Tunable Magnetic Coupling in Graphene Nanoribbon Quantum Dots

Carbon-based quantum dots (QDs) enable flexible manipulation of electronic behavior at the nanoscale, but controlling their magnetic properties requires atomically precise structural control. While magnetism is observed in organic molecules and graphene nanoribbons (GNRs), GNR precursors enabling bottom-up fabrication of QDs with various spin ground states have not yet been reported. Here the development of a new GNR precursor that results in magnetic QD structures embedded in semiconducting GNRs is reported. Inserting one such molecule into the GNR backbone and graphitizing it results in a QD region hosting one unpaired electron. QDs composed of two precursor molecules exhibit nonmagnetic, antiferromagnetic, or antiferromagnetic ground states, depending on the structural details that determine the coupling behavior of the spins originating from each molecule. The synthesis of these QDs and the emergence of localized states are demonstrated through high-resolution atomic force microscopy (HR-AFM), scanning tunneling microscopy (STM) imaging, and spectroscopy, and the relationship between QD atomic structure and magnetic properties is uncovered. GNR QDs provide a useful platform for controlling the spin-degree of freedom in carbon-based nanostructures.

Beyond the electrical double layer model: ion-dependent effects in nanoscale solvent organization

The nanoscale organization of electrolyte solutions at interfaces is often described well by the electrical double-layer model. However, a recent study has shown that this model breaks down in solutions of LiClO4 in acetonitrile at a silica interface, because the interface imposes a strong structuring in the solvent that in turn determines the preferred locations of cations and anions. As a surprising consequence of this organisation, the effective surface potential changes from negative at low electrolyte concentration to positive at high electrolyte concentration. Here we combine previous ion-current measurements with vibrational sum-frequency-generation spectroscopy experiments and molecular dynamics simulations to explore how the localization of ions at the acetonitrile-silica interface depends on the sizes of the anions and cations. We observe a strong, synergistic effect of the cation and anion identities that can prompt a large difference in the ability of ions to partition to the silica surface, and thereby influence the effective surface potential. Our results have implications for a wide range of applications that involve electrolyte solutions in polar aprotic solvents at nanoscale interfaces.

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.

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.

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.

Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement

We propose a data-driven framework for identifying coarse-grained (CG) Lennard-Jones (LJ) potential parameters in confined systems for simple liquids. Our approach involves the use of a Deep Neural Network (DNN) that is trained to approximate the solution of the Inverse Liquid State (ILST) problem for confined systems. The DNN model inherently incorporates essential physical characteristics specific to confined fluids, enabling an accurate prediction of inhomogeneity effects. By utilizing transfer learning, we predict single-site LJ potentials of simple multiatomic liquids confined in a slit-like channel, which effectively replicate both the fluid structure and molecular force of the target All-Atom (AA) system when the electrostatic interactions are not dominant. In addition, we showcase the synergy between the data-driven approach and the well-known Bottom-Up coarse-graining method utilizing Relative-Entropy (RE) Minimization. Through the sequential utilization of these two methods, the robustness of the iterative RE method is significantly augmented, leading to a remarkable enhancement in convergence.

Mapping Nanoscale Electrostatic Field Fluctuations around Graphene Dislocation Cores Using Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM)

Defects in crystalline lattices cause modulation of the atomic density, and this leads to variations in the associated electrostatics at the nanoscale. Mapping these spatially varying charge fluctuations using transmission electron microscopy has typically been challenging due to complicated contrast transfer inherent to conventional phase contrast imaging. To overcome this, we used four-dimensional scanning transmission electron microscopy (4D-STEM) to measure electrostatic fields near point dislocations in a monolayer. The asymmetry of the atomic density in a (1,0) edge dislocation core in graphene yields a local enhancement of the electric field in part of the dislocation core. Through experiment and simulation, the increased electric field magnitude is shown to arise from "long-range" interactions from beyond the nearest atomic neighbor. These results provide insights into the use of 4D-STEM to quantify electrostatics in thin materials and map out the lateral potential variations that are important for molecular and atomic bonding through Coulombic interactions.

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.

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 projected electron charge density in monolayer MoS2. We evaluate the contributions of both the core electrons and the valence electrons 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 and the need for smaller electron probes.

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.

Ion transport in two-dimensional flexible nanoporous membranes

Ion transport is a fundamental mechanism in living systems that plays a role in cell proliferation, energy conversion, and maintaining homeostasis. This has inspired various nanofluidic applications such as electricity harvesting, molecular sensors, and molecular separation. Two dimensional (2D) nanoporous membranes are particularly promising for these applications due to their ultralow transport barriers. We investigated ion conduction across flexible 2D membranes via extensive molecular dynamics simulations. We found that the microscopic fluctuations of these membranes can significantly increase ion conductance, for example, by 320% in Cu-HAB with 0.5 M KCl. Our analysis of ion dynamics near the flexible membranes revealed that ion hydration is destabilized when the membrane fluctuated within a specific frequency range leading to improved ion conduction. Our results show that the dynamic coupling between the fluctuating membrane and ions can play a crucial role in ion conduction across 2D nanoporous membranes.

Inducing Electric Current in Graphene Using Ionic Flow

Classical nanofluidic frameworks account for the confined fluid and ion transport under an electrostatic field at the solid-liquid interface, but the electronic property of the solid is often overlooked. Harvesting the interaction of the nanofluidic transport with the electron transport in solid requires a route effectively coupling ion and electron dynamics. Here we report a nanofluidic analogy of Coulomb drag for exploring the dynamic ion-electron interactions at the liquid-graphene interface. An induced electric current in graphene by ionic flow with no bias directly applied to the graphene channel is observed experimentally, featuring an opposite electron current direction to the ion current. Our experiments and ab initio calculations show that the current generation stems from the confined ion-electron interactions via a nanofluidic Coulomb drag mechanism. Our findings may open up a new dimension for nanofluidics and transport control by ion-electron coupling.

Immunoglobulin adsorption and film formation on mechanically wrinkled and crumpled surfaces at submonolayer coverage

Understanding protein adsorption behavior on rough and wrinkled surfaces is vital to applications including biosensors and flexible biomedical devices. Despite this, there is a dearth of study on protein interaction with regularly undulating surface topographies, particularly in regions of negative curvature. Here we report nanoscale adsorption behavior of immunoglobulin M (IgM) and immunoglobulin G (IgG) on wrinkled and crumpled surfaces via atomic force microscopy (AFM). Hydrophilic plasma treated poly(dimethylsiloxane) (PDMS) wrinkles with varying dimensions exhibit higher surface coverage of IgM on wrinkle peaks over valleys. Negative curvature in the valleys is determined to reduce protein surface coverage based both on an increase in geometric hindrance on concave surfaces, and reduced binding energy as calculated in coarse-grained molecular dynamics simulations. The smaller IgG molecule in contrast shows no observable effects on coverage from this degree of curvature. The same wrinkles with an overlayer of monolayer graphene show hydrophobic spreading and network formation, and inhomogeneous coverage across wrinkle peaks and valleys attributed to filament wetting and drying effects in the valleys. Additionally, adsorption onto uniaxial buckle delaminated graphene shows that when wrinkle features are on the length scale of the protein diameter, hydrophobic deformation and spreading do not occur and both IgM and IgG molecules retain their dimensions. These results demonstrate that undulating wrinkled surfaces characteristic of flexible substrates can have significant effects on protein surface distribution with potential implications for design of materials for biological applications.

Fluids and Electrolytes under Confinement in Single-Digit Nanopores

Confined fluids and electrolyte solutions in nanopores exhibit rich and surprising physics and chemistry that impact the mass transport and energy efficiency in many important natural systems and industrial applications. Existing theories often fail to predict the exotic effects observed in the narrowest of such pores, called single-digit nanopores (SDNs), which have diameters or conduit widths of less than 10 nm, and have only recently become accessible for experimental measurements. What SDNs reveal has been surprising, including a rapidly increasing number of examples such as extraordinarily fast water transport, distorted fluid-phase boundaries, strong ion-correlation and quantum effects, and dielectric anomalies that are not observed in larger pores. Exploiting these effects presents myriad opportunities in both basic and applied research that stand to impact a host of new technologies at the water-energy nexus, from new membranes for precise separations and water purification to new gas permeable materials for water electrolyzers and energy-storage devices. SDNs also present unique opportunities to achieve ultrasensitive and selective chemical sensing at the single-ion and single-molecule limit. In this review article, we summarize the progress on nanofluidics of SDNs, with a focus on the confinement effects that arise in these extremely narrow nanopores. The recent development of precision model systems, transformative experimental tools, and multiscale theories that have played enabling roles in advancing this frontier are reviewed. We also identify new knowledge gaps in our understanding of nanofluidic transport and provide an outlook for the future challenges and opportunities at this rapidly advancing frontier.

Observation and Isochoric Thermodynamic Analysis of Partially Water-Filled 1.32 and 1.45 nm Diameter Carbon Nanotubes

Recent measurements of fluids under extreme confinement, including water within narrow carbon nanotubes, exhibit marked deviations from continuum theoretical descriptions. In this work, we generate precise carbon nanotube replicates that are filled with water, closed from external mass transfer, and studied over a wide temperature range by Raman spectroscopy. We study segments that are empty, partially filled, and completely filled with condensed water from -80 to 120 °C. Partially filled, nanodroplet states contain submicron vapor-like and liquid-like domains and are analyzed using a Clausius-Clapeyron-type model, yielding heats of condensation of water inside closed 1.32 nm diameter carbon nanotubes (3.32 ± 0.10 kJ/mol and 3.72 ± 0.11 kJ/mol) and 1.45 nm diameter carbon nanotubes (3.50 ± 0.07 kJ/mol) that are lower than the bulk enthalpy of vaporization and closer to the bulk enthalpy of fusion. Favored partial filling fractions are calculated, highlighting the effect of subnanometer changes in confining diameter on fluid properties and suggesting the promise of molecular engineering of nanoconfined liquid/vapor interfaces for water treatment or membrane distillation.

Multiscale compression-induced restructuring of stacked lipid bilayers: From buckling delamination to molecular packing

Lipid membranes in nature adapt and reconfigure to changes in composition, temperature, humidity, and mechanics. For instance, the oscillating mechanical forces on lung cells and alveoli influence membrane synthesis and structure during breathing. However, despite advances in the understanding of lipid membrane phase behavior and mechanics of tissue, there is a critical knowledge gap regarding the response of lipid membranes to micromechanical forces. Most studies of lipid membrane mechanics use supported lipid bilayer systems missing the structural complexity of pulmonary lipids in alveolar membranes comprising multi-bilayer interconnected stacks. Here, we elucidate the collective response of the major component of pulmonary lipids to strain in the form of multi-bilayer stacks supported on flexible elastomer substrates. We utilize X-ray diffraction, scanning probe microscopy, confocal microscopy, and molecular dynamics simulation to show that lipid multilayered films both in gel and fluid states evolve structurally and mechanically in response to compression at multiple length scales. Specifically, compression leads to increased disorder of lipid alkyl chains comparable to the effect of cholesterol on gel phases as a direct result of the formation of nanoscale undulations in the lipid multilayers, also inducing buckling delamination and enhancing multi-bilayer alignment. We propose this cooperative short- and long-range reconfiguration of lipid multilayered films under compression constitutes a mechanism to accommodate stress and substrate topography. Our work raises fundamental insights regarding the adaptability of complex lipid membranes to mechanical stimuli. This is critical to several technologies requiring mechanically reconfigurable surfaces such as the development of electronic devices interfacing biological materials.

Real-Space Charge Density Profiling of Electrode-Electrolyte Interfaces with Angstrom Depth Resolution

The accumulation and depletion of charges at electrode-electrolyte interfaces is crucial for all types of electrochemical processes. However, the spatial profile of such interfacial charges remains largely elusive. Here we develop charge profiling three-dimensional (3D) atomic force microscopy (CP-3D-AFM) to experimentally quantify the real-space charge distribution of the electrode surface and electric double layers (EDLs) with angstrom depth resolution. We first measure the 3D force maps at different electrode potentials using our recently developed electrochemical 3D-AFM. Through statistical analysis, peak deconvolution, and electrostatic calculations, we derive the depth profile of the local charge density. We perform such charge profiling for two types of emergent electrolytes, ionic liquids, and highly concentrated aqueous solutions, observe pronounced sub-nanometer charge variations, and find the integrated charge densities to agree with those derived from macroscopic electrochemical measurements.

Innermost Ion Association Configuration Is a Key Structural Descriptor of Ionic Liquids at Electrified Interfaces

The structure of electric double layers (EDLs) is crucial for all types of electrochemical processes. While in dilute solutions EDL structure can be approximately treated within the Gouy-Chapman-Stern regime, in highly ionic electrolytes the description of EDL has been largely elusive. Here we study the EDL structure of an ionic liquid on a series of crystalline electrodes. Through molecular dynamics (MD) simulations, we observe strong intermolecular interaction among cations and anions and propose that the cation-anion association structure at the innermost layer is a key descriptor of the EDL. Using our recently developed electrochemical 3D atomic force microscopy (EC-3D-AFM) technique, we confirm the theoretical prediction and further find that the width of the first EDL is an experimental gauge of the ion association structure in that layer. We expect such ion association descriptors to be broadly applicable to a large range of highly ionic electrolytes on various electrode surfaces.

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.

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.

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.

Modified Lucas-Washburn theory for fluid filling in nanotubes

Ultrafast water transport in carbon nanotubes (CNTs) has drawn a great deal of attention in a number of applications, such as water desalination, power generation, and biomolecule detection. With the recent experimental advances in water filling of isolated CNTs, the Lucas-Washburn theory for capillary rise in tubes needs to be revisited for a better understanding of the physics and dynamics of water filling in CNTs. Here, the Lucas-Washburn theory is corrected for the hydrodynamic entrance effects as well as the variation of capillary pressure and hydrodynamic properties with the radius and length of CNTs. Due to the large slippage in CNTs, inclusion of the entrance effects is important particularly for the initial stages of filling where a L∝t scaling, as opposed to L^{2}∝t, is observed in our molecular dynamics (MD) simulations. The corrected Lucas-Washburn theory is shown to predict the water filling dynamics in CNTs as observed in MD simulations. With the corrected theory, we achieve a better understanding of capillary rise and water filling dynamics in CNTs.

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.

Machine Learning Assisted Screening of Two-Dimensional Materials for Water Desalination

There exists a vast expanse of data in the literature which can be harnessed for accelerated design and discovery of advanced materials for various applications of importance ─ for example, desalination of seawater. Here, we develop a machine learning (ML) model, training it with ∼260 molecular dynamics (MD) computation results, to predict the desalination performance of 2D membranes that exist in the literature. The desalination performance variables of water flux and salt rejection rates are correlated to 49 material features related to the chemistry of the pores and the membranes along with applied pressure, salt concentration, partial charges on the atoms, geometry of the pore, the mechanical properties of the membranes, and the properties of water for the water model used. We used the ML model to screen 3814 structurally optimized 2D materials for maximum water flux and salt rejection rates from the literature. We found some candidates that perform ∼4 times better than the more popularly known 2D materials such as graphene and MoS₂. This result is verified using data obtained from MD simulations performed on several representative 2D membranes for different classes. Such validated statistical frameworks using literature data can be very useful in guiding experiments in the field of functional materials for varied applications.

Nonlinear electrohydrodynamic ion transport in graphene nanopores

Mechanosensitivity is one of the essential functionalities of biological ion channels. Synthesizing an artificial nanofluidic system to mimic such sensations will not only improve our understanding of these fluidic systems but also inspire applications. In contrast to the electrohydrodynamic ion transport in long nanoslits and nanotubes, coupling hydrodynamical and ion transport at the single-atom thickness remains challenging. Here, we report the pressure-modulated ion conduction in graphene nanopores featuring nonlinear electrohydrodynamic coupling. Increase of ionic conductance, ranging from a few percent to 204.5% induced by the pressure—an effect that was not predicted by the classical linear coupling of molecular streaming to voltage-driven ion transport—was observed experimentally. Computational and theoretical studies reveal that the pressure sensitivity of graphene nanopores arises from the transport of capacitively accumulated ions near the graphene surface. Our findings may help understand the electrohydrodynamic ion transport in nanopores and offer a new ion transport controlling methodology.

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.

Ultrasensitive Detection of Dopamine, IL-6 and SARS-CoV-2 Proteins on Crumpled Graphene FET Biosensor

Universal platforms for biomolecular analysis using label-free sensing modalities can address important diagnostic challenges. Electrical field effect-sensors are an important class of devices that can enable point-of-care sensing by probing the charge in the biological entities. Use of crumpled graphene for this application is especially promising. It is previously reported that the limit of detection (LoD) on electrical field effect-based sensors using DNA molecules on the crumpled graphene FET (field-effect transistor) platform. Here, the crumpled graphene FET-based biosensing of important biomarkers including small molecules and proteins is reported. The performance of devices is systematically evaluated and optimized by studying the effect of the crumpling ratio on electrical double layer (EDL) formation and bandgap opening on the graphene. It is also shown that a small and electroneutral molecule dopamine can be captured by an aptamer and its conformation change induced electrical signal changes. Three kinds of proteins were captured with specific antibodies including interleukin-6 (IL-6) and two viral proteins. All tested biomarkers are detectable with the highest sensitivity reported on the electrical platform. Significantly, two COVID-19 related proteins, nucleocapsid (N-) and spike (S-) proteins antigens are successfully detected with extremely low LoDs. This electrical antigen tests can contribute to the challenge of rapid, point-of-care diagnostics.

Anomalous interfacial dynamics of single proton charges in binary aqueous solutions

Our understanding of the dynamics of charge transfer between solid surfaces and liquid electrolytes has been hampered by the difficulties in obtaining interface, charge, and solvent-specific information at both high spatial and temporal resolution. Here, we measure at the single charge scale the dynamics of protons at the interface between an hBN crystal and binary mixtures of water and organic amphiphilic solvents (alcohols and acetone), evidencing a marked influence of solvation on interfacial dynamics. Applying single-molecule localization microscopy to emissive crystal defects, we observe correlated activation between adjacent ionizable surface defects, mediated by the transport of single excess protons along the solid/liquid interface. Solvent content has a nontrivial effect on interfacial dynamics, leading at intermediate water fraction to an increased surface diffusivity, as well as an increased affinity of the proton charges to the solid surface. Our measurements evidence the notable role of solvation on interfacial proton charge transport.

Culture-free biphasic approach for sensitive detection of Escherichia coli O157:H7 from beef samples

Foodborne illnesses are a major threat to public health also leading to significant mortality and financial and reputational damage to industry. It is very important to detect pathogen presence in food products early, rapidly, and accurately to avoid potential outbreaks and economic loss. However, "gold standard" culture methods, including enrichment of pathogens, can take up to several days. Moreover, the food matrix often interferes with nucleic acid amplification methods of detection, requiring DNA extraction from the sample for successful molecular detection of pathogens. Here, we introduce a "biphasic" amplification method that can achieve high sensitivity detection with background noise from ground beef food samples without culture or other extraction methods in 2.5 h. Homogenized ground beef is dried resulting in an increase in porosity of the dried food matrix to allowing amplification enzymes and primers to access the target DNA and initiate the reaction within the dried food matrix. Using Loop Mediated Isothermal Amplification, we demonstrate the detection of 1-3 cfu of Escherichia coli bacteria in 30 mg of dried food matrix. Our approach significantly lowers the time to result to less than a few hours and have a pronounced impact on reduction of instrumentation complexity and costs.

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 ^ξ_cf 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.

Highly Strain-Tunable Interlayer Excitons in MoS2/WSe2 Heterobilayers

Interlayer excitons in heterobilayers of transition-metal dichalcogenides (TMDCs) have generated enormous interest due to their permanent vertical dipole moments and long lifetimes. However, the effects of mechanical strain on the optoelectronic properties of interlayer excitons in heterobilayers remain relatively uncharacterized. Here, we experimentally demonstrate strain tuning of Γ-K interlayer excitons in molybdenum disulfide and tungsten diselenide (MoS₂/WSe₂) wrinkled heterobilayers and obtain a deformation potential constant of ∼107 meV/% uniaxial strain, which is approximately twice that of the intralayer excitons in the constituent monolayers. We further observe a nonmonotonic dependence of the interlayer exciton photoluminescence intensity with strain, which we interpret as being due to the sensitivity of the Γ point to band hybridization arising from the competition between in-plane strain and out-of-plane interlayer coupling. Strain engineering with interlayer excitons in TMDC heterobilayers offers higher strain tunability and new degrees of freedom compared to their monolayer counterparts.

Super-resolved Optical Mapping of Reactive Sulfur-Vacancies in Two-Dimensional Transition Metal Dichalcogenides

Transition metal dichalcogenides (TMDs) represent a class of semiconducting two-dimensional (2D) materials with exciting properties. In particular, defects in 2D-TMDs and their molecular interactions with the environment can crucially affect their physical and chemical properties. However, mapping the spatial distribution and chemical reactivity of defects in liquid remains a challenge. Here, we demonstrate large area mapping of reactive sulfur-deficient defects in 2D-TMDs in aqueous solutions by coupling single-molecule localization microscopy with fluorescence labeling using thiol chemistry. Our method, reminiscent of PAINT strategies, relies on the specific binding of fluorescent probes hosting a thiol group to sulfur vacancies, allowing localization of the defects with an uncertainty down to 15 nm. Tuning the distance between the fluorophore and the docking thiol site allows us to control Föster resonance energy transfer (FRET) process and reveal grain boundaries and line defects due to the local irregular lattice structure. We further characterize the binding kinetics over a large range of pH conditions, evidencing the reversible adsorption of the thiol probes to the defects with a subsequent transitioning to irreversible binding in basic conditions. Our methodology provides a simple and fast alternative for large-scale mapping of nonradiative defects in 2D materials and can be used for in situ and spatially resolved monitoring of the interaction between chemical agents and defects in 2D materials that has general implications for defect engineering in aqueous condition.

Dynamic and weak electric double layers in ultrathin nanopores

The unique properties of aqueous electrolytes in ultrathin nanopores have drawn a great deal of attention in a variety of applications, such as power generation, water desalination, and disease diagnosis. Inside the nanopore, at the interface, properties of ions differ from those predicted by the classical ionic layering models (e.g., Gouy-Chapman electric double layer) when the thickness of the nanopore approaches the size of a single atom (e.g., nanopores in a single-layer graphene membrane). Here, using extensive molecular dynamics simulations, the structure and dynamics of aqueous ions inside nanopores are studied for different thicknesses, diameters, and surface charge densities of carbon-based nanopores [ultrathin graphene and finite-thickness carbon nanotubes (CNTs)]. The ion concentration and diffusion coefficient in ultrathin nanopores show no indication of the formation of a Stern layer (an immobile counter-ionic layer) as the counter-ions and nanopore atoms are weakly correlated in time compared to the strong correlation observed in thick nanopores. The weak correlation observed in ultrathin nanopores is indicative of a weak adsorption of counter-ions onto the surface compared to that of thick pores. The vanishing counter-ion adsorption (ion-wall correlation) in ultrathin nanopores leads to several orders of magnitude shorter ionic residence times (picoseconds) compared to the residence times in thick CNTs (seconds). The results of this study will help better understand the structure and dynamics of aqueous ions in ultrathin nanopores.

Ion Solvation and Transport in Narrow Carbon Nanotubes: Effects of Polarizability, Cation-π Interaction, and Confinement

Understanding ion solvation and transport under confinement is critical for a wide range of emerging technologies, including water desalination and energy storage. While molecular dynamics (MD) simulations have been widely used to study the behavior of confined ions, considerable deviations between simulation results depending on the specific treatment of intermolecular interactions remain. In the following, we present a systematic investigation of the structure and dynamics of two representative solutions, that is, KCl and LiCl, confined in narrow carbon nanotubes (CNTs) with a diameter of 1.1 and 1.5 nm, using a combination of first-principles and classical MD simulations. Our simulations show that the inclusion of both polarization and cation-π interactions is essential for the description of ion solvation under confinement, particularly for large ions with weak hydration energies. Beyond the variation in ion solvation, we find that cation-π interactions can significantly influence the transport properties of ions in CNTs, particularly for KCl, where our simulations point to a strong correlation between ion dehydration and diffusion. Our study highlights the complex interplay between nanoconfinement and specific intermolecular interactions that strongly control the solvation and transport properties of ions.

Strain-resilient electrical functionality in thin-film metal electrodes using two-dimensional interlayers

Flexible electrodes that allow electrical conductance to be maintained during mechanical deformation are required for the development of wearable electronics. However, flexible electrodes based on metal thin-films on elastomeric substrates can suffer from complete and unexpected electrical disconnection after the onset of mechanical fracture across the metal. Here we show that the strain-resilient electrical performance of thin-film metal electrodes under multimodal deformation can be enhanced by using a two-dimensional (2D) interlayer. Insertion of atomically-thin interlayers - graphene, molybdenum disulfide, or hexagonal boron nitride - induce continuous in-plane crack deflection in thin-film metal electrodes. This leads to unique electrical characteristics (termed electrical ductility) in which electrical resistance gradually increases with strain, creating extended regions of stable resistance. Our 2D-interlayer electrodes can maintain a low electrical resistance beyond a strain in which conventional metal electrodes would completely disconnect. We use the approach to create a flexible electroluminescent light emitting device with an augmented strain-resilient electrical functionality and an early-damage diagnosis capability.

Selective filling of n-hexane in a tight nanopore

Molecular sieving may occur when two molecules compete for a nanopore. In nearly all known examples, the nanopore is larger than the molecule that selectively enters the pore. Here, we experimentally demonstrate the ability of single-wall carbon nanotubes with a van der Waals pore size of 0.42 nm to separate n-hexane from cyclohexane—despite the fact that both molecules have kinetic diameters larger than the rigid nanopore. This unexpected finding challenges our current understanding of nanopore selectivity and how molecules may enter a tight channel. Ab initio molecular dynamics simulations reveal that n-hexane molecules stretch by nearly 11.2% inside the nanotube pore. Although at a relatively low probability (28.5% overall), the stretched state of n-hexane does exist in the bulk solution, allowing the molecule to enter the tight pore even at room temperature. These insights open up opportunities to engineer nanopore selectivity based on the molecular degrees of freedom.

Molecular sieving typically occurs when molecules with smaller kinetic diameter than a nanopore selectively enter the pore. Here the authors show, using photoluminescence imaging and ab initio molecular dynamics simulations, that single-walled carbon nanotubes can separate n-hexane from cyclohexane, despite both having larger kinetic diameter than the nanopore.

Three-Dimensional Molecular Mapping of Ionic Liquids at Electrified Interfaces

Electric double layers (EDLs), occurring ubiquitously at solid-liquid interfaces, are critical for electrochemical energy conversion and storage processes such as capacitive charging and redox reactions. However, to date the molecular-scale structure of EDLs remains elusive. Here we report an advanced technique, electrochemical three-dimensional atomic force microscopy (EC-3D-AFM), and use it to directly image the molecular-scale EDL structure of an ionic liquid under different electrode potentials. We observe not only multiple discrete ionic layers in the EDL on a graphite electrode but also a quasi-periodic molecular density distribution within each layer. Furthermore, we find pronounced 3D reconfiguration of the EDL at different voltages, especially in the first layer. Combining the experimental results with molecular dynamics simulations, we find potential-dependent molecular redistribution and reorientation in the innermost EDL layer, both of which are critical to EDL capacitive charging. We expect this mechanistic understanding to have profound impacts on the rational design of electrode-electrolyte interfaces for energy conversion and storage.

Confinement-Induced Enhancement of Parallel Dielectric Permittivity: Super Permittivity Under Extreme Confinement

Enhancement of parallel (x-y plane) dielectric permittivity of confined fluids has been shown previously. However, a theoretical model that explains this enhancement is lacking thus far. In this study, using statistical-mechanical theories and molecular dynamics simulations, we show an explicit relation between the parallel dielectric permittivity, density variations, and dipolar correlations for protic and aprotic fluids confined in slit-like channels. We analyze the importance of dipolar correlations on enhancement of parallel dielectric permittivity inside large channels and extreme confinements. In large channels, beyond the interfacial region, dipolar correlations exhibit bulk-like behavior. Under extreme confinement, the correlations become stronger to the extent that they give rise to a giant increase in the parallel dielectric permittivity. This sudden increase in dielectric permittivity can be a signature of a liquid transition into higher-ordered structures and has important consequences for understanding ion transport, molecular dissociation, and chemical reactions inside nanoconfined environments.

Universal Reduction in Dielectric Response of Confined Fluids

Dielectric permittivity is central to many biological and physiochemical systems, as it affects the long-range electrostatic interactions. Similar to many fluid properties, confinement greatly alters the dielectric response of polar liquids. Many studies have focused on the reduction of the dielectric response of water under confinement. Here, using molecular dynamics simulations, statistical-mechanical theories, and multiscale methods, we study the out-of-plane (z-axis) dielectric response of protic and aprotic fluids confined inside slit-like graphene channels. We show that the reduction in perpendicular permittivity is universal for all the fluids and exhibits a Langevin-like behavior as a function of channel width. We show that this reduction is due to the favorable in-plane (x-y plane) dipole-dipole electrostatic interactions of the interfacial fluid layer. Furthermore, we observe an anomalously low dielectric response under an extreme confinement.

Ion Transport in Electrically Imperfect Nanopores

Ionic transport through a charged nanopore at low ion concentration is governed by the surface conductance. Several experiments have reported various power-law relations between the surface conductance and ion concentration, i.e., G_surf ∝ c₀^α. However, the physical origin of the varying exponent, α, is not yet clearly understood. By performing extensive coarse-grained Molecular Dynamics simulations for various pore diameters, lengths, and surface charge densities, we observe varying power-law exponents even with a constant surface charge and show that α depends on how electrically "perfect" the nanopore is. Specifically, when the net charge of the solution in the pore is insufficient to ensure electroneutrality, the pore is electrically "imperfect" and such nanopores can exhibit varying α depending on the degree of "imperfectness". We present an ionic conductance theory for electrically "imperfect" nanopores that not only explains the various power-law relationships but also describes most of the experimental data available in the literature.

Ultrasensitive detection of nucleic acids using deformed graphene channel field effect biosensors

Field-effect transistor (FET)-based biosensors allow label-free detection of biomolecules by measuring their intrinsic charges. The detection limit of these sensors is determined by the Debye screening of the charges from counter ions in solutions. Here, we use FETs with a deformed monolayer graphene channel for the detection of nucleic acids. These devices with even millimeter scale channels show an ultra-high sensitivity detection in buffer and human serum sample down to 600 zM and 20 aM, respectively, which are ∼18 and ∼600 nucleic acid molecules. Computational simulations reveal that the nanoscale deformations can form 'electrical hot spots' in the sensing channel which reduce the charge screening at the concave regions. Moreover, the deformed graphene could exhibit a band-gap, allowing an exponential change in the source-drain current from small numbers of charges. Collectively, these phenomena allow for ultrasensitive electronic biomolecular detection in millimeter scale structures.

Ultrasensitive detection of nucleic acids using deformed graphene channel field effect biosensors

Field-effect transistor (FET)-based biosensors allow label-free detection of biomolecules by measuring their intrinsic charges. The detection limit of these sensors is determined by the Debye screening of the charges from counter ions in solutions. Here, we use FETs with a deformed monolayer graphene channel for the detection of nucleic acids. These devices with even millimeter scale channels show an ultra-high sensitivity detection in buffer and human serum sample down to 600 zM and 20 aM, respectively, which are ∼18 and ∼600 nucleic acid molecules. Computational simulations reveal that the nanoscale deformations can form 'electrical hot spots' in the sensing channel which reduce the charge screening at the concave regions. Moreover, the deformed graphene could exhibit a band-gap, allowing an exponential change in the source-drain current from small numbers of charges. Collectively, these phenomena allow for ultrasensitive electronic biomolecular detection in millimeter scale structures.

Nanofluidic Transport Theory with Enhancement Factors Approaching One

High performance water transport in nanopores has drawn a great deal of attention in a variety of applications, such as water desalination, power generation, and biosensing. High water transport enhancement factors in carbon-based nanopores have been reported over the classical Hagen-Poiseuille (HP) equation which does not account for the physics of transport at molecular scale. Instead, comparing the experimentally measured transport rates to that of a theory, that accounts for the microscopic physics of transport, would result in enhancement factors approaching unity. Such a theory is currently missing. Here, molecular corrections are introduced into the HP equation by considering the variation of key hydrodynamical properties (viscosity and friction) with thickness and diameter of pores in ultrathin graphene and finite-length carbon nanotubes (CNTs) using Green-Kubo relations and molecular dynamics (MD) simulations. The corrected HP (CHP) theory successfully predicts the permeation rates from nonequilibrium MD pressure driven flows. The previously reported enhancement factors over no-slip HP (of the order of 1000) approach unity when the permeations are normalized by the CHP flow rates. The results of our study will help better understand nanoscale flows in carbon-based pores and tubes.

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.

Strong Electroosmotic Coupling Dominates Ion Conductance of 1.5 nm Diameter Carbon Nanotube Porins

Extreme confinement in nanometer-sized channels can alter fluid and ion transport in significant ways, leading to significant water flow enhancement and unusual ion correlation effects. These effects are especially pronounced in carbon nanotube porins (CNTPs) that combine strong confinement in the inner lumen of carbon nanotubes with the high slip flow enhancement due to smooth hydrophobic pore walls. We have studied ion transport and ion selectivity in 1.5 nm diameter CNTPs embedded in lipid membranes using a single nanopore measurement setup. Our data show that CNTPs are weakly cation selective at pH 7.5 and become nonselective at pH 3.0. Ion conductance of CNTPs exhibits an unusual 2/3 power law scaling with the ion concentration at both neutral and acidic pH values. Coupled Navier-Stokes and Poisson-Nernst-Planck simulations and atomistic molecular dynamics simulations reveal that this scaling originates from strong coupling between water and ion transport in these channels. These effects could result in development of a next generation of biomimetic membranes and carbon nanotube-based electroosmotic pumps.

Electrical Double Layer of Supported Atomically Thin Materials

The electrical double layer (EDL), consisting of two parallel layers of opposite charges, is foundational to many interfacial phenomena and unique in atomically thin materials. An important but unanswered question is how the "transparency" of atomically thin materials to their substrates influences the formation of the EDL. Here, we report that the EDL of graphene is directly affected by the surface energy of the underlying substrates. Cyclic voltammetry and electrochemical impedance spectroscopy measurements demonstrate that graphene on hydrophobic substrates exhibits an anomalously low EDL capacitance, much lower than what was previously measured for highly oriented pyrolytic graphite, suggesting disturbance of the EDL ("disordered EDL") formation due to the substrate-induced hydrophobicity to graphene. Similarly, electrostatic gating using EDL of graphene field-effect transistors shows much lower transconductance levels or even no gating for graphene on hydrophobic substrates, further supporting our hypothesis. Molecular dynamics simulations show that the EDL structure of graphene on a hydrophobic substrate is disordered, caused by the disruption of water dipole assemblies. Our study advances understanding of EDL in atomically thin limit.

Transfer-Learning-Based Coarse-Graining Method for Simple Fluids: Toward Deep Inverse Liquid-State Theory

Machine learning is an attractive paradigm to circumvent difficulties associated with the development and optimization of force-field parameters. In this study, a deep neural network (DNN) is used to study the inverse problem of the liquid-state theory, in particular, to obtain the relation between the radial distribution function (RDF) and the Lennard-Jones (LJ) potential parameters at various thermodynamic states. Using molecular dynamics (MD), many observables, including RDF, are determined once the interatomic potential is specified. However, the inverse problem (parametrization of the potential for a specific RDF) is not straightforward. Here we present a framework integrating DNN with big data from 1.5 TB of MD trajectories with a cumulative simulation time of 52 μs for 26 000 distinct systems to predict LJ potential parameters. Our results show that DNN is successful not only in the parametrization of the atomic LJ liquids but also in parametrizing the LJ potential for coarse-grained models of simple multiatom molecules.