of calculating the rate of homogeneous nucleation of ice in realistic models of water has been dubbed as one of the most challenging open problems in computational statistical physics, besting the efforts of generations of computational scientists. Another aqueous system that is of utmost interest in the context of clean sources of energy is the water-gas mixtures that can transform into clathrate hydrates. Clathrates are crystalline solids in which water molecules organize into polyhedral cages that encompass small guest molecules such as hydrocarbons, carbon dioxide and nitrogen. Methane hydrates are present in the ocean bed and in the permafrost and it is believed that the methane trapped in the form of hydrates in those environments is orders of magnitude larger in quantity than the methane in known oil and natural gas resources. Methane hydrates can become sources of clean energy in the future. However, the mechanism and the kinetics of hydrate formation are not fully understood, due to the very same reasons that make computational studies of ice so challenging. In my postdoctoral years in Princeton, I developed a coarse-grained variant of the forward-flux sampling algorithm, and successfully used it for calculating nucleation rates in a fully atomistic model of water. As an assistant professor, I intend to expand on my postdoctoral work and study the problem of ice nucleation under atmospherically relevant circumstances, e.g. in droplets and films of supercooled water. There are several interesting questions to be asked in this regard, such as the effect of vapor-liquid interfaces on crystallization, or the role that inorganic impurities play in modifying the rate and the mechanism of crystallization. The findings of this investigation will be pivotal to the area of atmospheric and environmental sciences and can help us gain a better understanding of phenomena such as global warming and climate change. It can also assist us in devising guidelines for engineering our climate, e.g. by engineering the radiation budget and the precipitation patterns across the biosphere via changing the properties of clouds. I am also interested in the problem of hydrate nucleation, mostly at realistic supersaturations that have been mostly overlooked in existing computational studies of hydrates. Understanding the mechanisms of nucleation in those systems can assist us in developing better technologies for energy storage and carbon sequestration. It can also help us prevent the problems that hydrate formation can cause in production and transportation facilities in the petroleum industry.
Another difficulty in studying crystallization is the issue of nonconventional symmetries that can make the characterization of the disordered and the crystalline states a nontrivial task. The most notable examples are the complex structures that assemble from anisotropic nano- and colloidal building blocks. Many of these nonconventional symmetries can result in interesting functional properties, such as photonic band gaps, negative refractive index. During my doctoral years at the University of Michigan, we established the striking capably of anisotropic building blocks to assemble into structures with complex crystalline and quasicrystalline order. For instance, we showed that hard tetrahedra can assemble into a dodecagonal quasicrystal, a discovery that was followed by further discoveries of other intriguing disorder-order transitions in other hard and soft particle systems. Today, it is relatively straightforward to study the thermodynamics and the self-assembly propensities of different building blocks. However, studying kinetics of such transformations are less straightforward. There are two types of difficulties in applying the existing advanced simulation techniques to these crystallization processes. First of all, many of these structures have nonconventional symmetries, and can thus not be properly characterized by the existing order parameters that are widely used for simple atomic and molecular systems. It is necessary to emphasize that the problem of identifying a good oder parameter for these advanced simulation techniques is not just an exercise in geometry, but instead requires a large deal of experience and a lot of effort. The second difficulty is that no efficient algorithms exist for following the dynamics of systems that are comprised of building blocks with singular surfaces e.g. hard polyhedra. Combining my graduate experience in studying such nontrivial symmetries, and my postdoctoral experience in using and modifying advanced sampling techniques, I intend to tackle these two problems to obtain a mechanistic understanding of the processes that give rise to such nontrivial structures.
HIGHLIGHTS [AS OF MAY 2014]:
-Five first-author publications.
-One submitted manuscript.
-Five to be submitted within the next few months.