Unveiling the hydroxyl-dependent viscosity of water in graphene oxide nanochannels via molecular dynamics simulations

Sun, Chengzhen; Zhou, Runfeng; Zhao, Zhixiang; Bai, Bofeng

doi:10.1016/j.cplett.2021.138808

Cited by 11 publications

(11 citation statements)

References 51 publications

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“…The red line signifies μ //, whereas the green line signifies μ ⊥ . The inset blue region showcases the viscosity result by Sun et al in GO nanochannels, substantiating the rationality of our work regarding viscosity. (c) Diffusion coefficient analysis: comparison of the water diffusion coefficient within GO nanochannels across different oxidation degrees.…”

Section: Resultssupporting

confidence: 91%

“…This behavior is linked to interlayer friction and inner-layer friction, as we previously outlined. 63 The concurrent shifts in interlayer and inner-layer friction are converse: a decrease in interlayer friction corresponds to an increase in inner-layer friction and vice versa. Furthermore, Figure 5 indicates that at lower oxidation degrees, hydrogen bonding primarily occurs between hydroxyl groups and water molecules, whereas at higher oxidation degrees, hydroxyl groups mainly form hydrogen bonds with other hydroxyl groups.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

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Deciphering the Liquid Continuum: Thermophysical and Slippage Dynamical Behavior of Water in Graphene Oxide Nanochannels

Li,

Ma,

Zhou

et al. 2024

J. Phys. Chem. C

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Understanding water behavior within graphene oxide (GO) nanochannels holds paramount significance, especially in the realms of nanofluidics and energy conversion. Presently, flow modeling predominantly relies on simplifications, overlooking phase states. Our study unveils a nuanced reality: the molecular arrangement of confined water mirrors the density fluctuations observed in the liquid phase of water, a behavior contingent on both the degree of oxidation and the proximity to GO membranes. Through an exhaustive analysis encompassing density, viscosity, specific heat capacity, and diffusion coefficient, we establish a robust correlation, revealing the manifestation of continuous liquid behavior tied intimately to thermophysical properties and hydrogen bonding networks. Incorporating boundary slip velocity into the Hagen− Poiseuille equation, we predict pressure-driven flow with remarkable accuracy, validated by molecular dynamics simulations. Our revelations illuminate the continuum nature of water flow in GO nanochannels, with far-reaching implications spanning biomechanics, energy conversion, and desalination. Additionally, our investigation delves into the impact of varying hydroxyl (−OH) group concentrations within GO systems, effectively representing the degree of oxidation. This facet offers profound insights into the consequences of surface functionalization on the water behavior of nanochannels. Notably, our findings underscore the sensitivity of slip velocity to surface modifications, with a striking reduction by a factor of 5 as the −OH group concentration escalates from 5 to 30% under an applied driving pressure of 0.5 GPa. This discovery accentuates the potent influence of increasing hydrophilicity, stemming from higher degrees of oxidation, on water flow behavior, thereby casting fresh perspectives on nanofluidic system design.

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Section: Resultssupporting

confidence: 91%

Section: ■ Results and Discussionmentioning

confidence: 99%

Deciphering the Liquid Continuum: Thermophysical and Slippage Dynamical Behavior of Water in Graphene Oxide Nanochannels

Li,

Ma,

Zhou

et al. 2024

J. Phys. Chem. C

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“…Detailed information on systems is listed in Table S1. The GO model was constructed by randomly functionalizing hydroxyl groups on both sides of graphene sheet with concentrations c = n O / n C = 20%, where n O and n C are the numbers of the oxygen and carbon atoms, according to the experimental evidence. , The model is illustrated in Figure . The CO self-interaction was modeled using a rigid three-site model, where two sites describe C and O, both with a partial negative charge, while the third site is a dummy atom carrying a large neutralizing positive charge, and the same configuration was set in H 2 .…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Assisted Exploration of a Two-Dimensional Nanoslit for Blast Furnace Gas Separation

Huan,

Qiu,

Sun

et al. 2023

Ind. Eng. Chem. Res.

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Diffusion-induced gas separation is crucial for industrial applications, while the determination of specific conditions is still challenging. Here, molecular dynamics simulation data were used to train machine learning models to identify the effective separation conditions for blast furnace gas confined in nanosilts with different absorption strengths (graphene and graphene oxide). The diffusion coefficients and exponents of the blast furnace gas were obtained as a database by molecular dynamics (MD) simulations. Several environmental and structural controlling factors (such as temperature, layer distance, atomic number, ionization potential, etc.) were extracted through importance analysis. And the relationships between these factors and diffusion properties were further established by the Kernel Ridge Regression algorithm. Based on the differences in diffusion coefficients, specific binary gas mixtures in the blast furnace gas with competitively high separation potential have been screened out by a trained machine learning model and verified by MD simulations. The simulation strategy can provide theoretical guidance for the structural design of membranes for diffusion-controlled gas separation.

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“…112,116 Similar to the density distribution, the shear viscosity distribution may also non-monotonic in the nanochannel and affected by the size of nanochannels, the properties of surface (chemical functional groups, charge density) and the liquid properties (salt concentration, ion liquids, organic liquids). 110,157 The strong interfacial liquid-wall interaction increases the local shear viscosity adjacent to the surface, resulting in the slow flow. 112,116,158 However, in the limited…”

Section: Viscositymentioning

confidence: 99%

Multiscale simulations of nanofluidics: Recent progress and perspective

Xie

2023

WIREs Comput Mol Sci

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Nanofluidics research has achieved a significant growth over the past few years. New phenomena of nanoscaled fluid flows are being reported continuously, such as altered liquid properties, fast flows, and ion rectification, which attract tremendous research interests in many fields, such as membrane science, biological nanochips, and energy conventions. Multiscale simulations, covering quantum mechanics, molecular mechanics, coarse‐grained particle dynamics (mesoscale), and continuum mechanics, have shown their great advantages in studying the new frontier of nanofluidics in academia and industry, which is in range of 1–1000 nm scale. These simulations provide the opportunity to visualize the nanofluidics applications existed in the minds of scientists and then guide experimental design to realize the potential of nanofluidics applications in industrial. In this article, we attempt to give a comprehensive review of nanofluidics from the aspect of multiscale simulations. The methodology and role of various simulation methods used in the investigation of nanofluidics are presented. The properties and characteristics of nanofluidics are summarized. The applications of nanofluidics in recent years are emphasized. And then the development of simulation methods and the application of nanofluidics are also prospected.This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Simulation Methods

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Unveiling the hydroxyl-dependent viscosity of water in graphene oxide nanochannels via molecular dynamics simulations

Cited by 11 publications

References 51 publications

Deciphering the Liquid Continuum: Thermophysical and Slippage Dynamical Behavior of Water in Graphene Oxide Nanochannels

Deciphering the Liquid Continuum: Thermophysical and Slippage Dynamical Behavior of Water in Graphene Oxide Nanochannels

Machine Learning-Assisted Exploration of a Two-Dimensional Nanoslit for Blast Furnace Gas Separation

Multiscale simulations of nanofluidics: Recent progress and perspective

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