Thermodynamics at Solid-Liquid Interfaces

Reassessing the transport properties of fluids: A symbolic regression approach

The viscosity and thermal conductivity coefficients of the Lennard-Jones fluid are extracted through symbolic regression (SR) techniques from data derived from simulations at the atomic scale. This data-oriented approach provides closed form relations that achieve fine accuracy when compared to well-established theoretical, empirical, or approximate equations, fully transparent, with small complexity and high interpretability. The novelty is further outlined by suggesting analytical expressions for estimating fluid transport properties across the whole phase space, from a dilute gas to a dense liquid, by considering only two macroscopic properties (density and temperature). In such expressions, the underlying physical mechanisms are reflected, while, at the same time, it can be a computationally efficient alternative to costly in time and size first principle and/or molecular dynamics simulations.

Molecular dynamics simulation of thermal transport across a solid/liquid interface created by a meniscus

Understandings heat transfer across a solid/liquid interface is crucial for establishing novel thermal control pathways in a range of energy applications. One of the major problems raised in this context is the impact of the three-phase contact line between solid, liquid, and gas on heat flux perturbations at the nanoscale. The focus of this research is the thermal transport via nanosized meniscus restricted between two solid walls. The molecular dynamics approach was used to consider different wetting states of the meniscus by varying the interaction potential between atoms of the substrate and the liquid. The influence of the meniscus size on the energy exchange between two solid walls was also studied. It was discovered that possessing a three-phase contact line reduces the interfacial boundary resistance between solid and liquid. Furthermore, the finite element method was employed to connect atomistic simulations with continuum mechanics. We show that the wetting angle and interfacial boundary resistance are essential important parameters for multiscale analysis of thermal engineering issues with precise microscale parametrization.

The Phonon Theory of Liquids and Biological Fluids: Developments and Applications

Among the three basic states of matter (solid, liquid, and gas), the liquid state has always eluded general theoretical approaches for describing liquid energy and heat capacity. In this Viewpoint, we derive the phonon theory of liquids and biological fluids stemming from Frenkel's microscopic picture of the liquid state. Specifically, the theory predicts the existence of phonon gaps in vibrational spectra of liquids and a thermodynamic boundary in the supercritical state. Direct experimental evidence reaffirming these theoretical predictions was achieved through a combination of techniques using static compression X-ray diffraction and inelastic X-ray scattering on deeply supercritical argon in a diamond anvil cell. Furthermore, these findings inspired and then led to the discovery of phonon gaps in liquid crystals (mesogens), block copolymers, and biological membranes. Importantly, phonon gaps define viscoelastic crossovers in cellular membranes responsible for lipid self-diffusion, lateral molecular-level stress propagation, and passive transmembrane transport of small molecules and solutes. Finally, molecular interactions mediated by external stimuli result in synaptic activity controlling biological membranes' plasticity resulting in learning and memory. Therefore, we also discuss learning and memory effects─equally important for neuroscience as well as for the development of neuromorphic devices─facilitated in biological membranes by external stimuli.

Ion-dependent protein-surface interactions from intrinsic solvent response

The phyllosilicate mineral muscovite mica is widely used as a surface template for the patterning of macromolecules, yet a molecular understanding of its surface chemistry under varying solution conditions, required to predict and control the self-assembly of adsorbed species, is lacking. We utilize all-atom molecular dynamics simulations in conjunction with an electrostatic analysis based in local molecular field theory that affords a clean separation of long-range and short-range electrostatics. Using water polarization response as a measure of the electric fields that arise from patterned, surface-bound ions that direct the adsorption of charged macromolecules, we apply a Landau theory of forces induced by asymmetrically polarized surfaces to compute protein-surface interactions for two muscovite-binding proteins (DHR10-mica6 and ^C98RhuA). Comparison of the pressure between surface and protein in high-concentration KCl and NaCl aqueous solutions reveals ion-specific differences in far-field protein-surface interactions, neatly capturing the ability of ions to modulate the surface charge of muscovite that in turn selectively attracts one binding face of each protein over all others.

Machine Learning Techniques for Fluid Flows at the Nanoscale

Simulations of fluid flows at the nanoscale feature massive data production and machine learning (ML) techniques have been developed during recent years to leverage them, presenting unique results. This work facilitates ML tools to provide an insight on properties among molecular dynamics (MD) simulations, covering missing data points and predicting states not previously located by the simulation. Taking the fluid flow of a simple Lennard-Jones liquid in nanoscale slits as a basis, ML regression-based algorithms are exploited to provide an alternative for the calculation of transport properties of fluids, e.g., the diffusion coefficient, shear viscosity and thermal conductivity and the average velocity across the nanochannels. Through appropriate training and testing, ML-predicted values can be extracted for various input variables, such as the geometrical characteristics of the slits, the interaction parameters between particles and the flow driving force. The proposed technique could act in parallel to simulation as a means of enriching the database of material properties, assisting in coupling between scales, and accelerating data-based scientific computations.

Understanding water slippage through carbon nanotubes

It is a formidable challenge to understand water slippage through carbon nanotubes (CNTs), despite its great significance in fundamental research and technology. Herein, we propose an effective scheme to describe water slippage properties by extending two friction models - the phononic friction model and Einstein's diffusion model, both relying on the potential corrugation of water slippage. Our scheme effectively captures the tube-size effect on the viscosity and slippage of water molecules through CNTs. It also identifies the experimentally reported size-dependent transition from continuum to sub-continuum flow and further reveals that this transition is likely to be determined by the hydrogen bond instead of the structural transition or entropic change. Besides, the size-dependence of slip lengths is found to be controllable by temperature. Our methods are thus expected to be a useful basis for further studies on substance transport under confinement.

Machine-Learning Methods for Computational Science and Engineering

The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.

NePCM Based on Silver Dispersions in Poly(Ethylene Glycol) as a Stable Solution for Thermal Storage

The main objective of this study is to design and characterize silver suspensions based on poly(ethylene glycol) PEG400, Ag/PEG400, as energy storage media for low-temperature applications. A polyvinylpyrrolidone (PVP) treatment was applied to ~22 nm silver nanoparticles to ensure good stability in poly(ethylene glycol). An array of different experimental techniques was utilized to analyze the molecular mass and purity of base poly(ethylene glycol), morphology of dry PVP-capped Ag nanoparticles, hydrodynamic average size of dispersed Ag particles, as well as thermal stability of PEG400 and Ag/PEG400 dispersions. Samples exhibited good temporal stabilities with average hydrodynamic diameter around 50 nm according to dynamic light scattering analyses. Melting and solidification transitions were investigated in terms of temperature and enthalpy from differential scanning calorimeter (DSC) thermograms. The thermophysical characterization was completed with thermal conductivity (k), dynamic viscosity (η), isobaric heat capacity (Cp), density (ρ), and surface tension (σ) measurements of designed materials using a Hot Disk thermal conductivimeter, a rotational rheometer, a DSC calorimeter working with a quasi-isothermal modulated method, a U-tube densimeter and a drop shape analyzer, respectively. For a nanoparticle loading of only 1.1% in mass, sub-cooling reduced by 7.1% and thermal conductive improved by 3.9%, with almost no penalization in dynamic viscosity (less than 5.4% of increase). Maximum modifications in Cp, ρ, and σ were 0.9%, 2.2%, and 2.2%, respectively. Experimental results were compared with the values provided by using different theoretical or semi-empirical equations. In particular, good descriptions of dynamic viscosity as functions of temperature and nanoparticle volume concentration were obtained by using the Vogel-Fulcher-Tammann equation and a first-order polynomial η( ϕ v , n p ) correlation, with absolute average deviations of 2.2% and 0.55%, respectively.

Heat transfer across a fractal surface

The effects of surface irregularities and imperfections on the thermal resistance at a solid-liquid interface have been investigated using molecular dynamics. The molecular model comprises liquid argon confined between silver walls. The surface roughness was designed using fractal theory, introducing stochastic patterns of multiple scales that resemble realistic surface geometries. In agreement with most previous studies, we find that increasing the strength of the solid-liquid interactions monotonically reduces the thermal resistance across smooth interfaces. Yet, the behavior of the thermal resistance across rough surfaces is more complex. Following the initially anticipated decrease, the thermal resistance starts to increase once the strength of solid-liquid interaction increases past a threshold. We attribute the above behavior to two competing phenomena, namely, the area of the solid-liquid interface and the introduction of vibrational anharmonicities and localization of phonons resulting from the surface roughness. Finally, we demonstrate that, for the same fractal dimension and depth of surface roughness, different surfaces practically have the same thermal resistance, solid-liquid radial distribution function, and liquid density profiles. We conclude that the above fractal parameters are useful in deriving reduced models for properties related to the surface geometry.

Multiscale Computational Fluid Dynamics

Computational Fluid Dynamics (CFD) has numerous applications in the field of energy research, in modelling the basic physics of combustion, multiphase flow and heat transfer; and in the simulation of mechanical devices such as turbines, wind wave and tidal devices, and other devices for energy generation. With the constant increase in available computing power, the fidelity and accuracy of CFD simulations have constantly improved, and the technique is now an integral part of research and development. In the past few years, the development of multiscale methods has emerged as a topic of intensive research. The variable scales may be associated with scales of turbulence, or other physical processes which operate across a range of different scales, and often lead to spatial and temporal scales crossing the boundaries of continuum and molecular mechanics. In this paper, we present a short review of multiscale CFD frameworks with potential applications to energy problems.