Memory effects in nanoparticle dynamics and transport

Sanghi, Tarun; Bhadauria, Ravi; Aluru, N. R.

doi:10.1063/1.4964287

Cited by 8 publications

(6 citation statements)

References 40 publications

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“…Indeed, mass-dependent friction has recently been observed in simulations of water-solvated fullerene nanoparticles. 53 Our results show that power-law relations of the form D ∝ m −β do not hold over the complete range of solute/solvent mass ratios. In particular, the finite friction constant observed for m → 0 is not recovered by such a power-law fit.…”

Section: The Journal Of Physical Chemistry Bmentioning

confidence: 65%

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Mass-Dependent Solvent Friction of a Hydrophobic Molecule

Daldrop

Netz

2019

J. Phys. Chem. B

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We show by molecular dynamics simulations that the friction constant of a freely diffusing methane molecule in explicit water increases with the methane mass by a factor of up to 1.8 in the infinite mass limit compared to the massless limit. This effect is rationalized by the mass dependence of the friction memory kernel which is extracted from the simulation data by mapping on the generalized Langevin equation. On the basis of the mass-dependent memory kernels, we obtain perfect agreement between simulation results and analytic predictions for both mean-square displacements and force autocorrelation functions. The memory kernels, which account for methane interactions with the solvent, decay significantly slower with increasing methane mass. The mass-dependent friction is correlated with the mean escape time of water molecules from the first hydration shell, which increases monotonically with the methane mass by a factor of 3 over the mass range considered. Our proposed scaling expression allows the direct prediction of diffusion constants for stable methane isotopes, which will help to better understand bacterial methane isotope fractionation. Our scaling analysis suggests that mass-dependent friction will be relevant also for larger solutes in sufficiently viscous solvents.

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Section: The Journal Of Physical Chemistry Bmentioning

confidence: 65%

“…Therefore, also in water, we expect mass-dependent friction for solutes that are larger than methane. Indeed, mass-dependent friction has recently been observed in simulations of water-solvated fullerene nanoparticles …”

Section: Conclusion and Discussionmentioning

confidence: 99%

Mass-Dependent Solvent Friction of a Hydrophobic Molecule

Daldrop

Netz

2019

J. Phys. Chem. B

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“…In this line, Sanghi et al used the GLE to characterize memory effects in fullerene nanoparticle dynamics and investigated the scaling of the memory kernel with the nanoparticle mass, shape, and size. They observed that the FACF and the random force ACF are indeed comparable in the large nanoparticle mass limit . Nonetheless, for finite mass CG models, an intermediate plateau can be found in several cases, and the plateau values can then be taken to determine the friction coefficient. , …”

Section: The Markovian Assumptionmentioning

confidence: 98%

Introducing Memory in Coarse-Grained Molecular Simulations

et al. 2021

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Preserving the correct dynamics at the coarse-grained (CG) level is a pressing problem in the development of systematic CG models in soft matter simulation. Starting from the seminal idea of simple time-scale mapping, there have been many efforts over the years toward establishing a meticulous connection between the CG and fine-grained (FG) dynamics based on fundamental statistical mechanics approaches. One of the most successful attempts in this context has been the development of CG models based on the Mori−Zwanzig (MZ) theory, where the resulting equation of motion has the form of a generalized Langevin equation (GLE) and closely preserves the underlying FG dynamics. In this Review, we describe some of the recent studies in this regard. We focus on the construction and simulation of dynamically consistent systematic CG models based on the GLE, both in the simple Markovian limit and the non-Markovian case. Some recent studies of physical effects of memory are also discussed. The Review is aimed at summarizing recent developments in the field while highlighting the major challenges and possible future directions.

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“…(13b)]. The numerical method to integrate the equations of motion in the GLE framework is described in detail in our previous work, 35 and the accuracy of the correlations computed using the GLE framework with respect to the EMD simulations is discussed in Sec. IV.…”

Section: B Interfacial Friction Modelmentioning

confidence: 99%

A multiscale transport model for non-classical nanochannel electroosmosis

Bhadauria

Aluru

2017

The Journal of Chemical Physics

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We present a multiscale model describing the electroosmotic flow (EOF) in nanoscale channels involving high surface charge liquid-solid interfaces. The departure of the EOF velocity profiles from classical predictions is explained by the non-classical charge distribution in the confined direction including charge inversion, reduced mobility of interfacial counter-ions, and subsequent enhancement of the local viscosity. The excess component of the local solvent viscosity is modeled by the local application of the Fuoss-Onsager theory and the Hubbard-Onsager electro-hydrodynamic equation based dielectric friction theory. The electroosmotic slip velocity is estimated from the interfacial friction coefficient, which in turn is calculated using a generalized Langevin equation based dynamical framework. The proposed model for local viscosity enhancement and EOF velocity shows good agreement of corresponding physical quantities against relevant molecular dynamics simulation results, including the cases of anomalous transport such as EOF reversal.

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Memory effects in nanoparticle dynamics and transport

Cited by 8 publications

References 40 publications

Mass-Dependent Solvent Friction of a Hydrophobic Molecule

Mass-Dependent Solvent Friction of a Hydrophobic Molecule

Introducing Memory in Coarse-Grained Molecular Simulations

A multiscale transport model for non-classical nanochannel electroosmosis

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