Water Flow in Single-Wall Nanotubes: Oxygen Makes It Slip, Hydrogen Makes It Stick

High-Pressure CO2 and CH4 Transport in Smooth Crystalline Silica Mesopores: A Molecular Dynamics Study

In this study, we use molecular dynamics (MD) simulation to study pressure-driven CO2 and CH4 flows and their slippage behaviors in β-cristobalite mesopores. The result illustrates that both CO2 and CH4 have an apparent adsorption layer on pore surface. However, significant differences in gas slippage are observed: CH4 flow shows considerable slippage, while it is negligible for CO2 flow. This disparity is attributed to the collective effect of gas molecular configurations and surface structure. The linear molecular structure of CO2 allows it to align perpendicular to the surface, even penetrating into the surface. Notably, the perpendicular orientation of CO2 molecules is energetically favored near the center of the equilateral triangle formed by adjacent oxygen atoms on β-cristobalite surface. Conversely, the symmetric molecular structure of CH4, coupled with its larger size, prevents its penetration into pore surfaces. Therefore, despite smooth crystalline surfaces, CO2 topological accessible plane is much more curved than that of CH4. Consequently, CO2 displays hesitating motions undergoing rotational movements, which significantly hinders its slippage. This study highlights the collective influences of gas molecular characteristics and surface structure on gas slippage, affording important insights into gas sequestration and the development of functional materials for gas separation.

The role of the water contact layer on hydration and transport at solid/liquid interfaces

Understanding the structure in the nanoscopic region of water that is in direct contact with solid surfaces, so-called contact layer, is key to quantifying macroscopic properties that are of interest to e.g. catalysis, ice nucleation, nanofluidics, gas adsorption, and sensing. We explore the structure of the water contact layer on various technologically relevant solid surfaces, namely graphene, MoS[Formula: see text], Au(111), Au(100), Pt(111), and Pt(100), which have been previously hampered by time and length scale limitations of ab initio approaches or force field inaccuracies, by means of molecular dynamics simulations based on ab initio machine learning potentials built using an active learning scheme. Our results reveal that the in-plane intermolecular correlations of the water contact layer vary greatly among different systems: Whereas the contact layer on graphene and on Au(111) is predominantly homogeneous and isotropic, it is inhomogeneous and anisotropic on MoS[Formula: see text], on Au(100), and on the Pt surfaces, where it additionally forms two distinct sublayers. We apply hydrodynamics and the theory of the hydrophobic effect, to relate the energy corrugation and the characteristic length-scales of the contact layer with wetting, slippage, the hydration of small hydrophobic solutes and diffusio-osmotic transport. Thus, this work provides a microscopic picture of the water contact layer and links it to macroscopic properties of liquid/solid interfaces that are measured experimentally and that are relevant to wetting, hydrophobic solvation, nanofluidics, and osmotic transport.

Ion transport and ultra-efficient osmotic power generation in boron nitride nanotube porins

Nanotube porins form transmembrane nanomaterial-derived scaffolds that mimic the geometry and functionality of biological membrane channels. We report synthesis, transport properties, and osmotic energy harvesting performance of another member of the nanotube porin family: boron nitride nanotube porins (BNNTPs). Cryo-transmission electron microscopy imaging, liposome transport assays, and DNA translocation experiments show that BNNTPs reconstitute into lipid membranes to form functional channels of ~2-nm diameter. Ion transport studies reveal ion conductance characteristics of individual BNNTPs, which show an unusual C1/4 scaling with ion concentration and pronounced pH sensitivity. Reversal potential measurements indicate that BNNTPs have strong cation selectivity at neutral pH, attributable to the high negative charge on the channel. BNNTPs also deliver very large power density up to 12 kW/m2 in the osmotic gradient transport experiments at neutral pH, surpassing that of other BNNT-based devices by two orders of magnitude under similar conditions. Our results suggest that BNNTPs are a promising platform for mass transport and osmotic power generation.

Slip Opacity and Fast Osmotic Transport of Hydrophobes at Aqueous Interfaces with Two-Dimensional Materials

We investigate the interfacial transport of water and hydrophobic solutes on van der Waals bilayers and heterostructures formed by stacking graphene, hBN, and MoS2 using extensive ab initio molecular dynamics simulations. We compute water slippage and the diffusio-osmotic transport coefficient of hydrophobic particles at the interface by combining hydrodynamics and the theory of the hydrophobic effect. We find that slippage is dominated by the layer that is in direct contact with water and only marginally altered by the second layer, leading to a so-called "slip opacity". The screening of the lateral forces, where the liquid does not feel the forces coming from the second nearest layer, is one of the factors leading to the "slip opacity" in our systems. The diffusio-osmotic transport of small hydrophobes (with a radius below 2.5 Å) is also affected by the slip opacity, being dramatically enhanced by slippage. Furthermore, the direction of diffusio-osmotic flow is controlled by the solute size, with the flow in the opposite direction of the concentration gradient for smaller hydrophobes, and vice versa for larger ones. We connect our findings to the wetting properties of two-dimensional materials, and we propose that slippage and wetting can be controlled separately: whereas the slippage is mostly determined by the layer in closer proximity to water, wetting can be finely tuned by stacking different two-dimensional materials. Our study advances the computational design of two-dimensional materials and van der Waals heterostructures, enabling precise control over wetting and slippage properties for applications in coatings and water purification membranes.

Atomic scale insights into NaCl nucleation in nanoconfined environments

In this work we examine the nucleation from NaCl aqueous solutions within nano-confined environments, employing enhanced sampling molecular dynamics simulations integrated with machine learning-derived reaction coordinates. Through our simulations, we successfully induce phase transitions between solid, liquid, and a hydrated phase, typically observed at lower temperatures in bulk environments. Interestingly, while generally speaking nano-confinement serves to stabilize the solid phase and elevate melting points, there are subtle variations in the thermodynamics of competing phases with the precise extent of confinement. Our simulations explain these findings by underscoring the significant role of water, alongside ion aggregation and subtle, anisotropic dielectric behavior, in driving nucleation within nano-confined environments. This report thus provides a framework for sampling, analyzing and understanding nucleation processes under nano-confinement.

Estimating water transport in carbon nanotubes: a critical review and inclusion of scale effects

The quasi-frictionless water flow across graphitic surfaces offers vast opportunities for a wide range of applications from biomedical science to energy. However, the conflicting experimental results impede a clear understanding of the transport mechanism and desired flow control. Existing literature proposes numerous modifications and updated boundary conditions to extend classical hydrodynamic theories for nanoflows, yet a consensus or definitive conclusion remains elusive. This study presents a critical review of the proposed modifications of the pressure driven flow or the Hagen-Poiseuille (HP) equations to estimate the flow enhancement through carbon nanotubes (CNTs). For such a case, we performed (semi-)classical molecular dynamics simulations of water flow in various sizes of CNTs, applied the different forms of boundary definitions from the literature, and derived HP equation models by implementing these modifications. By aggregating seven distinct experimental datasets, we tested various flow enhancement models against our measurements. Our findings indicate that including the interfacial layering-based dynamic slip-definition in the proposed HP equations yields accurate estimations. While considering interfacial viscosity predicts the individual CNT experiments well, using the experimental viscosity yields better estimations of measurements for the water flow enhancement through membranes of CNTs. This critical review testing existing literature demonstrates how to refine continuum fluid mechanics to predict water flow enhancement at the nanoscale providing holistic multiscale modeling.

Shale Gas Nanofluid in the Curved Carbon Nanotube: A Molecular Dynamics Simulation Study

Curved nanochannels are prevalent in porous and tortuous materials, with shale matrices being a noteworthy example. The tortuosity of shale matrices significantly influences the behavior of shale gas, holding crucial implications for gas recovery engineering. In this study, we employ molecular dynamics simulation (MD) to investigate the impact of curvature and radius in tortuous nanochannel formed by a curved single-walled carbon nanotube (SWCNT) on the adsorption and transport properties of methane gas fluid. Our findings reveal that the inner half surface of the SWCNT, characterized by negative curvature, exhibits enhanced methane adsorption. Methane in straighter and narrower channels displays higher flow velocities, while wider channels exhibit higher flow flux. The nonzero flow velocity alters adsorption strength, causing the outer half to surpass the inner half. Tangent and vertical velocities of the flow are heterogeneously distributed in the channel, with the outer half having higher tangent velocities. Additionally, a vertical velocity pulse near the entrance induces turbulent vortex flow, slowing down the tangent flow velocity. This research contributes to a deeper understanding of shale gas properties in matrices with bent and curved channels, offering insights into nanofluids in carbon nanotubes and porous media featuring curved nanochannels.

Do Molecules Tunnel through Nanoporous Graphene?

The molecular transport and quantum tunneling of H2 and H2O molecules through nanoporous graphene is studied using computational modeling and first-principles density functional theory. It is demonstrated that molecules with sufficiently high kinetic energies can tunnel through nanopores. It is also demonstrated that molecules can be trapped in front of a nanopore or behind it. These investigations help us learn the behavior of molecules in and around the nanopores of graphene. They also help us learn the fundamentals of molecular tunneling. We believe nanoporous graphene can play important roles for gas separation and nanofiltration.

Tuning Interfacial Water Friction through Moiré Twist

Foundations of nanofluidics can enable advances in diverse applications such as water desalination, energy harvesting, and biological analysis. Dynamically manipulating nanofluidic properties, such as diffusion and friction, is an area of great scientific interest. Twisted bilayer graphene, particularly at the magic angle, has garnered attention for its unconventional superconductivity and correlated insulator behavior due to strong electronic correlations. The impact of the electronic properties of moiré patterns in twisted bilayer graphene on structural and dynamic properties of water remains largely unexplored. Computational challenges, stemming from simulating large unit cells using density functional theory, have hindered progress. This study addresses this gap by investigating water behavior on twisted bilayer graphene, employing a deep neural network potential (DP) model trained with a data set from ab initio molecular dynamics simulations. It is found that as the twisted angle approaches the magic angle, interfacial water friction increases, leading to a reduced water diffusion. Notably, the analysis shows that at smaller twisted angles with larger moiré patterns, water is more likely to reside in AA stacking regions than AB (or BA) stacking regions, a distinction that diminishes with smaller moiré patterns. This study illustrates the potential for leveraging the distinctive properties of moiré systems to effectively control and optimize interfacial fluid behavior.

Probing structural superlubricity of two-dimensional water transport with atomic resolution

Low-dimensional water transport can be drastically enhanced under atomic-scale confinement. However, its microscopic origin is still under debate. In this work, we directly imaged the atomic structure and transport of two-dimensional water islands on graphene and hexagonal boron nitride surfaces using qPlus-based atomic force microscopy. The lattice of the water island was incommensurate with the graphene surface but commensurate with the boron nitride surface owing to different surface electrostatics. The area-normalized static friction on the graphene diminished as the island area was increased by a power of ~-0.58, suggesting superlubricity behavior. By contrast, the friction on the boron nitride appeared insensitive to the area. Molecular dynamic simulations further showed that the friction coefficient of the water islands on the graphene could reduce to <0.01.

Reorientation of Hydrogen Bonds Renders Unusual Enhancement in Thermal Transport of Water in Nanoconfined Environments

Liquid confined in a nanochannel or nanotube has exhibited a superfast transport phenomenon, providing an ideal heat and mass transfer platform to meet the increasingly stringent challenge of thermal management in developing high-power-density nanoelectronics and nanochips. However, understanding the thermal transport of confined liquid is currently lacking and is speculated to be fundamentally different from that of bulk counterparts due to the unprecedented thermodynamics of liquid in nanoconfined environments. Here, we report that the thermal conductivity of water confined in a silica nanotube is nearly 2-fold as that of bulk status. Further molecular dynamics simulations reveal that this unusual enhancement originates from the densification and reorientation of local hydrogen bonds close to the nanotubes. Thermal-confinement scaling law is established and quantitatively supported by comprehensive simulations with remarkable agreement. Our findings lay a theoretical foundation for designing nanofluidics-enabled cooling strategies and devices.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Homogeneous water vapor condensation with a deep neural network potential model

Molecular-level nucleation has not been clearly understood due to the complexity of multi-body potentials and the stochastic, rare nature of the process. This work utilizes molecular dynamics (MD) simulations, incorporating a first-principles-based deep neural network (DNN) potential model, to investigate homogeneous water vapor condensation. The nucleation rates and critical nucleus sizes predicted by the DNN model are compared against commonly used semi-empirical models, namely extended simple point charge (SPC/E), TIP4P, and OPC, in addition to classical nucleation theory (CNT). The nucleation rates from the DNN model are comparable with those from the OPC model yet surpass the rates from the SPC/E and TIP4P models, a discrepancy that could mainly arise from the overestimated bulk free energy by SPC/E and TIP4P. The surface free energy predicted by CNT is lower than that in MD simulations, while its bulk free energy is higher than that in MD simulations, irrespective of the potential model used. Further analysis of cluster properties with the DNN model unveils pronounced variations of O-H bond length and H-O-H bond angle, along with averaged bond lengths and angles that are enlarged during embryonic cluster formation. Properties such as cluster surface free energy and liquid-to-vapor density transition profiles exhibit significant deviations from CNT assumptions.

Water Electric Field Induced Modulation of the Wetting of Hexagonal Boron Nitride: Insights from Multiscale Modeling of Many-Body Polarization

Understanding the behavior of water contacting two-dimensional materials, such as hexagonal boron nitride (hBN), is important in practical applications, including seawater desalination and energy harvesting. Water, being a polar solvent, can strongly polarize the hBN surface via the electric fields that it generates. However, there is a lack of molecular-level understanding about the role of polarization effects at the hBN/water interface, including its effect on the wetting properties of water. In this study, we develop a theoretical framework that introduces an all-atomistic polarizable force field to accurately model the interactions of water molecules with hBN surfaces. The force field is then utilized to self-consistently describe the water-induced polarization of hBN using the classical Drude oscillator model, including predicting the hBN-water binding energies which are found to be in excellent agreement with diffusion Monte Carlo (DMC) predictions. By carrying out molecular dynamics (MD) simulations, we demonstrate that the polarizable force field yields a water contact angle on multilayered hBN which is in close agreement with the recent experimentally reported values. Conversely, an implicit modeling of the hBN-water polarization energy utilizing a Lennard-Jones (LJ) potential, a commonly utilized approximation in previous MD simulation studies, leads to a considerably lower water contact angle. This difference in the predicted contact angles is attributed to the significant energy-entropy compensation resulting from the incorporation of polarization effects at the hBN-water interface. Our work highlights the importance of self-consistently modeling the hBN-water polarization energy and offers insights into the wetting-related interfacial phenomena of water on polarizable materials.

Uniqueness of Nanoscale Confinement for Fast Water Transport: Effect of Nanotube Diameter and Hydrophobicity

Inspired by the enhanced water permeability of carbon nanotubes (CNTs), molecular dynamics simulations were performed to investigate the transport behavior through nanotubes made of boron nitride (BNNT), silicon carbide (SiC), and silicon nitride (SiN) alongside carbon nanotubes (which have different hydrophobic attributes) considering their implication for reverse osmosis (RO) membranes under different practical environments. According to our findings, not only do CNTs but also other kinds of nanotubes exhibit transition anomalies with increasing diameter. Utilizing the robust two-phase thermodynamic (2PT) methods, the current examinations shed light on thermodynamic origin of favorable water filling of these nanotubes. The results show that regardless of the nanotube material, the filling of water inside small nanopores (d < 10 Å) as well as within pores of diameter larger than 15 Å will always be favored by the entropy of filling. However, the entropic preference for filling nanotubes with a diameter of 10-15 Å depends on the constituent material. In particular, the enhancement in total entropy of confined water was mainly due to the increased rotational freedom of confined water molecules. The thermodynamic origin of water transport was correlated with the structural and fluidic behavior of water inside these nanotubes. The observed data for density, flow, structure correlation functions, water-water coordination, tetrahedral order parameter, hydrogen bonds, and density of states functions quantitatively support the observed entropy behavior. Of critical importance is that the present study demonstrates the effectiveness of RO filtration using nanotubes of boron nitride rather than carbon. Furthermore, it was found that one should avoid the use of silicon nanotubes unless filtration needs to be performed under harsh environments where nanotube of other materials cannot survive. Specifically, the results show that both the structural and dynamic properties of water confined in BNNTs are similar to those of CNT's, and for SiNT it is similar as SiC. Our results show that besides the nanotube material, the chirality index of the nanotube also plays a significant role in determining the structure, dynamics and thermodynamics of confined water molecules.

Ion and Hydrodynamic Translucency in 1D van der Waals Heterostructured Boron-Nitride Single-Walled Carbon Nanotubes

An unresolved challenge in nanofluidics is tuning ion selectivity and hydrodynamic transport in pores, particularly for those with diameters larger than a nanometer. In contrast to conventional strategies that focus on changing surface functionalization or confinement degree by varying the radial dimension of the pores, we explore a unique approach for manipulating ion selectivity and hydrodynamic flow enhancement by externally coating single-walled carbon nanotubes (SWCNTs) with a few layers of hexagonal boron nitride (h-BN). For van der Waals heterostructured BN-SWCNTs, we observed a 9-fold increase in cation selectivity for K+ versus Cl- compared to pristine SWCNTs of the same 2.2 nm diameter, while hydrodynamic slip lengths decreased by more than an order of magnitude. These results suggest that the single-layer graphene inner surface may be translucent to charge-regulation and hydrodynamic-slip effects arising from h-BN on the outside of the SWCNT. Such 1D heterostructures could serve as synthetic platforms with tunable properties for exploring distinct nanofluidic phenomena and their potential applications.

Origin of dielectric polarization suppression in confined water from first principles

It has long been known that the dielectric constant of confined water should be different from that in bulk. Recent experiments have shown that it is vanishingly small, however the origin of the phenomenon remains unclear. Here we used ab initio molecular dynamics simulations (AIMD) and AIMD-trained machine-learning potentials to understand water's structure and electronic properties underpinning this effect. For the graphene and hexagonal boron-nitride substrates considered, we find that it originates in the spontaneous anti-parallel alignment of the water dipoles in the first two water layers near the solid interface. The interfacial layers exhibit net ferroelectric ordering, resulting in an overall anti-ferroelectric arrangement of confined water. Together with constrained hydrogen-bonding orientations, this leads to much reduced out-of-plane polarization. Furthermore, we directly contrast AIMD and simple classical force-field simulations, revealing important differences. This work offers insight into a property of water that is critical in modulating surface forces, the electric-double-layer formation and molecular solvation, and shows a way to compute it.

Transferability evaluation of the deep potential model for simulating water-graphene confined system

Machine learning potentials (MLPs) are poised to combine the accuracy of ab initio predictions with the computational efficiency of classical molecular dynamics (MD) simulation. While great progress has been made over the last two decades in developing MLPs, there is still much to be done to evaluate their model transferability and facilitate their development. In this work, we construct two deep potential (DP) models for liquid water near graphene surfaces, Model S and Model F, with the latter having more training data. A concurrent learning algorithm (DP-GEN) is adopted to explore the configurational space beyond the scope of conventional ab initio MD simulation. By examining the performance of Model S, we find that an accurate prediction of atomic force does not imply an accurate prediction of system energy. The deviation from the relative atomic force alone is insufficient to assess the accuracy of the DP models. Based on the performance of Model F, we propose that the relative magnitude of the model deviation and the corresponding root-mean-square error of the original test dataset, including energy and atomic force, can serve as an indicator for evaluating the accuracy of the model prediction for a given structure, which is particularly applicable for large systems where density functional theory calculations are infeasible. In addition to the prediction accuracy of the model described above, we also briefly discuss simulation stability and its relationship to the former. Both are important aspects in assessing the transferability of the MLP model.

A numerical model for water hydration on nanosurfaces: from friction to hydrophilicity and hydrophobicity

Fluidic transport down to the nanometer scale is of great importance for a wide range of applications such as energy harvesting, seawater desalination, and water treatment and may help to understand many biological processes. In this work, we studied the interfacial friction of liquid water on a series of nanostructures through molecular dynamics (MD) simulations. Our results reveal that the friction coefficient of the water-solid interface cannot be described using a previously reported simple function of the free energy corrugation. Considering that the water-solid friction is firmly correlated with the microscopic water motion, we proposed a probability parameter P(d, t) to classify water motion modes on a surface. We demonstrate that this parameter can be used to accurately predict the water-solid friction by simply monitoring the water binding time on a nanosurface. More importantly, according to the relationship between P(d, t) and friction, we found that the friction coefficient can be used as an indicative criterion for quantitatively assessing hydrophobic or hydrophilic materials, where the borderline is roughly 2 × 10⁵ N s m^-3. That is if the water-solid friction is less than 2 × 10⁵ N s m^-3, the surface is considered hydrophobic. But if the friction is larger than this value, the surface is hydrophilic. The present findings could help to better understand fluidic transport at the nanoscale and guide the future design of functional materials, such as super-hydrophobic and super-hydrophilic surfaces by structure engineering.

Probing Confinement Effects on the Infrared Spectra of Water with Deep Potential Molecular Dynamics Simulations

The hydrogen-bond network of confined water is expected to deviate from that of the bulk liquid, yet probing these deviations remains a significant challenge. In this work, we combine large-scale molecular dynamics simulations with machine learning potential derived from first-principles calculations to examine the hydrogen bonding of water confined in carbon nanotubes (CNTs). We computed and compared the infrared spectrum (IR) of confined water to existing experiments to elucidate confinement effects. For CNTs with diameters >1.2 nm, we find that confinement imposes a monotonic effect on the hydrogen-bond network and on the IR spectrum of water. In contrast, confinement below 1.2 nm CNT diameter affects the water structure in a complex fashion, leading to a strong directional dependence of hydrogen bonding that varies nonlinearly with the CNT diameter. When integrated with existing IR measurements, our simulations provide a new interpretation for the IR spectrum of water confined in CNTs, pointing to previously unreported aspects of hydrogen bonding in this system. This work also offers a general platform for simulating water in CNTs with quantum accuracy on time and length scales beyond the reach of conventional first-principles approaches.

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.

Classical Quantum Friction at Water-Carbon Interfaces

Friction at water-carbon interfaces remains a major puzzle with theories and simulations unable to explain experimental trends in nanoscale waterflow. A recent theoretical framework─quantum friction (QF)─proposes to resolve these experimental observations by considering nonadiabatic coupling between dielectric fluctuations in water and graphitic surfaces. Here, using a classical model that enables fine-tuning of the solid's dielectric spectrum, we provide evidence from simulations in general support of QF. In particular, as features in the solid's dielectric spectrum begin to overlap with water's librational and Debye modes, we find an increase in friction in line with that proposed by QF. At the microscopic level, we find that this contribution to friction manifests more distinctly in the dynamics of the solid's charge density than that of water. Our findings suggest that experimental signatures of QF may be more pronounced in the solid's response rather than liquid water's.

Ab Initio Molecular Dynamics Simulation of Water Transport through Short Carbon Nanotubes

Water transport through short single-walled (6, 6) carbon nanotubes (CNTs) was investigated with ab initio molecular dynamics (AIMD) simulation at different temperatures. The water molecules under extreme confinement present a one-dimensional jagged pattern owing to hydrogen bonding, with the near-perfect alignment of the dipole orientations. CNTs ending with dangling bonds can promote water dissociation near the entrance and the occurrence of dipole flipping along the water wire at high temperatures, accompanied by the formation of D defects and L defects in the hydrogen-bond network. In contrast, dissociation of water molecules rarely takes place if the dangling bonds at the ends of the CNTs are terminated with H atoms. Angular jumps of water molecules are commonplace inside the narrow CNTs, implying a low-energy barrier for hydrogen-bond exchange among water molecules in narrow CNTs. The simulation results demonstrate the high activity of dangling bonds at the ends of short CNTs, accompanying passivation processes and their profound impact on water structure and transport, which is important for diverse technological applications.

Recent Progress in Fabrication and Application of BN Nanostructures and BN-Based Nanohybrids

Due to its unique physical, chemical, and mechanical properties, such as a low specific density, large specific surface area, excellent thermal stability, oxidation resistance, low friction, good dispersion stability, enhanced adsorbing capacity, large interlayer shear force, and wide bandgap, hexagonal boron nitride (h-BN) nanostructures are of great interest in many fields. These include, but are not limited to, (i) heterogeneous catalysts, (ii) promising nanocarriers for targeted drug delivery to tumor cells and nanoparticles containing therapeutic agents to fight bacterial and fungal infections, (iii) reinforcing phases in metal, ceramics, and polymer matrix composites, (iv) additives to liquid lubricants, (v) substrates for surface enhanced Raman spectroscopy, (vi) agents for boron neutron capture therapy, (vii) water purifiers, (viii) gas and biological sensors, and (ix) quantum dots, single photon emitters, and heterostructures for electronic, plasmonic, optical, optoelectronic, semiconductor, and magnetic devices. All of these areas are developing rapidly. Thus, the goal of this review is to analyze the critical mass of knowledge and the current state-of-the-art in the field of BN-based nanomaterial fabrication and application based on their amazing properties.

Water Flow in Single-Wall Nanotubes: Oxygen Makes It Slip, Hydrogen Makes It Stick

Experimental measurements have reported ultrafast and radius-dependent water transport in carbon nanotubes which are absent in boron nitride nanotubes. Despite considerable effort, the origin of this contrasting (and fascinating) behavior is not understood. Here, with the aid of machine learning-based molecular dynamics simulations that deliver first-principles accuracy, we investigate water transport in single-wall carbon and boron nitride nanotubes. Our simulations reveal a large, radius-dependent hydrodynamic slippage on both materials, with water experiencing indeed a ≈5 times lower friction on carbon surfaces compared to boron nitride. Analysis of the diffusion mechanisms across the two materials reveals that the fast water transport on carbon is governed by facile oxygen motion, whereas the higher friction on boron nitride arises from specific hydrogen-nitrogen interactions. This work not only delivers a clear reference of quantum mechanical accuracy for water flow in single-wall nanotubes but also provides detailed mechanistic insight into its radius and material dependence for future technological application.