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28  
29   \begin{document}
30  
# Line 44 | Line 44 | The abstract version 2
44   \begin{doublespace}
45  
46   \begin{abstract}
47 < The abstract version 2
47 >
48 > With the Non-Isotropic Velocity Scaling algorithm (NIVS) we have
49 > developed, an unphysical thermal flux can be effectively set up even
50 > for non-homogeneous systems like interfaces in non-equilibrium
51 > molecular dynamics simulations. In this work, this algorithm is
52 > applied for simulating thermal conductance at metal / organic solvent
53 > interfaces with various coverages of butanethiol capping
54 > agents. Different solvents and force field models were tested. Our
55 > results suggest that the United-Atom models are able to provide an
56 > estimate of the interfacial thermal conductivity comparable to
57 > experiments in our simulations with satisfactory computational
58 > efficiency. From our results, the acoustic impedance mismatch between
59 > metal and liquid phase is effectively reduced by the capping
60 > agents, and thus leads to interfacial thermal conductance
61 > enhancement. Furthermore, this effect is closely related to the
62 > capping agent coverage on the metal surfaces and the type of solvent
63 > molecules, and is affected by the models used in the simulations.
64 >
65   \end{abstract}
66  
67   \newpage
# Line 56 | Line 73 | The abstract version 2
73   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
74  
75   \section{Introduction}
76 + Due to the importance of heat flow in nanotechnology, interfacial
77 + thermal conductance has been studied extensively both experimentally
78 + and computationally.\cite{cahill:793} Unlike bulk materials, nanoscale
79 + materials have a significant fraction of their atoms at interfaces,
80 + and the chemical details of these interfaces govern the heat transfer
81 + behavior. Furthermore, the interfaces are
82 + heterogeneous (e.g. solid - liquid), which provides a challenge to
83 + traditional methods developed for homogeneous systems.
84  
85 < The intro.
85 > Experimentally, various interfaces have been investigated for their
86 > thermal conductance. Cahill and coworkers studied nanoscale thermal
87 > transport from metal nanoparticle/fluid interfaces, to epitaxial
88 > TiN/single crystal oxides interfaces, to hydrophilic and hydrophobic
89 > interfaces between water and solids with different self-assembled
90 > monolayers.\cite{Wilson:2002uq,PhysRevB.67.054302,doi:10.1021/jp048375k,PhysRevLett.96.186101}
91 > Wang {\it et al.} studied heat transport through
92 > long-chain hydrocarbon monolayers on gold substrate at individual
93 > molecular level,\cite{Wang10082007} Schmidt {\it et al.} studied the
94 > role of CTAB on thermal transport between gold nanorods and
95 > solvent,\cite{doi:10.1021/jp8051888} and Juv\'e {\it et al.} studied
96 > the cooling dynamics, which is controlled by thermal interface
97 > resistence of glass-embedded metal
98 > nanoparticles.\cite{PhysRevB.80.195406} Although interfaces are
99 > normally considered barriers for heat transport, Alper {\it et al.}
100 > suggested that specific ligands (capping agents) could completely
101 > eliminate this barrier
102 > ($G\rightarrow\infty$).\cite{doi:10.1021/la904855s}
103 >
104 > Theoretical and computational models have also been used to study the
105 > interfacial thermal transport in order to gain an understanding of
106 > this phenomena at the molecular level. Recently, Hase and coworkers
107 > employed Non-Equilibrium Molecular Dynamics (NEMD) simulations to
108 > study thermal transport from hot Au(111) substrate to a self-assembled
109 > monolayer of alkylthiol with relatively long chain (8-20 carbon
110 > atoms).\cite{hase:2010,hase:2011} However, ensemble averaged
111 > measurements for heat conductance of interfaces between the capping
112 > monolayer on Au and a solvent phase have yet to be studied with their
113 > approach. The comparatively low thermal flux through interfaces is
114 > difficult to measure with Equilibrium
115 > MD\cite{doi:10.1080/0026897031000068578} or forward NEMD simulation
116 > methods. Therefore, the Reverse NEMD (RNEMD)
117 > methods\cite{MullerPlathe:1997xw,kuang:164101} would have the
118 > advantage of applying this difficult to measure flux (while measuring
119 > the resulting gradient), given that the simulation methods being able
120 > to effectively apply an unphysical flux in non-homogeneous systems.
121 > Garde and coworkers\cite{garde:nl2005,garde:PhysRevLett2009} applied
122 > this approach to various liquid interfaces and studied how thermal
123 > conductance (or resistance) is dependent on chemistry details of
124 > interfaces, e.g. hydrophobic and hydrophilic aqueous interfaces.
125 >
126 > Recently, we have developed a Non-Isotropic Velocity Scaling (NIVS)
127 > algorithm for RNEMD simulations\cite{kuang:164101}. This algorithm
128 > retains the desirable features of RNEMD (conservation of linear
129 > momentum and total energy, compatibility with periodic boundary
130 > conditions) while establishing true thermal distributions in each of
131 > the two slabs. Furthermore, it allows effective thermal exchange
132 > between particles of different identities, and thus makes the study of
133 > interfacial conductance much simpler.
134 >
135 > The work presented here deals with the Au(111) surface covered to
136 > varying degrees by butanethiol, a capping agent with short carbon
137 > chain, and solvated with organic solvents of different molecular
138 > properties. Different models were used for both the capping agent and
139 > the solvent force field parameters. Using the NIVS algorithm, the
140 > thermal transport across these interfaces was studied and the
141 > underlying mechanism for the phenomena was investigated.
142 >
143 > \section{Methodology}
144 > \subsection{Imposd-Flux Methods in MD Simulations}
145 > Steady state MD simulations have an advantage in that not many
146 > trajectories are needed to study the relationship between thermal flux
147 > and thermal gradients. For systems with low interfacial conductance,
148 > one must have a method capable of generating or measuring relatively
149 > small fluxes, compared to those required for bulk conductivity. This
150 > requirement makes the calculation even more difficult for
151 > slowly-converging equilibrium methods.\cite{Viscardy:2007lq} Forward
152 > NEMD methods impose a gradient (and measure a flux), but at interfaces
153 > it is not clear what behavior should be imposed at the boundaries
154 > between materials.  Imposed-flux reverse non-equilibrium
155 > methods\cite{MullerPlathe:1997xw} set the flux {\it a priori} and
156 > the thermal response becomes an easy-to-measure quantity.  Although
157 > M\"{u}ller-Plathe's original momentum swapping approach can be used
158 > for exchanging energy between particles of different identity, the
159 > kinetic energy transfer efficiency is affected by the mass difference
160 > between the particles, which limits its application on heterogeneous
161 > interfacial systems.
162 >
163 > The non-isotropic velocity scaling (NIVS) \cite{kuang:164101} approach
164 > to non-equilibrium MD simulations is able to impose a wide range of
165 > kinetic energy fluxes without obvious perturbation to the velocity
166 > distributions of the simulated systems. Furthermore, this approach has
167 > the advantage in heterogeneous interfaces in that kinetic energy flux
168 > can be applied between regions of particles of arbitary identity, and
169 > the flux will not be restricted by difference in particle mass.
170 >
171 > The NIVS algorithm scales the velocity vectors in two separate regions
172 > of a simulation system with respective diagonal scaling matricies. To
173 > determine these scaling factors in the matricies, a set of equations
174 > including linear momentum conservation and kinetic energy conservation
175 > constraints and target energy flux satisfaction is solved. With the
176 > scaling operation applied to the system in a set frequency, bulk
177 > temperature gradients can be easily established, and these can be used
178 > for computing thermal conductivities. The NIVS algorithm conserves
179 > momenta and energy and does not depend on an external thermostat.
180 >
181 > \subsection{Defining Interfacial Thermal Conductivity ($G$)}
182 >
183 > For an interface with relatively low interfacial conductance, and a
184 > thermal flux between two distinct bulk regions, the regions on either
185 > side of the interface rapidly come to a state in which the two phases
186 > have relatively homogeneous (but distinct) temperatures. The
187 > interfacial thermal conductivity $G$ can therefore be approximated as:
188 > \begin{equation}
189 >  G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
190 >    \langle T_\mathrm{cold}\rangle \right)}
191 > \label{lowG}
192 > \end{equation}
193 > where ${E_{total}}$ is the total imposed non-physical kinetic energy
194 > transfer during the simulation and ${\langle T_\mathrm{hot}\rangle}$
195 > and ${\langle T_\mathrm{cold}\rangle}$ are the average observed
196 > temperature of the two separated phases.  For an applied flux $J_z$
197 > operating over a simulation time $t$ on a periodically-replicated slab
198 > of dimensions $L_x \times L_y$, $E_{total} = J_z *(t)*(2 L_x L_y)$.
199 >
200 > When the interfacial conductance is {\it not} small, there are two
201 > ways to define $G$. One common way is to assume the temperature is
202 > discrete on the two sides of the interface. $G$ can be calculated
203 > using the applied thermal flux $J$ and the maximum temperature
204 > difference measured along the thermal gradient max($\Delta T$), which
205 > occurs at the Gibbs deviding surface (Figure \ref{demoPic}). This is
206 > known as the Kapitza conductance, which is the inverse of the Kapitza
207 > resistance.
208 > \begin{equation}
209 >  G=\frac{J}{\Delta T}
210 > \label{discreteG}
211 > \end{equation}
212 >
213 > \begin{figure}
214 > \includegraphics[width=\linewidth]{method}
215 > \caption{Interfacial conductance can be calculated by applying an
216 >  (unphysical) kinetic energy flux between two slabs, one located
217 >  within the metal and another on the edge of the periodic box.  The
218 >  system responds by forming a thermal response or a gradient.  In
219 >  bulk liquids, this gradient typically has a single slope, but in
220 >  interfacial systems, there are distinct thermal conductivity
221 >  domains.  The interfacial conductance, $G$ is found by measuring the
222 >  temperature gap at the Gibbs dividing surface, or by using second
223 >  derivatives of the thermal profile.}
224 > \label{demoPic}
225 > \end{figure}
226 >
227 > The other approach is to assume a continuous temperature profile along
228 > the thermal gradient axis (e.g. $z$) and define $G$ at the point where
229 > the magnitude of thermal conductivity ($\lambda$) change reaches its
230 > maximum, given that $\lambda$ is well-defined throughout the space:
231 > \begin{equation}
232 > G^\prime = \Big|\frac{\partial\lambda}{\partial z}\Big|
233 >         = \Big|\frac{\partial}{\partial z}\left(-J_z\Big/
234 >           \left(\frac{\partial T}{\partial z}\right)\right)\Big|
235 >         = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
236 >         \Big/\left(\frac{\partial T}{\partial z}\right)^2
237 > \label{derivativeG}
238 > \end{equation}
239 >
240 > With temperature profiles obtained from simulation, one is able to
241 > approximate the first and second derivatives of $T$ with finite
242 > difference methods and calculate $G^\prime$. In what follows, both
243 > definitions have been used, and are compared in the results.
244 >
245 > To investigate the interfacial conductivity at metal / solvent
246 > interfaces, we have modeled a metal slab with its (111) surfaces
247 > perpendicular to the $z$-axis of our simulation cells. The metal slab
248 > has been prepared both with and without capping agents on the exposed
249 > surface, and has been solvated with simple organic solvents, as
250 > illustrated in Figure \ref{gradT}.
251 >
252 > With the simulation cell described above, we are able to equilibrate
253 > the system and impose an unphysical thermal flux between the liquid
254 > and the metal phase using the NIVS algorithm. By periodically applying
255 > the unphysical flux, we obtained a temperature profile and its spatial
256 > derivatives. Figure \ref{gradT} shows how an applied thermal flux can
257 > be used to obtain the 1st and 2nd derivatives of the temperature
258 > profile.
259 >
260 > \begin{figure}
261 > \includegraphics[width=\linewidth]{gradT}
262 > \caption{A sample of Au-butanethiol/hexane interfacial system and the
263 >  temperature profile after a kinetic energy flux is imposed to
264 >  it. The 1st and 2nd derivatives of the temperature profile can be
265 >  obtained with finite difference approximation (lower panel).}
266 > \label{gradT}
267 > \end{figure}
268 >
269 > \section{Computational Details}
270 > \subsection{Simulation Protocol}
271 > The NIVS algorithm has been implemented in our MD simulation code,
272 > OpenMD\cite{Meineke:2005gd,openmd}, and was used for our simulations.
273 > Metal slabs of 6 or 11 layers of Au atoms were first equilibrated
274 > under atmospheric pressure (1 atm) and 200K. After equilibration,
275 > butanethiol capping agents were placed at three-fold hollow sites on
276 > the Au(111) surfaces. These sites are either {\it fcc} or {\it
277 >  hcp} sites, although Hase {\it et al.} found that they are
278 > equivalent in a heat transfer process,\cite{hase:2010} so we did not
279 > distinguish between these sites in our study. The maximum butanethiol
280 > capacity on Au surface is $1/3$ of the total number of surface Au
281 > atoms, and the packing forms a $(\sqrt{3}\times\sqrt{3})R30^\circ$
282 > structure\cite{doi:10.1021/ja00008a001,doi:10.1021/cr9801317}. A
283 > series of lower coverages was also prepared by eliminating
284 > butanethiols from the higher coverage surface in a regular manner. The
285 > lower coverages were prepared in order to study the relation between
286 > coverage and interfacial conductance.
287 >
288 > The capping agent molecules were allowed to migrate during the
289 > simulations. They distributed themselves uniformly and sampled a
290 > number of three-fold sites throughout out study. Therefore, the
291 > initial configuration does not noticeably affect the sampling of a
292 > variety of configurations of the same coverage, and the final
293 > conductance measurement would be an average effect of these
294 > configurations explored in the simulations.
295 >
296 > After the modified Au-butanethiol surface systems were equilibrated in
297 > the canonical (NVT) ensemble, organic solvent molecules were packed in
298 > the previously empty part of the simulation cells.\cite{packmol} Two
299 > solvents were investigated, one which has little vibrational overlap
300 > with the alkanethiol and which has a planar shape (toluene), and one
301 > which has similar vibrational frequencies to the capping agent and
302 > chain-like shape ({\it n}-hexane).
303 >
304 > The simulation cells were not particularly extensive along the
305 > $z$-axis, as a very long length scale for the thermal gradient may
306 > cause excessively hot or cold temperatures in the middle of the
307 > solvent region and lead to undesired phenomena such as solvent boiling
308 > or freezing when a thermal flux is applied. Conversely, too few
309 > solvent molecules would change the normal behavior of the liquid
310 > phase. Therefore, our $N_{solvent}$ values were chosen to ensure that
311 > these extreme cases did not happen to our simulations. The spacing
312 > between periodic images of the gold interfaces is $45 \sim 75$\AA.
313 >
314 > The initial configurations generated are further equilibrated with the
315 > $x$ and $y$ dimensions fixed, only allowing the $z$-length scale to
316 > change. This is to ensure that the equilibration of liquid phase does
317 > not affect the metal's crystalline structure. Comparisons were made
318 > with simulations that allowed changes of $L_x$ and $L_y$ during NPT
319 > equilibration. No substantial changes in the box geometry were noticed
320 > in these simulations. After ensuring the liquid phase reaches
321 > equilibrium at atmospheric pressure (1 atm), further equilibration was
322 > carried out under canonical (NVT) and microcanonical (NVE) ensembles.
323 >
324 > After the systems reach equilibrium, NIVS was used to impose an
325 > unphysical thermal flux between the metal and the liquid phases. Most
326 > of our simulations were done under an average temperature of
327 > $\sim$200K. Therefore, thermal flux usually came from the metal to the
328 > liquid so that the liquid has a higher temperature and would not
329 > freeze due to lowered temperatures. After this induced temperature
330 > gradient had stablized, the temperature profile of the simulation cell
331 > was recorded. To do this, the simulation cell is devided evenly into
332 > $N$ slabs along the $z$-axis. The average temperatures of each slab
333 > are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
334 > the same, the derivatives of $T$ with respect to slab number $n$ can
335 > be directly used for $G^\prime$ calculations: \begin{equation}
336 >  G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
337 >         \Big/\left(\frac{\partial T}{\partial z}\right)^2
338 >         = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
339 >         \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
340 >         = |J_z|\Big|\frac{\partial^2 T}{\partial n^2}\Big|
341 >         \Big/\left(\frac{\partial T}{\partial n}\right)^2
342 > \label{derivativeG2}
343 > \end{equation}
344 >
345 > All of the above simulation procedures use a time step of 1 fs. Each
346 > equilibration stage took a minimum of 100 ps, although in some cases,
347 > longer equilibration stages were utilized.
348 >
349 > \subsection{Force Field Parameters}
350 > Our simulations include a number of chemically distinct components.
351 > Figure \ref{demoMol} demonstrates the sites defined for both
352 > United-Atom and All-Atom models of the organic solvent and capping
353 > agents in our simulations. Force field parameters are needed for
354 > interactions both between the same type of particles and between
355 > particles of different species.
356 >
357 > \begin{figure}
358 > \includegraphics[width=\linewidth]{structures}
359 > \caption{Structures of the capping agent and solvents utilized in
360 >  these simulations. The chemically-distinct sites (a-e) are expanded
361 >  in terms of constituent atoms for both United Atom (UA) and All Atom
362 >  (AA) force fields.  Most parameters are from
363 >  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes,TraPPE-UA.thiols}
364 >  (UA) and \protect\cite{OPLSAA} (AA). Cross-interactions with the Au
365 >  atoms are given in Table \ref{MnM}.}
366 > \label{demoMol}
367 > \end{figure}
368 >
369 > The Au-Au interactions in metal lattice slab is described by the
370 > quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
371 > potentials include zero-point quantum corrections and are
372 > reparametrized for accurate surface energies compared to the
373 > Sutton-Chen potentials.\cite{Chen90}
374 >
375 > For the two solvent molecules, {\it n}-hexane and toluene, two
376 > different atomistic models were utilized. Both solvents were modeled
377 > using United-Atom (UA) and All-Atom (AA) models. The TraPPE-UA
378 > parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
379 > for our UA solvent molecules. In these models, sites are located at
380 > the carbon centers for alkyl groups. Bonding interactions, including
381 > bond stretches and bends and torsions, were used for intra-molecular
382 > sites closer than 3 bonds. For non-bonded interactions, Lennard-Jones
383 > potentials are used.
384 >
385 > By eliminating explicit hydrogen atoms, the TraPPE-UA models are
386 > simple and computationally efficient, while maintaining good accuracy.
387 > However, the TraPPE-UA model for alkanes is known to predict a slighly
388 > lower boiling point than experimental values. This is one of the
389 > reasons we used a lower average temperature (200K) for our
390 > simulations. If heat is transferred to the liquid phase during the
391 > NIVS simulation, the liquid in the hot slab can actually be
392 > substantially warmer than the mean temperature in the simulation. The
393 > lower mean temperatures therefore prevent solvent boiling.
394 >
395 > For UA-toluene, the non-bonded potentials between intermolecular sites
396 > have a similar Lennard-Jones formulation. The toluene molecules were
397 > treated as a single rigid body, so there was no need for
398 > intramolecular interactions (including bonds, bends, or torsions) in
399 > this solvent model.
400 >
401 > Besides the TraPPE-UA models, AA models for both organic solvents are
402 > included in our studies as well. The OPLS-AA\cite{OPLSAA} force fields
403 > were used. For hexane, additional explicit hydrogen sites were
404 > included. Besides bonding and non-bonded site-site interactions,
405 > partial charges and the electrostatic interactions were added to each
406 > CT and HC site. For toluene, a flexible model for the toluene molecule
407 > was utilized which included bond, bend, torsion, and inversion
408 > potentials to enforce ring planarity.
409 >
410 > The butanethiol capping agent in our simulations, were also modeled
411 > with both UA and AA model. The TraPPE-UA force field includes
412 > parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
413 > UA butanethiol model in our simulations. The OPLS-AA also provides
414 > parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
415 > surfaces do not have the hydrogen atom bonded to sulfur. To derive
416 > suitable parameters for butanethiol adsorbed on Au(111) surfaces, we
417 > adopt the S parameters from Luedtke and Landman\cite{landman:1998} and
418 > modify the parameters for the CTS atom to maintain charge neutrality
419 > in the molecule.  Note that the model choice (UA or AA) for the capping
420 > agent can be different from the solvent. Regardless of model choice,
421 > the force field parameters for interactions between capping agent and
422 > solvent can be derived using Lorentz-Berthelot Mixing Rule:
423 > \begin{eqnarray}
424 >  \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
425 >  \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
426 > \end{eqnarray}
427 >
428 > To describe the interactions between metal (Au) and non-metal atoms,
429 > we refer to an adsorption study of alkyl thiols on gold surfaces by
430 > Vlugt {\it et al.}\cite{vlugt:cpc2007154} They fitted an effective
431 > Lennard-Jones form of potential parameters for the interaction between
432 > Au and pseudo-atoms CH$_x$ and S based on a well-established and
433 > widely-used effective potential of Hautman and Klein for the Au(111)
434 > surface.\cite{hautman:4994} As our simulations require the gold slab
435 > to be flexible to accommodate thermal excitation, the pair-wise form
436 > of potentials they developed was used for our study.
437 >
438 > The potentials developed from {\it ab initio} calculations by Leng
439 > {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
440 > interactions between Au and aromatic C/H atoms in toluene. However,
441 > the Lennard-Jones parameters between Au and other types of particles,
442 > (e.g. AA alkanes) have not yet been established. For these
443 > interactions, the Lorentz-Berthelot mixing rule can be used to derive
444 > effective single-atom LJ parameters for the metal using the fit values
445 > for toluene. These are then used to construct reasonable mixing
446 > parameters for the interactions between the gold and other atoms.
447 > Table \ref{MnM} summarizes the ``metal/non-metal'' parameters used in
448 > our simulations.
449 >
450 > \begin{table*}
451 >  \begin{minipage}{\linewidth}
452 >    \begin{center}
453 >      \caption{Non-bonded interaction parameters (including cross
454 >        interactions with Au atoms) for both force fields used in this
455 >        work.}      
456 >      \begin{tabular}{lllllll}
457 >        \hline\hline
458 >        & Site  & $\sigma_{ii}$ & $\epsilon_{ii}$ & $q_i$ &
459 >        $\sigma_{Au-i}$ & $\epsilon_{Au-i}$ \\
460 >        & & (\AA) & (kcal/mol) & ($e$) & (\AA) & (kcal/mol) \\
461 >        \hline
462 >        United Atom (UA)
463 >        &CH3  & 3.75  & 0.1947  & -      & 3.54   & 0.2146  \\
464 >        &CH2  & 3.95  & 0.0914  & -      & 3.54   & 0.1749  \\
465 >        &CHar & 3.695 & 0.1003  & -      & 3.4625 & 0.1680  \\
466 >        &CRar & 3.88  & 0.04173 & -      & 3.555  & 0.1604  \\
467 >        \hline
468 >        All Atom (AA)
469 >        &CT3  & 3.50  & 0.066   & -0.18  & 3.365  & 0.1373  \\
470 >        &CT2  & 3.50  & 0.066   & -0.12  & 3.365  & 0.1373  \\
471 >        &CTT  & 3.50  & 0.066   & -0.065 & 3.365  & 0.1373  \\
472 >        &HC   & 2.50  & 0.030   &  0.06  & 2.865  & 0.09256 \\
473 >        &CA   & 3.55  & 0.070   & -0.115 & 3.173  & 0.0640  \\
474 >        &HA   & 2.42  & 0.030   &  0.115 & 2.746  & 0.0414  \\
475 >        \hline
476 >        Both UA and AA
477 >        & S   & 4.45  & 0.25    & -      & 2.40   & 8.465   \\
478 >        \hline\hline
479 >      \end{tabular}
480 >      \label{MnM}
481 >    \end{center}
482 >  \end{minipage}
483 > \end{table*}
484 >
485 >
486 > \section{Results}
487 > There are many factors contributing to the measured interfacial
488 > conductance; some of these factors are physically motivated
489 > (e.g. coverage of the surface by the capping agent coverage and
490 > solvent identity), while some are governed by parameters of the
491 > methodology (e.g. applied flux and the formulas used to obtain the
492 > conductance). In this section we discuss the major physical and
493 > calculational effects on the computed conductivity.
494 >
495 > \subsection{Effects due to capping agent coverage}
496 >
497 > A series of different initial conditions with a range of surface
498 > coverages was prepared and solvated with various with both of the
499 > solvent molecules. These systems were then equilibrated and their
500 > interfacial thermal conductivity was measured with the NIVS
501 > algorithm. Figure \ref{coverage} demonstrates the trend of conductance
502 > with respect to surface coverage.
503 >
504 > \begin{figure}
505 > \includegraphics[width=\linewidth]{coverage}
506 > \caption{Comparison of interfacial thermal conductivity ($G$) values
507 >  for the Au-butanethiol/solvent interface with various UA models and
508 >  different capping agent coverages at $\langle T\rangle\sim$200K.}
509 > \label{coverage}
510 > \end{figure}
511 >
512 > In partially covered surfaces, the derivative definition for
513 > $G^\prime$ (Eq. \ref{derivativeG}) becomes difficult to apply, as the
514 > location of maximum change of $\lambda$ becomes washed out.  The
515 > discrete definition (Eq. \ref{discreteG}) is easier to apply, as the
516 > Gibbs dividing surface is still well-defined. Therefore, $G$ (not
517 > $G^\prime$) was used in this section.
518 >
519 > From Figure \ref{coverage}, one can see the significance of the
520 > presence of capping agents. When even a small fraction of the Au(111)
521 > surface sites are covered with butanethiols, the conductivity exhibits
522 > an enhancement by at least a factor of 3.  Cappping agents are clearly
523 > playing a major role in thermal transport at metal / organic solvent
524 > surfaces.
525 >
526 > We note a non-monotonic behavior in the interfacial conductance as a
527 > function of surface coverage. The maximum conductance (largest $G$)
528 > happens when the surfaces are about 75\% covered with butanethiol
529 > caps.  The reason for this behavior is not entirely clear.  One
530 > explanation is that incomplete butanethiol coverage allows small gaps
531 > between butanethiols to form. These gaps can be filled by transient
532 > solvent molecules.  These solvent molecules couple very strongly with
533 > the hot capping agent molecules near the surface, and can then carry
534 > away (diffusively) the excess thermal energy from the surface.
535  
536 + There appears to be a competition between the conduction of the
537 + thermal energy away from the surface by the capping agents (enhanced
538 + by greater coverage) and the coupling of the capping agents with the
539 + solvent (enhanced by interdigitation at lower coverages).  This
540 + competition would lead to the non-monotonic coverage behavior observed
541 + here.
542 +
543 + Results for rigid body toluene solvent, as well as the UA hexane, are
544 + within the ranges expected from prior experimental
545 + work.\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406} This suggests
546 + that explicit hydrogen atoms might not be required for modeling
547 + thermal transport in these systems.  C-H vibrational modes do not see
548 + significant excited state population at low temperatures, and are not
549 + likely to carry lower frequency excitations from the solid layer into
550 + the bulk liquid.
551 +
552 + The toluene solvent does not exhibit the same behavior as hexane in
553 + that $G$ remains at approximately the same magnitude when the capping
554 + coverage increases from 25\% to 75\%.  Toluene, as a rigid planar
555 + molecule, cannot occupy the relatively small gaps between the capping
556 + agents as easily as the chain-like {\it n}-hexane.  The effect of
557 + solvent coupling to the capping agent is therefore weaker in toluene
558 + except at the very lowest coverage levels.  This effect counters the
559 + coverage-dependent conduction of heat away from the metal surface,
560 + leading to a much flatter $G$ vs. coverage trend than is observed in
561 + {\it n}-hexane.
562 +
563 + \subsection{Effects due to Solvent \& Solvent Models}
564 + In addition to UA solvent and capping agent models, AA models have
565 + also been included in our simulations.  In most of this work, the same
566 + (UA or AA) model for solvent and capping agent was used, but it is
567 + also possible to utilize different models for different components.
568 + We have also included isotopic substitutions (Hydrogen to Deuterium)
569 + to decrease the explicit vibrational overlap between solvent and
570 + capping agent. Table \ref{modelTest} summarizes the results of these
571 + studies.
572 +
573 + \begin{table*}
574 +  \begin{minipage}{\linewidth}
575 +    \begin{center}
576 +      
577 +      \caption{Computed interfacial thermal conductance ($G$ and
578 +        $G^\prime$) values for interfaces using various models for
579 +        solvent and capping agent (or without capping agent) at
580 +        $\langle T\rangle\sim$200K. (D stands for deuterated solvent
581 +        or capping agent molecules; ``Avg.'' denotes results that are
582 +        averages of simulations under different applied thermal flux
583 +        values $(J_z)$. Error estimates are indicated in
584 +        parentheses.)}
585 +      
586 +      \begin{tabular}{llccc}
587 +        \hline\hline
588 +        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
589 +        (or bare surface) & model & (GW/m$^2$) &
590 +        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
591 +        \hline
592 +        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
593 +              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
594 +              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
595 +              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
596 +              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
597 +        \hline
598 +        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
599 +              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
600 +              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
601 +              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
602 +              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
603 +        \hline
604 +        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
605 +              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
606 +              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
607 +        \hline
608 +        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
609 +              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
610 +              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
611 +              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
612 +        \hline\hline
613 +      \end{tabular}
614 +      \label{modelTest}
615 +    \end{center}
616 +  \end{minipage}
617 + \end{table*}
618 +
619 + To facilitate direct comparison between force fields, systems with the
620 + same capping agent and solvent were prepared with the same length
621 + scales for the simulation cells.
622 +
623 + On bare metal / solvent surfaces, different force field models for
624 + hexane yield similar results for both $G$ and $G^\prime$, and these
625 + two definitions agree with each other very well. This is primarily an
626 + indicator of weak interactions between the metal and the solvent, and
627 + is a typical case for acoustic impedance mismatch between these two
628 + phases.  
629 +
630 + For the fully-covered surfaces, the choice of force field for the
631 + capping agent and solvent has a large impact on the calulated values
632 + of $G$ and $G^\prime$.  The AA thiol to AA solvent conductivities are
633 + much larger than their UA to UA counterparts, and these values exceed
634 + the experimental estimates by a large measure.  The AA force field
635 + allows significant energy to go into C-H (or C-D) stretching modes,
636 + and since these modes are high frequency, this non-quantum behavior is
637 + likely responsible for the overestimate of the conductivity.  Compared
638 + to the AA model, the UA model yields more reasonable conductivity
639 + values with much higher computational efficiency.
640 +
641 + \subsubsection{Are electronic excitations in the metal important?}
642 + Because they lack electronic excitations, the QSC and related embedded
643 + atom method (EAM) models for gold are known to predict unreasonably
644 + low values for bulk conductivity
645 + ($\lambda$).\cite{kuang:164101,ISI:000207079300006,Clancy:1992} If the
646 + conductance between the phases ($G$) is governed primarily by phonon
647 + excitation (and not electronic degrees of freedom), one would expect a
648 + classical model to capture most of the interfacial thermal
649 + conductance.  Our results for $G$ and $G^\prime$ indicate that this is
650 + indeed the case, and suggest that the modeling of interfacial thermal
651 + transport depends primarily on the description of the interactions
652 + between the various components at the interface.  When the metal is
653 + chemically capped, the primary barrier to thermal conductivity appears
654 + to be the interface between the capping agent and the surrounding
655 + solvent, so the excitations in the metal have little impact on the
656 + value of $G$.
657 +
658 + \subsection{Effects due to methodology and simulation parameters}
659 +
660 + We have varied the parameters of the simulations in order to
661 + investigate how these factors would affect the computation of $G$.  Of
662 + particular interest are: 1) the length scale for the applied thermal
663 + gradient (modified by increasing the amount of solvent in the system),
664 + 2) the sign and magnitude of the applied thermal flux, 3) the average
665 + temperature of the simulation (which alters the solvent density during
666 + equilibration), and 4) the definition of the interfacial conductance
667 + (Eqs. (\ref{discreteG}) or (\ref{derivativeG})) used in the
668 + calculation.
669 +
670 + Systems of different lengths were prepared by altering the number of
671 + solvent molecules and extending the length of the box along the $z$
672 + axis to accomodate the extra solvent.  Equilibration at the same
673 + temperature and pressure conditions led to nearly identical surface
674 + areas ($L_x$ and $L_y$) available to the metal and capping agent,
675 + while the extra solvent served mainly to lengthen the axis that was
676 + used to apply the thermal flux.  For a given value of the applied
677 + flux, the different $z$ length scale has only a weak effect on the
678 + computed conductivities (Table \ref{AuThiolHexaneUA}).
679 +
680 + \subsubsection{Effects of applied flux}
681 + The NIVS algorithm allows changes in both the sign and magnitude of
682 + the applied flux.  It is possible to reverse the direction of heat
683 + flow simply by changing the sign of the flux, and thermal gradients
684 + which would be difficult to obtain experimentally ($5$ K/\AA) can be
685 + easily simulated.  However, the magnitude of the applied flux is not
686 + arbitary if one aims to obtain a stable and reliable thermal gradient.
687 + A temperature gradient can be lost in the noise if $|J_z|$ is too
688 + small, and excessive $|J_z|$ values can cause phase transitions if the
689 + extremes of the simulation cell become widely separated in
690 + temperature.  Also, if $|J_z|$ is too large for the bulk conductivity
691 + of the materials, the thermal gradient will never reach a stable
692 + state.  
693 +
694 + Within a reasonable range of $J_z$ values, we were able to study how
695 + $G$ changes as a function of this flux.  In what follows, we use
696 + positive $J_z$ values to denote the case where energy is being
697 + transferred by the method from the metal phase and into the liquid.
698 + The resulting gradient therefore has a higher temperature in the
699 + liquid phase.  Negative flux values reverse this transfer, and result
700 + in higher temperature metal phases.  The conductance measured under
701 + different applied $J_z$ values is listed in Tables
702 + \ref{AuThiolHexaneUA} and \ref{AuThiolToluene}. These results do not
703 + indicate that $G$ depends strongly on $J_z$ within this flux
704 + range. The linear response of flux to thermal gradient simplifies our
705 + investigations in that we can rely on $G$ measurement with only a
706 + small number $J_z$ values.  
707 +
708 + \begin{table*}
709 +  \begin{minipage}{\linewidth}
710 +    \begin{center}
711 +      \caption{Computed interfacial thermal conductivity ($G$ and
712 +        $G^\prime$) values for the 100\% covered Au-butanethiol/hexane
713 +        interfaces with UA model and different hexane molecule numbers
714 +        at different temperatures using a range of energy
715 +        fluxes. Error estimates indicated in parenthesis.}
716 +      
717 +      \begin{tabular}{ccccccc}
718 +        \hline\hline
719 +        $\langle T\rangle$ & $N_{hexane}$  & $\rho_{hexane}$ &
720 +        $J_z$ & $G$ & $G^\prime$ \\
721 +        (K) &  & (g/cm$^3$) & (GW/m$^2$) &
722 +        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
723 +        \hline
724 +        200 & 266 &  0.672 & -0.96 & 102(3)    & 80.0(0.8) \\
725 +            & 200 &  0.688 &  0.96 & 125(16)   & 90.2(15)  \\
726 +            &     &        &  1.91 & 139(10)   & 101(10)   \\
727 +            &     &        &  2.83 & 141(6)    & 89.9(9.8) \\
728 +            & 166 &  0.681 &  0.97 & 141(30)   & 78(22)    \\
729 +            &     &        &  1.92 & 138(4)    & 98.9(9.5) \\
730 +        \hline
731 +        250 & 200 &  0.560 &  0.96 & 75(10)    & 61.8(7.3) \\
732 +            &     &        & -0.95 & 49.4(0.3) & 45.7(2.1) \\
733 +            & 166 &  0.569 &  0.97 & 80.3(0.6) & 67(11)    \\
734 +            &     &        &  1.44 & 76.2(5.0) & 64.8(3.8) \\
735 +            &     &        & -0.95 & 56.4(2.5) & 54.4(1.1) \\
736 +            &     &        & -1.85 & 47.8(1.1) & 53.5(1.5) \\
737 +        \hline\hline
738 +      \end{tabular}
739 +      \label{AuThiolHexaneUA}
740 +    \end{center}
741 +  \end{minipage}
742 + \end{table*}
743 +
744 + The sign of $J_z$ is a different matter, however, as this can alter
745 + the temperature on the two sides of the interface. The average
746 + temperature values reported are for the entire system, and not for the
747 + liquid phase, so at a given $\langle T \rangle$, the system with
748 + positive $J_z$ has a warmer liquid phase.  This means that if the
749 + liquid carries thermal energy via convective transport, {\it positive}
750 + $J_z$ values will result in increased molecular motion on the liquid
751 + side of the interface, and this will increase the measured
752 + conductivity.
753 +
754 + \subsubsection{Effects due to average temperature}
755 +
756 + We also studied the effect of average system temperature on the
757 + interfacial conductance.  The simulations are first equilibrated in
758 + the NPT ensemble at 1 atm.  The TraPPE-UA model for hexane tends to
759 + predict a lower boiling point (and liquid state density) than
760 + experiments.  This lower-density liquid phase leads to reduced contact
761 + between the hexane and butanethiol, and this accounts for our
762 + observation of lower conductance at higher temperatures as shown in
763 + Table \ref{AuThiolHexaneUA}.  In raising the average temperature from
764 + 200K to 250K, the density drop of ~20\% in the solvent phase leads to
765 + a ~65\% drop in the conductance.
766 +
767 + Similar behavior is observed in the TraPPE-UA model for toluene,
768 + although this model has better agreement with the experimental
769 + densities of toluene.  The expansion of the toluene liquid phase is
770 + not as significant as that of the hexane (8.3\% over 100K), and this
771 + limits the effect to ~20\% drop in thermal conductivity  (Table
772 + \ref{AuThiolToluene}).
773 +
774 + Although we have not mapped out the behavior at a large number of
775 + temperatures, is clear that there will be a strong temperature
776 + dependence in the interfacial conductance when the physical properties
777 + of one side of the interface (notably the density) change rapidly as a
778 + function of temperature.
779 +
780 + \begin{table*}
781 +  \begin{minipage}{\linewidth}
782 +    \begin{center}
783 +      \caption{Computed interfacial thermal conductivity ($G$ and
784 +        $G^\prime$) values for a 90\% coverage Au-butanethiol/toluene
785 +        interface at different temperatures using a range of energy
786 +        fluxes. Error estimates indicated in parenthesis.}
787 +      
788 +      \begin{tabular}{ccccc}
789 +        \hline\hline
790 +        $\langle T\rangle$ & $\rho_{toluene}$ & $J_z$ & $G$ & $G^\prime$ \\
791 +        (K) & (g/cm$^3$) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
792 +        \hline
793 +        200 & 0.933 &  2.15 & 204(12) & 113(12) \\
794 +            &       & -1.86 & 180(3)  & 135(21) \\
795 +            &       & -3.93 & 176(5)  & 113(12) \\
796 +        \hline
797 +        300 & 0.855 & -1.91 & 143(5)  & 125(2)  \\
798 +            &       & -4.19 & 135(9)  & 113(12) \\
799 +        \hline\hline
800 +      \end{tabular}
801 +      \label{AuThiolToluene}
802 +    \end{center}
803 +  \end{minipage}
804 + \end{table*}
805 +
806 + Besides the lower interfacial thermal conductance, surfaces at
807 + relatively high temperatures are susceptible to reconstructions,
808 + particularly when butanethiols fully cover the Au(111) surface. These
809 + reconstructions include surface Au atoms which migrate outward to the
810 + S atom layer, and butanethiol molecules which embed into the surface
811 + Au layer. The driving force for this behavior is the strong Au-S
812 + interactions which are modeled here with a deep Lennard-Jones
813 + potential. This phenomenon agrees with reconstructions that have beeen
814 + experimentally
815 + observed.\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}.  Vlugt
816 + {\it et al.} kept their Au(111) slab rigid so that their simulations
817 + could reach 300K without surface
818 + reconstructions.\cite{vlugt:cpc2007154} Since surface reconstructions
819 + blur the interface, the measurement of $G$ becomes more difficult to
820 + conduct at higher temperatures.  For this reason, most of our
821 + measurements are undertaken at $\langle T\rangle\sim$200K where
822 + reconstruction is minimized.
823 +
824 + However, when the surface is not completely covered by butanethiols,
825 + the simulated system appears to be more resistent to the
826 + reconstruction. O ur Au / butanethiol / toluene system had the Au(111)
827 + surfaces 90\% covered by butanethiols, but did not see this above
828 + phenomena even at $\langle T\rangle\sim$300K.  That said, we did
829 + observe butanethiols migrating to neighboring three-fold sites during
830 + a simulation.  Since the interface persisted in these simulations,
831 + were able to obtain $G$'s for these interfaces even at a relatively
832 + high temperature without being affected by surface reconstructions.
833 +
834 + \section{Discussion}
835 +
836 + The primary result of this work is that the capping agent acts as an
837 + efficient thermal coupler between solid and solvent phases.  One of
838 + the ways the capping agent can carry out this role is to down-shift
839 + between the phonon vibrations in the solid (which carry the heat from
840 + the gold) and the molecular vibrations in the liquid (which carry some
841 + of the heat in the solvent).
842 +
843 + To investigate the mechanism of interfacial thermal conductance, the
844 + vibrational power spectrum was computed. Power spectra were taken for
845 + individual components in different simulations. To obtain these
846 + spectra, simulations were run after equilibration in the
847 + microcanonical (NVE) ensemble and without a thermal
848 + gradient. Snapshots of configurations were collected at a frequency
849 + that is higher than that of the fastest vibrations occuring in the
850 + simulations. With these configurations, the velocity auto-correlation
851 + functions can be computed:
852 + \begin{equation}
853 + C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
854 + \label{vCorr}
855 + \end{equation}
856 + The power spectrum is constructed via a Fourier transform of the
857 + symmetrized velocity autocorrelation function,
858 + \begin{equation}
859 +  \hat{f}(\omega) =
860 +  \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
861 + \label{fourier}
862 + \end{equation}
863 +
864 + \subsection{The role of specific vibrations}
865 + The vibrational spectra for gold slabs in different environments are
866 + shown as in Figure \ref{specAu}. Regardless of the presence of
867 + solvent, the gold surfaces which are covered by butanethiol molecules
868 + exhibit an additional peak observed at a frequency of
869 + $\sim$165cm$^{-1}$.  We attribute this peak to the S-Au bonding
870 + vibration. This vibration enables efficient thermal coupling of the
871 + surface Au layer to the capping agents. Therefore, in our simulations,
872 + the Au / S interfaces do not appear to be the primary barrier to
873 + thermal transport when compared with the butanethiol / solvent
874 + interfaces.
875 +
876 + \begin{figure}
877 + \includegraphics[width=\linewidth]{vibration}
878 + \caption{Vibrational power spectra for gold in different solvent
879 +  environments.  The presence of the butanethiol capping molecules
880 +  adds a vibrational peak at $\sim$165cm$^{-1}$. The butanethiol
881 +  spectra exhibit a corresponding peak.}
882 + \label{specAu}
883 + \end{figure}
884 +
885 + Also in this figure, we show the vibrational power spectrum for the
886 + bound butanethiol molecules, which also exhibits the same
887 + $\sim$165cm$^{-1}$ peak.
888 +
889 + \subsection{Overlap of power spectra}
890 + A comparison of the results obtained from the two different organic
891 + solvents can also provide useful information of the interfacial
892 + thermal transport process.  In particular, the vibrational overlap
893 + between the butanethiol and the organic solvents suggests a highly
894 + efficient thermal exchange between these components.  Very high
895 + thermal conductivity was observed when AA models were used and C-H
896 + vibrations were treated classically.  The presence of extra degrees of
897 + freedom in the AA force field yields higher heat exchange rates
898 + between the two phases and results in a much higher conductivity than
899 + in the UA force field.
900 +
901 + The similarity in the vibrational modes available to solvent and
902 + capping agent can be reduced by deuterating one of the two components
903 + (Fig. \ref{aahxntln}).  Once either the hexanes or the butanethiols
904 + are deuterated, one can observe a significantly lower $G$ and
905 + $G^\prime$ values (Table \ref{modelTest}).
906 +
907 + \begin{figure}
908 + \includegraphics[width=\linewidth]{aahxntln}
909 + \caption{Spectra obtained for all-atom (AA) Au / butanethiol / solvent
910 +  systems. When butanethiol is deuterated (lower left), its
911 +  vibrational overlap with hexane decreases significantly.  Since
912 +  aromatic molecules and the butanethiol are vibrationally dissimilar,
913 +  the change is not as dramatic when toluene is the solvent (right).}
914 + \label{aahxntln}
915 + \end{figure}
916 +
917 + For the Au / butanethiol / toluene interfaces, having the AA
918 + butanethiol deuterated did not yield a significant change in the
919 + measured conductance. Compared to the C-H vibrational overlap between
920 + hexane and butanethiol, both of which have alkyl chains, the overlap
921 + between toluene and butanethiol is not as significant and thus does
922 + not contribute as much to the heat exchange process.
923 +
924 + Previous observations of Zhang {\it et al.}\cite{hase:2010} indicate
925 + that the {\it intra}molecular heat transport due to alkylthiols is
926 + highly efficient.  Combining our observations with those of Zhang {\it
927 +  et al.}, it appears that butanethiol acts as a channel to expedite
928 + heat flow from the gold surface and into the alkyl chain.  The
929 + acoustic impedance mismatch between the metal and the liquid phase can
930 + therefore be effectively reduced with the presence of suitable capping
931 + agents.
932 +
933 + Deuterated models in the UA force field did not decouple the thermal
934 + transport as well as in the AA force field.  The UA models, even
935 + though they have eliminated the high frequency C-H vibrational
936 + overlap, still have significant overlap in the lower-frequency
937 + portions of the infrared spectra (Figure \ref{uahxnua}).  Deuterating
938 + the UA models did not decouple the low frequency region enough to
939 + produce an observable difference for the results of $G$ (Table
940 + \ref{modelTest}).
941 +
942 + \begin{figure}
943 + \includegraphics[width=\linewidth]{uahxnua}
944 + \caption{Vibrational spectra obtained for normal (upper) and
945 +  deuterated (lower) hexane in Au-butanethiol/hexane
946 +  systems. Butanethiol spectra are shown as reference. Both hexane and
947 +  butanethiol were using United-Atom models.}
948 + \label{uahxnua}
949 + \end{figure}
950 +
951 + \section{Conclusions}
952 + The NIVS algorithm has been applied to simulations of
953 + butanethiol-capped Au(111) surfaces in the presence of organic
954 + solvents. This algorithm allows the application of unphysical thermal
955 + flux to transfer heat between the metal and the liquid phase. With the
956 + flux applied, we were able to measure the corresponding thermal
957 + gradients and to obtain interfacial thermal conductivities. Under
958 + steady states, 2-3 ns trajectory simulations are sufficient for
959 + computation of this quantity.
960 +
961 + Our simulations have seen significant conductance enhancement in the
962 + presence of capping agent, compared with the bare gold / liquid
963 + interfaces. The acoustic impedance mismatch between the metal and the
964 + liquid phase is effectively eliminated by a chemically-bonded capping
965 + agent. Furthermore, the coverage precentage of the capping agent plays
966 + an important role in the interfacial thermal transport
967 + process. Moderately low coverages allow higher contact between capping
968 + agent and solvent, and thus could further enhance the heat transfer
969 + process, giving a non-monotonic behavior of conductance with
970 + increasing coverage.
971 +
972 + Our results, particularly using the UA models, agree well with
973 + available experimental data.  The AA models tend to overestimate the
974 + interfacial thermal conductance in that the classically treated C-H
975 + vibrations become too easily populated. Compared to the AA models, the
976 + UA models have higher computational efficiency with satisfactory
977 + accuracy, and thus are preferable in modeling interfacial thermal
978 + transport.
979 +
980 + Of the two definitions for $G$, the discrete form
981 + (Eq. \ref{discreteG}) was easier to use and gives out relatively
982 + consistent results, while the derivative form (Eq. \ref{derivativeG})
983 + is not as versatile. Although $G^\prime$ gives out comparable results
984 + and follows similar trend with $G$ when measuring close to fully
985 + covered or bare surfaces, the spatial resolution of $T$ profile
986 + required for the use of a derivative form is limited by the number of
987 + bins and the sampling required to obtain thermal gradient information.
988 +
989 + Vlugt {\it et al.} have investigated the surface thiol structures for
990 + nanocrystalline gold and pointed out that they differ from those of
991 + the Au(111) surface.\cite{landman:1998,vlugt:cpc2007154} This
992 + difference could also cause differences in the interfacial thermal
993 + transport behavior. To investigate this problem, one would need an
994 + effective method for applying thermal gradients in non-planar
995 + (i.e. spherical) geometries.
996 +
997   \section{Acknowledgments}
998   Support for this project was provided by the National Science
999   Foundation under grant CHE-0848243. Computational time was provided by
1000   the Center for Research Computing (CRC) at the University of Notre
1001 < Dame.  \newpage
1001 > Dame.
1002 > \newpage
1003  
1004   \bibliography{interfacial}
1005  

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