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The Nanosphere Electrostatics Lab empowers users to simulate the self-assembly of ions near a spherically shaped nanoparticle, and extract the effective electrostatic properties.
The Nanosphere Electrostatics Lab empowers users to simulate the self-assembly of ions near a spherically shaped nanoparticle, and extract the effective electrostatic properties. Accurate knowledge of effective charge of nanoparticles (NPs) and ionic structure near NPs is useful in the design of engineered nanocontainers for biological applications. This information also enables the understanding of nanoscale phenomena such as protein conformational changes, DNA precipitation, NP self-assembly, stability of emulsions, and charging/discharging processes in supercapacitor systems. Users can change the NP radius and charge to study their effects on the ionic density profile near the NP. In addition, the app enables the study of both salt-free (counterion only) and salty systems via the salt concentration parameter. One computing parameter -- the number of simulation steps, is provided for the users to monitor the convergence of density profiles. Converged density profiles are expected for simulation steps greater than 1 million. The solvent near the NP (inside and outside) is considered as water in this study. The output tabs include real-time monitoring of the energies associated with this system and the evolution of the density profile for the ions (ionic structure). App enables users to change the dielectric properties of the NP and its environment (solvent, e.g. water) and monitor the effect of polarized charges on the ionic distributions and effective electrostatic properties of the NP. App supports the calculation of the effective charge of the NP determined by the counterion condensation on the NP surface and other NP attributes. This app is based on a coarse-grained model of NP and ions with implicit solvent that was simulated using molecular dynamics method. The app was enhanced using a Hybrid MPI/OpenMP parallelization method as well as a machine learning approach designed to automate the evolution of the polarized charges. The app is being tested experimentally by measuring zeta potentials of NPs of different radius and bare charge under various ionic conditions; numerical validation has been performed via LAMMPS.
NSF award 1720625 (Network for Computational Nanotechnology - Engineered nanoBIO Node)
V. Jadhao, F. J. Solis, and M. Olvera de la Cruz, “Simulation of charged systems in heterogeneous dielectric media via a true energy functional”, Phys. Rev. Lett. 109, 223905 (2012)
A. Diehl and Y. Levin, Effective charge of colloidal particles. The Journal of Chemical Physics 121, 12100–12103 (2004).
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