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\begin{document} |
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\title{ENTER TITLE HERE} |
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\author{Shenyu Kuang and J. Daniel |
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Gezelter\footnote{Corresponding author. \ Electronic mail: gezelter@nd.edu} \\ |
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Department of Chemistry and Biochemistry,\\ |
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University of Notre Dame\\ |
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Notre Dame, Indiana 46556} |
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\date{\today} |
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\maketitle |
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\begin{doublespace} |
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\begin{abstract} |
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REPLACE ABSTRACT HERE |
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With the Non-Isotropic Velocity Scaling (NIVS) approach to Reverse |
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Non-Equilibrium Molecular Dynamics (RNEMD) it is possible to impose |
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an unphysical thermal flux between different regions of |
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inhomogeneous systems such as solid / liquid interfaces. We have |
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applied NIVS to compute the interfacial thermal conductance at a |
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metal / organic solvent interface that has been chemically capped by |
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butanethiol molecules. Our calculations suggest that coupling |
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between the metal and liquid phases is enhanced by the capping |
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agents, leading to a greatly enhanced conductivity at the interface. |
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Specifically, the chemical bond between the metal and the capping |
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agent introduces a vibrational overlap that is not present without |
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the capping agent, and the overlap between the vibrational spectra |
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(metal to cap, cap to solvent) provides a mechanism for rapid |
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thermal transport across the interface. Our calculations also |
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suggest that this is a non-monotonic function of the fractional |
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coverage of the surface, as moderate coverages allow diffusive heat |
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transport of solvent molecules that have been in close contact with |
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the capping agent. |
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\end{abstract} |
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\newpage |
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%\narrowtext |
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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% BODY OF TEXT |
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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\section{Introduction} |
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[REFINE LATER, ADD MORE REF.S] |
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Imposed-flux methods in Molecular Dynamics (MD) |
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simulations\cite{MullerPlathe:1997xw} can establish steady state |
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systems with a set applied flux vs a corresponding gradient that can |
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be measured. These methods does not need many trajectories to provide |
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information of transport properties of a given system. Thus, they are |
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utilized in computing thermal and mechanical transfer of homogeneous |
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or bulk systems as well as heterogeneous systems such as liquid-solid |
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interfaces.\cite{kuang:AuThl} |
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|
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The Reverse Non-Equilibrium MD (RNEMD) methods adopt constraints that |
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satisfy linear momentum and total energy conservation of a system when |
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imposing fluxes in a simulation. Thus they are compatible with various |
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ensembles, including the micro-canonical (NVE) ensemble, without the |
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need of an external thermostat. The original approaches by |
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M\"{u}ller-Plathe {\it et |
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al.}\cite{MullerPlathe:1997xw,ISI:000080382700030} utilize simple |
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momentum swapping for generating energy/momentum fluxes, which is also |
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compatible with particles of different identities. Although simple to |
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implement in a simulation, this approach can create nonthermal |
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velocity distributions, as discovered by Tenney and |
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Maginn\cite{Maginn:2010}. Furthermore, this approach to kinetic energy |
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transfer between particles of different identities is less efficient |
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when the mass difference between the particles becomes significant, |
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which also limits its application on heterogeneous interfacial |
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systems. |
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Recently, we developed a different approach, using Non-Isotropic |
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Velocity Scaling (NIVS) \cite{kuang:164101} algorithm to impose |
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fluxes. Compared to the momentum swapping move, it scales the velocity |
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vectors in two separate regions of a simulated system with respective |
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diagonal scaling matrices. These matrices are determined by solving a |
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set of equations including linear momentum and kinetic energy |
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conservation constraints and target flux satisfaction. This method is |
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able to effectively impose a wide range of kinetic energy fluxes |
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without obvious perturbation to the velocity distributions of the |
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simulated systems, regardless of the presence of heterogeneous |
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interfaces. We have successfully applied this approach in studying the |
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interfacial thermal conductance at metal-solvent |
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interfaces.\cite{kuang:AuThl} |
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However, the NIVS approach limits its application in imposing momentum |
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fluxes. Temperature anisotropy can happen under high momentum fluxes, |
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due to the nature of the algorithm. Thus, combining thermal and |
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momentum flux is also difficult to implement with this |
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approach. However, such combination may provide a means to simulate |
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thermal/momentum gradient coupled processes such as freeze |
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desalination. Therefore, developing novel approaches to extend the |
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application of imposed-flux method is desired. |
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In this paper, we improve the NIVS method and propose a novel approach |
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to impose fluxes. This approach separate the means of applying |
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momentum and thermal flux with operations in one time step and thus is |
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able to simutaneously impose thermal and momentum flux. Furthermore, |
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the approach retains desirable features of previous RNEMD approaches |
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and is simpler to implement compared to the NIVS method. In what |
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follows, we first present the method to implement the method in a |
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simulation. Then we compare the method on bulk fluids to previous |
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methods. Also, interfacial frictions are computed for a series of |
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interfaces. |
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\section{Methodology} |
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Similar to the NIVS methodology,\cite{kuang:164101} we consider a |
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periodic system divided into a series of slabs along a certain axis |
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(e.g. $z$). The unphysical thermal and/or momentum flux is designated |
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from the center slab to one of the end slabs, and thus the center slab |
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would have a lower temperature than the end slab (unless the thermal |
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flux is negative). Therefore, the center slab is denoted as ``$c$'' |
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while the end slab as ``$h$''. |
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To impose these fluxes, we periodically apply separate operations to |
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velocities of particles {$i$} within the center slab and of particles |
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{$j$} within the end slab: |
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\begin{eqnarray} |
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\vec{v}_i & \leftarrow & c\cdot\left(\vec{v}_i - \langle\vec{v}_c |
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\rangle\right) + \left(\langle\vec{v}_c\rangle + \vec{a}_c\right) \\ |
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\vec{v}_j & \leftarrow & h\cdot\left(\vec{v}_j - \langle\vec{v}_h |
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\rangle\right) + \left(\langle\vec{v}_h\rangle + \vec{a}_h\right) |
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\end{eqnarray} |
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where $\langle\vec{v}_c\rangle$ and $\langle\vec{v}_h\rangle$ denotes |
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the instantaneous bulk velocity of slabs $c$ and $h$ respectively |
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before an operation occurs. When a momentum flux $\vec{j}_z(\vec{p})$ |
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presents, these bulk velocities would have a corresponding change |
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($\vec{a}_c$ and $\vec{a}_h$ respectively) according to Newton's |
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second law: |
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\begin{eqnarray} |
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M_c \vec{a}_c & = & -\vec{j}_z(\vec{p}) \Delta t \\ |
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M_h \vec{a}_h & = & \vec{j}_z(\vec{p}) \Delta t |
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\end{eqnarray} |
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where |
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\begin{eqnarray} |
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M_c & = & \sum_{i = 1}^{N_c} m_i \\ |
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M_h & = & \sum_{j = 1}^{N_h} m_j |
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\end{eqnarray} |
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and $\Delta t$ is the interval between two operations. |
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The above operations conserve the linear momentum of a periodic |
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system. To satisfy total energy conservation as well as to impose a |
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thermal flux $J_z$, one would have |
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[SUPPORT INFO MIGHT BE NECESSARY TO PUT EXTRA MATH IN] |
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\begin{eqnarray} |
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K_c - J_z\Delta t & = & c^2 (K_c - \frac{1}{2}M_c \langle\vec{v}_c |
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\rangle^2) + \frac{1}{2}M_c (\langle \vec{v}_c \rangle + \vec{a}_c)^2 \\ |
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K_h + J_z\Delta t & = & h^2 (K_h - \frac{1}{2}M_h \langle\vec{v}_h |
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\rangle^2) + \frac{1}{2}M_h (\langle \vec{v}_h \rangle + \vec{a}_h)^2 |
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\end{eqnarray} |
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where $K_c$ and $K_h$ denotes translational kinetic energy of slabs |
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$c$ and $h$ respectively before an operation occurs. These |
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translational kinetic energy conservation equations are sufficient to |
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ensure total energy conservation, as the operations applied do not |
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change the potential energy of a system, given that the potential |
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energy does not depend on particle velocity. |
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The above sets of equations are sufficient to determine the velocity |
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scaling coefficients ($c$ and $h$) as well as $\vec{a}_c$ and |
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$\vec{a}_h$. Note that two roots of $c$ and $h$ exist |
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respectively. However, to avoid dramatic perturbations to a system, |
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the positive roots (which are closer to 1) are chosen. Figure |
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\ref{method} illustrates the implementation of this algorithm in an |
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individual step. |
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\begin{figure} |
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\includegraphics[width=\linewidth]{method} |
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\caption{Illustration of the implementation of the algorithm in a |
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single step. Starting from an ideal velocity distribution, the |
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transformation is used to apply both thermal and momentum flux from |
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the ``c'' slab to the ``h'' slab. As the figure shows, the thermal |
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distributions preserve after this operation.} |
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\label{method} |
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\end{figure} |
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By implementing these operations at a certain frequency, a steady |
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thermal and/or momentum flux can be applied and the corresponding |
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temperature and/or momentum gradients can be established. |
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This approach is more computationaly efficient compared to the |
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previous NIVS method, in that only quadratic equations are involved, |
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while the NIVS method needs to solve a quartic equations. Furthermore, |
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the method implements isotropic scaling of velocities in respective |
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slabs, unlike the NIVS, where an extra criteria function is necessary |
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to choose a set of coefficients that performs the most isotropic |
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scaling. More importantly, separating the momentum flux imposing from |
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velocity scaling avoids the underlying cause that NIVS produced |
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thermal anisotropy when applying a momentum flux. |
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%NEW METHOD DOESN'T CAUSE UNDESIRED CONCOMITENT MOMENTUM FLUX WHEN |
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%IMPOSING A THERMAL FLUX |
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The advantages of the approach over the original momentum swapping |
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approach lies in its nature to preserve a Gaussian |
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distribution. Because the momentum swapping tends to render a |
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nonthermal distribution, when the imposed flux is relatively large, |
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diffusion of the neighboring slabs could no longer remedy this effect, |
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and nonthermal distributions would be observed. Results in later |
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section will illustrate this effect. |
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%NEW METHOD (AND NIVS) HAVE LESS PERTURBATION THAN MOMENTUM SWAPPING |
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\section{Computational Details} |
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The algorithm has been implemented in our MD simulation code, |
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OpenMD\cite{Meineke:2005gd,openmd}. We compare the method with |
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previous RNEMD methods or equilibrium MD methods in homogeneous fluids |
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(Lennard-Jones and SPC/E water). And taking advantage of the method, |
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we simulate the interfacial friction of different heterogeneous |
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interfaces (gold-organic solvent and gold-SPC/E water and ice-liquid |
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water). |
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\subsection{Simulation Protocols} |
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The systems to be investigated are set up in a orthorhombic simulation |
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cell with periodic boundary conditions in all three dimensions. The |
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$z$ axis of these cells were longer and was set as the gradient axis |
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of temperature and/or momentum. Thus the cells were divided into $N$ |
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slabs along this axis, with various $N$ depending on individual |
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system. The $x$ and $y$ axis were usually of the same length in |
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homogeneous systems or close to each other where interfaces |
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presents. In all cases, before introducing a nonequilibrium method to |
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establish steady thermal and/or momentum gradients for further |
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measurements and calculations, canonical ensemble with a Nos\'e-Hoover |
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thermostat\cite{hoover85} and microcanonical ensemble equilibrations |
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were used to prepare systems ready for data |
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collections. Isobaric-isothermal equilibrations are performed before |
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this for SPC/E water systems to reach normal pressure (1 bar), while |
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similar equilibrations are used for interfacial systems to relax the |
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surface tensions. |
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While homogeneous fluid systems can be set up with random |
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configurations, our interfacial systems needs extra steps to ensure |
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the interfaces be established properly for computations. The |
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preparation and equilibration of butanethiol covered gold (111) |
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surface and further solvation and equilibration process is described |
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as in reference \cite{kuang:AuThl}. |
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|
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As for the ice/liquid water interfaces, the basal surface of ice |
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lattice was first constructed. Hirsch {\it et |
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al.}\cite{doi:10.1021/jp048434u} explored the energetics of ice |
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lattices with different proton orders. We refer to their results and |
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choose the configuration of the lowest energy after geometry |
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optimization as the unit cells of our ice lattices. Although |
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experimental solid/liquid coexistant temperature near normal pressure |
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is 273K, Bryk and Haymet's simulations of ice/liquid water interfaces |
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with different models suggest that for SPC/E, the most stable |
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interface is observed at 225$\pm$5K. Therefore, all our ice/liquid |
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water simulations were carried out under 225K. To have extra |
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protection of the ice lattice during initial equilibration (when the |
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randomly generated liquid phase configuration could release large |
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amount of energy in relaxation), a constraint method (REF?) was |
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adopted until the high energy configuration was relaxed. |
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[MAY ADD A FIGURE HERE FOR BASAL PLANE, MAY INCLUDE PRISM IF POSSIBLE] |
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\subsection{Force Field Parameters} |
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For comparison of our new method with previous work, we retain our |
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force field parameters consistent with the results we will compare |
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with. The Lennard-Jones fluid used here for argon , and reduced unit |
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results are reported for direct comparison purpose. |
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As for our water simulations, SPC/E model is used throughout this work |
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for consistency. Previous work for transport properties of SPC/E water |
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model is available\cite{Bedrov:2000,10.1063/1.3330544,Medina2011} so |
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that unnecessary repetition of previous methods can be avoided. |
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The Au-Au interaction parameters in all simulations are described by |
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the quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The |
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QSC potentials include zero-point quantum corrections and are |
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reparametrized for accurate surface energies compared to the |
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Sutton-Chen potentials.\cite{Chen90} For gold/water interfaces, the |
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Spohr potential was adopted\cite{ISI:000167766600035} to depict |
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Au-H$_2$O interactions. |
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The small organic molecules included in our simulations are the Au |
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surface capping agent butanethiol and liquid hexane and toluene. The |
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United-Atom |
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models\cite{TraPPE-UA.thiols,TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} |
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for these components were used in this work for better computational |
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efficiency, while maintaining good accuracy. We refer readers to our |
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previous work\cite{kuang:AuThl} for further details of these models, |
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as well as the interactions between Au and the above organic molecule |
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components. |
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\subsection{Thermal conductivities} |
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When $\vec{j}_z(\vec{p})$ is set to zero and a target $J_z$ is set to |
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impose kinetic energy transfer, the method can be used for thermal |
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conductivity computations. Similar to previous RNEMD methods, we |
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assume linear response of the temperature gradient with respect to the |
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thermal flux in general case. And the thermal conductivity ($\lambda$) |
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can be obtained with the imposed kinetic energy flux and the measured |
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thermal gradient: |
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\begin{equation} |
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J_z = -\lambda \frac{\partial T}{\partial z} |
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\end{equation} |
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Like other imposed-flux methods, the energy flux was calculated using |
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the total non-physical energy transferred (${E_{total}}$) from slab |
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|
``c'' to slab ``h'', which is recorded throughout a simulation, and |
327 |
|
|
the time for data collection $t$: |
328 |
|
|
\begin{equation} |
329 |
|
|
J_z = \frac{E_{total}}{2 t L_x L_y} |
330 |
|
|
\end{equation} |
331 |
|
|
where $L_x$ and $L_y$ denotes the dimensions of the plane in a |
332 |
|
|
simulation cell perpendicular to the thermal gradient, and a factor of |
333 |
|
|
two in the denominator is present for the heat transport occurs in |
334 |
|
|
both $+z$ and $-z$ directions. The temperature gradient |
335 |
|
|
${\langle\partial T/\partial z\rangle}$ can be obtained by a linear |
336 |
|
|
regression of the temperature profile, which is recorded during a |
337 |
|
|
simulation for each slab in a cell. For Lennard-Jones simulations, |
338 |
|
|
thermal conductivities are reported in reduced units |
339 |
|
|
(${\lambda^*=\lambda \sigma^2 m^{1/2} k_B^{-1}\varepsilon^{-1/2}}$). |
340 |
|
|
|
341 |
skuang |
3774 |
\subsection{Shear viscosities} |
342 |
skuang |
3775 |
Alternatively, the method can carry out shear viscosity calculations |
343 |
|
|
by switching off $J_z$. One can specify the vector |
344 |
|
|
$\vec{j}_z(\vec{p})$ by choosing the three components |
345 |
|
|
respectively. For shear viscosity simulations, $j_z(p_z)$ is usually |
346 |
|
|
set to zero. Although for isotropic systems, the direction of |
347 |
|
|
$\vec{j}_z(\vec{p})$ on the $xy$ plane should not matter, the ability |
348 |
|
|
of arbitarily specifying the vector direction in our method provides |
349 |
|
|
convenience in anisotropic simulations. |
350 |
|
|
|
351 |
|
|
Similar to thermal conductivity computations, linear response of the |
352 |
|
|
momentum gradient with respect to the shear stress is assumed, and the |
353 |
|
|
shear viscosity ($\eta$) can be obtained with the imposed momentum |
354 |
|
|
flux (e.g. in $x$ direction) and the measured gradient: |
355 |
|
|
\begin{equation} |
356 |
|
|
j_z(p_x) = -\eta \frac{\partial v_x}{\partial z} |
357 |
|
|
\end{equation} |
358 |
|
|
where the flux is similarly defined: |
359 |
|
|
\begin{equation} |
360 |
|
|
j_z(p_x) = \frac{P_x}{2 t L_x L_y} |
361 |
|
|
\end{equation} |
362 |
|
|
with $P_x$ being the total non-physical momentum transferred within |
363 |
|
|
the data collection time. Also, the velocity gradient |
364 |
|
|
${\langle\partial v_x/\partial z\rangle}$ can be obtained using linear |
365 |
|
|
regression of the $x$ component of the mean velocity, $\langle |
366 |
|
|
v_x\rangle$, in each of the bins. For Lennard-Jones simulations, shear |
367 |
|
|
viscosities are reported in reduced units |
368 |
|
|
(${\eta^*=\eta\sigma^2(\varepsilon m)^{-1/2}}$). |
369 |
|
|
|
370 |
skuang |
3774 |
\subsection{Interfacial friction and Slip length} |
371 |
skuang |
3775 |
While the shear stress results in a velocity gradient within bulk |
372 |
|
|
fluid phase, its effect at a solid-liquid interface could vary due to |
373 |
|
|
the interaction strength between the two phases. The interfacial |
374 |
|
|
friction coefficient $\kappa$ is defined to relate the shear stress |
375 |
|
|
(e.g. along $x$-axis) and the relative fluid velocity tangent to the |
376 |
|
|
interface: |
377 |
|
|
\begin{equation} |
378 |
|
|
j_z(p_x)|_{interface} = \kappa\Delta v_x|_{interface} |
379 |
|
|
\end{equation} |
380 |
|
|
Under ``stick'' boundary condition, $\Delta v_x|_{interface} |
381 |
|
|
\rightarrow 0$, which leads to $\kappa\rightarrow\infty$. However, for |
382 |
|
|
``slip'' boundary condition at the solid-liquid interface, $\kappa$ |
383 |
|
|
becomes finite. To characterize the interfacial boundary conditions, |
384 |
|
|
slip length ($\delta$) is defined using $\kappa$ and the shear |
385 |
|
|
viscocity of liquid phase ($\eta$): |
386 |
|
|
\begin{equation} |
387 |
|
|
\delta = \frac{\eta}{\kappa} |
388 |
|
|
\end{equation} |
389 |
|
|
so that $\delta\rightarrow 0$ in the ``no-slip'' boundary condition, |
390 |
|
|
and depicts how ``slippery'' an interface is. Figure \ref{slipLength} |
391 |
|
|
illustrates how this quantity is defined and computed for a |
392 |
|
|
solid-liquid interface. |
393 |
gezelter |
3769 |
|
394 |
skuang |
3775 |
\begin{figure} |
395 |
|
|
\includegraphics[width=\linewidth]{defDelta} |
396 |
|
|
\caption{The slip length $\delta$ can be obtained from a velocity |
397 |
|
|
profile of a solid-liquid interface. An example of Au/hexane |
398 |
|
|
interfaces is shown.} |
399 |
|
|
\label{slipLength} |
400 |
|
|
\end{figure} |
401 |
gezelter |
3769 |
|
402 |
skuang |
3775 |
In our method, a shear stress can be applied similar to shear |
403 |
|
|
viscosity computations by applying an unphysical momentum flux |
404 |
|
|
(e.g. $j_z(p_x)$). A corresponding velocity profile can be obtained as |
405 |
|
|
shown in Figure \ref{slipLength}, in which the velocity gradients |
406 |
|
|
within liquid phase and velocity difference at the liquid-solid |
407 |
|
|
interface can be measured respectively. Further calculations and |
408 |
|
|
characterizations of the interface can be carried out using these |
409 |
|
|
data. |
410 |
|
|
[MENTION IN RESULTS THAT ETA OBTAINED HERE DOES NOT NECESSARILY EQUAL |
411 |
|
|
TO BULK VALUES] |
412 |
|
|
|
413 |
gezelter |
3769 |
\section{Results} |
414 |
skuang |
3773 |
[L-J COMPARED TO RNEMD NIVS; WATER COMPARED TO RNEMD NIVS AND EMD; |
415 |
skuang |
3770 |
SLIP BOUNDARY VS STICK BOUNDARY; ICE-WATER INTERFACES] |
416 |
|
|
|
417 |
gezelter |
3769 |
There are many factors contributing to the measured interfacial |
418 |
|
|
conductance; some of these factors are physically motivated |
419 |
|
|
(e.g. coverage of the surface by the capping agent coverage and |
420 |
|
|
solvent identity), while some are governed by parameters of the |
421 |
|
|
methodology (e.g. applied flux and the formulas used to obtain the |
422 |
|
|
conductance). In this section we discuss the major physical and |
423 |
|
|
calculational effects on the computed conductivity. |
424 |
|
|
|
425 |
|
|
\subsection{Effects due to capping agent coverage} |
426 |
|
|
|
427 |
|
|
A series of different initial conditions with a range of surface |
428 |
|
|
coverages was prepared and solvated with various with both of the |
429 |
|
|
solvent molecules. These systems were then equilibrated and their |
430 |
|
|
interfacial thermal conductivity was measured with the NIVS |
431 |
|
|
algorithm. Figure \ref{coverage} demonstrates the trend of conductance |
432 |
|
|
with respect to surface coverage. |
433 |
|
|
|
434 |
|
|
\begin{figure} |
435 |
|
|
\includegraphics[width=\linewidth]{coverage} |
436 |
|
|
\caption{The interfacial thermal conductivity ($G$) has a |
437 |
|
|
non-monotonic dependence on the degree of surface capping. This |
438 |
|
|
data is for the Au(111) / butanethiol / solvent interface with |
439 |
|
|
various UA force fields at $\langle T\rangle \sim $200K.} |
440 |
|
|
\label{coverage} |
441 |
|
|
\end{figure} |
442 |
|
|
|
443 |
|
|
In partially covered surfaces, the derivative definition for |
444 |
|
|
$G^\prime$ (Eq. \ref{derivativeG}) becomes difficult to apply, as the |
445 |
|
|
location of maximum change of $\lambda$ becomes washed out. The |
446 |
|
|
discrete definition (Eq. \ref{discreteG}) is easier to apply, as the |
447 |
|
|
Gibbs dividing surface is still well-defined. Therefore, $G$ (not |
448 |
|
|
$G^\prime$) was used in this section. |
449 |
|
|
|
450 |
|
|
From Figure \ref{coverage}, one can see the significance of the |
451 |
|
|
presence of capping agents. When even a small fraction of the Au(111) |
452 |
|
|
surface sites are covered with butanethiols, the conductivity exhibits |
453 |
|
|
an enhancement by at least a factor of 3. Capping agents are clearly |
454 |
|
|
playing a major role in thermal transport at metal / organic solvent |
455 |
|
|
surfaces. |
456 |
|
|
|
457 |
|
|
We note a non-monotonic behavior in the interfacial conductance as a |
458 |
|
|
function of surface coverage. The maximum conductance (largest $G$) |
459 |
|
|
happens when the surfaces are about 75\% covered with butanethiol |
460 |
|
|
caps. The reason for this behavior is not entirely clear. One |
461 |
|
|
explanation is that incomplete butanethiol coverage allows small gaps |
462 |
|
|
between butanethiols to form. These gaps can be filled by transient |
463 |
|
|
solvent molecules. These solvent molecules couple very strongly with |
464 |
|
|
the hot capping agent molecules near the surface, and can then carry |
465 |
|
|
away (diffusively) the excess thermal energy from the surface. |
466 |
|
|
|
467 |
|
|
There appears to be a competition between the conduction of the |
468 |
|
|
thermal energy away from the surface by the capping agents (enhanced |
469 |
|
|
by greater coverage) and the coupling of the capping agents with the |
470 |
|
|
solvent (enhanced by interdigitation at lower coverages). This |
471 |
|
|
competition would lead to the non-monotonic coverage behavior observed |
472 |
|
|
here. |
473 |
|
|
|
474 |
|
|
Results for rigid body toluene solvent, as well as the UA hexane, are |
475 |
|
|
within the ranges expected from prior experimental |
476 |
|
|
work.\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406} This suggests |
477 |
|
|
that explicit hydrogen atoms might not be required for modeling |
478 |
|
|
thermal transport in these systems. C-H vibrational modes do not see |
479 |
|
|
significant excited state population at low temperatures, and are not |
480 |
|
|
likely to carry lower frequency excitations from the solid layer into |
481 |
|
|
the bulk liquid. |
482 |
|
|
|
483 |
|
|
The toluene solvent does not exhibit the same behavior as hexane in |
484 |
|
|
that $G$ remains at approximately the same magnitude when the capping |
485 |
|
|
coverage increases from 25\% to 75\%. Toluene, as a rigid planar |
486 |
|
|
molecule, cannot occupy the relatively small gaps between the capping |
487 |
|
|
agents as easily as the chain-like {\it n}-hexane. The effect of |
488 |
|
|
solvent coupling to the capping agent is therefore weaker in toluene |
489 |
|
|
except at the very lowest coverage levels. This effect counters the |
490 |
|
|
coverage-dependent conduction of heat away from the metal surface, |
491 |
|
|
leading to a much flatter $G$ vs. coverage trend than is observed in |
492 |
|
|
{\it n}-hexane. |
493 |
|
|
|
494 |
|
|
\subsection{Effects due to Solvent \& Solvent Models} |
495 |
|
|
In addition to UA solvent and capping agent models, AA models have |
496 |
|
|
also been included in our simulations. In most of this work, the same |
497 |
|
|
(UA or AA) model for solvent and capping agent was used, but it is |
498 |
|
|
also possible to utilize different models for different components. |
499 |
|
|
We have also included isotopic substitutions (Hydrogen to Deuterium) |
500 |
|
|
to decrease the explicit vibrational overlap between solvent and |
501 |
|
|
capping agent. Table \ref{modelTest} summarizes the results of these |
502 |
|
|
studies. |
503 |
|
|
|
504 |
|
|
\begin{table*} |
505 |
|
|
\begin{minipage}{\linewidth} |
506 |
|
|
\begin{center} |
507 |
|
|
|
508 |
|
|
\caption{Computed interfacial thermal conductance ($G$ and |
509 |
|
|
$G^\prime$) values for interfaces using various models for |
510 |
|
|
solvent and capping agent (or without capping agent) at |
511 |
|
|
$\langle T\rangle\sim$200K. Here ``D'' stands for deuterated |
512 |
|
|
solvent or capping agent molecules. Error estimates are |
513 |
|
|
indicated in parentheses.} |
514 |
|
|
|
515 |
|
|
\begin{tabular}{llccc} |
516 |
|
|
\hline\hline |
517 |
|
|
Butanethiol model & Solvent & $G$ & $G^\prime$ \\ |
518 |
|
|
(or bare surface) & model & \multicolumn{2}{c}{(MW/m$^2$/K)} \\ |
519 |
|
|
\hline |
520 |
|
|
UA & UA hexane & 131(9) & 87(10) \\ |
521 |
|
|
& UA hexane(D) & 153(5) & 136(13) \\ |
522 |
|
|
& AA hexane & 131(6) & 122(10) \\ |
523 |
|
|
& UA toluene & 187(16) & 151(11) \\ |
524 |
|
|
& AA toluene & 200(36) & 149(53) \\ |
525 |
|
|
\hline |
526 |
|
|
AA & UA hexane & 116(9) & 129(8) \\ |
527 |
|
|
& AA hexane & 442(14) & 356(31) \\ |
528 |
|
|
& AA hexane(D) & 222(12) & 234(54) \\ |
529 |
|
|
& UA toluene & 125(25) & 97(60) \\ |
530 |
|
|
& AA toluene & 487(56) & 290(42) \\ |
531 |
|
|
\hline |
532 |
|
|
AA(D) & UA hexane & 158(25) & 172(4) \\ |
533 |
|
|
& AA hexane & 243(29) & 191(11) \\ |
534 |
|
|
& AA toluene & 364(36) & 322(67) \\ |
535 |
|
|
\hline |
536 |
|
|
bare & UA hexane & 46.5(3.2) & 49.4(4.5) \\ |
537 |
|
|
& UA hexane(D) & 43.9(4.6) & 43.0(2.0) \\ |
538 |
|
|
& AA hexane & 31.0(1.4) & 29.4(1.3) \\ |
539 |
|
|
& UA toluene & 70.1(1.3) & 65.8(0.5) \\ |
540 |
|
|
\hline\hline |
541 |
|
|
\end{tabular} |
542 |
|
|
\label{modelTest} |
543 |
|
|
\end{center} |
544 |
|
|
\end{minipage} |
545 |
|
|
\end{table*} |
546 |
|
|
|
547 |
|
|
To facilitate direct comparison between force fields, systems with the |
548 |
|
|
same capping agent and solvent were prepared with the same length |
549 |
|
|
scales for the simulation cells. |
550 |
|
|
|
551 |
|
|
On bare metal / solvent surfaces, different force field models for |
552 |
|
|
hexane yield similar results for both $G$ and $G^\prime$, and these |
553 |
|
|
two definitions agree with each other very well. This is primarily an |
554 |
|
|
indicator of weak interactions between the metal and the solvent. |
555 |
|
|
|
556 |
|
|
For the fully-covered surfaces, the choice of force field for the |
557 |
|
|
capping agent and solvent has a large impact on the calculated values |
558 |
|
|
of $G$ and $G^\prime$. The AA thiol to AA solvent conductivities are |
559 |
|
|
much larger than their UA to UA counterparts, and these values exceed |
560 |
|
|
the experimental estimates by a large measure. The AA force field |
561 |
|
|
allows significant energy to go into C-H (or C-D) stretching modes, |
562 |
|
|
and since these modes are high frequency, this non-quantum behavior is |
563 |
|
|
likely responsible for the overestimate of the conductivity. Compared |
564 |
|
|
to the AA model, the UA model yields more reasonable conductivity |
565 |
|
|
values with much higher computational efficiency. |
566 |
|
|
|
567 |
|
|
\subsubsection{Are electronic excitations in the metal important?} |
568 |
|
|
Because they lack electronic excitations, the QSC and related embedded |
569 |
|
|
atom method (EAM) models for gold are known to predict unreasonably |
570 |
|
|
low values for bulk conductivity |
571 |
|
|
($\lambda$).\cite{kuang:164101,ISI:000207079300006,Clancy:1992} If the |
572 |
|
|
conductance between the phases ($G$) is governed primarily by phonon |
573 |
|
|
excitation (and not electronic degrees of freedom), one would expect a |
574 |
|
|
classical model to capture most of the interfacial thermal |
575 |
|
|
conductance. Our results for $G$ and $G^\prime$ indicate that this is |
576 |
|
|
indeed the case, and suggest that the modeling of interfacial thermal |
577 |
|
|
transport depends primarily on the description of the interactions |
578 |
|
|
between the various components at the interface. When the metal is |
579 |
|
|
chemically capped, the primary barrier to thermal conductivity appears |
580 |
|
|
to be the interface between the capping agent and the surrounding |
581 |
|
|
solvent, so the excitations in the metal have little impact on the |
582 |
|
|
value of $G$. |
583 |
|
|
|
584 |
|
|
\subsection{Effects due to methodology and simulation parameters} |
585 |
|
|
|
586 |
|
|
We have varied the parameters of the simulations in order to |
587 |
|
|
investigate how these factors would affect the computation of $G$. Of |
588 |
|
|
particular interest are: 1) the length scale for the applied thermal |
589 |
|
|
gradient (modified by increasing the amount of solvent in the system), |
590 |
|
|
2) the sign and magnitude of the applied thermal flux, 3) the average |
591 |
|
|
temperature of the simulation (which alters the solvent density during |
592 |
|
|
equilibration), and 4) the definition of the interfacial conductance |
593 |
|
|
(Eqs. (\ref{discreteG}) or (\ref{derivativeG})) used in the |
594 |
|
|
calculation. |
595 |
|
|
|
596 |
|
|
Systems of different lengths were prepared by altering the number of |
597 |
|
|
solvent molecules and extending the length of the box along the $z$ |
598 |
|
|
axis to accomodate the extra solvent. Equilibration at the same |
599 |
|
|
temperature and pressure conditions led to nearly identical surface |
600 |
|
|
areas ($L_x$ and $L_y$) available to the metal and capping agent, |
601 |
|
|
while the extra solvent served mainly to lengthen the axis that was |
602 |
|
|
used to apply the thermal flux. For a given value of the applied |
603 |
|
|
flux, the different $z$ length scale has only a weak effect on the |
604 |
|
|
computed conductivities. |
605 |
|
|
|
606 |
|
|
\subsubsection{Effects of applied flux} |
607 |
|
|
The NIVS algorithm allows changes in both the sign and magnitude of |
608 |
|
|
the applied flux. It is possible to reverse the direction of heat |
609 |
|
|
flow simply by changing the sign of the flux, and thermal gradients |
610 |
|
|
which would be difficult to obtain experimentally ($5$ K/\AA) can be |
611 |
|
|
easily simulated. However, the magnitude of the applied flux is not |
612 |
|
|
arbitrary if one aims to obtain a stable and reliable thermal gradient. |
613 |
|
|
A temperature gradient can be lost in the noise if $|J_z|$ is too |
614 |
|
|
small, and excessive $|J_z|$ values can cause phase transitions if the |
615 |
|
|
extremes of the simulation cell become widely separated in |
616 |
|
|
temperature. Also, if $|J_z|$ is too large for the bulk conductivity |
617 |
|
|
of the materials, the thermal gradient will never reach a stable |
618 |
|
|
state. |
619 |
|
|
|
620 |
|
|
Within a reasonable range of $J_z$ values, we were able to study how |
621 |
|
|
$G$ changes as a function of this flux. In what follows, we use |
622 |
|
|
positive $J_z$ values to denote the case where energy is being |
623 |
|
|
transferred by the method from the metal phase and into the liquid. |
624 |
|
|
The resulting gradient therefore has a higher temperature in the |
625 |
|
|
liquid phase. Negative flux values reverse this transfer, and result |
626 |
|
|
in higher temperature metal phases. The conductance measured under |
627 |
|
|
different applied $J_z$ values is listed in Tables 2 and 3 in the |
628 |
|
|
supporting information. These results do not indicate that $G$ depends |
629 |
|
|
strongly on $J_z$ within this flux range. The linear response of flux |
630 |
|
|
to thermal gradient simplifies our investigations in that we can rely |
631 |
|
|
on $G$ measurement with only a small number $J_z$ values. |
632 |
|
|
|
633 |
|
|
The sign of $J_z$ is a different matter, however, as this can alter |
634 |
|
|
the temperature on the two sides of the interface. The average |
635 |
|
|
temperature values reported are for the entire system, and not for the |
636 |
|
|
liquid phase, so at a given $\langle T \rangle$, the system with |
637 |
|
|
positive $J_z$ has a warmer liquid phase. This means that if the |
638 |
|
|
liquid carries thermal energy via diffusive transport, {\it positive} |
639 |
|
|
$J_z$ values will result in increased molecular motion on the liquid |
640 |
|
|
side of the interface, and this will increase the measured |
641 |
|
|
conductivity. |
642 |
|
|
|
643 |
|
|
\subsubsection{Effects due to average temperature} |
644 |
|
|
|
645 |
|
|
We also studied the effect of average system temperature on the |
646 |
|
|
interfacial conductance. The simulations are first equilibrated in |
647 |
|
|
the NPT ensemble at 1 atm. The TraPPE-UA model for hexane tends to |
648 |
|
|
predict a lower boiling point (and liquid state density) than |
649 |
|
|
experiments. This lower-density liquid phase leads to reduced contact |
650 |
|
|
between the hexane and butanethiol, and this accounts for our |
651 |
|
|
observation of lower conductance at higher temperatures. In raising |
652 |
|
|
the average temperature from 200K to 250K, the density drop of |
653 |
|
|
$\sim$20\% in the solvent phase leads to a $\sim$40\% drop in the |
654 |
|
|
conductance. |
655 |
|
|
|
656 |
|
|
Similar behavior is observed in the TraPPE-UA model for toluene, |
657 |
|
|
although this model has better agreement with the experimental |
658 |
|
|
densities of toluene. The expansion of the toluene liquid phase is |
659 |
|
|
not as significant as that of the hexane (8.3\% over 100K), and this |
660 |
|
|
limits the effect to $\sim$20\% drop in thermal conductivity. |
661 |
|
|
|
662 |
|
|
Although we have not mapped out the behavior at a large number of |
663 |
|
|
temperatures, is clear that there will be a strong temperature |
664 |
|
|
dependence in the interfacial conductance when the physical properties |
665 |
|
|
of one side of the interface (notably the density) change rapidly as a |
666 |
|
|
function of temperature. |
667 |
|
|
|
668 |
|
|
Besides the lower interfacial thermal conductance, surfaces at |
669 |
|
|
relatively high temperatures are susceptible to reconstructions, |
670 |
|
|
particularly when butanethiols fully cover the Au(111) surface. These |
671 |
|
|
reconstructions include surface Au atoms which migrate outward to the |
672 |
|
|
S atom layer, and butanethiol molecules which embed into the surface |
673 |
|
|
Au layer. The driving force for this behavior is the strong Au-S |
674 |
|
|
interactions which are modeled here with a deep Lennard-Jones |
675 |
|
|
potential. This phenomenon agrees with reconstructions that have been |
676 |
|
|
experimentally |
677 |
|
|
observed.\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}. Vlugt |
678 |
|
|
{\it et al.} kept their Au(111) slab rigid so that their simulations |
679 |
|
|
could reach 300K without surface |
680 |
|
|
reconstructions.\cite{vlugt:cpc2007154} Since surface reconstructions |
681 |
|
|
blur the interface, the measurement of $G$ becomes more difficult to |
682 |
|
|
conduct at higher temperatures. For this reason, most of our |
683 |
|
|
measurements are undertaken at $\langle T\rangle\sim$200K where |
684 |
|
|
reconstruction is minimized. |
685 |
|
|
|
686 |
|
|
However, when the surface is not completely covered by butanethiols, |
687 |
|
|
the simulated system appears to be more resistent to the |
688 |
|
|
reconstruction. Our Au / butanethiol / toluene system had the Au(111) |
689 |
|
|
surfaces 90\% covered by butanethiols, but did not see this above |
690 |
|
|
phenomena even at $\langle T\rangle\sim$300K. That said, we did |
691 |
|
|
observe butanethiols migrating to neighboring three-fold sites during |
692 |
|
|
a simulation. Since the interface persisted in these simulations, we |
693 |
|
|
were able to obtain $G$'s for these interfaces even at a relatively |
694 |
|
|
high temperature without being affected by surface reconstructions. |
695 |
|
|
|
696 |
|
|
\section{Discussion} |
697 |
skuang |
3770 |
[COMBINE W. RESULTS] |
698 |
gezelter |
3769 |
The primary result of this work is that the capping agent acts as an |
699 |
|
|
efficient thermal coupler between solid and solvent phases. One of |
700 |
|
|
the ways the capping agent can carry out this role is to down-shift |
701 |
|
|
between the phonon vibrations in the solid (which carry the heat from |
702 |
|
|
the gold) and the molecular vibrations in the liquid (which carry some |
703 |
|
|
of the heat in the solvent). |
704 |
|
|
|
705 |
|
|
To investigate the mechanism of interfacial thermal conductance, the |
706 |
|
|
vibrational power spectrum was computed. Power spectra were taken for |
707 |
|
|
individual components in different simulations. To obtain these |
708 |
|
|
spectra, simulations were run after equilibration in the |
709 |
|
|
microcanonical (NVE) ensemble and without a thermal |
710 |
|
|
gradient. Snapshots of configurations were collected at a frequency |
711 |
|
|
that is higher than that of the fastest vibrations occurring in the |
712 |
|
|
simulations. With these configurations, the velocity auto-correlation |
713 |
|
|
functions can be computed: |
714 |
|
|
\begin{equation} |
715 |
|
|
C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle |
716 |
|
|
\label{vCorr} |
717 |
|
|
\end{equation} |
718 |
|
|
The power spectrum is constructed via a Fourier transform of the |
719 |
|
|
symmetrized velocity autocorrelation function, |
720 |
|
|
\begin{equation} |
721 |
|
|
\hat{f}(\omega) = |
722 |
|
|
\int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt |
723 |
|
|
\label{fourier} |
724 |
|
|
\end{equation} |
725 |
|
|
|
726 |
|
|
\subsection{The role of specific vibrations} |
727 |
|
|
The vibrational spectra for gold slabs in different environments are |
728 |
|
|
shown as in Figure \ref{specAu}. Regardless of the presence of |
729 |
|
|
solvent, the gold surfaces which are covered by butanethiol molecules |
730 |
|
|
exhibit an additional peak observed at a frequency of |
731 |
|
|
$\sim$165cm$^{-1}$. We attribute this peak to the S-Au bonding |
732 |
|
|
vibration. This vibration enables efficient thermal coupling of the |
733 |
|
|
surface Au layer to the capping agents. Therefore, in our simulations, |
734 |
|
|
the Au / S interfaces do not appear to be the primary barrier to |
735 |
|
|
thermal transport when compared with the butanethiol / solvent |
736 |
|
|
interfaces. This supports the results of Luo {\it et |
737 |
|
|
al.}\cite{Luo20101}, who reported $G$ for Au-SAM junctions roughly |
738 |
|
|
twice as large as what we have computed for the thiol-liquid |
739 |
|
|
interfaces. |
740 |
|
|
|
741 |
|
|
\begin{figure} |
742 |
|
|
\includegraphics[width=\linewidth]{vibration} |
743 |
|
|
\caption{The vibrational power spectrum for thiol-capped gold has an |
744 |
|
|
additional vibrational peak at $\sim $165cm$^{-1}$. Bare gold |
745 |
|
|
surfaces (both with and without a solvent over-layer) are missing |
746 |
|
|
this peak. A similar peak at $\sim $165cm$^{-1}$ also appears in |
747 |
|
|
the vibrational power spectrum for the butanethiol capping agents.} |
748 |
|
|
\label{specAu} |
749 |
|
|
\end{figure} |
750 |
|
|
|
751 |
|
|
Also in this figure, we show the vibrational power spectrum for the |
752 |
|
|
bound butanethiol molecules, which also exhibits the same |
753 |
|
|
$\sim$165cm$^{-1}$ peak. |
754 |
|
|
|
755 |
|
|
\subsection{Overlap of power spectra} |
756 |
|
|
A comparison of the results obtained from the two different organic |
757 |
|
|
solvents can also provide useful information of the interfacial |
758 |
|
|
thermal transport process. In particular, the vibrational overlap |
759 |
|
|
between the butanethiol and the organic solvents suggests a highly |
760 |
|
|
efficient thermal exchange between these components. Very high |
761 |
|
|
thermal conductivity was observed when AA models were used and C-H |
762 |
|
|
vibrations were treated classically. The presence of extra degrees of |
763 |
|
|
freedom in the AA force field yields higher heat exchange rates |
764 |
|
|
between the two phases and results in a much higher conductivity than |
765 |
|
|
in the UA force field. The all-atom classical models include high |
766 |
|
|
frequency modes which should be unpopulated at our relatively low |
767 |
|
|
temperatures. This artifact is likely the cause of the high thermal |
768 |
|
|
conductance in all-atom MD simulations. |
769 |
|
|
|
770 |
|
|
The similarity in the vibrational modes available to solvent and |
771 |
|
|
capping agent can be reduced by deuterating one of the two components |
772 |
|
|
(Fig. \ref{aahxntln}). Once either the hexanes or the butanethiols |
773 |
|
|
are deuterated, one can observe a significantly lower $G$ and |
774 |
|
|
$G^\prime$ values (Table \ref{modelTest}). |
775 |
|
|
|
776 |
|
|
\begin{figure} |
777 |
|
|
\includegraphics[width=\linewidth]{aahxntln} |
778 |
|
|
\caption{Spectra obtained for all-atom (AA) Au / butanethiol / solvent |
779 |
|
|
systems. When butanethiol is deuterated (lower left), its |
780 |
|
|
vibrational overlap with hexane decreases significantly. Since |
781 |
|
|
aromatic molecules and the butanethiol are vibrationally dissimilar, |
782 |
|
|
the change is not as dramatic when toluene is the solvent (right).} |
783 |
|
|
\label{aahxntln} |
784 |
|
|
\end{figure} |
785 |
|
|
|
786 |
|
|
For the Au / butanethiol / toluene interfaces, having the AA |
787 |
|
|
butanethiol deuterated did not yield a significant change in the |
788 |
|
|
measured conductance. Compared to the C-H vibrational overlap between |
789 |
|
|
hexane and butanethiol, both of which have alkyl chains, the overlap |
790 |
|
|
between toluene and butanethiol is not as significant and thus does |
791 |
|
|
not contribute as much to the heat exchange process. |
792 |
|
|
|
793 |
|
|
Previous observations of Zhang {\it et al.}\cite{hase:2010} indicate |
794 |
|
|
that the {\it intra}molecular heat transport due to alkylthiols is |
795 |
|
|
highly efficient. Combining our observations with those of Zhang {\it |
796 |
|
|
et al.}, it appears that butanethiol acts as a channel to expedite |
797 |
|
|
heat flow from the gold surface and into the alkyl chain. The |
798 |
|
|
vibrational coupling between the metal and the liquid phase can |
799 |
|
|
therefore be enhanced with the presence of suitable capping agents. |
800 |
|
|
|
801 |
|
|
Deuterated models in the UA force field did not decouple the thermal |
802 |
|
|
transport as well as in the AA force field. The UA models, even |
803 |
|
|
though they have eliminated the high frequency C-H vibrational |
804 |
|
|
overlap, still have significant overlap in the lower-frequency |
805 |
|
|
portions of the infrared spectra (Figure \ref{uahxnua}). Deuterating |
806 |
|
|
the UA models did not decouple the low frequency region enough to |
807 |
|
|
produce an observable difference for the results of $G$ (Table |
808 |
|
|
\ref{modelTest}). |
809 |
|
|
|
810 |
|
|
\begin{figure} |
811 |
|
|
\includegraphics[width=\linewidth]{uahxnua} |
812 |
|
|
\caption{Vibrational power spectra for UA models for the butanethiol |
813 |
|
|
and hexane solvent (upper panel) show the high degree of overlap |
814 |
|
|
between these two molecules, particularly at lower frequencies. |
815 |
|
|
Deuterating a UA model for the solvent (lower panel) does not |
816 |
|
|
decouple the two spectra to the same degree as in the AA force |
817 |
|
|
field (see Fig \ref{aahxntln}).} |
818 |
|
|
\label{uahxnua} |
819 |
|
|
\end{figure} |
820 |
|
|
|
821 |
|
|
\section{Conclusions} |
822 |
|
|
The NIVS algorithm has been applied to simulations of |
823 |
|
|
butanethiol-capped Au(111) surfaces in the presence of organic |
824 |
|
|
solvents. This algorithm allows the application of unphysical thermal |
825 |
|
|
flux to transfer heat between the metal and the liquid phase. With the |
826 |
|
|
flux applied, we were able to measure the corresponding thermal |
827 |
|
|
gradients and to obtain interfacial thermal conductivities. Under |
828 |
|
|
steady states, 2-3 ns trajectory simulations are sufficient for |
829 |
|
|
computation of this quantity. |
830 |
|
|
|
831 |
|
|
Our simulations have seen significant conductance enhancement in the |
832 |
|
|
presence of capping agent, compared with the bare gold / liquid |
833 |
|
|
interfaces. The vibrational coupling between the metal and the liquid |
834 |
|
|
phase is enhanced by a chemically-bonded capping agent. Furthermore, |
835 |
|
|
the coverage percentage of the capping agent plays an important role |
836 |
|
|
in the interfacial thermal transport process. Moderately low coverages |
837 |
|
|
allow higher contact between capping agent and solvent, and thus could |
838 |
|
|
further enhance the heat transfer process, giving a non-monotonic |
839 |
|
|
behavior of conductance with increasing coverage. |
840 |
|
|
|
841 |
|
|
Our results, particularly using the UA models, agree well with |
842 |
|
|
available experimental data. The AA models tend to overestimate the |
843 |
|
|
interfacial thermal conductance in that the classically treated C-H |
844 |
|
|
vibrations become too easily populated. Compared to the AA models, the |
845 |
|
|
UA models have higher computational efficiency with satisfactory |
846 |
|
|
accuracy, and thus are preferable in modeling interfacial thermal |
847 |
|
|
transport. |
848 |
|
|
|
849 |
|
|
Of the two definitions for $G$, the discrete form |
850 |
|
|
(Eq. \ref{discreteG}) was easier to use and gives out relatively |
851 |
|
|
consistent results, while the derivative form (Eq. \ref{derivativeG}) |
852 |
|
|
is not as versatile. Although $G^\prime$ gives out comparable results |
853 |
|
|
and follows similar trend with $G$ when measuring close to fully |
854 |
|
|
covered or bare surfaces, the spatial resolution of $T$ profile |
855 |
|
|
required for the use of a derivative form is limited by the number of |
856 |
|
|
bins and the sampling required to obtain thermal gradient information. |
857 |
|
|
|
858 |
|
|
Vlugt {\it et al.} have investigated the surface thiol structures for |
859 |
|
|
nanocrystalline gold and pointed out that they differ from those of |
860 |
|
|
the Au(111) surface.\cite{landman:1998,vlugt:cpc2007154} This |
861 |
|
|
difference could also cause differences in the interfacial thermal |
862 |
|
|
transport behavior. To investigate this problem, one would need an |
863 |
|
|
effective method for applying thermal gradients in non-planar |
864 |
|
|
(i.e. spherical) geometries. |
865 |
|
|
|
866 |
|
|
\section{Acknowledgments} |
867 |
|
|
Support for this project was provided by the National Science |
868 |
|
|
Foundation under grant CHE-0848243. Computational time was provided by |
869 |
|
|
the Center for Research Computing (CRC) at the University of Notre |
870 |
|
|
Dame. |
871 |
|
|
|
872 |
|
|
\newpage |
873 |
|
|
|
874 |
skuang |
3770 |
\bibliography{stokes} |
875 |
gezelter |
3769 |
|
876 |
|
|
\end{doublespace} |
877 |
|
|
\end{document} |
878 |
|
|
|