Dielectric boundary effects on the interaction between planar charged surfaces with counterions only

Santos, Alexandre Pereira dos; Netz, Roland R.

doi:10.1063/1.5022226

Cited by 16 publications

(10 citation statements)

References 50 publications

(75 reference statements)

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“…It is well known that like-charged colloidal particles can attract one another if suspension contains multivalent counterions 21,[31][32][33][34][35][36][37][38][39][40][41][42] . This attraction results from the electrostatic correlations between the double layers of condensed multivalent counterions 21 .…”

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confidence: 99%

Like-Charge Attraction between Metal Nanoparticles in a1∶1Electrolyte Solution

Santos

Levin

2019

Phys. Rev. Lett.

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We calculate the force between two spherical metal nanoparticles of charge Q 1 and Q 2 in a dilute 1:1 electrolyte solution. Numerically solving the non-linear Poisson-Boltzmann equation, we find that metal nanoparticles with the same sign of charge can attract one another. This is fundamentally different from what is found for likecharged, non-polarizable, colloidal particles, the two body interaction potential for which is always repulsive inside a dilute 1:1 electrolyte. Furthermore, existence of like-charge attraction between spherical metal nanoparticles is even more surprising in view of the result that such attraction is impossible between parallel metal slabs, showing the fundamental importance of curvature. To overcome a slow convergence of the numerical solution of the full non-linear Poisson-Boltzmann equation, we developed a modified Derjaguin approximation which allows us to accurate and rapidly calculate the interaction potential between two metal nanoparticles, or between a metal nanoparticle and a phospholipid membrane.

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Like-Charge Attraction between Metal Nanoparticles in a1∶1Electrolyte Solution

Santos

Levin

2019

Phys. Rev. Lett.

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“…It is known that surface polarization is very important to the electrical double layer. It influences, for example, the ionic distribution around surfaces, − the conformations of confined polyelectrolytes, the differential capacitance in supercapacitors, and the structure of polyelectrolyte brushes. , How does the surface polarization influence electroosmosis? There is a lack of literature related to the effect of surface polarization on induced electroosmotic flow to our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Electroosmotic Flow Grows with Electrostatic Coupling in Confining Charged Dielectric Surfaces

Telles

Santos

2021

Langmuir

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In this work, the effects of polarization of confining charged planar dielectric surfaces on induced electroosmotic flow are investigated. To this end, we use dissipative particle dynamics to model solvent and ionic particles together with a modified Ewald sum method to model electrostatic interactions and surfaces polarization. A relevant difference between counterions number density profiles, velocity profiles, and volumetric flow rates obtained with and without surface polarization for moderate and high electrostatic coupling parameters is observed. For low coupling parameters, the effect is negligible. An increase of almost 500% in volumetric flow rate for moderate/high electrostatic coupling and surface separation is found when polarizable surfaces are considered. The most important result is that the increase in electrostatic coupling substantially increases the electroosmotic flow in all studied range of separations when the dielectric constant of electrolytes is much higher than the dielectric constant of confining walls. For the higher separation simulated, an increase of around 340% in volumetric flow rate when the electrostatic coupling is increased by a factor of two orders of magnitude is obtained.

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“…This computation typically involves solving the second-order Poisson differential equation in three-dimensional space at each simulation timestep, making the use of conventional nanoscale simulation methods very time-consuming and inefficient. Because of these computational challenges, the problem of extracting ionic structure near polarizable NPs has been a subject of intense research (Allen et al, 2001;Boda et al, 2004;dos Santos et al, 2011;dos Santos and Netz, 2018;Fahrenberger et al, 2014;Gan et al, 2015;Jadhao et al, 2012Jadhao et al, , 2013Marchi et al, 2001;Qin et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…This computation typically involves solving the second-order Poisson differential equation in 3-dimensional space at each simulation timestep, making the use of conventional nanoscale simulation methods very time consuming and inefficient. Because of these computational challenges, the problem of extracting ionic structure near polarizable NPs has been a subject of intense research [3,15,28,29,31,37,43,44,58,63].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles

Kadupitiya¹,

Fox²,

Jadhao

2020

The International Journal of High Performance Computing Applica

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Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method based on a dynamical optimization framework bypassed this obstacle by representing the polarization charge density as virtual dynamic variables, and evolving them in parallel with the physical dynamics of ions. We highlight the computational gains accessible with the integration of machine learning (ML) methods for parameter prediction in MD simulations by demonstrating how they were realized in MD simulations of ions near polarizable NPs. An artificial neural network based regression model was integrated with MD simulation and predicted the optimal simulation timestep and optimization parameters characterizing the virtual system with 94.3% success. The ML-enabled auto-tuning of parameters generated accurate dynamics of ions for ≈ 10 million steps while improving the stability of the simulation by over an order of magnitude. The integration of ML-enhanced framework with hybrid OpenMP/MPI parallelization techniques reduced the computational time of simulating systems with thousands of ions and induced charges from thousands of hours to tens of hours, yielding a maximum speedup of ≈ 3 from ML-only acceleration and a maximum speedup of ≈ 600 from the combination of ML and parallel computing methods. Extraction of ionic structure in concentrated electrolytes near oil-water emulsions demonstrates the success of the method. The approach can be generalized to select optimal parameters in other MD applications and energy minimization problems.

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Dielectric boundary effects on the interaction between planar charged surfaces with counterions only

Cited by 16 publications

References 50 publications

Like-Charge Attraction between Metal Nanoparticles in a1∶1Electrolyte Solution

Like-Charge Attraction between Metal Nanoparticles in a1∶1Electrolyte Solution

Electroosmotic Flow Grows with Electrostatic Coupling in Confining Charged Dielectric Surfaces

Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles

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