Nanoscale electrohydrodynamic ion transport: Influences of channel geometry and polarization-induced surface charges.

Electrohydrodynamic ion transport has been studied in nanotubes, nanoslits, and nanopores to mimic the advanced functionalities of biological ion channels. However, probing how the intricate interplay between the electrical and mechanical interactions affects ion conduction in asymmetric nanoconduits presents further obstacles. Here, ion transport across a conical nanopore embedded in a polarizable membrane under an electric field and pressure is analyzed by numerically solving a continuum model based on the Poisson, Nernst-Planck, and Navier-Stokes equations. We report an anomalous ionic current depletion, of up to 75%, and an unexpected rise in current rectification when pressure is exerted along the external electric field. Membrane polarization is revealed as the prerequisite to obtain this previously undetected electrohydrodynamic coupling. The electric field induces large surface charges at the pore tip due to its conical shape, creating nonuniform electrical double layers (EDL) with a massive accumulation of electrolyte ions near the orifice. Once applied, the pressure distorts the quasiequilibrium distribution of the EDL ions to influence the nanopore conductivity. Our fundamental approach to inspect the effect of pressure on the channel EDL (and thus ionic conductance) in contrast to its effect on the current arising from the hydrodynamic streaming of ions further explains the pressure-sensitive ion transport in different nanochannels and physical regimes manifested in past experiments, including the hitherto inexplicit mechanism behind the mechanically activated ion transport in carbon nanotubes. This enhances our broad understanding of nanoscale electrohydrodynamic ion transport, yielding a platform to build nanofluidic devices and ionic circuits with more robust and tunable responses to electrical and mechanical stimuli.

Scaling of ionic conductance in a fluctuating single-layer nanoporous membrane.

Single-layer membranes have emerged as promising candidates for applications requiring high transport rates due to their low resistance to molecular transport. Owing to their atomically thin structure, these membranes experience significant microscopic fluctuations, emphasizing the need to explore their impact on ion transport processes. In this study, we investigate the effects of membrane fluctuations on the elementary scaling behavior of ion conductance [Formula: see text] as a function of ion concentration [Formula: see text], represented as [Formula: see text], using molecular dynamics simulations. Our findings reveal that membrane fluctuations not only alter the conductance coefficient [Formula: see text] but also the power-law exponent [Formula: see text]. We identify two distinct frequency regimes of membrane fluctuations, GHz-scale and THz-scale fluctuations, and examine their roles in conductance scaling. Furthermore, we demonstrate that the alteration of conductance scaling arises from the non-linearity between ion conductance and membrane shape. This work provides a fundamental understanding of ion transport in fluctuating membranes.

From ab initio to continuum: Linking multiple scales using deep-learned forces.

We develop a deep learning-based algorithm, called DeepForce, to link ab initio physics with the continuum theory to predict concentration profiles of confined water. We show that the deep-learned forces can be used to predict the structural properties of water confined in a nanochannel with quantum scale accuracy by solving the continuum theory given by Nernst-Planck equation. The DeepForce model has an excellent predictive performance with a relative error less than 7.6% not only for confined water in small channel systems (L < 6 nm) but also for confined water in large channel systems (L = 20 nm) which are computationally inaccessible through the high accuracy ab initio molecular dynamics simulations. Finally, we note that classical Molecular dynamics simulations can be inaccurate in capturing the interfacial physics of water in confinement (L < 4.0 nm) when quantum scale physics are neglected.

Spontaneous sieving of water from ethanol using angstrom-sized nanopores.

Ethanol is widely used as a precursor in products ranging from drugs to cosmetics. However, distillation of ethanol from aqueous solution is energy intensive and expensive. Here, we show that angstrom-sized nanopores with precisely controlled pore sizes can spontaneously remove water from ethanol-water mixtures through molecular sieving at room temperature and pressure. For small-diameter nanotubes, water-filling is observed, but ethanol is completely excluded, as evidenced by time-dependent density functional theory (TD-DFT) calculations and spectroscopy measurements. Potential of mean force calculations were performed to determine how the free energy barriers for water and ethanol-filling of the nanotubes change with increasing pore size. Water/ethanol selectivity ratio reaching as high as 6700 is observed with a (6,4) nanotube, which has a pore size of 0.204 nm. This selectivity vanishes as the pore size increases beyond 0.306 nm. These findings provide insights that may help realize energy efficient molecular sieving of ethanol and water.

Structural and Dynamical Properties of H2O and D2O under Confinement.

Water (H2O) is of great societal importance, and there has been a significant amount of research on its fundamental properties and related physical phenomena. Deuterium dioxide (D2O), known as heavy water, also draws much interest as an important medium for medical imaging, nuclear reactors, etc. Although many experimental studies on the fundamental properties of H2O and D2O have been conducted, they have been primarily limited to understanding the differences between H2O and D2O in the bulk state. In this paper, using path integral molecular dynamics simulations, the structural and dynamical properties of H2O and D2O in bulk and under nanoscale confinement in a (14,0) carbon nanotube are studied. We find that in bulk, structural properties such as bond angle and bond length of D2O are slightly smaller than those of H2O while D2O is slightly more structured than H2O. The dipole moment of D2O tends to be 4% higher than that of H2O, and the hydrogen bonding of D2O is also stronger than that of H2O. Under nanoscale confinement in a (14,0) carbon nanotube, H2O and D2O exhibit a smaller bond length and bond angle. The hydrogen bond number decreases, which demonstrates a weakened hydrogen bond interaction. Moreover, confinement results in a lower libration frequency and a higher OH(OD) bond stretching frequency with an almost unchanged HOH(DOD) bending frequency. The D2O-filled (14,0) carbon nanotube is found to have a smaller radial breathing mode than the H2O-filled (14,0) carbon nanotube.

Benzo[a]pyrene-induced multigenerational changes in gene expression, behavior, and DNA methylation are primarily influenced by paternal exposure.

Benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon (PAH), is implicated in many developmental and behavioral adverse outcomes in offspring of exposed parents. The objective of this study was to investigate sex-dependent multigenerational effects of preconceptional effects of BaP exposure. Adult wild-type (5D) zebrafish were fed 708 µg BaP/g diet (measured) at a rate of 1% body weight twice/day (14 µg BaP/g fish/day) for 21 days. Fish were spawned using a crossover design, and parental (F0) behavior and reproductive indexes were measured. In offspring, behavioral effects were measured at 96 h post fertilization (hpf) in F1 & F2 larvae, and again when F1s were adults. Compared to controls, there was no significant effect on F0 adult behavior immediately following exposure, but locomotor activity was significantly increased in F1 adults of both sexes. Larval behavior (96 hpf, photomotor response assay) was significantly altered in both the F1 and F2 generations. To assess molecular changes associated with BaP exposure, we conducted transcriptome and DNA methylation profiling in F0 gametes (sperm and eggs) and F1 embryos (10 hpf) from all four crosses. Embryos resulting from the BaP male and control female cross had the most differentially expressed genes (DEGs) and differentially methylated regions (DMRs). Some DMRs were associated with genes encoding chromatin modifying enzymes suggesting regulation of chromatin conformation by DNA methylation. Overall, these results suggest that parental dietary BaP exposure significantly contributes to the multigenerational adverse outcomes.

Deep learning-based quasi-continuum theory for structure of confined fluids.

Predicting the structural properties of water and simple fluids confined in nanometer scale pores and channels is essential in, for example, energy storage and biomolecular systems. Classical continuum theories fail to accurately capture the interfacial structure of fluids. In this work, we develop a deep learning-based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nanochannel. The deep learning model is built based on a convolutional encoder-decoder network (CED) and is applied for high-dimensional surrogate modeling to relate the fluid properties to the fluid-fluid potential. The CED model is then combined with the interatomic potential-based continuum theory to determine the concentration profiles of a confined LJ fluid and confined water. We show that the DL-QT model exhibits robust predictive performance for a confined LJ fluid under various thermodynamic states and for water confined in a nanochannel of different widths. The DL-QT model seamlessly connects molecular physics at the nanoscale with continuum theory by using a deep learning model.

Multi-scale simulation of proton diffusion in dislocation cores in BaZrO3.

Dislocations are important for their effects on the chemical, electrical, magnetic, and transport properties of oxide materials, especially for electrochemical devices such as solid fuel cells and resistive memories, but these effects are still under-studied at the atomic level. We have developed a quantum mechanical/molecular mechanical (QM/MM)-based multiscale simulation program to reveal the diffusion properties of protons on 〈100〉 edge dislocations in BaZrO3 perovskite oxide. We find that the large free space and the presence of hydrogen bonds in the dislocation core structure lead to significant trapping of protons. The diffusion properties of protons in dislocation cores were investigated, and no evidence of pipeline diffusion was found from the calculated migration energy barriers, which not only did not accelerate ion diffusion but rather decreases the conductivity of ions. The proton diffusion properties of Y-doped BaZrO3 (BZY), with a dislocation core structure (BZY-D) and with a grain boundary structure (BZY-GB) were also compared. In all three structures, local lattice deformation occupies an essential part in the proton transfer and rotation processes. The change in bond order is calculated and it is found that the interaction with oxygen and Zr ions during proton transfer and rotation controls the energy barrier for local lattice deformation of the O-B-O motion, which affects the proton diffusion in the structure. Our study provides insight into proton diffusion in dislocations in terms of mechanical behavior, elucidates the origin of the energy barrier associated with proton diffusion in dislocations, and provides guidance for the preparation and application of proton conductors.

Effect of interfacial vibrational coupling on surface wettability and water transport.

We report that the atomic-scale vibrational coupling at the solid-fluid interface can substantially alter the interfacial properties such as wettability and fluid slip. The wettability of water droplets on substrates subjected to various vibrational frequencies is studied using molecular dynamics simulation. The contact angle increases (i.e., becomes more hydrophobic) when the oscillation frequency of the substrate matches the intermolecular bending frequency of liquid water. We investigate the underlying mechanism by examining the dynamics of water molecules at the interface and find that the temporal contact between the solid and fluid is shorter when the frequencies match, resulting in weak solid-fluid adsorption. We further report that the vibrational match at the interface reduces wall-fluid friction and enhances water transport through the nanopore. Our findings demonstrate the importance of the atomic-scale vibrational coupling at the solid-fluid interface on the physicochemical behavior of nanodevices and biological nanochannels.

Phospholipid Monolayer/Graphene Interfaces: Curvature Effect on Lipid Morphology and Dynamics.

Phospholipids are an important class of lipids that are widely used as model platforms for the study of biological processes and interactions. These lipids can form stable interfaces with solid substrates, such as graphene, and these interfaces have potential applications in biosensing and targeted drug delivery. In this paper, we perform molecular dynamics simulations of graphene-supported lipid monolayers to characterize the lipid properties of such interfaces. We observed substantial differences between the supported monolayer and free-standing bilayer in terms of the lipid properties, such as the tail order parameters, density profiles, diffusion rates, and so on. Furthermore, we studied these interfaces on sinusoidally deformed graphene substrates to understand the effect of curvature on the supported lipids. Here, we observed that the nature of the substrate curvature, that is, concave or convex, can locally affect the lipid/substrate adhesion strength and induce structural and dynamic changes in the adsorbed lipid monolayer. Together, these results help characterize the properties of lipid/graphene interfaces and provide insights into the substrate curvature effect on these interfaces, which can enable the tuning of lipid properties for various sensor devices and drug delivery applications.

Nonlocal hydrodynamic model for gravity-driven transport in nanochannels.

It has been established that Newton's law of viscosity fails for fluids under strong confinement as the strain-rate varies significantly over molecular length-scales. We thereby investigate if a nonlocal shear stress accounting for the strain-rate of an adjoining region by a convolution relation with a nonlocal viscosity kernel can be employed to predict the gravity-driven isothermal flow of a Weeks-Chandler-Andersen fluid in a nanochannel. We estimate, using the local average density model, the fluid's viscosity kernel from isotropic bulk systems of corresponding state points by the sinusoidal transverse force method. A continuum model is proposed to solve the nonlocal hydrodynamics whose solutions capture the key features and agree qualitatively with the results of non-equilibrium molecular dynamics simulations, with deviations observed mostly near the fluid-channel interface.

Many-Body Neural Network-Based Force Field for Structure-Based Coarse-Graining of Water.

High-fidelity results from atomistic simulations can only be obtained by using accurate force-field (FF) parameters. Although empirical FFs are commonly used in the modeling of atomistic systems due to their simplicity, they have many limitations inherent in the crude approximations associated with their analytical form. Recent advances in neural network-based FFs have led to more accurate FFs by using symmetry functions or full many-body expansions. However, this approach leads to several issues including the arbitrariness of the symmetry functions, and the intangible and uninterpretable interactions which are only known once the positions of all atoms are set. More importantly, training is another bottleneck, as high-quality force and energy information is required, which is usually not accessible from experimental data. To solve these issues within the context of structure-based coarse-graining methods, we switch in this work to a local-search method to target the reference structure instead of using conventional backpropagation algorithms used to target the forces and energies of the reference structure. Our FF is decomposed into two-, three-, and higher-order terms, where each term is modeled with a separate neural network. To show the versatility of our method, we study four different systems, namely, Stillinger-Weber particles as an atomistic case and three water models, namely SPC/E, MB-pol, and ab initio, as coarse-graining cases. We show the successful application of our approach, by reproducing structural properties of different water models, followed by providing insight into the role of two-and three-body interactions. The results of all models indicate that the double-well isotropic pair potential, the signature of water-like behavior in an isotropic system, vanishes upon inclusion of the three-body interaction, showing dominance of the three-body interaction over the two-body interaction in water-like behavior with the single-well isotropic pair potential.

Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining.

Molecular dynamics (MD) simulations are widely used to obtain the microscopic properties of atomistic systems when the interatomic potential or the coarse-grained potential is known. In many practical situations, however, it is necessary to predict the interatomic or coarse-grained potential, which is a tremendous challenge. Many approaches have been developed to predict the potential parameters based on various techniques, including the relative entropy method, integral equation theory, etc., but these methods lack transferability and are limited to a specific range of thermodynamic states. Recently, data-driven and machine learning approaches have been developed to overcome such limitations. In this study, we expand the range of thermodynamic states used to train deep inverse liquid-state theory (DeepILST)1, a deep learning framework for solving the inverse problem of liquid-state theory. We also assess the performance of DeepILST in coarse-graining various multiatom molecules and identify the molecular characteristics that affect the coarse-graining performance of DeepILST.

Phonon-Fluid Coupling Enhanced Water Desalination in Flexible Two-Dimensional Porous Membranes.

Water purification using 2D nanoporous membranes has been drawing significant attention for over a decade because of fast water transport in ultrathin membranes. We perform a comprehensive study using molecular dynamics (MD) simulations on water desalination using 2D flexible membranes where the coupling between the fluid dynamics and mechanics of the membrane plays an important role. We observe that a considerable deformation and fluctuation in the 2D membrane results in an enhanced water permeability (up to 122%) along with a slight decrease in the salt rejection rate (less than 11%). Simulations on harmonically vibrating membranes indicate that the vibrational match at the membrane-water interface can significantly increase the permeance. We conduct mechanical stability tests and discuss the maximum endurable pressure of 2D porous membranes for water desalination. These findings will contribute to advances in applications using ultrathin membranes, such as energy harvesting and molecular separation.

Understanding simple liquids through statistical and deep learning approaches.

Statistical and deep learning-based methods are employed to obtain insights into the quasi-universal properties of simple liquids. In the first part, a statistical model is employed to provide a probabilistic explanation for the similarity in the structure of simple liquids interacting with different pair potential forms, collectively known as simple liquids. The methodology works by sampling the radial distribution function and the number of interacting particles within the cutoff distance, and it produces the probability density function of the net force. We show that matching the probability distribution of the net force can be a direct route to parameterize simple liquid pair potentials with a similar structure, as the net force is the main component of the Newtonian equations of motion. The statistical model is assessed and validated against various cases. In the second part, we exploit DeepILST [A. Moradzadeh and N. R. Aluru, J. Phys. Chem. Lett. 10, 1242-1250 (2019)], a data-driven and deep-learning assisted framework to parameterize the standard 12-6 Lennard-Jones (LJ) pair potential, to find structurally equivalent/isomorphic LJ liquids that identify constant order parameter [τ=∫0 ξcf gξ-1ξ2dξ, where gξ and ξ(=rρ13) are the reduced radial distribution function and radial distance, respectively] systems in the space of non-dimensional temperature and density of the LJ liquids. We also investigate the consistency of DeepILST in reproducibility of radial distribution functions of various quasi-universal potentials, e.g., exponential, inverse-power-law, and Yukawa pair potentials, quantified based on the radial distribution functions and Kullback-Leibler errors. Our results provide insights into the quasi-universality of simple liquids using the statistical and deep learning methods.

Molecular Dynamics Properties without the Full Trajectory: A Denoising Autoencoder Network for Properties of Simple Liquids.

Molecular dynamics (MD) simulation is a popularly used computational tool to compute microscopic and macroscopic properties of a variety of systems including liquids, solids, biological systems, etc. To determine properties of atomic systems to a good level of accuracy with minimal noise or fluctuation, MD simulations are performed over a long time ranging from a few nanoseconds to several tens to hundreds of nanoseconds depending on the system and the properties of interest. In this study, by considering simple liquids, we explore the feasibility of significantly reducing the MD simulation time to compute various properties of monatomic systems such as the structure, pressure, and isothermal compressibility. To do so, extensive MD simulations are performed on 12 000 distinct Lennard-Jones systems at various thermodynamic states. Then, a deep denoising autoencoder network is trained to take the radial distribution function (RDF) from a single snapshot of a Lennard-Jones liquid to compute the mean, temporally averaged RDF. We show that the method is successful in the prediction of RDF and other properties such as the pressure and isothermal compressibility that can be computed based on the RDF not only for Lennard-Jones liquids at various thermodynamic states but also for various simple liquids described by exponential, Yukawa, and inverse-power-law pair potentials.

Integral equation theory based direct and accelerated systematic coarse-graining approaches.

Coarse-grained (CG) molecular dynamics (MD) simulations have become popular for investigating systems on multiple length and time scales ranging from atomistic to mesoscales. In CGMD, several atoms are mapped onto a single CG bead and the effective interactions between CG beads are determined. Iterative coarse-graining methods, such as iterative Boltzmann inversion (IBI), are computationally expensive and can have convergence issues. In this paper, we present a direct and computationally efficient theoretical procedure for coarse-graining based on the Ornstein-Zernike (OZ) and hypernetted chain (HNC) integral equation theory. We demonstrate the OZ-HNC-based CG method by coarse-graining a bulk water system, a water-methanol mixture system, and an electrolyte system. We show that the accuracy of the CG potentials obtained from the OZ-HNC-based coarse-graining is comparable to iterative systematic coarse-graining methods. Furthermore, we show that the CG potentials from OZ-HNC can be used to reduce the number of iterations and hence the computational cost of the iterative systematic coarse-graining approaches, like IBI and relative entropy minimization.

A multiscale model for charge inversion in electric double layers.

Charge inversion is a widely observed phenomenon. It is a result of the rich statistical mechanics of the molecular interactions between ions, solvent, and charged surfaces near electric double layers (EDLs). Electrostatic correlations between ions and hydration interactions between ions and water molecules play a dominant role in determining the distribution of ions in EDLs. Due to highly polar nature of water, near a surface, an inhomogeneous and anisotropic arrangement of water molecules gives rise to pronounced variations in the electrostatic and hydration energies of ions. Classical continuum theories fail to accurately describe electrostatic correlations and molecular effects of water in EDLs. In this work, we present an empirical potential based quasi-continuum theory (EQT) to accurately predict the molecular-level properties of aqueous electrolytes. In EQT, we employ rigorous statistical mechanics tools to incorporate interatomic interactions, long-range electrostatics, correlations, and orientation polarization effects at a continuum-level. Explicit consideration of atomic interactions of water molecules is both theoretically and numerically challenging. We develop a systematic coarse-graining approach to coarse-grain interactions of water molecules and electrolyte ions from a high-resolution atomistic scale to the continuum scale. To demonstrate the ability of EQT to incorporate the water orientation polarization, ion hydration, and electrostatic correlations effects, we simulate confined KCl aqueous electrolyte and show that EQT can accurately predict the distribution of ions in a thin EDL and also predict the complex phenomenon of charge inversion.

A multiscale transport model for non-classical nanochannel electroosmosis.

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.

An EQT-based cDFT approach for thermodynamic properties of confined fluid mixtures.

We present an empirical potential-based quasi-continuum theory (EQT) to predict the structure and thermodynamic properties of confined fluid mixtures. The central idea in the EQT is to construct potential energies that integrate important atomistic details into a continuum-based model such as the Nernst-Planck equation. The EQT potentials can be also used to construct the excess free energy functional, which is required for the grand potential in the classical density functional theory (cDFT). In this work, we use the EQT-based grand potential to predict various thermodynamic properties of a confined binary mixture of hydrogen and methane molecules inside graphene slit channels of different widths. We show that the EQT-cDFT predictions for the structure, surface tension, solvation force, and local pressure tensor profiles are in good agreement with the molecular dynamics simulations. Moreover, we study the effect of different bulk compositions and channel widths on the thermodynamic properties. Our results reveal that the composition of methane in the mixture can significantly affect the ordering of molecules and thermodynamic properties under confinement. In addition, we find that graphene is selective to methane molecules.