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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Development of DPD coarse-grained models: From bulk to interfacial properties

A new Bayesian method was recently introduced for developing coarse-grain (CG) force fields for molecular dynamics. The CG models designed for dissipative particle dynamics (DPD) are optimized based on trajectory matching. Here we extend this method to improve transferability across thermodynamic conditions. We demonstrate the capability of the method by developing a CG model of n-pentane from constant-NPT atomistic simulations of bulk liquid phases and we apply the CG-DPD model to the calculation of the surface tension of the liquid-vapor interface over a large range of temperatures. The coexisting densities, vapor pressures, and surface tensions calculated with different CG and atomistic models are compared to experiments. Depending on the database used for the development of the potentials, it is possible to build a CG model which performs very well in the reproduction of the surface tension on the orthobaric curve.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Understanding the origin of softness in structurally tailored and engineered macromolecular (STEM) gels: A DPD study

Structurally tailored and engineered macromolecular (STEM) gels are polymer networks containing a primary network (the “STEM-0” gel) and side chains that are anchored to this network (forming the “STEM-1” gel). The ability to vary the features of the side chains provides a robust means of tailoring the macroscopic properties of the material. We use dissipative particle dynamics (DPD) simulations to determine the mechanical response of STEM-1 gels to uniaxial compression for various values of the side chain length, n sc . While the Young’s modulus of the material is significantly decreased with increasing n sc (at a fixed density of grafted side chains), above a certain saturation value, increasing n sc does not lead to any further softening of the sample. Using the simulations, we calculate the relevant stresses, which are related to the Young’s modulus and the number of entanglements in the network. We show that the backbone chains become more spread out upon addition of sidechains, leading to a decrease in the physical entanglements between backbone segments. This observation accounts for the decrease of stress in the backbone network and the softening of the networks through the addition of sidechains. For long chains, however, the number density of physical crosslinks between side chains is independent of side chain length; this observation explains the observed stress saturation in the STEM-1 gels. Our approach allows us to correlate the molecular architecture of the gels to the resultant macroscopic mechanical behavior and provide guidelines for fabricating STEM gels with well-defined mechanical properties, which allow the materials to be used for a variety of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Correction to “Frank–Kasper Phases of Diblock Copolymer Melts Studied with the DPD Model: SCF Results”

In a recent study, (1) we performed the self-consistent field (SCF) calculations of a dissipative particle dynamics (DPD) model of conformationally asymmetric diblock copolymer A-B melts to study the stability of various Frank–Kasper (FK) phases formed by such A-B melts. While all the equations in ref (1) are correct, we did not calculate the dimensionless (mean-field) Helmholtz free energies per chain βf c = βu c,χ + βu c,κ – s c /k B and the dimensionless stresses dβf c /dθ of ordered phases accurately. For example, the dimensionless (nonbonded) internal energy per chain due to the system compressibility βu c,κ = (N/2κ)∑ q βû 0 (|q|)(ϕ̂ A (q) + ϕ̂ B (q) – δ q,0 )(ϕ̂ A (−q) + ϕ̂ B (−q) – δ– q,0 ) should be calculated in the reciprocal space as given (note that βû 0 (|q|) is obtained analytically, and that since ϕ A (r) is real 𝜙̂ A (−𝐪)= $\overline{𝜙̂ A}$(𝐪), i.e., the complex conjugate of ϕ̂ A (q)); this corresponds to numerically evaluating βu c,κ = (N/2κ)∫dr(ϕ A (r) + ϕ B (r) – 1)ψ(r) with ψ(r) ≡ (∫dr′βu 0 (|r–r′|)(ϕ A (r′) + ϕ B (r′) – 1)/V in the real space via the (composite) trapezoidal rule. While the trapezoidal rule is less accurate than the Romberg integration (2) in general, the opposite occurs when the integration domain is periodic; this was pointed out by Matsen in ref (3) and explained in detail in refs (4and5). With our ignorance of this exceptional convergence property of the trapezoidal rule for periodic functions, we calculated all convolutions having the form of ψ(r) in the reciprocal space via the fast Fourier transforms (6) but those having the form of βu c,κ in the real space via Romberg integration; the latter leads to inaccurate results and unfortunately incorrect phase diagrams reported in ref (1), which are corrected here.

He, Juntong↗
DOE OSTI2024

Machine Learning of Performance Space Mapping for the DPD Simulation of Drug Delivery to Endothelial Cells

Despite huge effort over the years, the design of functionalised nanocarriers (NCs) for targeted drug delivery to endothelial cells is still to be completely unveiled. Dissipative particle dynamics (DPD) simulation is used to study the adhesion of NCs to endothelial cells under the influence of series of parameters such as shape, size, and ligand density of the NCs. However, preparing a performance space mapping that illustrates the penetration depths of NCs as a function of variations in their properties requires simulations of all possible NCs with the above-mentioned properties, which is not feasible. This challenge was addressed by leveraging a Gaussian process regression (GPR)-informed active learning strategy and an extensive exploration of numerous samples, each representing different properties of NCs. The performance space mapping reveals that NCs with rod and disc shapes exhibit superior penetration capabilities compared to those with a spherical shape. Furthermore, it demonstrates that smaller-sized rod-shaped NCs and larger-sized disc-shaped NCs tend to achieve better penetration. When considering smaller NCs, the influence of ligand density appears to be limited. On the contrary, for larger NCs, an increase in ligand density correlates with greater penetration depth, underscoring its substantial role in shaping their penetration capabilities.

36 MATERIALS SCIENCE↗
DOE OSTI2018

DPD Parameters Estimation for Simultaneously Simulating Water–Oil Interfaces and Aqueous Nonionic Surfactants

The outcome of a coarse-grained simulation within the dissipative particle dynamics framework strongly depends on the choice of the repulsive parameter between different species. Different methodologies have been used in the literature to determine these parameters toward reproducing selected experimental system properties. In this work, a systematic investigation on possible procedures for estimating the simulation parameters is conducted. We compare methods based on the Hildebrand and the Hansen solubility parameter theories, mapped into the Flory–Huggins model. We find that using the Hansen solubility parameters it is possible to achieve a high degree of coarse graining, with parameters that yield realistic values for the interfacial tension. The procedure was first applied to the water/benzene system and then validated for water/n-octane, water/1,1-dichloroethane, water/methyl cyclohexane, and water/isobutyl acetate. In all these cases, the experimental interfacial tension could be reproduced by adjusting a single correction factor. In the case of the water–benzene system, the dissipative particle dynamics parameters derived using our approach were able to simultaneously describe both the interfacial tension and micellar properties of aqueous nonionic surfactants representative of the octyl poly(ethylene oxide) C8H17O(C2H4O)mH family. We show how the parameters can be used, within the dissipative particle dynamics framework, to simulate the water/oil interface in the presence of surfactants at varying concentrations. The results show, as expected, that as the surfactant concentration increases, the interfacial tension decreases, and micelles form in bulk water.

Chemistry↗
DOE OSTI2015

Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism

The Mori-Zwanzig formalism for coarse-graining a complex dynamical system typically introduces memory effects. The Markovian assumption of delta-correlated fluctuating forces is often employed to simplify the formulation of coarse-grained (CG) models and numerical implementations. However, when the time scales of a system are not clearly separated, the memory effects become strong and the Markovian assumption becomes inaccurate. To this end, we incorporate memory effects into CG modeling by preserving non-Markovian interactions between CG variables, and the memory kernel is evaluated directly from microscopic dynamics. For a specific example, molecular dynamics (MD) simulations of star polymer melts are performed while the corresponding CG system is defined by grouping many bonded atoms into single clusters. Then, the effective interactions between CG clusters as well as the memory kernel are obtained from the MD simulations. The constructed CG force field with a memory kernel leads to a non-Markovian dissipative particle dynamics (NM-DPD). Quantitative comparisons between the CG models with Markovian and non-Markovian approximations indicate that including the memory effects using NM-DPD yields similar results as the Markovian-based DPD if the system has clear time scale separation. However, for systems with small separation of time scales, NM-DPD can reproduce correct short-time properties that are related to how the system responds to high-frequency disturbances, which cannot be captured by the Markovian-based DPD model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

Processing the Interspecies Quorum-sensing Signal Autoinducer-2 (AI-2)

The molecule (S)-4,5-dihydroxy-2,3-pentanedione (DPD) is produced by many different species of bacteria and is the precursor of the signal molecule autoinducer-2 (AI-2). AI-2 mediates interspecies communication and facilitates regulation of bacterial behaviors such as biofilm formation and virulence. A variety of bacterial species have the ability to sequester and process the AI-2 present in their environment, thereby interfering with the cell-cell communication of other bacteria. This process involves the AI-2-regulated lsr operon, comprised of the Lsr transport system that facilitates uptake of the signal, a kinase that phosphorylates the signal to phospho-DPD (P-DPD), and enzymes (like LsrG) that are responsible for processing the phosphorylated signal. Because P-DPD is the intracellular inducer of the lsr operon, enzymes involved in P-DPD processing impact the levels of Lsr expression. Here we show that LsrG catalyzes isomerization of P-DPD into 3,4,4-trihydroxy-2-pentanone-5-phosphate. We present the crystal structure of LsrG, identify potential catalytic residues, and determine which of these residues affects P-DPD processing in vivo and in vitro. We also show that an lsrG deletion mutant accumulates at least 10 times more P-DPD than wild type cells. Consistent with this result, we find that the lsrG mutant has increased expression of the lsr operon and an altered profile of AI-2 accumulation and removal. Understanding of the biochemical mechanisms employed by bacteria to quench signaling of other species can be of great utility in the development of therapies to control bacterial behavior.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows

Active- and transfer-learning are applied to microscale dynamics of polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The result is macroscopic rheology directly connected to a microstructural model. Micro and macroscale simulations are adaptively coupled by means of Gaussian process regression (GPR) to run the expensive microscale computations only as necessary. This multiscale method is demonstrated with flows of a polymer solution as a model system. At the microscale level dissipative particle dynamics (DPD) is employed to model the fluid as a suspension of bead-spring micro-structures subjected to steady shear flow. The results yield the non-Newtonian viscosity and the first normal stress difference at strain rates as training data used in a GPR model. DPD parameters are calibrated with respect to experimental data for a real polymer solution. Compliance with these data requires adjustment of the DPD model's cutoff radius, which then becomes a function of the second invariant of the strain rate tensor. The FENE-P model is chosen for the macroscale description using the spectral element method (SEM) to simulate channel flow and flow past a circular cylinder. The DPD results at the lowest possible shear strain rate yield an estimate of the zero-shear rate viscosity, which allows the initiation of the macroscale flow by SEM as a Newtonian fluid. The resulting strain-rate field is surveyed to determine additional shear strain rate sampling points for the DPD system. This new information allows an initial fitting of parameters of the constitutive equation followed by new SEM simulations at the macroscale. Additionally, guided by active-learning GPR to select new sampling points, this process continues until convergence is achieved. The effectiveness of this new simulation paradigm for viscoelastic flows is tested with different macroscale operating conditions. The effective closure learned in the channel simulation is then transferred directly to the flow past a circular cylinder at low Reynolds number, where the results show that only two additional DPD simulations are required to achieve a satisfactory constitutive model. With an increase of the Reynolds number, the active-learning scheme automatically detects the inaccuracy of the learned constitutive model, and initiates additional DPD simulations for the extra data needed to once again close the microscale-macroscale coupled system. This new paradigm of active- and transfer-learning for multiscale modeling is readily applicable to other microscale-macroscale coupled simulations of complex fluids and other materials. Furthermore, the coupling between microscale and macroscale solvers can be seamlessly implemented with our open source multiscale universal interface (MUI) library.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Computing the non-Markovian coarse-grained interactions derived from the Mori–Zwanzig formalism in molecular systems: Application to polymer melts

Memory effects are often presented during coarse-graining of a complex dynamical system. In particular, a generalized Langevin equation (GLE) for the coarse-grained (CG) system arises in the context of Mori–Zwanzig formalism. Upon a pairwise decomposition, GLE can be reformulated into its pairwise version, i.e., non-Markovian dissipative particle dynamics (DPD). GLE models the dynamics of a single coarse particle, while DPD considers the dynamics of many interacting CG particles, with both CG systems governed by non-Markovian interactions. We compare two different methods for the practical implementation of the non-Markovian interactions in GLE and DPD systems. In particular, a direct evaluation of the non-Markovian (NM) terms is performed in LE-NM and DPD-NM models, which requires the storage of historical information that significantly increases computational complexity. Alternatively, we use a few auxiliary variables in LE-AUX and DPD-AUX models to replace the non-Markovian dynamics with a Markovian dynamics in a higher dimensional space, leading to a much reduced memory footprint and computational cost. In our numerical benchmarks, the GLE and non-Markovian DPD models are constructed from molecular dynamics (MD) simulations of star-polymer melts. Results show that a Markovian dynamics with auxiliary variables successfully generates equivalent non-Markovian dynamics consistent with the reference MD system, while maintaining a tractable computational cost. Also, transient subdiffusion of the star-polymers observed in the MD system can be reproduced by the coarse-grained models. The non-interacting particle models, LE-NM/AUX, are computationally much cheaper than the interacting particle models, DPD-NM/AUX. Yet, the pairwise models with momentum conservation are more appropriate for correctly reproducing the long-time hydrodynamics characterised by an algebraic decay in the velocity autocorrelation function.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Water desalination through the dewpoint evaporative system

This paper presents the first numerical analysis of a novel Dew Point Desalination unit (DPD), which utilizes the phenomenon of the evaporative cooling process to minimize energy consumption. The proposed solution is based on a modular structure, which allows it to obtain any required water production capacity. The analyses for various operational parameters and various climatic conditions are based on the numerical simulations conducted using a validated mathematical model. The system performance was described with different factors, including specific energy consumption (SEC) and daily water production rate respected to 1 m^3*s^-1 of passing air stream (DWP). It is shown that the DPD system can operate in almost every climate zone, and the desalinated water production consumes less than 1.5 kWh*m^-3 of electric energy It is noted the best performance of the DPD system is achieved in the semi-arid, desert and subtropical climate zones. In these climate zones, the 2-stage DPD system allows the production of desalinated water with an electric energy consumption SEC lower than 0.5 kWh*m^-3. The additional benefit of the DPD system is its modularity. The presented DPD system stands as a module, that can be multiplied as needed to achieve the desired size of the desalination plant.

Desalination↗
DOE OSTI2018

Active learning of constitutive relation from mesoscopic dynamics for macroscopic modeling of non-Newtonian flows

We simulate complex fluids by means of an on-the-fly coupling of the bulk rheology to the underlying microstructure dynamics. In particular, a continuum model of polymeric fluids is constructed without a pre-specified constitutive relation, but instead it is actively learned from mesoscopic simulations where the dynamics of polymer chains is explicitly computed. To couple the bulk rheology of polymeric fluids and the microscale dynamics of polymer chains, the continuum approach (based on the finite volume method) provides the transient flow field as inputs for the (mesoscopic) dissipative particle dynamics (DPD), and in turn DPD returns an effective constitutive relation to close the continuum equations. In this multiscale modeling procedure, we employ an active learning strategy based on Gaussian process regression (GPR) to minimize the number of expensive DPD simulations, where adaptively selected DPD simulations are performed only as necessary. Numerical experiments are carried out for flow past a circular cylinder of a non-Newtonian fluid, modeled at the mesoscopic level by bead-spring chains. The results show that only five DPD simulations are required to achieve an effective closure of the continuum equations at Reynolds number RE = 10. Furthermore, when Re is increased to 100, only one additional DPD simulation is required for constructing an extended GPR-informed model closure. Compared to traditional message-passing multiscale approaches, applying an active learning scheme to multiscale modeling of non-Newtonian fluids can significantly increase the computational efficiency. Although the method demonstrated here obtains only a local viscosity from the polymer dynamics, it can be extended to other multiscale models of complex fluids whose macro-rheology is unknown.

Computer Science↗
DOE OSTI2019

Concurrent coupling of atomistic simulation and mesoscopic hydrodynamics for flows over soft multi-functional surfaces

We develop an e?cient parallel multiscale method that bridges the atomistic and mesoscale regimes, from nanometers to microns and beyond, via concurrent coupling of atomistic simulation and mesoscopic dynamics. In particular, we combine an all-atom molecular dynamics (MD) description for specific atomistic details in the vicinity of the functional surface with a dissipative particle dynamics (DPD) approach that captures mesoscopic hydrodynamics in the domain away from the functional surface. In order to achieve a seamless transition in dynamic properties we endow the MD simulation with a DPD thermostat, which is validated against experimental results by modeling water at di?erent temperatures. We then validate the MD-DPD coupling method for transient Couette and Poiseuille flows, demonstrating that the concurrent MD-DPD coupling can resolve accurately the continuumbased analytical solutions. Subsequently, we simulate shear flows over grafted polydimethylsiloxane (PDMS) surfaces (polymer brushes) for various grafting densities, and investigate the slip flow as a function of the shear stress. We verify that a ‘‘universal’’ power law exists for the slip length, in agreement with published results. Having validated the MD-DPD coupling method, we simulate timedependent flows past an endothelial glycocalyx layer (EGL) in a microchannel. Coupled simulation results elucidate the dynamics of the EGL changing from an equilibrium state to a compressed state under shear by aligning the molecular structures along the shear direction. MD-DPD simulation results agree well with results of a single MD simulation, but with the former more than two orders of magnitude faster than the latter for system sizes above one micron.

Wang, Yuying↗
DOE OSTI2016

Dissipative particle dynamics of diffusion-NMR requires high Schmidt-numbers

We present an efficient mesoscale model to simulate the diffusion measurement with nuclear magnetic resonance (NMR). On the level of mesoscopic thermal motion of fluid particles, we couple the Bloch equations with dissipative particle dynamics (DPD). Thereby we establish a physically consistent scaling relation between the diffusion constant measured for DPD-particles and the diffusion constant of a real fluid. The latter is based on a splitting into a centre-of-mass contribution represented by DPD, and an internal contribution which is not resolved in the DPD-level of description. As a consequence, simulating the centre-of-mass contribution with DPD requires high Schmidt numbers. After a verification for fundamental pulse sequences, we apply the NMR-DPD method to NMR diffusion measurements of anisotropic fluids, and of fluids restricted by walls of microfluidic channels. For the latter, the free diffusion and the localisation regime are considered.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Self-Assembly and Critical Aggregation Concentration Measurements of ABA Triblock Copolymers with Varying B Block Types: Model Development, Prediction, and Validation

The dissipative particle dynamics (DPD) simulation technique is a coarse-grained (CG) molecular dynamics-based approach that can effectively capture the hydrodynamics of complex systems while retaining essential information about the structural properties of the molecular species. An advantageous feature of DPD is that it utilizes soft repulsive interactions between the beads, which are CG representation of groups of atoms or molecules. In this study, we used the DPD simulation technique to study the aggregation characteristics of ABA triblock copolymers in aqueous medium. Pluronic polymers (PEG-PPO-PEG) were modeled as two segments of hydrophilic beads and one segment of hydrophobic beads. Tyrosine-derived PEG 5K -b-oligo(desaminotyrosyl tyrosine octyl ester-suberate)-b-PEG 5K (PEG5K-oligo(DTO-SA)-PEG 5K ) block copolymers possess alternate rigid and flexible components along the hydrophobic oligo(DTO-SA) chain, and were modeled as two segments of hydrophilic beads and one segment of hydrophobic, alternate soft and hard beads. The formation, structure, and morphology of the initial aggregation of the polymer molecules in aqueous medium were investigated by following the aggregation dynamics. The dimensions of the aggregates predicted by the computational approach were in good agreement with corresponding results from experiments, for the Pluronic and PEG 5K -oligo(DTO-SA)-PEG 5K block copolymers. In addition, DPD simulations were utilized to determine the critical aggregation concentration (CAC), which was compared with corresponding results from an experimental approach. For Pluronic polymers F68, F88, F108, and F127, the computational results agreed well with experimental measurements of the CAC measurements. For PEG 5K -b-oligo(DTO-SA)-b-PEG 5K block polymers, the complexity in polymer structure made it difficult to directly determine their CAC values via the CG scheme. Therefore, we determined CAC values of a series of triblock copolymers with 3–8 DTO-SA units using DPD simulations, and used these results to predict the CAC values of triblock copolymers with higher molecular weights by extrapolation. In parallel, a PEG 5K -b-oligo(DTO-SA)-b-PEG 5K block copolymer was synthesized, and the CAC value was determined experimentally using the pyrene method. The experimental CAC value agreed well with the CAC value predicted by simulation. Furthermore, these results validate our CG models, and demonstrate an avenue to simulate and predict aggregation characteristics of ABA amphiphilic triblock copolymers with complex structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Dissipative Particle Dynamics Simulation Study of Poly(2-Oxazoline)-based Multicompartment Micelle Nanoreactor

We investigate multicompartment micelle consisting of poly(2-oxazoline)-based triblock copolymers for nanoreactor application, using DPD simulation method to characterize the internal structure of micelle and the distribution of reactant. Here, the DPD simulation parameters are determined from the Flory-Huggins interaction parameter (χ FH ). From the snapshots of the micellar structures and radial distribution function of polymer blocks, it is clearly presented that the micelle has the feature of the multicompartment. In addition, by implementing the DPD simulations in the presence of reactants, it is found that Reac-C4 and Reac-OPh are well associated with the hydrophilic shell of the micelle whereas other two reactants such as Reac-Ph and Reac-Cl are not incorporated into the micelle. From our DPD simulations, we confirm that the miscibility (solubility) of reactant with the micelle has strong correlation with the rate of hydrolysis kinetic resolution. Utilizing accurate methods evaluating accurate χ FH parameters for molecular interactions in micelle system, this DPD simulation can have a great potential to predict the micelle structures consisting of designed multiblock copolymers for useful reactions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Bottom-up derivation of conservative and dissipative interactions for coarse-grained molecular liquids with the conditional reversible work method

Molecular simulations of soft matter systems have been performed in recent years using a variety of systematically coarse-grained models. With these models, structural or thermodynamic properties can be quite accurately represented while the prediction of dynamic properties remains difficult, especially for multi-component systems. In this work, we use constraint molecular dynamics simulations for calculating dissipative pair forces which are used together with conditional reversible work (CRW) conservative forces in dissipative particle dynamics (DPD) simulations. The combined CRW-DPD approach aims to extend the representability of CRW models to dynamic properties and uses a bottom-up approach. Dissipative pair forces are derived from fluctuations of the direct atomistic forces between mapped groups. The conservative CRW potential is obtained from a similar series of constraint dynamics simulations and represents the reversible work performed to couple the direct atomistic interactions between the mapped atom groups. Neopentane, tetrachloromethane, cyclohexane, and n-hexane have been considered as model systems. These molecular liquids are simulated with atomistic molecular dynamics, coarse-grained molecular dynamics, and DPD. We find that the CRW-DPD models reproduce the liquid structure and diffusive dynamics of the liquid systems in reasonable agreement with the atomistic models when using single-site mapping schemes with beads containing five or six heavy atoms. For a two-site representation of n-hexane (3 carbons per bead), time scale separation can no longer be assumed and the DPD approach consequently fails to reproduce the atomistic dynamics.

2-2-DIMETHYLPROPANE↗
DOE OSTI2016

Coarse-grained model of water diffusion and proton conductivity in hydrated polyelectrolyte membrane

Using dissipative particle dynamics (DPD), we simulate nanoscale segregation, water diffusion, and proton conductivity in hydrated sulfonated polystyrene (sPS). We employ a novel model [Lee et al. J. Chem. Theory Comput. 11(9), 4395-4403 (2015)] that incorporates protonation/deprotonation equilibria into DPD simulations. The polymer and water are modeled by coarse-grained beads interacting via short-range soft repulsion and smeared charge electrostatic potentials. The proton is introduced as a separate charged bead that forms dissociable Morse bonds with the base beads representing water and sulfonate anions. Morse bond formation and breakup artificially mimics the Grotthuss mechanism of proton hopping between the bases. The DPD model is parameterized by matching the proton mobility in bulk water, dissociation constant of benzenesulfonic acid, and liquid-liquid equilibrium of water-ethylbenzene solutions. The DPD simulations semi-quantitatively predict nanoscale segregation in the hydrated sPS into hydrophobic and hydrophilic subphases, water self-diffusion, and proton mobility. As the hydration level increases, the hydrophilic subphase exhibits a percolation transition from isolated water clusters to a 3D network. The analysis of hydrophilic subphase connectivity and water diffusion demonstrates the importance of the dynamic percolation effect of formation and breakup of temporary junctions between water clusters. The proposed DPD model qualitatively predicts the ratio of proton to water self-diffusion and its dependence on the hydration level that is in reasonable agreement with experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗