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24  
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27 > \bibliographystyle{achemso}
28  
29   \begin{document}
30  
31 < \title{Simulating interfacial thermal conductance at metal-solvent
32 <  interfaces: the role of chemical capping agents}
31 > \title{Simulating Interfacial Thermal Conductance at Metal-Solvent
32 >  Interfaces: the Role of Chemical Capping Agents}
33  
34   \author{Shenyu Kuang and J. Daniel
35   Gezelter\footnote{Corresponding author. \ Electronic mail: gezelter@nd.edu} \\
# Line 44 | Line 44 | The abstract version 2
44   \begin{doublespace}
45  
46   \begin{abstract}
47 < The abstract version 2
47 >  With the Non-Isotropic Velocity Scaling (NIVS) approach to Reverse
48 >  Non-Equilibrium Molecular Dynamics (RNEMD) it is possible to impose
49 >  an unphysical thermal flux between different regions of
50 >  inhomogeneous systems such as solid / liquid interfaces.  We have
51 >  applied NIVS to compute the interfacial thermal conductance at a
52 >  metal / organic solvent interface that has been chemically capped by
53 >  butanethiol molecules.  Our calculations suggest that vibrational
54 >  coupling between the metal and liquid phases is enhanced by the
55 >  capping agents, leading to a greatly enhanced conductivity at the
56 >  interface.  Specifically, the chemical bond between the metal and
57 >  the capping agent introduces a vibrational overlap that is not
58 >  present without the capping agent, and the overlap between the
59 >  vibrational spectra (metal to cap, cap to solvent) provides a
60 >  mechanism for rapid thermal transport across the interface. Our
61 >  calculations also suggest that this is a non-monotonic function of
62 >  the fractional coverage of the surface, as moderate coverages allow
63 >  diffusive heat transport of solvent molecules that have been in
64 >  close contact with the capping agent.
65 >
66 > Keywords: non-equilibrium, molecular dynamics, vibrational overlap,
67 > coverage dependent.
68   \end{abstract}
69  
70   \newpage
# Line 56 | Line 76 | The abstract version 2
76   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
77  
78   \section{Introduction}
79 + Due to the importance of heat flow (and heat removal) in
80 + nanotechnology, interfacial thermal conductance has been studied
81 + extensively both experimentally and computationally.\cite{cahill:793}
82 + Nanoscale materials have a significant fraction of their atoms at
83 + interfaces, and the chemical details of these interfaces govern the
84 + thermal transport properties.  Furthermore, the interfaces are often
85 + heterogeneous (e.g. solid - liquid), which provides a challenge to
86 + computational methods which have been developed for homogeneous or
87 + bulk systems.
88  
89 < The intro.
89 > Experimentally, the thermal properties of a number of interfaces have
90 > been investigated.  Cahill and coworkers studied nanoscale thermal
91 > transport from metal nanoparticle/fluid interfaces, to epitaxial
92 > TiN/single crystal oxides interfaces, and hydrophilic and hydrophobic
93 > interfaces between water and solids with different self-assembled
94 > monolayers.\cite{Wilson:2002uq,PhysRevB.67.054302,doi:10.1021/jp048375k,PhysRevLett.96.186101}
95 > Wang {\it et al.} studied heat transport through long-chain
96 > hydrocarbon monolayers on gold substrate at individual molecular
97 > level,\cite{Wang10082007} Schmidt {\it et al.} studied the role of
98 > cetyltrimethylammonium bromide (CTAB) on the thermal transport between
99 > gold nanorods and solvent,\cite{doi:10.1021/jp8051888} and Juv\'e {\it
100 >  et al.} studied the cooling dynamics, which is controlled by thermal
101 > interface resistance of glass-embedded metal
102 > nanoparticles.\cite{PhysRevB.80.195406} Although interfaces are
103 > normally considered barriers for heat transport, Alper {\it et al.}
104 > suggested that specific ligands (capping agents) could completely
105 > eliminate this barrier
106 > ($G\rightarrow\infty$).\cite{doi:10.1021/la904855s}
107 >
108 > The acoustic mismatch model for interfacial conductance utilizes the
109 > acoustic impedance ($Z_a = \rho_a v^s_a$) on both sides of the
110 > interface.\cite{schwartz} Here, $\rho_a$ and $v^s_a$ are the density
111 > and speed of sound in material $a$.  The phonon transmission
112 > probability at the $a-b$ interface is
113 > \begin{equation}
114 > t_{ab} = \frac{4 Z_a Z_b}{\left(Z_a + Z_b \right)^2},
115 > \end{equation}
116 > and the interfacial conductance can then be approximated as
117 > \begin{equation}
118 > G_{ab} \approx \frac{1}{4} C_D v_D t_{ab}
119 > \end{equation}
120 > where $C_D$ is the Debye heat capacity of the hot side, and $v_D$ is
121 > the Debye phonon velocity ($1/v_D^3 = 1/3v_L^3 + 2/3 v_T^3$) where
122 > $v_L$ and $v_T$ are the longitudinal and transverse speeds of sound,
123 > respectively.  For the Au/hexane and Au/toluene interfaces, the
124 > acoustic mismatch model predicts room-temperature $G \approx 87 \mbox{
125 >  and } 129$ MW m$^{-2}$ K$^{-1}$, respectively.  However, it is not
126 > clear how one might apply the acoustic mismatch model to a
127 > chemically-modified surface, particularly when the acoustic properties
128 > of a monolayer film may not be well characterized.
129 >
130 > More precise computational models have also been used to study the
131 > interfacial thermal transport in order to gain an understanding of
132 > this phenomena at the molecular level. Recently, Hase and coworkers
133 > employed Non-Equilibrium Molecular Dynamics (NEMD) simulations to
134 > study thermal transport from hot Au(111) substrate to a self-assembled
135 > monolayer of alkylthiol with relatively long chain (8-20 carbon
136 > atoms).\cite{hase:2010,hase:2011} However, ensemble averaged
137 > measurements for heat conductance of interfaces between the capping
138 > monolayer on Au and a solvent phase have yet to be studied with their
139 > approach. The comparatively low thermal flux through interfaces is
140 > difficult to measure with Equilibrium
141 > MD\cite{doi:10.1080/0026897031000068578} or forward NEMD simulation
142 > methods. Therefore, the Reverse NEMD (RNEMD)
143 > methods\cite{MullerPlathe:1997xw,kuang:164101} would be advantageous
144 > in that they {\it apply} the difficult to measure quantity (flux),
145 > while {\it measuring} the easily-computed quantity (the thermal
146 > gradient).  This is particularly true for inhomogeneous interfaces
147 > where it would not be clear how to apply a gradient {\it a priori}.
148 > Garde and coworkers\cite{garde:nl2005,garde:PhysRevLett2009} applied
149 > this approach to various liquid interfaces and studied how thermal
150 > conductance (or resistance) is dependent on chemical details of a
151 > number of hydrophobic and hydrophilic aqueous interfaces. And
152 > recently, Luo {\it et al.} studied the thermal conductance of
153 > Au-SAM-Au junctions using the same approach, comparing to a constant
154 > temperature difference method.\cite{Luo20101} While this latter
155 > approach establishes more ideal Maxwell-Boltzmann distributions than
156 > previous RNEMD methods, it does not guarantee momentum or kinetic
157 > energy conservation.
158 >
159 > Recently, we have developed a Non-Isotropic Velocity Scaling (NIVS)
160 > algorithm for RNEMD simulations\cite{kuang:164101}. This algorithm
161 > retains the desirable features of RNEMD (conservation of linear
162 > momentum and total energy, compatibility with periodic boundary
163 > conditions) while establishing true thermal distributions in each of
164 > the two slabs. Furthermore, it allows effective thermal exchange
165 > between particles of different identities, and thus makes the study of
166 > interfacial conductance much simpler.
167 >
168 > The work presented here deals with the Au(111) surface covered to
169 > varying degrees by butanethiol, a capping agent with short carbon
170 > chain, and solvated with organic solvents of different molecular
171 > properties. Different models were used for both the capping agent and
172 > the solvent force field parameters. Using the NIVS algorithm, the
173 > thermal transport across these interfaces was studied and the
174 > underlying mechanism for the phenomena was investigated.
175 >
176 > \section{Methodology}
177 > \subsection{Imposed-Flux Methods in MD Simulations}
178 > Steady state MD simulations have an advantage in that not many
179 > trajectories are needed to study the relationship between thermal flux
180 > and thermal gradients. For systems with low interfacial conductance,
181 > one must have a method capable of generating or measuring relatively
182 > small fluxes, compared to those required for bulk conductivity. This
183 > requirement makes the calculation even more difficult for
184 > slowly-converging equilibrium methods.\cite{Viscardy:2007lq} Forward
185 > NEMD methods impose a gradient (and measure a flux), but at interfaces
186 > it is not clear what behavior should be imposed at the boundaries
187 > between materials.  Imposed-flux reverse non-equilibrium
188 > methods\cite{MullerPlathe:1997xw} set the flux {\it a priori} and
189 > the thermal response becomes an easy-to-measure quantity.  Although
190 > M\"{u}ller-Plathe's original momentum swapping approach can be used
191 > for exchanging energy between particles of different identity, the
192 > kinetic energy transfer efficiency is affected by the mass difference
193 > between the particles, which limits its application on heterogeneous
194 > interfacial systems.
195 >
196 > The non-isotropic velocity scaling (NIVS) \cite{kuang:164101} approach
197 > to non-equilibrium MD simulations is able to impose a wide range of
198 > kinetic energy fluxes without obvious perturbation to the velocity
199 > distributions of the simulated systems. Furthermore, this approach has
200 > the advantage in heterogeneous interfaces in that kinetic energy flux
201 > can be applied between regions of particles of arbitrary identity, and
202 > the flux will not be restricted by difference in particle mass.
203 >
204 > The NIVS algorithm scales the velocity vectors in two separate regions
205 > of a simulation system with respective diagonal scaling matrices. To
206 > determine these scaling factors in the matrices, a set of equations
207 > including linear momentum conservation and kinetic energy conservation
208 > constraints and target energy flux satisfaction is solved. With the
209 > scaling operation applied to the system in a set frequency, bulk
210 > temperature gradients can be easily established, and these can be used
211 > for computing thermal conductivities. The NIVS algorithm conserves
212 > momenta and energy and does not depend on an external thermostat.
213 >
214 > \subsection{Defining Interfacial Thermal Conductivity ($G$)}
215 >
216 > For an interface with relatively low interfacial conductance, and a
217 > thermal flux between two distinct bulk regions, the regions on either
218 > side of the interface rapidly come to a state in which the two phases
219 > have relatively homogeneous (but distinct) temperatures. The
220 > interfacial thermal conductivity $G$ can therefore be approximated as:
221 > \begin{equation}
222 >  G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
223 >    \langle T_\mathrm{cold}\rangle \right)}
224 > \label{lowG}
225 > \end{equation}
226 > where ${E_{total}}$ is the total imposed non-physical kinetic energy
227 > transfer during the simulation and ${\langle T_\mathrm{hot}\rangle}$
228 > and ${\langle T_\mathrm{cold}\rangle}$ are the average observed
229 > temperature of the two separated phases.  For an applied flux $J_z$
230 > operating over a simulation time $t$ on a periodically-replicated slab
231 > of dimensions $L_x \times L_y$, $E_{total} = J_z *(t)*(2 L_x L_y)$.
232 >
233 > When the interfacial conductance is {\it not} small, there are two
234 > ways to define $G$. One common way is to assume the temperature is
235 > discrete on the two sides of the interface. $G$ can be calculated
236 > using the applied thermal flux $J$ and the maximum temperature
237 > difference measured along the thermal gradient max($\Delta T$), which
238 > occurs at the Gibbs dividing surface (Figure \ref{demoPic}). This is
239 > known as the Kapitza conductance, which is the inverse of the Kapitza
240 > resistance.
241 > \begin{equation}
242 >  G=\frac{J}{\Delta T}
243 > \label{discreteG}
244 > \end{equation}
245 >
246 > \begin{figure}
247 > \includegraphics[width=\linewidth]{method}
248 > \caption{Interfacial conductance can be calculated by applying an
249 >  (unphysical) kinetic energy flux between two slabs, one located
250 >  within the metal and another on the edge of the periodic box.  The
251 >  system responds by forming a thermal gradient.  In bulk liquids,
252 >  this gradient typically has a single slope, but in interfacial
253 >  systems, there are distinct thermal conductivity domains.  The
254 >  interfacial conductance, $G$ is found by measuring the temperature
255 >  gap at the Gibbs dividing surface, or by using second derivatives of
256 >  the thermal profile.}
257 > \label{demoPic}
258 > \end{figure}
259 >
260 > Another approach is to assume that the temperature is continuous and
261 > differentiable throughout the space. Given that $\lambda$ is also
262 > differentiable, $G$ can be defined as its gradient ($\nabla\lambda$)
263 > projected along a vector normal to the interface ($\mathbf{\hat{n}}$)
264 > and evaluated at the interface location ($z_0$). This quantity,
265 > \begin{align}
266 > G^\prime &= \left(\nabla\lambda \cdot \mathbf{\hat{n}}\right)_{z_0} \\
267 >         &= \frac{\partial}{\partial z}\left(-\frac{J_z}{
268 >           \left(\frac{\partial T}{\partial z}\right)}\right)_{z_0} \\
269 >         &= J_z\left(\frac{\partial^2 T}{\partial z^2}\right)_{z_0} \Big/
270 >         \left(\frac{\partial T}{\partial z}\right)_{z_0}^2 \label{derivativeG}
271 > \end{align}
272 > has the same units as the common definition for $G$, and the maximum
273 > of its magnitude denotes where thermal conductivity has the largest
274 > change, i.e. the interface.  In the geometry used in this study, the
275 > vector normal to the interface points along the $z$ axis, as do
276 > $\vec{J}$ and the thermal gradient.  This yields the simplified
277 > expressions in Eq. \ref{derivativeG}.
278 >
279 > With temperature profiles obtained from simulation, one is able to
280 > approximate the first and second derivatives of $T$ with finite
281 > difference methods and calculate $G^\prime$. In what follows, both
282 > definitions have been used, and are compared in the results.
283 >
284 > To investigate the interfacial conductivity at metal / solvent
285 > interfaces, we have modeled a metal slab with its (111) surfaces
286 > perpendicular to the $z$-axis of our simulation cells. The metal slab
287 > has been prepared both with and without capping agents on the exposed
288 > surface, and has been solvated with simple organic solvents, as
289 > illustrated in Figure \ref{gradT}.
290 >
291 > With the simulation cell described above, we are able to equilibrate
292 > the system and impose an unphysical thermal flux between the liquid
293 > and the metal phase using the NIVS algorithm. By periodically applying
294 > the unphysical flux, we obtained a temperature profile and its spatial
295 > derivatives. Figure \ref{gradT} shows how an applied thermal flux can
296 > be used to obtain the 1st and 2nd derivatives of the temperature
297 > profile.
298 >
299 > \begin{figure}
300 > \includegraphics[width=\linewidth]{gradT}
301 > \caption{A sample of Au (111) / butanethiol / hexane interfacial
302 >  system with the temperature profile after a kinetic energy flux has
303 >  been imposed.  Note that the largest temperature jump in the thermal
304 >  profile (corresponding to the lowest interfacial conductance) is at
305 >  the interface between the butanethiol molecules (blue) and the
306 >  solvent (grey).  First and second derivatives of the temperature
307 >  profile are obtained using a finite difference approximation (lower
308 >  panel).}
309 > \label{gradT}
310 > \end{figure}
311 >
312 > \section{Computational Details}
313 > \subsection{Simulation Protocol}
314 > The NIVS algorithm has been implemented in our MD simulation code,
315 > OpenMD\cite{Meineke:2005gd,openmd}, and was used for our simulations.
316 > Metal slabs of 6 or 11 layers of Au atoms were first equilibrated
317 > under atmospheric pressure (1 atm) and 200K. After equilibration,
318 > butanethiol capping agents were placed at three-fold hollow sites on
319 > the Au(111) surfaces. These sites are either {\it fcc} or {\it
320 >  hcp} sites, although Hase {\it et al.} found that they are
321 > equivalent in a heat transfer process,\cite{hase:2010} so we did not
322 > distinguish between these sites in our study. The maximum butanethiol
323 > capacity on Au surface is $1/3$ of the total number of surface Au
324 > atoms, and the packing forms a $(\sqrt{3}\times\sqrt{3})R30^\circ$
325 > structure\cite{doi:10.1021/ja00008a001,doi:10.1021/cr9801317}. A
326 > series of lower coverages was also prepared by eliminating
327 > butanethiols from the higher coverage surface in a regular manner. The
328 > lower coverages were prepared in order to study the relation between
329 > coverage and interfacial conductance.
330 >
331 > The capping agent molecules were allowed to migrate during the
332 > simulations. They distributed themselves uniformly and sampled a
333 > number of three-fold sites throughout out study. Therefore, the
334 > initial configuration does not noticeably affect the sampling of a
335 > variety of configurations of the same coverage, and the final
336 > conductance measurement would be an average effect of these
337 > configurations explored in the simulations.
338 >
339 > After the modified Au-butanethiol surface systems were equilibrated in
340 > the canonical (NVT) ensemble, organic solvent molecules were packed in
341 > the previously empty part of the simulation cells.\cite{packmol} Two
342 > solvents were investigated, one which has little vibrational overlap
343 > with the alkanethiol and which has a planar shape (toluene), and one
344 > which has similar vibrational frequencies to the capping agent and
345 > chain-like shape ({\it n}-hexane).
346 >
347 > The simulation cells were not particularly extensive along the
348 > $z$-axis, as a very long length scale for the thermal gradient may
349 > cause excessively hot or cold temperatures in the middle of the
350 > solvent region and lead to undesired phenomena such as solvent boiling
351 > or freezing when a thermal flux is applied. Conversely, too few
352 > solvent molecules would change the normal behavior of the liquid
353 > phase. Therefore, our $N_{solvent}$ values were chosen to ensure that
354 > these extreme cases did not happen to our simulations. The spacing
355 > between periodic images of the gold interfaces is $45 \sim 75$\AA in
356 > our simulations.
357 >
358 > The initial configurations generated are further equilibrated with the
359 > $x$ and $y$ dimensions fixed, only allowing the $z$-length scale to
360 > change. This is to ensure that the equilibration of liquid phase does
361 > not affect the metal's crystalline structure. Comparisons were made
362 > with simulations that allowed changes of $L_x$ and $L_y$ during NPT
363 > equilibration. No substantial changes in the box geometry were noticed
364 > in these simulations. After ensuring the liquid phase reaches
365 > equilibrium at atmospheric pressure (1 atm), further equilibration was
366 > carried out under canonical (NVT) and microcanonical (NVE) ensembles.
367 >
368 > After the systems reach equilibrium, NIVS was used to impose an
369 > unphysical thermal flux between the metal and the liquid phases. Most
370 > of our simulations were done under an average temperature of
371 > $\sim$200K. Therefore, thermal flux usually came from the metal to the
372 > liquid so that the liquid has a higher temperature and would not
373 > freeze due to lowered temperatures. After this induced temperature
374 > gradient had stabilized, the temperature profile of the simulation cell
375 > was recorded. To do this, the simulation cell is divided evenly into
376 > $N$ slabs along the $z$-axis. The average temperatures of each slab
377 > are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
378 > the same, the derivatives of $T$ with respect to slab number $n$ can
379 > be directly used for $G^\prime$ calculations: \begin{equation}
380 >  G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
381 >         \Big/\left(\frac{\partial T}{\partial z}\right)^2
382 >         = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
383 >         \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
384 >         = |J_z|\Big|\frac{\partial^2 T}{\partial n^2}\Big|
385 >         \Big/\left(\frac{\partial T}{\partial n}\right)^2
386 > \label{derivativeG2}
387 > \end{equation}
388 > The absolute values in Eq. \ref{derivativeG2} appear because the
389 > direction of the flux $\vec{J}$ is in an opposing direction on either
390 > side of the metal slab.
391 >
392 > All of the above simulation procedures use a time step of 1 fs. Each
393 > equilibration stage took a minimum of 100 ps, although in some cases,
394 > longer equilibration stages were utilized.
395 >
396 > \subsection{Force Field Parameters}
397 > Our simulations include a number of chemically distinct components.
398 > Figure \ref{demoMol} demonstrates the sites defined for both
399 > United-Atom and All-Atom models of the organic solvent and capping
400 > agents in our simulations. Force field parameters are needed for
401 > interactions both between the same type of particles and between
402 > particles of different species.
403 >
404 > \begin{figure}
405 > \includegraphics[width=\linewidth]{structures}
406 > \caption{Structures of the capping agent and solvents utilized in
407 >  these simulations. The chemically-distinct sites (a-e) are expanded
408 >  in terms of constituent atoms for both United Atom (UA) and All Atom
409 >  (AA) force fields.  Most parameters are from References
410 >  \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes,TraPPE-UA.thiols}
411 >  (UA) and \protect\cite{OPLSAA} (AA). Cross-interactions with the Au
412 >  atoms are given in Table \ref{MnM}.}
413 > \label{demoMol}
414 > \end{figure}
415 >
416 > The Au-Au interactions in metal lattice slab is described by the
417 > quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
418 > potentials include zero-point quantum corrections and are
419 > reparametrized for accurate surface energies compared to the
420 > Sutton-Chen potentials.\cite{Chen90}
421 >
422 > For the two solvent molecules, {\it n}-hexane and toluene, two
423 > different atomistic models were utilized. Both solvents were modeled
424 > using United-Atom (UA) and All-Atom (AA) models. The TraPPE-UA
425 > parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
426 > for our UA solvent molecules. In these models, sites are located at
427 > the carbon centers for alkyl groups. Bonding interactions, including
428 > bond stretches and bends and torsions, were used for intra-molecular
429 > sites closer than 3 bonds. For non-bonded interactions, Lennard-Jones
430 > potentials are used.
431 >
432 > By eliminating explicit hydrogen atoms, the TraPPE-UA models are
433 > simple and computationally efficient, while maintaining good accuracy.
434 > However, the TraPPE-UA model for alkanes is known to predict a slightly
435 > lower boiling point than experimental values. This is one of the
436 > reasons we used a lower average temperature (200K) for our
437 > simulations. If heat is transferred to the liquid phase during the
438 > NIVS simulation, the liquid in the hot slab can actually be
439 > substantially warmer than the mean temperature in the simulation. The
440 > lower mean temperatures therefore prevent solvent boiling.
441 >
442 > For UA-toluene, the non-bonded potentials between intermolecular sites
443 > have a similar Lennard-Jones formulation. The toluene molecules were
444 > treated as a single rigid body, so there was no need for
445 > intramolecular interactions (including bonds, bends, or torsions) in
446 > this solvent model.
447 >
448 > Besides the TraPPE-UA models, AA models for both organic solvents are
449 > included in our studies as well. The OPLS-AA\cite{OPLSAA} force fields
450 > were used. For hexane, additional explicit hydrogen sites were
451 > included. Besides bonding and non-bonded site-site interactions,
452 > partial charges and the electrostatic interactions were added to each
453 > CT and HC site. For toluene, a flexible model for the toluene molecule
454 > was utilized which included bond, bend, torsion, and inversion
455 > potentials to enforce ring planarity.
456 >
457 > The butanethiol capping agent in our simulations, were also modeled
458 > with both UA and AA model. The TraPPE-UA force field includes
459 > parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
460 > UA butanethiol model in our simulations. The OPLS-AA also provides
461 > parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
462 > surfaces do not have the hydrogen atom bonded to sulfur. To derive
463 > suitable parameters for butanethiol adsorbed on Au(111) surfaces, we
464 > adopt the S parameters from Luedtke and Landman\cite{landman:1998} and
465 > modify the parameters for the CTS atom to maintain charge neutrality
466 > in the molecule.  Note that the model choice (UA or AA) for the capping
467 > agent can be different from the solvent. Regardless of model choice,
468 > the force field parameters for interactions between capping agent and
469 > solvent can be derived using Lorentz-Berthelot Mixing Rule:
470 > \begin{eqnarray}
471 >  \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
472 >  \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
473 > \end{eqnarray}
474 >
475 > To describe the interactions between metal (Au) and non-metal atoms,
476 > we refer to an adsorption study of alkyl thiols on gold surfaces by
477 > Vlugt {\it et al.}\cite{vlugt:cpc2007154} They fitted an effective
478 > Lennard-Jones form of potential parameters for the interaction between
479 > Au and pseudo-atoms CH$_x$ and S based on a well-established and
480 > widely-used effective potential of Hautman and Klein for the Au(111)
481 > surface.\cite{hautman:4994} As our simulations require the gold slab
482 > to be flexible to accommodate thermal excitation, the pair-wise form
483 > of potentials they developed was used for our study.
484 >
485 > The potentials developed from {\it ab initio} calculations by Leng
486 > {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
487 > interactions between Au and aromatic C/H atoms in toluene. However,
488 > the Lennard-Jones parameters between Au and other types of particles,
489 > (e.g. AA alkanes) have not yet been established. For these
490 > interactions, the Lorentz-Berthelot mixing rule can be used to derive
491 > effective single-atom LJ parameters for the metal using the fit values
492 > for toluene. These are then used to construct reasonable mixing
493 > parameters for the interactions between the gold and other atoms.
494 > Table \ref{MnM} summarizes the ``metal/non-metal'' parameters used in
495 > our simulations.
496 >
497 > \begin{table*}
498 >  \begin{minipage}{\linewidth}
499 >    \begin{center}
500 >      \caption{Non-bonded interaction parameters (including cross
501 >        interactions with Au atoms) for both force fields used in this
502 >        work.}      
503 >      \begin{tabular}{lllllll}
504 >        \hline\hline
505 >        & Site  & $\sigma_{ii}$ & $\epsilon_{ii}$ & $q_i$ &
506 >        $\sigma_{Au-i}$ & $\epsilon_{Au-i}$ \\
507 >        & & (\AA) & (kcal/mol) & ($e$) & (\AA) & (kcal/mol) \\
508 >        \hline
509 >        United Atom (UA)
510 >        &CH3  & 3.75  & 0.1947  & -      & 3.54   & 0.2146  \\
511 >        &CH2  & 3.95  & 0.0914  & -      & 3.54   & 0.1749  \\
512 >        &CHar & 3.695 & 0.1003  & -      & 3.4625 & 0.1680  \\
513 >        &CRar & 3.88  & 0.04173 & -      & 3.555  & 0.1604  \\
514 >        \hline
515 >        All Atom (AA)
516 >        &CT3  & 3.50  & 0.066   & -0.18  & 3.365  & 0.1373  \\
517 >        &CT2  & 3.50  & 0.066   & -0.12  & 3.365  & 0.1373  \\
518 >        &CTT  & 3.50  & 0.066   & -0.065 & 3.365  & 0.1373  \\
519 >        &HC   & 2.50  & 0.030   &  0.06  & 2.865  & 0.09256 \\
520 >        &CA   & 3.55  & 0.070   & -0.115 & 3.173  & 0.0640  \\
521 >        &HA   & 2.42  & 0.030   &  0.115 & 2.746  & 0.0414  \\
522 >        \hline
523 >        Both UA and AA
524 >        & S   & 4.45  & 0.25    & -      & 2.40   & 8.465   \\
525 >        \hline\hline
526 >      \end{tabular}
527 >      \label{MnM}
528 >    \end{center}
529 >  \end{minipage}
530 > \end{table*}
531  
532 +
533 + \section{Results}
534 + There are many factors contributing to the measured interfacial
535 + conductance; some of these factors are physically motivated
536 + (e.g. coverage of the surface by the capping agent coverage and
537 + solvent identity), while some are governed by parameters of the
538 + methodology (e.g. applied flux and the formulas used to obtain the
539 + conductance). In this section we discuss the major physical and
540 + calculational effects on the computed conductivity.
541 +
542 + \subsection{Effects due to capping agent coverage}
543 +
544 + A series of different initial conditions with a range of surface
545 + coverages was prepared and solvated with various with both of the
546 + solvent molecules. These systems were then equilibrated and their
547 + interfacial thermal conductivity was measured with the NIVS
548 + algorithm. Figure \ref{coverage} demonstrates the trend of conductance
549 + with respect to surface coverage.
550 +
551 + \begin{figure}
552 + \includegraphics[width=\linewidth]{coverage}
553 + \caption{The interfacial thermal conductivity ($G$) has a
554 +  non-monotonic dependence on the degree of surface capping.  This
555 +  data is for the Au(111) / butanethiol / solvent interface with
556 +  various UA force fields at $\langle T\rangle \sim $200K.}
557 + \label{coverage}
558 + \end{figure}
559 +
560 + In partially covered surfaces, the derivative definition for
561 + $G^\prime$ (Eq. \ref{derivativeG}) becomes difficult to apply, as the
562 + location of maximum change of $\lambda$ becomes washed out.  The
563 + discrete definition (Eq. \ref{discreteG}) is easier to apply, as the
564 + Gibbs dividing surface is still well-defined. Therefore, $G$ (not
565 + $G^\prime$) was used in this section.
566 +
567 + From Figure \ref{coverage}, one can see the significance of the
568 + presence of capping agents. When even a small fraction of the Au(111)
569 + surface sites are covered with butanethiols, the conductivity exhibits
570 + an enhancement by at least a factor of 3.  Capping agents are clearly
571 + playing a major role in thermal transport at metal / organic solvent
572 + surfaces.
573 +
574 + We note a non-monotonic behavior in the interfacial conductance as a
575 + function of surface coverage. The maximum conductance (largest $G$)
576 + happens when the surfaces are about 75\% covered with butanethiol
577 + caps.  The reason for this behavior is not entirely clear.  One
578 + explanation is that incomplete butanethiol coverage allows small gaps
579 + between butanethiols to form. These gaps can be filled by transient
580 + solvent molecules.  These solvent molecules couple very strongly with
581 + the hot capping agent molecules near the surface, and can then carry
582 + away (diffusively) the excess thermal energy from the surface.
583 +
584 + There appears to be a competition between the conduction of the
585 + thermal energy away from the surface by the capping agents (enhanced
586 + by greater coverage) and the coupling of the capping agents with the
587 + solvent (enhanced by interdigitation at lower coverages).  This
588 + competition would lead to the non-monotonic coverage behavior observed
589 + here.
590 +
591 + Results for rigid body toluene solvent, as well as the UA hexane, are
592 + within the ranges expected from prior experimental
593 + work.\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406} This suggests
594 + that explicit hydrogen atoms might not be required for modeling
595 + thermal transport in these systems.  C-H vibrational modes do not see
596 + significant excited state population at low temperatures, and are not
597 + likely to carry lower frequency excitations from the solid layer into
598 + the bulk liquid.
599 +
600 + The toluene solvent does not exhibit the same behavior as hexane in
601 + that $G$ remains at approximately the same magnitude when the capping
602 + coverage increases from 25\% to 75\%.  Toluene, as a rigid planar
603 + molecule, cannot occupy the relatively small gaps between the capping
604 + agents as easily as the chain-like {\it n}-hexane.  The effect of
605 + solvent coupling to the capping agent is therefore weaker in toluene
606 + except at the very lowest coverage levels.  This effect counters the
607 + coverage-dependent conduction of heat away from the metal surface,
608 + leading to a much flatter $G$ vs. coverage trend than is observed in
609 + {\it n}-hexane.
610 +
611 + \subsection{Effects due to Solvent \& Solvent Models}
612 + In addition to UA solvent and capping agent models, AA models have
613 + also been included in our simulations.  In most of this work, the same
614 + (UA or AA) model for solvent and capping agent was used, but it is
615 + also possible to utilize different models for different components.
616 + We have also included isotopic substitutions (Hydrogen to Deuterium)
617 + to decrease the explicit vibrational overlap between solvent and
618 + capping agent. Table \ref{modelTest} summarizes the results of these
619 + studies.
620 +
621 + \begin{table*}
622 +  \begin{minipage}{\linewidth}
623 +    \begin{center}
624 +      
625 +      \caption{Computed interfacial thermal conductance ($G$ and
626 +        $G^\prime$) values for interfaces using various models for
627 +        solvent and capping agent (or without capping agent) at
628 +        $\langle T\rangle\sim$200K.  Here ``D'' stands for deuterated
629 +        solvent or capping agent molecules. Error estimates are
630 +        indicated in parentheses.}
631 +      
632 +      \begin{tabular}{llccc}
633 +        \hline\hline
634 +        Butanethiol model & Solvent & $G$ & $G^\prime$ \\
635 +        (or bare surface) & model &
636 +        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
637 +        \hline
638 +        UA    & UA hexane    & 131(9)    & 87(10)    \\
639 +              & UA hexane(D) & 153(5)    & 136(13)   \\
640 +              & AA hexane    & 131(6)    & 122(10)   \\
641 +              & UA toluene   & 187(16)   & 151(11)   \\
642 +              & AA toluene   & 200(36)   & 149(53)   \\
643 +        \hline
644 +        AA    & UA hexane    & 116(9)    & 129(8)    \\
645 +              & AA hexane    & 442(14)   & 356(31)   \\
646 +              & AA hexane(D) & 222(12)   & 234(54)   \\
647 +              & UA toluene   & 125(25)   & 97(60)    \\
648 +              & AA toluene   & 487(56)   & 290(42)   \\
649 +        \hline
650 +        AA(D) & UA hexane    & 158(25)   & 172(4)    \\
651 +              & AA hexane    & 243(29)   & 191(11)   \\
652 +              & AA toluene   & 364(36)   & 322(67)   \\
653 +        \hline
654 +        bare  & UA hexane    & 46.5(3.2) & 49.4(4.5) \\
655 +              & UA hexane(D) & 43.9(4.6) & 43.0(2.0) \\
656 +              & AA hexane    & 31.0(1.4) & 29.4(1.3) \\
657 +              & UA toluene   & 70.1(1.3) & 65.8(0.5) \\
658 +        \hline\hline
659 +      \end{tabular}
660 +      \label{modelTest}
661 +    \end{center}
662 +  \end{minipage}
663 + \end{table*}
664 +
665 + To facilitate direct comparison between force fields, systems with the
666 + same capping agent and solvent were prepared with the same length
667 + scales for the simulation cells.
668 +
669 + On bare metal / solvent surfaces, different force field models for
670 + hexane yield similar results for both $G$ and $G^\prime$, and these
671 + two definitions agree with each other very well. This is primarily an
672 + indicator of weak interactions between the metal and the solvent.
673 +
674 + For the fully-covered surfaces, the choice of force field for the
675 + capping agent and solvent has a large impact on the calculated values
676 + of $G$ and $G^\prime$.  The AA thiol to AA solvent conductivities are
677 + much larger than their UA to UA counterparts, and these values exceed
678 + the experimental estimates by a large measure.  The AA force field
679 + allows significant energy to go into C-H (or C-D) stretching modes,
680 + and since these modes are high frequency, this non-quantum behavior is
681 + likely responsible for the overestimate of the conductivity.  Compared
682 + to the AA model, the UA model yields more reasonable conductivity
683 + values with much higher computational efficiency.
684 +
685 + \subsubsection{Are electronic excitations in the metal important?}
686 + Because they lack electronic excitations, the QSC and related embedded
687 + atom method (EAM) models for gold are known to predict unreasonably
688 + low values for bulk conductivity
689 + ($\lambda$).\cite{kuang:164101,ISI:000207079300006,Clancy:1992} If the
690 + conductance between the phases ($G$) is governed primarily by phonon
691 + excitation (and not electronic degrees of freedom), one would expect a
692 + classical model to capture most of the interfacial thermal
693 + conductance.  Our results for $G$ and $G^\prime$ indicate that this is
694 + indeed the case, and suggest that the modeling of interfacial thermal
695 + transport depends primarily on the description of the interactions
696 + between the various components at the interface.  When the metal is
697 + chemically capped, the primary barrier to thermal conductivity appears
698 + to be the interface between the capping agent and the surrounding
699 + solvent, so the excitations in the metal have little impact on the
700 + value of $G$.
701 +
702 + \subsection{Effects due to methodology and simulation parameters}
703 +
704 + We have varied the parameters of the simulations in order to
705 + investigate how these factors would affect the computation of $G$.  Of
706 + particular interest are: 1) the length scale for the applied thermal
707 + gradient (modified by increasing the amount of solvent in the system),
708 + 2) the sign and magnitude of the applied thermal flux, 3) the average
709 + temperature of the simulation (which alters the solvent density during
710 + equilibration), and 4) the definition of the interfacial conductance
711 + (Eqs. (\ref{discreteG}) or (\ref{derivativeG})) used in the
712 + calculation.
713 +
714 + Systems of different lengths were prepared by altering the number of
715 + solvent molecules and extending the length of the box along the $z$
716 + axis to accomodate the extra solvent.  Equilibration at the same
717 + temperature and pressure conditions led to nearly identical surface
718 + areas ($L_x$ and $L_y$) available to the metal and capping agent,
719 + while the extra solvent served mainly to lengthen the axis that was
720 + used to apply the thermal flux.  For a given value of the applied
721 + flux, the different $z$ length scale has only a weak effect on the
722 + computed conductivities (Table \ref{AuThiolHexaneUA}).
723 +
724 + \subsubsection{Effects of applied flux}
725 + The NIVS algorithm allows changes in both the sign and magnitude of
726 + the applied flux.  It is possible to reverse the direction of heat
727 + flow simply by changing the sign of the flux, and thermal gradients
728 + which would be difficult to obtain experimentally ($5$ K/\AA) can be
729 + easily simulated.  However, the magnitude of the applied flux is not
730 + arbitrary if one aims to obtain a stable and reliable thermal gradient.
731 + A temperature gradient can be lost in the noise if $|J_z|$ is too
732 + small, and excessive $|J_z|$ values can cause phase transitions if the
733 + extremes of the simulation cell become widely separated in
734 + temperature.  Also, if $|J_z|$ is too large for the bulk conductivity
735 + of the materials, the thermal gradient will never reach a stable
736 + state.  
737 +
738 + Within a reasonable range of $J_z$ values, we were able to study how
739 + $G$ changes as a function of this flux.  In what follows, we use
740 + positive $J_z$ values to denote the case where energy is being
741 + transferred by the method from the metal phase and into the liquid.
742 + The resulting gradient therefore has a higher temperature in the
743 + liquid phase.  Negative flux values reverse this transfer, and result
744 + in higher temperature metal phases.  The conductance measured under
745 + different applied $J_z$ values is listed in Tables 1 and 2 in the
746 + supporting information. These results do not indicate that $G$ depends
747 + strongly on $J_z$ within this flux range. The linear response of flux
748 + to thermal gradient simplifies our investigations in that we can rely
749 + on $G$ measurement with only a small number $J_z$ values.
750 +
751 + The sign of $J_z$ is a different matter, however, as this can alter
752 + the temperature on the two sides of the interface. The average
753 + temperature values reported are for the entire system, and not for the
754 + liquid phase, so at a given $\langle T \rangle$, the system with
755 + positive $J_z$ has a warmer liquid phase.  This means that if the
756 + liquid carries thermal energy via diffusive transport, {\it positive}
757 + $J_z$ values will result in increased molecular motion on the liquid
758 + side of the interface, and this will increase the measured
759 + conductivity.
760 +
761 + \subsubsection{Effects due to average temperature}
762 +
763 + We also studied the effect of average system temperature on the
764 + interfacial conductance.  The simulations are first equilibrated in
765 + the NPT ensemble at 1 atm.  The TraPPE-UA model for hexane tends to
766 + predict a lower boiling point (and liquid state density) than
767 + experiments.  This lower-density liquid phase leads to reduced contact
768 + between the hexane and butanethiol, and this accounts for our
769 + observation of lower conductance at higher temperatures.  In raising
770 + the average temperature from 200K to 250K, the density drop of
771 + $\sim$20\% in the solvent phase leads to a $\sim$40\% drop in the
772 + conductance.
773 +
774 + Similar behavior is observed in the TraPPE-UA model for toluene,
775 + although this model has better agreement with the experimental
776 + densities of toluene.  The expansion of the toluene liquid phase is
777 + not as significant as that of the hexane (8.3\% over 100K), and this
778 + limits the effect to $\sim$20\% drop in thermal conductivity.
779 +
780 + Although we have not mapped out the behavior at a large number of
781 + temperatures, is clear that there will be a strong temperature
782 + dependence in the interfacial conductance when the physical properties
783 + of one side of the interface (notably the density) change rapidly as a
784 + function of temperature.
785 +
786 + Besides the lower interfacial thermal conductance, surfaces at
787 + relatively high temperatures are susceptible to reconstructions,
788 + particularly when butanethiols fully cover the Au(111) surface. These
789 + reconstructions include surface Au atoms which migrate outward to the
790 + S atom layer, and butanethiol molecules which embed into the surface
791 + Au layer. The driving force for this behavior is the strong Au-S
792 + interactions which are modeled here with a deep Lennard-Jones
793 + potential. This phenomenon agrees with reconstructions that have been
794 + experimentally
795 + observed.\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}.  Vlugt
796 + {\it et al.} kept their Au(111) slab rigid so that their simulations
797 + could reach 300K without surface
798 + reconstructions.\cite{vlugt:cpc2007154} Since surface reconstructions
799 + blur the interface, the measurement of $G$ becomes more difficult to
800 + conduct at higher temperatures.  For this reason, most of our
801 + measurements are undertaken at $\langle T\rangle\sim$200K where
802 + reconstruction is minimized.
803 +
804 + However, when the surface is not completely covered by butanethiols,
805 + the simulated system appears to be more resistent to the
806 + reconstruction. Our Au / butanethiol / toluene system had the Au(111)
807 + surfaces 90\% covered by butanethiols, but did not see this above
808 + phenomena even at $\langle T\rangle\sim$300K.  That said, we did
809 + observe butanethiols migrating to neighboring three-fold sites during
810 + a simulation.  Since the interface persisted in these simulations, we
811 + were able to obtain $G$'s for these interfaces even at a relatively
812 + high temperature without being affected by surface reconstructions.
813 +
814 + \section{Discussion}
815 +
816 + The primary result of this work is that the capping agent acts as an
817 + efficient thermal coupler between solid and solvent phases.  One of
818 + the ways the capping agent can carry out this role is to down-shift
819 + between the phonon vibrations in the solid (which carry the heat from
820 + the gold) and the molecular vibrations in the liquid (which carry some
821 + of the heat in the solvent).
822 +
823 + To investigate the mechanism of interfacial thermal conductance, the
824 + vibrational power spectrum was computed. Power spectra were taken for
825 + individual components in different simulations. To obtain these
826 + spectra, simulations were run after equilibration in the
827 + microcanonical (NVE) ensemble and without a thermal
828 + gradient. Snapshots of configurations were collected at a frequency
829 + that is higher than that of the fastest vibrations occurring in the
830 + simulations. With these configurations, the velocity auto-correlation
831 + functions can be computed:
832 + \begin{equation}
833 + C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
834 + \label{vCorr}
835 + \end{equation}
836 + The power spectrum is constructed via a Fourier transform of the
837 + symmetrized velocity autocorrelation function,
838 + \begin{equation}
839 +  \hat{f}(\omega) =
840 +  \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
841 + \label{fourier}
842 + \end{equation}
843 +
844 + \subsection{The role of specific vibrations}
845 + The vibrational spectra for gold slabs in different environments are
846 + shown as in Figure \ref{specAu}. Regardless of the presence of
847 + solvent, the gold surfaces which are covered by butanethiol molecules
848 + exhibit an additional peak observed at a frequency of
849 + $\sim$165cm$^{-1}$.  We attribute this peak to the S-Au bonding
850 + vibration. This vibration enables efficient thermal coupling of the
851 + surface Au layer to the capping agents. Therefore, in our simulations,
852 + the Au / S interfaces do not appear to be the primary barrier to
853 + thermal transport when compared with the butanethiol / solvent
854 + interfaces.  This supports the results of Luo {\it et
855 +  al.}\cite{Luo20101}, who reported $G$ for Au-SAM junctions roughly
856 + twice as large as what we have computed for the thiol-liquid
857 + interfaces.
858 +
859 + \begin{figure}
860 + \includegraphics[width=\linewidth]{vibration}
861 + \caption{The vibrational power spectrum for thiol-capped gold has an
862 +  additional vibrational peak at $\sim $165cm$^{-1}$.  Bare gold
863 +  surfaces (both with and without a solvent over-layer) are missing
864 +  this peak.   A similar peak at  $\sim $165cm$^{-1}$ also appears in
865 +  the vibrational power spectrum for the butanethiol capping agents.}
866 + \label{specAu}
867 + \end{figure}
868 +
869 + Also in this figure, we show the vibrational power spectrum for the
870 + bound butanethiol molecules, which also exhibits the same
871 + $\sim$165cm$^{-1}$ peak.
872 +
873 + \subsection{Overlap of power spectra}
874 + A comparison of the results obtained from the two different organic
875 + solvents can also provide useful information of the interfacial
876 + thermal transport process.  In particular, the vibrational overlap
877 + between the butanethiol and the organic solvents suggests a highly
878 + efficient thermal exchange between these components.  Very high
879 + thermal conductivity was observed when AA models were used and C-H
880 + vibrations were treated classically. The presence of extra degrees of
881 + freedom in the AA force field yields higher heat exchange rates
882 + between the two phases and results in a much higher conductivity than
883 + in the UA force field. The all-atom classical models include high
884 + frequency modes which should be unpopulated at our relatively low
885 + temperatures.  This artifact is likely the cause of the high thermal
886 + conductance in all-atom MD simulations.
887 +
888 + The similarity in the vibrational modes available to solvent and
889 + capping agent can be reduced by deuterating one of the two components
890 + (Fig. \ref{aahxntln}).  Once either the hexanes or the butanethiols
891 + are deuterated, one can observe a significantly lower $G$ and
892 + $G^\prime$ values (Table \ref{modelTest}).
893 +
894 + \begin{figure}
895 + \includegraphics[width=\linewidth]{aahxntln}
896 + \caption{Spectra obtained for all-atom (AA) Au / butanethiol / solvent
897 +  systems. When butanethiol is deuterated (lower left), its
898 +  vibrational overlap with hexane decreases significantly.  Since
899 +  aromatic molecules and the butanethiol are vibrationally dissimilar,
900 +  the change is not as dramatic when toluene is the solvent (right).}
901 + \label{aahxntln}
902 + \end{figure}
903 +
904 + For the Au / butanethiol / toluene interfaces, having the AA
905 + butanethiol deuterated did not yield a significant change in the
906 + measured conductance. Compared to the C-H vibrational overlap between
907 + hexane and butanethiol, both of which have alkyl chains, the overlap
908 + between toluene and butanethiol is not as significant and thus does
909 + not contribute as much to the heat exchange process.
910 +
911 + Previous observations of Zhang {\it et al.}\cite{hase:2010} indicate
912 + that the {\it intra}molecular heat transport due to alkylthiols is
913 + highly efficient.  Combining our observations with those of Zhang {\it
914 +  et al.}, it appears that butanethiol acts as a channel to expedite
915 + heat flow from the gold surface and into the alkyl chain.  The
916 + vibrational coupling between the metal and the liquid phase can
917 + therefore be enhanced with the presence of suitable capping agents.
918 +
919 + Deuterated models in the UA force field did not decouple the thermal
920 + transport as well as in the AA force field.  The UA models, even
921 + though they have eliminated the high frequency C-H vibrational
922 + overlap, still have significant overlap in the lower-frequency
923 + portions of the infrared spectra (Figure \ref{uahxnua}).  Deuterating
924 + the UA models did not decouple the low frequency region enough to
925 + produce an observable difference for the results of $G$ (Table
926 + \ref{modelTest}).
927 +
928 + \begin{figure}
929 + \includegraphics[width=\linewidth]{uahxnua}
930 + \caption{Vibrational power spectra for UA models for the butanethiol
931 +  and hexane solvent (upper panel) show the high degree of overlap
932 +  between these two molecules, particularly at lower frequencies.
933 +  Deuterating a UA model for the solvent (lower panel) does not
934 +  decouple the two spectra to the same degree as in the AA force
935 +  field (see Fig \ref{aahxntln}).}
936 + \label{uahxnua}
937 + \end{figure}
938 +
939 + \section{Conclusions}
940 + The NIVS algorithm has been applied to simulations of
941 + butanethiol-capped Au(111) surfaces in the presence of organic
942 + solvents. This algorithm allows the application of unphysical thermal
943 + flux to transfer heat between the metal and the liquid phase. With the
944 + flux applied, we were able to measure the corresponding thermal
945 + gradients and to obtain interfacial thermal conductivities. Under
946 + steady states, 2-3 ns trajectory simulations are sufficient for
947 + computation of this quantity.
948 +
949 + Our simulations have seen significant conductance enhancement in the
950 + presence of capping agent, compared with the bare gold / liquid
951 + interfaces. The vibrational coupling between the metal and the liquid
952 + phase is enhanced by a chemically-bonded capping agent. Furthermore,
953 + the coverage percentage of the capping agent plays an important role
954 + in the interfacial thermal transport process. Moderately low coverages
955 + allow higher contact between capping agent and solvent, and thus could
956 + further enhance the heat transfer process, giving a non-monotonic
957 + behavior of conductance with increasing coverage.
958 +
959 + Our results, particularly using the UA models, agree well with
960 + available experimental data.  The AA models tend to overestimate the
961 + interfacial thermal conductance in that the classically treated C-H
962 + vibrations become too easily populated. Compared to the AA models, the
963 + UA models have higher computational efficiency with satisfactory
964 + accuracy, and thus are preferable in modeling interfacial thermal
965 + transport.
966 +
967 + Of the two definitions for $G$, the discrete form
968 + (Eq. \ref{discreteG}) was easier to use and gives out relatively
969 + consistent results, while the derivative form (Eq. \ref{derivativeG})
970 + is not as versatile. Although $G^\prime$ gives out comparable results
971 + and follows similar trend with $G$ when measuring close to fully
972 + covered or bare surfaces, the spatial resolution of $T$ profile
973 + required for the use of a derivative form is limited by the number of
974 + bins and the sampling required to obtain thermal gradient information.
975 +
976 + Vlugt {\it et al.} have investigated the surface thiol structures for
977 + nanocrystalline gold and pointed out that they differ from those of
978 + the Au(111) surface.\cite{landman:1998,vlugt:cpc2007154} This
979 + difference could also cause differences in the interfacial thermal
980 + transport behavior. To investigate this problem, one would need an
981 + effective method for applying thermal gradients in non-planar
982 + (i.e. spherical) geometries.
983 +
984   \section{Acknowledgments}
985   Support for this project was provided by the National Science
986   Foundation under grant CHE-0848243. Computational time was provided by
987   the Center for Research Computing (CRC) at the University of Notre
988 < Dame.  \newpage
988 > Dame.
989  
990 + \section{Supporting Information}
991 + This information is available free of charge via the Internet at
992 + http://pubs.acs.org.
993 +
994 + \newpage
995 +
996   \bibliography{interfacial}
997  
998   \end{doublespace}

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