We developed a method that allows us to create simulated aluminum nanoparticles of different sizes and with a variety of crystalline compositions by melting and recrystallizing the nanoparticles at various cooling rates. By varying the cooling rate of the nanoparticle and using different initial seeds, we can produce simulated aluminum nanoparticles with any natural crystalline composition and size. For larger nanoparticles (5 and 4 nm in diameter), we find that slower cooling rates are necessary to obtain a more ordered face-centered cubic crystal structure. For smaller crystallized nanoparticles (diameter 3.5 nm), there is an increase in the proportion of the hexagonal close-packed crystal structure compared to the face-centered cubic crystal structure in the resulting nanoparticle. The smallest crystallized nanoparticles (diameter 3 nm) have a predominantly amorphous structure. We also occasionally observed the formation of an atypical crystalline state for the chosen aluminum potential model. Potentially, our technique can be applied to any material of choice as long as it can be heated up and cooled down (crystallized). Our method can be used to create aluminum (or any other material) nanoparticles of various sizes and different crystalline compositions for further use in computational studies. In addition, this method can be used to test new potential interaction models for possible pitfalls and atypical crystalline structures, which might lead to the improvement of interatomic potential models, especially for nanoparticles.

Nanopores in solid-state membranes have been used to detect, identify, filter, and characterize nanoparticles and biological molecules. In this work, we simulate an ionic flow through a nanopore while an ellipsoidal nanoparticle translocates through a pore. We numerically solve the Poisson-Nernst-Planck equations to obtain the ionic current values for different aspect ratios, sizes, and orientations of a translocating particle. By extending the existing theoretical model for the ionic current in the nanopore to the particles of ellipsoidal shape, we propose semiempirical fitting formulas which describe our computed data within 5% accuracy. We also demonstrate how the derived formulas can be used to identify the dimensions of nanoparticles from the available experimental data which may have useful applications in bionanotechnology.

When a nanoparticle (or a biomolecule) translocates through a nanopore in a solid-state membrane submereged in an electrolyte solution, the ionic current changes and the ionic current blockadeIonic current blockade appears. In this work we elucidate how the concentration polarizationConcentration polarization, the surface charge on the nanopore and nanoparticle as well as the applied voltage affect the ionic current blockadeIonic current blockade. We use Brownian dynamics approach in conjunction with a full three-dimensional self-consistent solution of the Poisson-Nernst-Planck and Navier-Stockes system of equations to describe realistic ionic current response arising due to the random motion of a nanoparticle through a nanopore. We then derive a closed form expression for the ionic current blockadeIonic current blockade ratio and apply it to analyze the computed and experimentally measured ionic current blockadesIonic current blockade.

We use Brownian dynamics simulations to study the motion of cylindrical capsule-like particles (capsules) as they translocate through nanopores of various radii in an electrically biased silicon membrane. We find that for all pore sizes the electrostatic interaction between the particle and the pore results in the particle localization towards the pore 's center when the membrane and the particle have charges of the same sign (case 1) while in case of the opposite sign charges, the capsule prefers to stay near and along the nanopore wall (case 2). The preferential localization leads to all capsules rotating less while inside the pore compared to the bulk solution, with a larger net charge and/or particle length resulting in a smaller range of rotational movement. It also strongly affects the whole translocation process: in the first case, the translocation is due to the free diffusion along the pore axis and is weakly dependent on the particle charge and the nanopore radius while in the second case, the translocation time dramatically increases with the particle size and charge as the capsule gets "stuck" to the nanopore surface.

This themed collection includes a collection of articles on molecular simulation of chemistry and physics in external electric fields.

Nanoporous membranes may provide an approach for rapidly filtering proteins at high throughput volume, a goal that many fields of study would find useful. Creating a system to separate proteins quickly would require an extensive knowledge of protein-nanopore and protein-protein dynamics. To further this knowledge, we use Brownian dynamics simulations to model the motion of two similarly sized proteins (insulin and ubiquitin) coarse-grained and interacting with each other in a system with a cylindrical nanopore (of radius ranging from 30 to 60 Å), an electrically biased membrane (−1 and 1 V), and a zero electrolyte bias. The time each protein takes to move from one side of the membrane to the other (cis chamber to the trans chamber) is compared in order to find the system that best encourages the separation of the two proteins. Additionally, we studied the interaction of two spherical beads of various sizes and charges in order to determine the favorable/viable circumstances of separating proteins. We use the results of these simple cases to gain insight on the more complex dynamics present in modeling specific proteins.

We elucidate how the concentration polarization, the surface charge densities on the nanopore and nanoparticle, as well as the applied voltage affect the ionic current blockade arising when a nanosized object translocates through a nanopore. We compare our results with self-consistent numerical calculations of the ionic current within the Poisson-Nernst-Planck model to determine their validity. We then apply them to explain the observed trends in the recently measured ionic current traces and extract the surface charge density values for the nanoparticles and nanopores. Our results show that the ionic current is much more sensitive to the passage of small charged objects as compared to the neutral ones of the same size.

Nanoporous membranes provide an attractive approach for rapid filtering of nanoparticles at high-throughput volume, a goal useful to many fields of science and technology. Creating a device to readily separate different particles would require an extensive knowledge of particle-nanopore interactions and particle translocation dynamics. To this end, we use a multiscale model for the separation of nanoparticles by combining microscopic Brownian dynamics simulations to simulate the motion of spherical nanoparticles of various sizes and charges in a system with nanopores in an electrically biased membrane with a macroscopic filtration model accounting for bulk diffusion of nanoparticles and membrane surface pore density. We find that, in general, the separation of differently sized particles is easier to accomplish than of differently charged particles. The separation by charge can be better performed in systems with low pore density and/or smaller filtration chambers when electric nanopore-particle interactions are significant. The results from these simple cases can be used to gain insight in the more complex dynamics of separating, for example, globular proteins.

Nanopore sensing and detection over the last few years has become an integral and useful method for identification and separation of biomolecules and DNA chains. In order to better identify DNA molecules with very slight variations in nucleotide composition, it is imperative to study the ionic current variations caused by individual nucleotides placed in a solid-state nanopore. In this work, we perform a systematic study, using a self-consistent method of computation, of the dependence of ionic current variations on the orientation of the translocating nucleotide. We simulate ionic current and electric potential trends for an individual nucleotide placed in a silicon dioxide nanopore for certain pre-defined orientations in different planes of rotations. We illustrate how slight variations in the nucleotide's orientation can alter the measured ionic current and electric potential and obscure the variations caused by the nucleotide substitution.

We develop an atomistic model of a cerium dioxide CeO2 nanoparticle, which we then extend to our model of a ceria nanoparticle with a varied Ce3 + /Ce4 +  composition. For a pure CeO2 particle we compute the radial distribution function for all pairs of atoms in the nanoparticle, which we find is in excellent agreement with the reported experimental data. For a particle with a mixed Ce3 + /Ce4 +  we adjust the parameters and modify the crystallization procedure to produce a realistic distribution of Ce3 +  atoms on the particle. We improve our initial guess of the Lennard-Jones parameters by melting and recrystallizing the nanoparticle, as well as computing the radial distribution function for the nanoparticle at room temperature.

We present a novel computational approach to self-consistently describe a nanoparticle moving through a nanopore embedded in a solid-state membrane. We calculate the electrostatic and hydrodynamic forces a particle experiences at various locations within a nanopore and incorporate these forces into a Brownian dynamics model at each time step. The value of the ionic current through the pore is determined based on the particle's location, and in conjunction with our Brownian dynamics model, a realistic ionic current trace is obtained due to the motion of a nanoparticle through a nanopore. We find that in addition to the usual geometric blockade, the variations of the current along the axis of the pore are largely caused by a concentration polarization induced by the presence of the translocating nanoparticle in the nanopore while the current changes in the radial (perpendicular to the axis) direction occur because of the local buildup of the ionic charge between the particle and the nanopore surface.

The ability to separate proteins is desirable for many fields of study, and nanoporous membranes may offer a method for rapid protein filtration at high throughput volume, provided there is an understanding of the protein dynamics involved. In this work, we use Brownian dynamics simulations to study the motion of coarse-grained proteins insulin and ubiquitin in an electrically biased membrane. In our model, the protein is subjected to various biases applied to the silicon membrane equipped with a nanopore of different radii. The time each protein takes to find a cylindrical nanopore embedded in a thin silicon membrane, attempt to translocate it (waiting time), and successfully translocate it in a single attempt (translocation time) is calculated. We observe insulin finding the nanopore and translocating it faster than the electrically neutral ubiquitin due to insulin's slightly smaller size and net negative charge. While ubiquitin's dynamics is also affected by the size of the pore, surprisingly, its translocation process is also noticeably changed by the membrane bias. By investigating the protein's multipole moments, we demonstrate that this behavior is largely due to the protein's dipole and quadrupole interactions with the membrane potential.

The ability to separate proteins is desirable for many fields of study and nanoporous membranes may offer a method for rapid protein filtration at high throughput volume provided there is an understanding of the protein dynamics involved. We use Brownian dynamics simulations to model the motion of coarse-grained proteins insulin and ubiquitin in an electrically biased membrane under zero electrolyte bias. The time each protein takes to find a cylindrical nanopore embedded in a thin silicon dioxide membrane (waiting time) and successfully translocate it in a single attempt (translocation time) are calculated. In our model, each protein is subjected to various biases applied to the silicon membrane equipped with a nanopore of different radii. We observe insulin finding the nanopore and translocating it faster than the electrically neutral ubiquitin due insulin's slightly smaller size and net negative charge. While ubiquitin's dynamics is also affected by the size of the pore, surprisingly, its translocation process is noticably changed by the membrane bias. By investigating the protein's dipole and quadrupole moments, we demonstrate that its waiting and translocation time dependency on the membrane bias is due to the protein's charge distribution.

In this work, the ionic current blockades due to the translocation of a neutral spherical nanoparticle through a nanopore in a solid state membrane are computed. We use a Brownian dynamics approach, in conjunction with a full three-dimensional self-consistent solution of the Poisson-Nernst-Planck and Navier-Stockes system of equations to describe realistic ionic current response arising due to the random motion of a nanoparticle through a nanopore. We find that in addition to the usual geometric blockade, the variations of the current along the axis of the pore are largely caused by a concentration polarization induced by the presence of the translocating nanoparticle in the nanopore while the current changes in the radial (perpendicular to the axis) direction occur because of the local build up of the ionic charge between the particle and the nanopore surface. By performing statistical analysis of the current traces, we also observe that, in general, smaller current blockade values correspond to faster translocation times, while increased dwell times result in a larger current decrease.

Silicon nanomembranes are ultrathin, highly permeable, optically transparent and biocompatible substrates for the construction of barrier tissue models. Trans-epithelial/endothelial electrical resistance (TEER) is often used as a non-invasive, sensitive and quantitative technique to assess barrier function. The current study characterizes the electrical behavior of devices featuring silicon nanomembranes to facilitate their application in TEER studies. In conventional practice with commercial systems, raw resistance values are multiplied by the area of the membrane supporting cell growth to normalize TEER measurements. We demonstrate that under most circumstances, this multiplication does not 'normalize' TEER values as is assumed, and that the assumption is worse if applied to nanomembrane chips with a limited active area. To compare the TEER values from nanomembrane devices to those obtained from conventional polymer track-etched (TE) membranes, we develop finite element models (FEM) of the electrical behavior of the two membrane systems. Using FEM and parallel cell-culture experiments on both types of membranes, we successfully model the evolution of resistance values during the growth of endothelial monolayers. Further, by exploring the relationship between the models we develop a 'correction' function, which when applied to nanomembrane TEER, maps to experiments on conventional TE membranes. In summary, our work advances the the utility of silicon nanomembranes as substrates for barrier tissue models by developing an interpretation of TEER values compatible with conventional systems.

We study the movement of a polymer attached to a large protein inside a nanopore in a thin silicon dioxide membrane submerged in an electrolyte solution. We use Brownian dynamics to describe the motion of a negatively charged polymer chain of varying lengths attached to a neutral protein modeled as a spherical bead with a radius larger than that of the nanopore, allowing the chain to thread the nanopore but preventing it from translocating. The motion of the protein-polymer complex within the pore is also compared to that of a freely translocating polymer. Our results show that the free polymer's standard deviations in the direction normal to the pore axis is greater than that of the protein-polymer complex. We find that restrictions imposed by the protein, bias, and neighboring chain segments aid in controlling the position of the chain in the pore. Understanding the behavior of the protein-polymer chain complex may lead to methods that improve molecule identification by increasing the resolution of ionic current measurements.

We theoretically study how the electro-osmotic fluid velocity in a charged cylindrical nanopore in a thin solid state membrane depends on the pore's geometry, membrane charge, and electrolyte concentration. We find that when the pore's length is comparable to its diameter, the velocity profile develops a concave shape with a minimum along the pore axis unlike the situation in very long nanopores with a maximum velocity along the central pore axis. This effect is attributed to the induced pressure along the nanopore axis due to the fluid flow expansion and contraction near the exit or entrance to the pore and to the reduction of electric field inside the nanopore. The induced pressure is maximal when the pore's length is about equal to its diameter while decreasing for both longer and shorter nanopores. A model for the fluid velocity incorporating these effects is developed and shown to be in a good agreement with numerically computed results.

In this work, we theoretically study the interaction between a solid-state membrane equipped with a nanopore and a tethered, negatively charged polymer chain subjected to a time-dependent applied electrolyte bias. In order to describe the movement of the chain in the biomolecule-membrane system immersed in an electrolyte solution, Brownian dynamics is used. We show that we can control the polymer’s equilibrium position with various applied electrolyte biases: for a sufficiently positive bias, the chain extends inside the pore, and the removal of the bias causes the polymer to leave the pore. Corresponding to a driven process, we find that the time it takes for a biomolecular chain to enter and extend into a nanopore in a positive bias almost increases linearly with chain length while the time it takes for a polymer chain to escape the nanopore is mainly governed by diffusion. In addition to attaching the polymer chain to the mouth of the nanopore, the chain is attached to a molecule with a radius larger than that of the nanopore’s, acting as a molecular stop. This allows the polymer to thread the nanopore but not translocate it. In this new system, the chain’s variation of movement was compared to that of the freely translocating polymer chain. The results show the free polymer having greater variation in the radial direction, indicating the restrictions imposed by the molecular stop and bias aid in controlling the position and movement of the polymer chain in the nanopore.

Protein filtration is important in many fields of science and technology such as medicine, biology, chemistry, and engineering. Recently, protein separation and filtering with nanoporous membranes has attracted interest due to the possibility of fast separation and high throughput volume. This, however, requires understanding of the protein's dynamics inside and in the vicinity of the nanopore. In this work, we utilize a Brownian dynamics approach to study the motion of the model protein insulin in the membrane-electrolyte electrostatic potential. We compare the results of the atomic model of the protein with the results of a coarse-grained and a single-bead model, and find that the coarse-grained representation of protein strikes the best balance between the accuracy of the results and the computational effort required. Contrary to common belief, we find that to adequately describe the protein, a single-bead model cannot be utilized without a significant effort to tabulate the simulation parameters. Similar to results for nanoparticle dynamics, our findings also indicate that the electric field and the electro-osmotic flow due to the applied membrane and electrolyte biases affect the capture and translocation of the biomolecule by either attracting or repelling it to or from the nanopore. Our computational model can also be applied to other types of proteins and separation conditions.