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We combine advanced theories and computer simulation techniques to study at nano- to meso-scales (i.e., from sub-nanometer to micrometers) the behavior of nanostructured polymeric materials. A characteristic of complex fluids such as polymers is that they span over largely different time and length scales (e.g., 10-12~100 s and 10-10~100 m). We therefore use a suite of computational tools ranging from particle-based molecular simulations (molecular dynamics, Monte Carlo simulations, dissipative particle dynamics) to molecular-level theories (field theories, integral-equation theories, density-functional theories) to mesoscopic simulations (e.g., phase-field modeling and cell dynamics simulations), to investigate both thermodynamic and dynamic behavior of these systems. The overarching goal is to establish the interconnections between these results at different levels, thus enabling hierarchical modeling bridging various time and length scales. We are currently working on self- and directed assembly of block copolymers, stimuli-responsive polymer brushes, polyelectrolyte adsorption and layer-by-layer assembly, and structure and properties of polymer nanocomposites.
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Self- and directed assembly of block copolymers
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| Block copolymers (BCPs) have great potential for applications in nanotechnology, due to their self-assembly into spatially periodic structures on the length scale of 10 to 100 nanometers, direct control of the size and shape of these nanostructures, and uniformity of these nanostructures.[1,2,3] Many applications (e.g., templates for nanolithography, nanowires, high-density storage devices, and nanostructured membranes) envisage thin films of BCPs supported on a substrate, and well-ordered nanostructures oriented perpendicular to the substrate are desirable. Unfortunately, due to defect formation and slow kinetics common in BCP systems, the order of their self-assembled nanostructures persists over only a few hundreds of nanometers. Control of both the perpendicular orientation and the in-plane ordering to achieve well-ordered nanostructures is therefore the key issue in many applications of BCPs. In our original DOE project, we have studied directing block copolymer assembly by external fields (including topographically and chemically patterned substrates, and electric field) to obtain well-ordered nanostructures, using a high-performance, parallel FORTRAN 90 code (PolySCF) for 3D real-space self-consistent field calculations developed in our group. This work allows knowledge-based rational design (instead of trial-and-error experiments in a large parameter space) to obtain well-ordered nanostructures of BCPs. |
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| Photovoltaic (PV) energy obtained using p-conjugated (semiconducting) polymers is very attractive due to its cheap materials, low processing cost, and ease of large-scale manufacture. Control of the polymer morphology and structure on the nanoscale is critically important for optimizing the efficiency of polymer optoelectronic devices.[4,5,6,7] Such control can be achieved with magnetic-field directed assembly in thin films of rod-coil (RC) BCPs containing conjugated rod blocks. In our renewed DOE project, we use both 3D real-space parallel self-consistent field calculations with high accuracy and the newly proposed fast off-lattice Monte Carlo simulations with soft potentials that allow particle overlapping[8] to understand, predict, and ultimately control the self-assembled nanostructures of RC BCPs in bulk, under thin-film confinement, and with applied magnetic field. This work will allow knowledge-based rational design of these nanomaterials, thus advancing their integration into a range of technologically important applications, including the fabrication of polymer-based PV cells, light-emitting diodes, field-effect transistors, and chemical and biological sensors. |
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Stimuli-responsive polymer brushes
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| This NSF CAREER project is a computational study on the response of two-component polymer brushes, particularly the polyelectrolyte brushes, to various external stimuli (solvent selectivity, solution pH, ionic strength, and applied electric field) and the influence on such response by various design factors (chain architecture, polymer/block lengths and charges, grafting densities, and incompatibility between the two components). The seemingly simple two-component brushes (including both binary homopolymer brushes and block copolymer brushes) can exhibit a rich variety of interesting and novel behavior through self-assembly of the two components, and have diverse applications in many fields as "smart" surfaces.[9,10,11] The design of these responsive brushes for practical applications such as chemical gates, sensors, biomaterials, and drug delivery, however, requires detailed understanding of the physics of their stimuli-response and knowledge of how various design parameters affect their stimuli-response, which are currently rather limited. |
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Here we use coarse-grained models describing the generic features of two-component brushes, and two judiciously designed and complementary methods: 3D parallel self-consistent field calculations and novel fast Monte Carlo simulations[8,12]. The computational results will be validated by experiments through collaborations to ensure that they capture the physics of stimuli-response of two-component brushes and can provide essential guidance to experimental design of such smart surfaces. This work will greatly advance current understanding and predictive capability on the stimuli-response of two-component polymer brushes and enable knowledge-based rational design of such smart surfaces best suited for targeted applications, while avoiding both labor- and time-intensive trial-and-error experiments.
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Polyelectrolyte layer-by-layer assembly
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| Polyelectrolyte layer-by-layer (LbL) assembly has attracted exponentially growing interest due to its simplicity, versatility, and great potential for many applications.[13] Our understanding on the formation mechanism, internal structure, and molecular properties of polyelectrolyte multilayer (PEM), however, is still at an early stage. In great contrast to thousands of experimental papers on LbL assembly, very few theoretical and simulation studies have been reported.11 Although it is known that many parameters (e.g., nature and concentration of adsorbing species and added salt, solvent composition, pH of depositing solution, adsorption and washing time and temperature, etc.) affect the PEM structure and properties, to date there is no predictive tool for even the most basic quantities such as composition, layer thickness, and total charges in the multilayer.
In this ACS-PRF project, we have creatively modeled the LbL assembly process as a series of kinetically trapped states using an equilibrium self-consistent field theory. We have studied the internal structure and charge compensation of PEM formed on flat substrates, and the effects of various parameters affecting long-range electrostatic interactions (including the substrate charge density, polymer charge fractions, bulk salt concentrations) and short-range interactions (including solvent qualities and repulsion between the two polymer species) in the system.[14] Our modeling of polyelectrolyte LbL assembly is in good qualitative agreement with most experiments and molecular dynamics simulations, helps us better understand the formation mechanism and internal structure of PEM, and can further guide experimental design to obtain PEM with desired properties. |
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Fast Monte Carlo simulations with soft potentials
My group has been very actively developing a class of novel Monte Carlo (MC) simulation methodologies, the so-called "fast Monte Carlo (FMC) simulations"[8,12], suitable for the study of equilibrium properties of various polymeric systems with generic and coarse-grained models. Due to their formidable computational requirements, full atomistic simulations cannot be applied at present to the study of multi-chain systems such as concentrated polymer solutions or melts at nano- to meso-scales, and coarse-grained models where each segment represents a group of real monomers have to be used instead. While atoms cannot overlap, these segments representing the center of mass of a group of atoms certainly can. In conventional molecular dynamics or MC simulations with such coarse-grained models, however, hard excluded-volume interactions preventing segment overlapping (e.g., the Lennard-Jones potential or the self- and mutual-avoiding walk) are commonly used, which significantly slow down chain relaxation towards equilibrium configurations and efficient sampling of configuration space.
The basic idea of FMC simulations is therefore to use soft potentials that allow segment overlapping (i.e., the interaction energy between two segments is finite even when they completely overlap). This results in at least several orders of magnitude speed-up in sampling, and still allows the implementation of all advanced MC techniques proposed to date to further improve the sampling efficiency of FMC simulations. Even more significantly, this very simple idea of using soft potentials is truly transformative in that it radically changes the way in which people think about coarse-grained models, fluctuations/correlations, phase transitions, and how to study them.
Just like conventional MC simulations, FMC simulations can be performed either in continuum[8] or on a lattice[12], with the latter being the fastest molecular simulation method to date. Since 2009, my group has been applying FMC simulations to the quantitative study of fluctuations/correlations in a wide variety of polymeric systems, including compressible homopolymer melts in either bulk[8,15] or thin film[12], grafted homopolymers in an either implicit[16] or explicit[17] solvent, phase separation of polymer blends, microphase separation of block copolymers, self- and directed assembly of rod-coil block copolymers, stimuli-response of two-component polymer brushes, and capillary-wave fluctuations in ordered block copolymers. We are extending FMC simulations to polymer microemulsions, polyelectrolytes, and polymer gels. On the other hand, guided by our simulation results, we have also been developing Gaussian fluctuation theories[15], integral-equation theories, and density-functional theories to better describe the fluctuation/correlation effects in these systems, which is of both fundamental and practical importance in polymer science and engineering.
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