(10aa) Multiscale Design of Nanomaterial Synthesis

• Multiscale modeling and simulation of aerosol reactor design for the synthesis of nanostructured materials (discrete models â?? Molecular Dynamics and Brownian Dynamics, continuum models â?? balance equations)
• Nanoparticle interactions with biological interfaces (adhesion, self-assembly, dynamics in confined geometries)
• Theory and simulation of polymer nanocomposites and modeling of their rheological and mechanical properties (effect of polymer architecture and molecular weight, particle size, polydispersity, temperature)

Teaching Interests:

• Molecular Simulations and Statistical Mechanics
• Physical Chemistry, Chemical Engineering Thermodynamics, Statistical Thermodynamics
• Reaction Engineering and Surface Chemistry
• Aerosol Dynamics and Growth

Abstract

Aerosol particle technology is used for manufacturing of carbon black, fumed SiO₂, pigmentary TiO₂, filamentary Ni and ZnO for rubber vulcanization as well as for advanced materials (e.g. photocatalysts, nanofluids and biomaterials) with a number of functionalities and high-performance applications (Goudeli and Pratsinis, 2016a). Even though such materials can be made by both gas or liquid processes, aerosol technology has attracted a lot of interest due to its undisputable capacity for large-scale production of nanostructured commodities (up to 25 t/h), high product particle purity, easier collection and fewer unit operations (Pratsinis, 2010). Such nanostructured commodities consist of clusters of primary particles (PPs) that are formed by chemical reactions, condensation/evaporation or surface growth and grow further by sintering and coagulation. Depending on process conditions and particle residence time, coagulation and sintering (or partial coalescence), gas-phase processes typically result in aggregates (highly-sintered particles) which are attractive in catalysis, lightguide preforms and electronics and, downstream in the process in agglomerates (PPs held together by rather weak, physical forces) that are attractive in nanocomposites, pigments and liquid suspensions.

This motivates research for understanding of the formation of such gas-phase-generated nanoparticles. Multiscale design of aerosol reactors for synthesis of nanomaterials includes continuum, mesoscale, molecular dynamics (MD) and quantum mechanics models allowing fundamental understanding of particle growth dynamics. More specifically, quantum mechanics account for the electronic structure of matter determining the interatomic potentials in MD models for accurate estimation of sintering and crystallization rates. Particle properties, like structure and crystallinity (that cannot be easily obtained experimentally) are determined from first principles by highly parallelized MD simulations (Goudeli and Pratsinis, 2016b) as they affect material performance in a score of applications (e.g. sensors, catalysts, biometerials). Furthermore, MD simulations can be used also to determine the surface structure of bimetallic nanoparticles (e.g. Ag-Au) that facilitates understanding of catalytic properties.

Such accurate MD-derived sintering rates and crystallinity dynamics can be incorporated in mesoscale models that provide the transport properties and coagulation rate of multi-particle structures and continuum models that describe the effect of process variables on product particle size and morphology at various process temperatures and residence times (Buesser and Pratsinis, 2012). Thus, Discrete Element Modeling (DEM) simulations can used to track the detailed structure and size distribution and agglomerate size of fractal-like agglomerates consisting of monodisperse (Goudeli et al., 2015a) or polydisperse primary particles undergoing Brownian coagulation. The DEM-obtained mobility size and number-based geometric standard deviation of the mobility radius are compared to real-time, online measurements (e.g. Scanning Mobility Particle Sizer and Differential Mobility Analyzer combined with Aerosol Particle Mass analyzers) as function of the number of primary particles per agglomerate (Goudeli et al., 2016). Particle morphology and structure are quantified by the fractal dimension, D_f, and mass-mobility exponent, D_fm, which are related to the cluster optical and transport properties, respectively, from spherical particles to fractal-like agglomerates until they attain their well-known asymptotic structure.

The above easy-to-use relations can be readily interfaced with climate dynamics, meteorological models or computational fluid dynamics describing the reactor operation and particle production. For example, the effect of varying particle structure during the formation of fractal-like crystalline TiO₂ and amorphous SiO₂ is investigated (Goudeli et al., 2015b) by employing the above DEM-derived D_f evolution in monodisperse continuum models. Neglecting the evolving particle structure overestimates the hard-agglomerate diameter by up to 25% for TiO₂ and 30% for SiO₂, especially at high maximum temperatures and cooling rates that hard-aggregates consist of only a few primary particles.

Interfacing the above models that span 10 â?? 15 orders of magnitude in length and time, respectively, can facilitate the understanding and scale-up design of aerosol reactors for synthesis of nanoparticles whose properties can be closely controlled during scale-up from laboratory scale to commercial products. This systematic approach to study particle formation can offer significant insight into fundamental physical principles and mechanisms that may be exploited by biomedical or pharmaceutical industry and nanotechnology.

Keywords: particle technology, multiscale modeling, aerosol reactors, gas-phase synthesis

References

Buesser, B., and Pratsinis, S.E. (2012) Annu. Rev. Chem. Biomol. Eng., 3, 103-127.

Goudeli, E., Eggersdorfer, M.L., and Pratsinis, S.E. (2015a) Langmuir 31, 1320-1327.

Goudeli, E., Eggersdorfer, M.L. and Pratsinis, S.E. (2015b) J. Aerosol Sci. 89, 58-68.

Goudeli, E., Gröhn, A.J. and Pratsinis, S.E. (2016) Aerosol Sci. Technol. 50, 591-604.

Goudeli, E. and Pratsinis, S.E. (2016a) Particul. Sci. Technol. in press.

Goudeli, E. and Pratsinis, S.E. (2016b) AIChE J. 62, 589-598.

Pratsinis, S.E. (2010) AIChE J. 56, 3028-3035.