arXiv subjects

Narayana R. Aluru

Publications and source records attributed to Narayana R. Aluru.

10 recordsLinked to original sources

arXiv2026

Invariant ionic conductance in an atomically thin polar nanopore

Ion channels regulate many essential properties of biological cells, especially the membrane potential. Despite decades of efforts on artificial channels, it remains a great challenge to mimic the dipole potential-an indispensable constituent of the membrane potential, due to its angstrom-scale characteristic length. Here, we explore nanopores in monolayer molybdenum sulfide selenide (MoSSe) considering its intrinsic dipole and atomic thickness. Remarkably, an invariant ionic conductance was observed over salt concentrations spanning six orders of magnitude, distinct from all known conductance-concentration scaling laws and reminiscent of the current saturation in cell membranes at high concentrations. Molecular dynamics simulations revealed the fundamental role of the dipole-modulated dielectric properties of nanoconfined water. Our findings highlight an exotic conductance scaling law and open up a novel avenue for controlling ion transport in unprecedented ways.

cond-mat.mes-hall↗

arXiv2026

Liquid structure adjacent to solid surfaces follows the superposition principle

Liquid structure at solid-liquid interfaces is critical for many natural and engineered processes ranging from biological signal transduction to electrochemical energy conversion. Advanced experimental and computational methods have provided insights into the structure of liquids adjacent to planar substrates at the nanoscale. However, realistic solid-liquid interfaces are inevitably inhomogeneous across multiple length scales, presenting a complexity that surpasses the capabilities of existing approaches. Here we bridge the complexity gap by discovering and utilizing a hitherto hidden principle of interfacial liquid--superposition. Experimentally, we use 3D atomic force microscopy (3D-AFM) to image the interfacial structure of a wide range of organic and aqueous solvents and electrolytes, uncovering universal liquid density oscillations and emergent liquid layer reconfigurations at heterogeneous substrate sites. We further develop an analytical model, coined solid-liquid superposition (SLS), which solves the interfacial liquid density distribution based on a key descriptor: the effective total correlation function (ETCF) between a liquid molecule and nearby solid atoms. SLS not only explains all the experimentally observed interfacial liquid distribution profiles from the angstrom to near-micron scale, but also predicts more precise atomic-scale interference patterns which are further corroborated by molecular dynamics (MD) simulations. This study unveils a key structural descriptor of interfacial liquids, and establishes a theoretical framework for rapidly and accurately predicting liquid structures adjacent to solid surfaces with arbitrary morphology and size scale.

physics.chem-ph↗

arXiv2025

Generative Quasi-Continuum Modeling of Confined Fluids at the Nanoscale

We present a data-efficient, multiscale framework for predicting the density profiles of confined fluids at the nanoscale. While accurate density estimates require prohibitively long timescales that are inaccessible by ab initio molecular dynamics (AIMD) simulations, machine-learned molecular dynamics (MLMD) offers a scalable alternative, enabling the generation of force predictions at ab initio accuracy with reduced computational cost. However, despite their efficiency, MLMD simulations remain constrained by femtosecond timesteps, which limit their practicality for computing long-time averages needed for accurate density estimation. To address this, we propose a conditional denoising diffusion probabilistic model (DDPM) based quasi-continuum approach that predicts the long-time behavior of force profiles along the confinement direction, conditioned on noisy forces extracted from a limited AIMD dataset. The predicted smooth forces are then linked to continuum theory via the Nernst-Planck equation to reveal the underlying density behavior. We test the framework on water confined between two graphene nanoscale slits and demonstrate that density profiles for channel widths outside of the training domain can be recovered with ab initio accuracy. Compared to AIMD and MLMD simulations, our method achieves orders-of-magnitude speed-up in runtime and requires significantly less training data than prior works.

physics.comp-ph↗

arXiv2025

Towards Quantitative Interpretation of 3D Atomic Force Microscopy at Solid-Liquid Interfaces

Three-dimensional atomic force microscopy (3D-AFM) has been a powerful tool to probe the atomic-scale structure of solid-liquid interfaces. As a nanoprobe moves along the 3D volume of interfacial liquid, the probe-sample interaction force is sensed and mapped, providing information on not only the solid morphology, but also the liquid density distribution. To date 3D-AFM force maps of a diverse set of solid-liquid interfaces have been recorded, revealing remarkable force oscillations that are typically attributed to solvation layers or electrical double layers. However, despite the high resolution down to sub-angstrom level, quantitative interpretation of the 3D force maps has been an outstanding challenge. Here we will review the technical details of 3D-AFM and the existing approaches for quantitative data interpretation. Based on evidences in recent literature, we conclude that the perturbation-induced AFM force paradoxically represents the intrinsic, unperturbed liquid density profile. We will further discuss how the oscillatory force profiles can be attributed to the probe-modulation of the liquid configurational entropy, and how the quantitative, atomic-scale liquid density distribution can be derived from the force maps.

physics.chem-ph↗

arXiv2024

Integrating Experiment with Theory to Determine the Structure of Electrode-Electrolyte Interfaces

Electrode-electrolyte interfaces are crucial for electrochemical energy conversion and storage. At these interfaces, the liquid electrolytes form electrical double layers (EDLs). However, despite more than a century of active research, the fundamental structure of EDLs remains elusive to date. Experimental characterization and theoretical calculations have both provided insights, yet each method by itself only offers incomplete or inexact information of the multifaceted EDL structure. Here we provide a survey of the mainstream approaches for EDL quantification, with a particular focus on the emerging 3D atomic force microscopy (3D-AFM) imaging which provides real-space atomic-scale EDL structures. To overcome the existing limits of EDL characterization methods, we propose a new approach to integrate 3D-AFM with classical molecular dynamics (MD) simulation, to enable realistic, precise, and high-throughput determination and prediction of EDL structures. As examples of real-world application, we will discuss the feasibility of using this joint experiment-theory method to unravel the EDL structure at various carbon-based electrodes for supercapacitors, batteries, and electrocatalysis. Looking forward, we believe 3D-AFM, future versions of scanning probe microscopy, and their integration with theory offer promising platforms to profile liquid structures in many electrochemical systems.

physics.chem-ph↗

arXiv2023

Imaging the electron charge density in monolayer MoS2 at the Ångstrom scale

Four-dimensional scanning transmission electron microscopy (4D-STEM) has recently gained widespread attention for its ability to image atomic electric fields with sub-Ångstrom spatial resolution. These electric field maps represent the integrated effect of the nucleus, core electrons and valence electrons, and separating their contributions is non-trivial. In this paper, we utilized simultaneously acquired 4D-STEM center of mass (CoM) images and annular dark field (ADF) images to determine the electron charge density in monolayer MoS2. We find that both the core electrons and the valence electrons contribute to the derived electron charge density. However, due to blurring by the probe shape, the valence electron contribution forms a nearly featureless background while most of the spatial modulation comes from the core electrons. Our findings highlight the importance of probe shape in interpreting charge densities derived from 4D STEM.

cond-mat.mtrl-sci↗

arXiv2022

Topology-based Phase Identification of Bulk, Interface, and Confined Water using Edge-Conditioned Convolutional Graph Neural Network

Water plays a significant role in various physicochemical and biological processes. Understanding and identifying water phases in various systems such as bulk, interface, and confined water is crucial in improving and engineering state-of-the-art nano-devices. Various order parameters have been developed to distinguish water phases, including bond-order parameters, local structure index, and tetrahedral order parameters. These order parameters are often developed with the assumption of homogenous bulk systems, while most applications involve heterogeneous and non-bulk systems, thus, limiting their generalizability. Our study develops a methodology based on the graph neural network to distinguish water phases directly from data and to learn features instead of predefining them. We provide comparisons between baseline methods trained using conventional order-parameters as features, and a graph neural network model trained using radial distance and hydrogen-bonding information for phase classification and phase transition of water in bulk, interface, and confined systems with continuous and discontinuous phase transitions.

physics.comp-ph↗

arXiv2019

Uncertainty Quantification in Three Dimensional Natural Convection using Polynomial Chaos Expansion and Deep Neural Networks

This paper analyzes the effects of input uncertainties on the outputs of a three dimensional natural convection problem in a differentially heated cubical enclosure. Two different cases are considered for parameter uncertainty propagation and global sensitivity analysis. In case A, stochastic variation is introduced in the two non-dimensional parameters (Rayleigh and Prandtl numbers) with an assumption that the boundary temperature is uniform. Being a two dimensional stochastic problem, the polynomial chaos expansion (PCE) method is used as a surrogate model. Case B deals with non-uniform stochasticity in the boundary temperature. Instead of the traditional Gaussian process model with the Karhunen-Lo$\grave{e}$ve expansion, a novel approach is successfully implemented to model uncertainty in the boundary condition. The boundary is divided into multiple domains and the temperature imposed on each domain is assumed to be an independent and identically distributed (i.i.d) random variable. Deep neural networks are trained with the boundary temperatures as inputs and Nusselt number, internal temperature or velocities as outputs. The number of domains which is essentially the stochastic dimension is 4, 8, 16 or 32. Rigorous training and testing process shows that the neural network is able to approximate the outputs to a reasonable accuracy. For a high stochastic dimension such as 32, it is computationally expensive to fit the PCE. This paper demonstrates a novel way of using the deep neural network as a surrogate modeling method for uncertainty quantification with the number of simulations much fewer than that required for fitting the PCE, thus, saving the computational cost.

math.NA↗

arXiv2018

Strain Modulation of Graphene by Nanoscale Substrate Curvatures: A Molecular View

Spatially nonuniform strain is important for engineering the pseudomagnetic field and band structure of graphene. Despite the wide interest in strain engineering, there is still a lack of control on device-compatible strain patterns due to the limited understanding of the structure-strain relationship. Here, we study the effect of substrate corrugation and curvature on the strain profiles of graphene via combined experimental and theoretical studies of a model system: graphene on closely packed SiO2 nanospheres with different diameters (20-200 nm). Experimentally, via quantitative Raman analysis, we observe partial adhesion and wrinkle features and find that smaller nanospheres induce larger tensile strain in graphene, theoretically, molecular dynamics simulations confirm the same microscopic structure and size dependence of strain and reveal that a larger strain is caused by a stronger, inhomogeneous interaction force between smaller nanospheres and graphene. This molecular-level understanding of the strain mechanism is important for strain engineering of graphene and other two-dimensional materials.

cond-mat.mes-hall↗

arXiv2015

Capacitive Sensing of Intercalated H2O Molecules Using Graphene

Understanding the interactions of ambient molecules with graphene and adjacent dielectrics is of fundamental importance for a range of graphene-based devices, particularly sensors, where such interactions could influence the operation of the device. It is well-known that water can be trapped underneath graphene and its host substrate, however, the electrical effect of water beneath graphene and the dynamics of how it changes with different ambient conditions has not been quantified. Here, using a metal-oxide-graphene variable-capacitor (varactor) structure, we show that graphene can be used to capacitively sense the intercalation of water between graphene and HfO2 and that this process is reversible on a fast time scale. Atomic force microscopy is used to confirm the intercalation and quantify the displacement of graphene as a function of humidity. Density functional theory simulations are used to quantify the displacement of graphene induced by intercalated water and also explain the observed Dirac point shifts as being due to the combined effect of water and oxygen on the carrier concentration in the graphene. Finally, molecular dynamics simulations indicate that a likely mechanism for the intercalation involves adsorption and lateral diffusion of water molecules beneath the graphene.

cond-mat.mes-hall↗