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28  
29   \begin{document}
30  
# Line 44 | Line 44 | The abstract
44   \begin{doublespace}
45  
46   \begin{abstract}
47 < The abstract
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
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 in
313 + our simulations.
314 +
315 + The initial configurations generated are further equilibrated with the
316 + $x$ and $y$ dimensions fixed, only allowing the $z$-length scale to
317 + change. This is to ensure that the equilibration of liquid phase does
318 + not affect the metal's crystalline structure. Comparisons were made
319 + with simulations that allowed changes of $L_x$ and $L_y$ during NPT
320 + equilibration. No substantial changes in the box geometry were noticed
321 + in these simulations. After ensuring the liquid phase reaches
322 + equilibrium at atmospheric pressure (1 atm), further equilibration was
323 + carried out under canonical (NVT) and microcanonical (NVE) ensembles.
324 +
325 + After the systems reach equilibrium, NIVS was used to impose an
326 + unphysical thermal flux between the metal and the liquid phases. Most
327 + of our simulations were done under an average temperature of
328 + $\sim$200K. Therefore, thermal flux usually came from the metal to the
329 + liquid so that the liquid has a higher temperature and would not
330 + freeze due to lowered temperatures. After this induced temperature
331 + gradient had stablized, the temperature profile of the simulation cell
332 + was recorded. To do this, the simulation cell is devided evenly into
333 + $N$ slabs along the $z$-axis. The average temperatures of each slab
334 + are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
335 + the same, the derivatives of $T$ with respect to slab number $n$ can
336 + be directly used for $G^\prime$ calculations: \begin{equation}
337 +  G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
338 +         \Big/\left(\frac{\partial T}{\partial z}\right)^2
339 +         = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
340 +         \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
341 +         = |J_z|\Big|\frac{\partial^2 T}{\partial n^2}\Big|
342 +         \Big/\left(\frac{\partial T}{\partial n}\right)^2
343 + \label{derivativeG2}
344 + \end{equation}
345 +
346 + All of the above simulation procedures use a time step of 1 fs. Each
347 + equilibration stage took a minimum of 100 ps, although in some cases,
348 + longer equilibration stages were utilized.
349 +
350 + \subsection{Force Field Parameters}
351 + Our simulations include a number of chemically distinct components.
352 + Figure \ref{demoMol} demonstrates the sites defined for both
353 + United-Atom and All-Atom models of the organic solvent and capping
354 + agents in our simulations. Force field parameters are needed for
355 + interactions both between the same type of particles and between
356 + particles of different species.
357 +
358 + \begin{figure}
359 + \includegraphics[width=\linewidth]{structures}
360 + \caption{Structures of the capping agent and solvents utilized in
361 +  these simulations. The chemically-distinct sites (a-e) are expanded
362 +  in terms of constituent atoms for both United Atom (UA) and All Atom
363 +  (AA) force fields.  Most parameters are from
364 +  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes,TraPPE-UA.thiols}
365 +  (UA) and \protect\cite{OPLSAA} (AA). Cross-interactions with the Au
366 +  atoms are given in Table \ref{MnM}.}
367 + \label{demoMol}
368 + \end{figure}
369 +
370 + The Au-Au interactions in metal lattice slab is described by the
371 + quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
372 + potentials include zero-point quantum corrections and are
373 + reparametrized for accurate surface energies compared to the
374 + Sutton-Chen potentials.\cite{Chen90}
375 +
376 + For the two solvent molecules, {\it n}-hexane and toluene, two
377 + different atomistic models were utilized. Both solvents were modeled
378 + using United-Atom (UA) and All-Atom (AA) models. The TraPPE-UA
379 + parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
380 + for our UA solvent molecules. In these models, sites are located at
381 + the carbon centers for alkyl groups. Bonding interactions, including
382 + bond stretches and bends and torsions, were used for intra-molecular
383 + sites closer than 3 bonds. For non-bonded interactions, Lennard-Jones
384 + potentials are used.
385 +
386 + By eliminating explicit hydrogen atoms, the TraPPE-UA models are
387 + simple and computationally efficient, while maintaining good accuracy.
388 + However, the TraPPE-UA model for alkanes is known to predict a slighly
389 + lower boiling point than experimental values. This is one of the
390 + reasons we used a lower average temperature (200K) for our
391 + simulations. If heat is transferred to the liquid phase during the
392 + NIVS simulation, the liquid in the hot slab can actually be
393 + substantially warmer than the mean temperature in the simulation. The
394 + lower mean temperatures therefore prevent solvent boiling.
395 +
396 + For UA-toluene, the non-bonded potentials between intermolecular sites
397 + have a similar Lennard-Jones formulation. The toluene molecules were
398 + treated as a single rigid body, so there was no need for
399 + intramolecular interactions (including bonds, bends, or torsions) in
400 + this solvent model.
401 +
402 + Besides the TraPPE-UA models, AA models for both organic solvents are
403 + included in our studies as well. The OPLS-AA\cite{OPLSAA} force fields
404 + were used. For hexane, additional explicit hydrogen sites were
405 + included. Besides bonding and non-bonded site-site interactions,
406 + partial charges and the electrostatic interactions were added to each
407 + CT and HC site. For toluene, a flexible model for the toluene molecule
408 + was utilized which included bond, bend, torsion, and inversion
409 + potentials to enforce ring planarity.
410 +
411 + The butanethiol capping agent in our simulations, were also modeled
412 + with both UA and AA model. The TraPPE-UA force field includes
413 + parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
414 + UA butanethiol model in our simulations. The OPLS-AA also provides
415 + parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
416 + surfaces do not have the hydrogen atom bonded to sulfur. To derive
417 + suitable parameters for butanethiol adsorbed on Au(111) surfaces, we
418 + adopt the S parameters from Luedtke and Landman\cite{landman:1998} and
419 + modify the parameters for the CTS atom to maintain charge neutrality
420 + in the molecule.  Note that the model choice (UA or AA) for the capping
421 + agent can be different from the solvent. Regardless of model choice,
422 + the force field parameters for interactions between capping agent and
423 + solvent can be derived using Lorentz-Berthelot Mixing Rule:
424 + \begin{eqnarray}
425 +  \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
426 +  \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
427 + \end{eqnarray}
428 +
429 + To describe the interactions between metal (Au) and non-metal atoms,
430 + we refer to an adsorption study of alkyl thiols on gold surfaces by
431 + Vlugt {\it et al.}\cite{vlugt:cpc2007154} They fitted an effective
432 + Lennard-Jones form of potential parameters for the interaction between
433 + Au and pseudo-atoms CH$_x$ and S based on a well-established and
434 + widely-used effective potential of Hautman and Klein for the Au(111)
435 + surface.\cite{hautman:4994} As our simulations require the gold slab
436 + to be flexible to accommodate thermal excitation, the pair-wise form
437 + of potentials they developed was used for our study.
438 +
439 + The potentials developed from {\it ab initio} calculations by Leng
440 + {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
441 + interactions between Au and aromatic C/H atoms in toluene. However,
442 + the Lennard-Jones parameters between Au and other types of particles,
443 + (e.g. AA alkanes) have not yet been established. For these
444 + interactions, the Lorentz-Berthelot mixing rule can be used to derive
445 + effective single-atom LJ parameters for the metal using the fit values
446 + for toluene. These are then used to construct reasonable mixing
447 + parameters for the interactions between the gold and other atoms.
448 + Table \ref{MnM} summarizes the ``metal/non-metal'' parameters used in
449 + our simulations.
450 +
451 + \begin{table*}
452 +  \begin{minipage}{\linewidth}
453 +    \begin{center}
454 +      \caption{Non-bonded interaction parameters (including cross
455 +        interactions with Au atoms) for both force fields used in this
456 +        work.}      
457 +      \begin{tabular}{lllllll}
458 +        \hline\hline
459 +        & Site  & $\sigma_{ii}$ & $\epsilon_{ii}$ & $q_i$ &
460 +        $\sigma_{Au-i}$ & $\epsilon_{Au-i}$ \\
461 +        & & (\AA) & (kcal/mol) & ($e$) & (\AA) & (kcal/mol) \\
462 +        \hline
463 +        United Atom (UA)
464 +        &CH3  & 3.75  & 0.1947  & -      & 3.54   & 0.2146  \\
465 +        &CH2  & 3.95  & 0.0914  & -      & 3.54   & 0.1749  \\
466 +        &CHar & 3.695 & 0.1003  & -      & 3.4625 & 0.1680  \\
467 +        &CRar & 3.88  & 0.04173 & -      & 3.555  & 0.1604  \\
468 +        \hline
469 +        All Atom (AA)
470 +        &CT3  & 3.50  & 0.066   & -0.18  & 3.365  & 0.1373  \\
471 +        &CT2  & 3.50  & 0.066   & -0.12  & 3.365  & 0.1373  \\
472 +        &CTT  & 3.50  & 0.066   & -0.065 & 3.365  & 0.1373  \\
473 +        &HC   & 2.50  & 0.030   &  0.06  & 2.865  & 0.09256 \\
474 +        &CA   & 3.55  & 0.070   & -0.115 & 3.173  & 0.0640  \\
475 +        &HA   & 2.42  & 0.030   &  0.115 & 2.746  & 0.0414  \\
476 +        \hline
477 +        Both UA and AA
478 +        & S   & 4.45  & 0.25    & -      & 2.40   & 8.465   \\
479 +        \hline\hline
480 +      \end{tabular}
481 +      \label{MnM}
482 +    \end{center}
483 +  \end{minipage}
484 + \end{table*}
485 +
486 +
487 + \section{Results}
488 + There are many factors contributing to the measured interfacial
489 + conductance; some of these factors are physically motivated
490 + (e.g. coverage of the surface by the capping agent coverage and
491 + solvent identity), while some are governed by parameters of the
492 + methodology (e.g. applied flux and the formulas used to obtain the
493 + conductance). In this section we discuss the major physical and
494 + calculational effects on the computed conductivity.
495 +
496 + \subsection{Effects due to capping agent coverage}
497 +
498 + A series of different initial conditions with a range of surface
499 + coverages was prepared and solvated with various with both of the
500 + solvent molecules. These systems were then equilibrated and their
501 + interfacial thermal conductivity was measured with the NIVS
502 + algorithm. Figure \ref{coverage} demonstrates the trend of conductance
503 + with respect to surface coverage.
504 +
505 + \begin{figure}
506 + \includegraphics[width=\linewidth]{coverage}
507 + \caption{Comparison of interfacial thermal conductivity ($G$) values
508 +  for the Au-butanethiol/solvent interface with various UA models and
509 +  different capping agent coverages at $\langle T\rangle\sim$200K.}
510 + \label{coverage}
511 + \end{figure}
512 +
513 + In partially covered surfaces, the derivative definition for
514 + $G^\prime$ (Eq. \ref{derivativeG}) becomes difficult to apply, as the
515 + location of maximum change of $\lambda$ becomes washed out.  The
516 + discrete definition (Eq. \ref{discreteG}) is easier to apply, as the
517 + Gibbs dividing surface is still well-defined. Therefore, $G$ (not
518 + $G^\prime$) was used in this section.
519 +
520 + From Figure \ref{coverage}, one can see the significance of the
521 + presence of capping agents. When even a small fraction of the Au(111)
522 + surface sites are covered with butanethiols, the conductivity exhibits
523 + an enhancement by at least a factor of 3.  Cappping agents are clearly
524 + playing a major role in thermal transport at metal / organic solvent
525 + surfaces.
526 +
527 + We note a non-monotonic behavior in the interfacial conductance as a
528 + function of surface coverage. The maximum conductance (largest $G$)
529 + happens when the surfaces are about 75\% covered with butanethiol
530 + caps.  The reason for this behavior is not entirely clear.  One
531 + explanation is that incomplete butanethiol coverage allows small gaps
532 + between butanethiols to form. These gaps can be filled by transient
533 + solvent molecules.  These solvent molecules couple very strongly with
534 + the hot capping agent molecules near the surface, and can then carry
535 + away (diffusively) the excess thermal energy from the surface.
536 +
537 + There appears to be a competition between the conduction of the
538 + thermal energy away from the surface by the capping agents (enhanced
539 + by greater coverage) and the coupling of the capping agents with the
540 + solvent (enhanced by interdigitation at lower coverages).  This
541 + competition would lead to the non-monotonic coverage behavior observed
542 + here.
543 +
544 + Results for rigid body toluene solvent, as well as the UA hexane, are
545 + within the ranges expected from prior experimental
546 + work.\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406} This suggests
547 + that explicit hydrogen atoms might not be required for modeling
548 + thermal transport in these systems.  C-H vibrational modes do not see
549 + significant excited state population at low temperatures, and are not
550 + likely to carry lower frequency excitations from the solid layer into
551 + the bulk liquid.
552 +
553 + The toluene solvent does not exhibit the same behavior as hexane in
554 + that $G$ remains at approximately the same magnitude when the capping
555 + coverage increases from 25\% to 75\%.  Toluene, as a rigid planar
556 + molecule, cannot occupy the relatively small gaps between the capping
557 + agents as easily as the chain-like {\it n}-hexane.  The effect of
558 + solvent coupling to the capping agent is therefore weaker in toluene
559 + except at the very lowest coverage levels.  This effect counters the
560 + coverage-dependent conduction of heat away from the metal surface,
561 + leading to a much flatter $G$ vs. coverage trend than is observed in
562 + {\it n}-hexane.
563 +
564 + \subsection{Effects due to Solvent \& Solvent Models}
565 + In addition to UA solvent and capping agent models, AA models have
566 + also been included in our simulations.  In most of this work, the same
567 + (UA or AA) model for solvent and capping agent was used, but it is
568 + also possible to utilize different models for different components.
569 + We have also included isotopic substitutions (Hydrogen to Deuterium)
570 + to decrease the explicit vibrational overlap between solvent and
571 + capping agent. Table \ref{modelTest} summarizes the results of these
572 + studies.
573 +
574 + \begin{table*}
575 +  \begin{minipage}{\linewidth}
576 +    \begin{center}
577 +      
578 +      \caption{Computed interfacial thermal conductance ($G$ and
579 +        $G^\prime$) values for interfaces using various models for
580 +        solvent and capping agent (or without capping agent) at
581 +        $\langle T\rangle\sim$200K. (D stands for deuterated solvent
582 +        or capping agent molecules; ``Avg.'' denotes results that are
583 +        averages of simulations under different applied thermal flux
584 +        values $(J_z)$. Error estimates are indicated in
585 +        parentheses.)}
586 +      
587 +      \begin{tabular}{llccc}
588 +        \hline\hline
589 +        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
590 +        (or bare surface) & model & (GW/m$^2$) &
591 +        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
592 +        \hline
593 +        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
594 +              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
595 +              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
596 +              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
597 +              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
598 +        \hline
599 +        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
600 +              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
601 +              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
602 +              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
603 +              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
604 +        \hline
605 +        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
606 +              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
607 +              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
608 +        \hline
609 +        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
610 +              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
611 +              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
612 +              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
613 +        \hline\hline
614 +      \end{tabular}
615 +      \label{modelTest}
616 +    \end{center}
617 +  \end{minipage}
618 + \end{table*}
619 +
620 + To facilitate direct comparison between force fields, systems with the
621 + same capping agent and solvent were prepared with the same length
622 + scales for the simulation cells.
623 +
624 + On bare metal / solvent surfaces, different force field models for
625 + hexane yield similar results for both $G$ and $G^\prime$, and these
626 + two definitions agree with each other very well. This is primarily an
627 + indicator of weak interactions between the metal and the solvent, and
628 + is a typical case for acoustic impedance mismatch between these two
629 + phases.  
630 +
631 + For the fully-covered surfaces, the choice of force field for the
632 + capping agent and solvent has a large impact on the calulated values
633 + of $G$ and $G^\prime$.  The AA thiol to AA solvent conductivities are
634 + much larger than their UA to UA counterparts, and these values exceed
635 + the experimental estimates by a large measure.  The AA force field
636 + allows significant energy to go into C-H (or C-D) stretching modes,
637 + and since these modes are high frequency, this non-quantum behavior is
638 + likely responsible for the overestimate of the conductivity.  Compared
639 + to the AA model, the UA model yields more reasonable conductivity
640 + values with much higher computational efficiency.
641 +
642 + \subsubsection{Are electronic excitations in the metal important?}
643 + Because they lack electronic excitations, the QSC and related embedded
644 + atom method (EAM) models for gold are known to predict unreasonably
645 + low values for bulk conductivity
646 + ($\lambda$).\cite{kuang:164101,ISI:000207079300006,Clancy:1992} If the
647 + conductance between the phases ($G$) is governed primarily by phonon
648 + excitation (and not electronic degrees of freedom), one would expect a
649 + classical model to capture most of the interfacial thermal
650 + conductance.  Our results for $G$ and $G^\prime$ indicate that this is
651 + indeed the case, and suggest that the modeling of interfacial thermal
652 + transport depends primarily on the description of the interactions
653 + between the various components at the interface.  When the metal is
654 + chemically capped, the primary barrier to thermal conductivity appears
655 + to be the interface between the capping agent and the surrounding
656 + solvent, so the excitations in the metal have little impact on the
657 + value of $G$.
658 +
659 + \subsection{Effects due to methodology and simulation parameters}
660 +
661 + We have varied the parameters of the simulations in order to
662 + investigate how these factors would affect the computation of $G$.  Of
663 + particular interest are: 1) the length scale for the applied thermal
664 + gradient (modified by increasing the amount of solvent in the system),
665 + 2) the sign and magnitude of the applied thermal flux, 3) the average
666 + temperature of the simulation (which alters the solvent density during
667 + equilibration), and 4) the definition of the interfacial conductance
668 + (Eqs. (\ref{discreteG}) or (\ref{derivativeG})) used in the
669 + calculation.
670 +
671 + Systems of different lengths were prepared by altering the number of
672 + solvent molecules and extending the length of the box along the $z$
673 + axis to accomodate the extra solvent.  Equilibration at the same
674 + temperature and pressure conditions led to nearly identical surface
675 + areas ($L_x$ and $L_y$) available to the metal and capping agent,
676 + while the extra solvent served mainly to lengthen the axis that was
677 + used to apply the thermal flux.  For a given value of the applied
678 + flux, the different $z$ length scale has only a weak effect on the
679 + computed conductivities (Table \ref{AuThiolHexaneUA}).
680 +
681 + \subsubsection{Effects of applied flux}
682 + The NIVS algorithm allows changes in both the sign and magnitude of
683 + the applied flux.  It is possible to reverse the direction of heat
684 + flow simply by changing the sign of the flux, and thermal gradients
685 + which would be difficult to obtain experimentally ($5$ K/\AA) can be
686 + easily simulated.  However, the magnitude of the applied flux is not
687 + arbitary if one aims to obtain a stable and reliable thermal gradient.
688 + A temperature gradient can be lost in the noise if $|J_z|$ is too
689 + small, and excessive $|J_z|$ values can cause phase transitions if the
690 + extremes of the simulation cell become widely separated in
691 + temperature.  Also, if $|J_z|$ is too large for the bulk conductivity
692 + of the materials, the thermal gradient will never reach a stable
693 + state.  
694 +
695 + Within a reasonable range of $J_z$ values, we were able to study how
696 + $G$ changes as a function of this flux.  In what follows, we use
697 + positive $J_z$ values to denote the case where energy is being
698 + transferred by the method from the metal phase and into the liquid.
699 + The resulting gradient therefore has a higher temperature in the
700 + liquid phase.  Negative flux values reverse this transfer, and result
701 + in higher temperature metal phases.  The conductance measured under
702 + different applied $J_z$ values is listed in Tables
703 + \ref{AuThiolHexaneUA} and \ref{AuThiolToluene}. These results do not
704 + indicate that $G$ depends strongly on $J_z$ within this flux
705 + range. The linear response of flux to thermal gradient simplifies our
706 + investigations in that we can rely on $G$ measurement with only a
707 + small number $J_z$ values.  
708 +
709 + \begin{table*}
710 +  \begin{minipage}{\linewidth}
711 +    \begin{center}
712 +      \caption{Computed interfacial thermal conductivity ($G$ and
713 +        $G^\prime$) values for the 100\% covered Au-butanethiol/hexane
714 +        interfaces with UA model and different hexane molecule numbers
715 +        at different temperatures using a range of energy
716 +        fluxes. Error estimates indicated in parenthesis.}
717 +      
718 +      \begin{tabular}{ccccccc}
719 +        \hline\hline
720 +        $\langle T\rangle$ & $N_{hexane}$  & $\rho_{hexane}$ &
721 +        $J_z$ & $G$ & $G^\prime$ \\
722 +        (K) &  & (g/cm$^3$) & (GW/m$^2$) &
723 +        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
724 +        \hline
725 +        200 & 266 &  0.672 & -0.96 & 102(3)    & 80.0(0.8) \\
726 +            & 200 &  0.688 &  0.96 & 125(16)   & 90.2(15)  \\
727 +            &     &        &  1.91 & 139(10)   & 101(10)   \\
728 +            &     &        &  2.83 & 141(6)    & 89.9(9.8) \\
729 +            & 166 &  0.681 &  0.97 & 141(30)   & 78(22)    \\
730 +            &     &        &  1.92 & 138(4)    & 98.9(9.5) \\
731 +        \hline
732 +        250 & 200 &  0.560 &  0.96 & 75(10)    & 61.8(7.3) \\
733 +            &     &        & -0.95 & 49.4(0.3) & 45.7(2.1) \\
734 +            & 166 &  0.569 &  0.97 & 80.3(0.6) & 67(11)    \\
735 +            &     &        &  1.44 & 76.2(5.0) & 64.8(3.8) \\
736 +            &     &        & -0.95 & 56.4(2.5) & 54.4(1.1) \\
737 +            &     &        & -1.85 & 47.8(1.1) & 53.5(1.5) \\
738 +        \hline\hline
739 +      \end{tabular}
740 +      \label{AuThiolHexaneUA}
741 +    \end{center}
742 +  \end{minipage}
743 + \end{table*}
744 +
745 + The sign of $J_z$ is a different matter, however, as this can alter
746 + the temperature on the two sides of the interface. The average
747 + temperature values reported are for the entire system, and not for the
748 + liquid phase, so at a given $\langle T \rangle$, the system with
749 + positive $J_z$ has a warmer liquid phase.  This means that if the
750 + liquid carries thermal energy via convective transport, {\it positive}
751 + $J_z$ values will result in increased molecular motion on the liquid
752 + side of the interface, and this will increase the measured
753 + conductivity.
754 +
755 + \subsubsection{Effects due to average temperature}
756 +
757 + We also studied the effect of average system temperature on the
758 + interfacial conductance.  The simulations are first equilibrated in
759 + the NPT ensemble at 1 atm.  The TraPPE-UA model for hexane tends to
760 + predict a lower boiling point (and liquid state density) than
761 + experiments.  This lower-density liquid phase leads to reduced contact
762 + between the hexane and butanethiol, and this accounts for our
763 + observation of lower conductance at higher temperatures as shown in
764 + Table \ref{AuThiolHexaneUA}.  In raising the average temperature from
765 + 200K to 250K, the density drop of $\sim$20\% in the solvent phase
766 + leads to a $\sim$65\% drop in the conductance. [BUT (125-75)/125 = .4?]
767 +
768 + Similar behavior is observed in the TraPPE-UA model for toluene,
769 + although this model has better agreement with the experimental
770 + densities of toluene.  The expansion of the toluene liquid phase is
771 + not as significant as that of the hexane (8.3\% over 100K), and this
772 + limits the effect to $\sim$20\% drop in thermal conductivity  (Table
773 + \ref{AuThiolToluene}).
774 +
775 + Although we have not mapped out the behavior at a large number of
776 + temperatures, is clear that there will be a strong temperature
777 + dependence in the interfacial conductance when the physical properties
778 + of one side of the interface (notably the density) change rapidly as a
779 + function of temperature.
780 +
781 + \begin{table*}
782 +  \begin{minipage}{\linewidth}
783 +    \begin{center}
784 +      \caption{Computed interfacial thermal conductivity ($G$ and
785 +        $G^\prime$) values for a 90\% coverage Au-butanethiol/toluene
786 +        interface at different temperatures using a range of energy
787 +        fluxes. Error estimates indicated in parenthesis.}
788 +      
789 +      \begin{tabular}{ccccc}
790 +        \hline\hline
791 +        $\langle T\rangle$ & $\rho_{toluene}$ & $J_z$ & $G$ & $G^\prime$ \\
792 +        (K) & (g/cm$^3$) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
793 +        \hline
794 +        200 & 0.933 &  2.15 & 204(12) & 113(12) \\
795 +            &       & -1.86 & 180(3)  & 135(21) \\
796 +            &       & -3.93 & 176(5)  & 113(12) \\
797 +        \hline
798 +        300 & 0.855 & -1.91 & 143(5)  & 125(2)  \\
799 +            &       & -4.19 & 135(9)  & 113(12) \\
800 +        \hline\hline
801 +      \end{tabular}
802 +      \label{AuThiolToluene}
803 +    \end{center}
804 +  \end{minipage}
805 + \end{table*}
806 +
807 + Besides the lower interfacial thermal conductance, surfaces at
808 + relatively high temperatures are susceptible to reconstructions,
809 + particularly when butanethiols fully cover the Au(111) surface. These
810 + reconstructions include surface Au atoms which migrate outward to the
811 + S atom layer, and butanethiol molecules which embed into the surface
812 + Au layer. The driving force for this behavior is the strong Au-S
813 + interactions which are modeled here with a deep Lennard-Jones
814 + potential. This phenomenon agrees with reconstructions that have beeen
815 + experimentally
816 + observed.\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}.  Vlugt
817 + {\it et al.} kept their Au(111) slab rigid so that their simulations
818 + could reach 300K without surface
819 + reconstructions.\cite{vlugt:cpc2007154} Since surface reconstructions
820 + blur the interface, the measurement of $G$ becomes more difficult to
821 + conduct at higher temperatures.  For this reason, most of our
822 + measurements are undertaken at $\langle T\rangle\sim$200K where
823 + reconstruction is minimized.
824 +
825 + However, when the surface is not completely covered by butanethiols,
826 + the simulated system appears to be more resistent to the
827 + reconstruction. Our Au / butanethiol / toluene system had the Au(111)
828 + surfaces 90\% covered by butanethiols, but did not see this above
829 + phenomena even at $\langle T\rangle\sim$300K.  That said, we did
830 + observe butanethiols migrating to neighboring three-fold sites during
831 + a simulation.  Since the interface persisted in these simulations,
832 + were able to obtain $G$'s for these interfaces even at a relatively
833 + high temperature without being affected by surface reconstructions.
834 +
835 + \section{Discussion}
836 +
837 + The primary result of this work is that the capping agent acts as an
838 + efficient thermal coupler between solid and solvent phases.  One of
839 + the ways the capping agent can carry out this role is to down-shift
840 + between the phonon vibrations in the solid (which carry the heat from
841 + the gold) and the molecular vibrations in the liquid (which carry some
842 + of the heat in the solvent).
843 +
844 + To investigate the mechanism of interfacial thermal conductance, the
845 + vibrational power spectrum was computed. Power spectra were taken for
846 + individual components in different simulations. To obtain these
847 + spectra, simulations were run after equilibration in the
848 + microcanonical (NVE) ensemble and without a thermal
849 + gradient. Snapshots of configurations were collected at a frequency
850 + that is higher than that of the fastest vibrations occuring in the
851 + simulations. With these configurations, the velocity auto-correlation
852 + functions can be computed:
853 + \begin{equation}
854 + C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
855 + \label{vCorr}
856 + \end{equation}
857 + The power spectrum is constructed via a Fourier transform of the
858 + symmetrized velocity autocorrelation function,
859 + \begin{equation}
860 +  \hat{f}(\omega) =
861 +  \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
862 + \label{fourier}
863 + \end{equation}
864 +
865 + \subsection{The role of specific vibrations}
866 + The vibrational spectra for gold slabs in different environments are
867 + shown as in Figure \ref{specAu}. Regardless of the presence of
868 + solvent, the gold surfaces which are covered by butanethiol molecules
869 + exhibit an additional peak observed at a frequency of
870 + $\sim$165cm$^{-1}$.  We attribute this peak to the S-Au bonding
871 + vibration. This vibration enables efficient thermal coupling of the
872 + surface Au layer to the capping agents. Therefore, in our simulations,
873 + the Au / S interfaces do not appear to be the primary barrier to
874 + thermal transport when compared with the butanethiol / solvent
875 + interfaces.
876 +
877 + \begin{figure}
878 + \includegraphics[width=\linewidth]{vibration}
879 + \caption{Vibrational power spectra for gold in different solvent
880 +  environments.  The presence of the butanethiol capping molecules
881 +  adds a vibrational peak at $\sim$165cm$^{-1}$. The butanethiol
882 +  spectra exhibit a corresponding peak.}
883 + \label{specAu}
884 + \end{figure}
885 +
886 + Also in this figure, we show the vibrational power spectrum for the
887 + bound butanethiol molecules, which also exhibits the same
888 + $\sim$165cm$^{-1}$ peak.
889 +
890 + \subsection{Overlap of power spectra}
891 + A comparison of the results obtained from the two different organic
892 + solvents can also provide useful information of the interfacial
893 + thermal transport process.  In particular, the vibrational overlap
894 + between the butanethiol and the organic solvents suggests a highly
895 + efficient thermal exchange between these components.  Very high
896 + thermal conductivity was observed when AA models were used and C-H
897 + vibrations were treated classically.  The presence of extra degrees of
898 + freedom in the AA force field yields higher heat exchange rates
899 + between the two phases and results in a much higher conductivity than
900 + in the UA force field.
901 +
902 + The similarity in the vibrational modes available to solvent and
903 + capping agent can be reduced by deuterating one of the two components
904 + (Fig. \ref{aahxntln}).  Once either the hexanes or the butanethiols
905 + are deuterated, one can observe a significantly lower $G$ and
906 + $G^\prime$ values (Table \ref{modelTest}).
907 +
908 + \begin{figure}
909 + \includegraphics[width=\linewidth]{aahxntln}
910 + \caption{Spectra obtained for all-atom (AA) Au / butanethiol / solvent
911 +  systems. When butanethiol is deuterated (lower left), its
912 +  vibrational overlap with hexane decreases significantly.  Since
913 +  aromatic molecules and the butanethiol are vibrationally dissimilar,
914 +  the change is not as dramatic when toluene is the solvent (right).}
915 + \label{aahxntln}
916 + \end{figure}
917 +
918 + For the Au / butanethiol / toluene interfaces, having the AA
919 + butanethiol deuterated did not yield a significant change in the
920 + measured conductance. Compared to the C-H vibrational overlap between
921 + hexane and butanethiol, both of which have alkyl chains, the overlap
922 + between toluene and butanethiol is not as significant and thus does
923 + not contribute as much to the heat exchange process.
924 +
925 + Previous observations of Zhang {\it et al.}\cite{hase:2010} indicate
926 + that the {\it intra}molecular heat transport due to alkylthiols is
927 + highly efficient.  Combining our observations with those of Zhang {\it
928 +  et al.}, it appears that butanethiol acts as a channel to expedite
929 + heat flow from the gold surface and into the alkyl chain.  The
930 + acoustic impedance mismatch between the metal and the liquid phase can
931 + therefore be effectively reduced with the presence of suitable capping
932 + agents.
933 +
934 + Deuterated models in the UA force field did not decouple the thermal
935 + transport as well as in the AA force field.  The UA models, even
936 + though they have eliminated the high frequency C-H vibrational
937 + overlap, still have significant overlap in the lower-frequency
938 + portions of the infrared spectra (Figure \ref{uahxnua}).  Deuterating
939 + the UA models did not decouple the low frequency region enough to
940 + produce an observable difference for the results of $G$ (Table
941 + \ref{modelTest}).
942 +
943 + \begin{figure}
944 + \includegraphics[width=\linewidth]{uahxnua}
945 + \caption{Vibrational spectra obtained for normal (upper) and
946 +  deuterated (lower) hexane in Au-butanethiol/hexane
947 +  systems. Butanethiol spectra are shown as reference. Both hexane and
948 +  butanethiol were using United-Atom models.}
949 + \label{uahxnua}
950 + \end{figure}
951 +
952 + \section{Conclusions}
953 + The NIVS algorithm has been applied to simulations of
954 + butanethiol-capped Au(111) surfaces in the presence of organic
955 + solvents. This algorithm allows the application of unphysical thermal
956 + flux to transfer heat between the metal and the liquid phase. With the
957 + flux applied, we were able to measure the corresponding thermal
958 + gradients and to obtain interfacial thermal conductivities. Under
959 + steady states, 2-3 ns trajectory simulations are sufficient for
960 + computation of this quantity.
961 +
962 + Our simulations have seen significant conductance enhancement in the
963 + presence of capping agent, compared with the bare gold / liquid
964 + interfaces. The acoustic impedance mismatch between the metal and the
965 + liquid phase is effectively eliminated by a chemically-bonded capping
966 + agent. Furthermore, the coverage precentage of the capping agent plays
967 + an important role in the interfacial thermal transport
968 + process. Moderately low coverages allow higher contact between capping
969 + agent and solvent, and thus could further enhance the heat transfer
970 + process, giving a non-monotonic behavior of conductance with
971 + increasing coverage.
972 +
973 + Our results, particularly using the UA models, agree well with
974 + available experimental data.  The AA models tend to overestimate the
975 + interfacial thermal conductance in that the classically treated C-H
976 + vibrations become too easily populated. Compared to the AA models, the
977 + UA models have higher computational efficiency with satisfactory
978 + accuracy, and thus are preferable in modeling interfacial thermal
979 + transport.
980 +
981 + Of the two definitions for $G$, the discrete form
982 + (Eq. \ref{discreteG}) was easier to use and gives out relatively
983 + consistent results, while the derivative form (Eq. \ref{derivativeG})
984 + is not as versatile. Although $G^\prime$ gives out comparable results
985 + and follows similar trend with $G$ when measuring close to fully
986 + covered or bare surfaces, the spatial resolution of $T$ profile
987 + required for the use of a derivative form is limited by the number of
988 + bins and the sampling required to obtain thermal gradient information.
989 +
990 + Vlugt {\it et al.} have investigated the surface thiol structures for
991 + nanocrystalline gold and pointed out that they differ from those of
992 + the Au(111) surface.\cite{landman:1998,vlugt:cpc2007154} This
993 + difference could also cause differences in the interfacial thermal
994 + transport behavior. To investigate this problem, one would need an
995 + effective method for applying thermal gradients in non-planar
996 + (i.e. spherical) geometries.
997 +
998   \section{Acknowledgments}
999   Support for this project was provided by the National Science
1000   Foundation under grant CHE-0848243. Computational time was provided by
1001   the Center for Research Computing (CRC) at the University of Notre
1002 < Dame.  \newpage
1002 > Dame.
1003 > \newpage
1004  
1005   \bibliography{interfacial}
1006  

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