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23   \setlength{\belowcaptionskip}{30 pt}
24  
25   %\renewcommand\citemid{\ } % no comma in optional reference note
26 < \bibpunct{[}{]}{,}{s}{}{;}
27 < \bibliographystyle{aip}
26 > \bibpunct{[}{]}{,}{n}{}{;}
27 > \bibliographystyle{achemso}
28  
29   \begin{document}
30  
31 < \title{Simulating interfacial thermal conductance at metal-solvent
32 <  interfaces: the role of chemical capping agents}
31 > \title{Simulating Interfacial Thermal Conductance at Metal-Solvent
32 >  Interfaces: the Role of Chemical Capping Agents}
33  
34   \author{Shenyu Kuang and J. Daniel
35   Gezelter\footnote{Corresponding author. \ Electronic mail: gezelter@nd.edu} \\
# Line 44 | Line 44 | Notre Dame, Indiana 46556}
44   \begin{doublespace}
45  
46   \begin{abstract}
47 <
48 < We have developed a Non-Isotropic Velocity Scaling algorithm for
49 < setting up and maintaining stable thermal gradients in non-equilibrium
50 < molecular dynamics simulations. This approach effectively imposes
51 < unphysical thermal flux even between particles of different
52 < identities, conserves linear momentum and kinetic energy, and
53 < minimally perturbs the velocity profile of a system when compared with
54 < previous RNEMD methods. We have used this method to simulate thermal
55 < conductance at metal / organic solvent interfaces both with and
56 < without the presence of thiol-based capping agents.  We obtained
57 < values comparable with experimental values, and observed significant
58 < conductance enhancement with the presence of capping agents. Computed
59 < power spectra indicate the acoustic impedance mismatch between metal
60 < and liquid phase is greatly reduced by the capping agents and thus
61 < leads to higher interfacial thermal transfer efficiency.
62 <
47 >  With the Non-Isotropic Velocity Scaling (NIVS) approach to Reverse
48 >  Non-Equilibrium Molecular Dynamics (RNEMD) it is possible to impose
49 >  an unphysical thermal flux between different regions of
50 >  inhomogeneous systems such as solid / liquid interfaces.  We have
51 >  applied NIVS to compute the interfacial thermal conductance at a
52 >  metal / organic solvent interface that has been chemically capped by
53 >  butanethiol molecules.  Our calculations suggest that the acoustic
54 >  impedance mismatch between the metal and liquid phases is
55 >  effectively reduced by the capping agents, leading to a greatly
56 >  enhanced conductivity at the interface.  Specifically, the chemical
57 >  bond between the metal and the capping agent introduces a
58 >  vibrational overlap that is not present without the capping agent,
59 >  and the overlap between the vibrational spectra (metal to cap, cap
60 >  to solvent) provides a mechanism for rapid thermal transport across
61 >  the interface. Our calculations also suggest that this is a
62 >  non-monotonic function of the fractional coverage of the surface, as
63 >  moderate coverages allow {\bf vibrational heat diffusion} of solvent
64 >  molecules that have been in close contact with the capping agent.
65   \end{abstract}
66  
67   \newpage
# Line 71 | Line 73 | leads to higher interfacial thermal transfer efficienc
73   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
74  
75   \section{Introduction}
76 < [BACKGROUND FOR INTERFACIAL THERMAL CONDUCTANCE PROBLEM]
77 < Interfacial thermal conductance is extensively studied both
78 < experimentally and computationally, and systems with interfaces
79 < present are generally heterogeneous. Although interfaces are commonly
80 < barriers to heat transfer, it has been
81 < reported\cite{doi:10.1021/la904855s} that under specific circustances,
82 < e.g. with certain capping agents present on the surface, interfacial
83 < conductance can be significantly enhanced. However, heat conductance
84 < of molecular and nano-scale interfaces will be affected by the
83 < chemical details of the surface and is challenging to
84 < experimentalist. The lower thermal flux through interfaces is even
85 < more difficult to measure with EMD and forward NEMD simulation
86 < methods. Therefore, developing good simulation methods will be
87 < desirable in order to investigate thermal transport across interfaces.
76 > Due to the importance of heat flow (and heat removal) in
77 > nanotechnology, interfacial thermal conductance has been studied
78 > extensively both experimentally and computationally.\cite{cahill:793}
79 > Nanoscale materials have a significant fraction of their atoms at
80 > interfaces, and the chemical details of these interfaces govern the
81 > thermal transport properties.  Furthermore, the interfaces are often
82 > heterogeneous (e.g. solid - liquid), which provides a challenge to
83 > computational methods which have been developed for homogeneous or
84 > bulk systems.
85  
86 < Recently, we have developed the Non-Isotropic Velocity Scaling (NIVS)
86 > Experimentally, the thermal properties of a number of interfaces have
87 > been investigated.  Cahill and coworkers studied nanoscale thermal
88 > transport from metal nanoparticle/fluid interfaces, to epitaxial
89 > TiN/single crystal oxides interfaces, and hydrophilic and hydrophobic
90 > interfaces between water and solids with different self-assembled
91 > monolayers.\cite{Wilson:2002uq,PhysRevB.67.054302,doi:10.1021/jp048375k,PhysRevLett.96.186101}
92 > Wang {\it et al.} studied heat transport through long-chain
93 > hydrocarbon monolayers on gold substrate at individual molecular
94 > level,\cite{Wang10082007} Schmidt {\it et al.} studied the role of
95 > cetyltrimethylammonium bromide (CTAB) on the thermal transport between
96 > gold nanorods and solvent,\cite{doi:10.1021/jp8051888} and Juv\'e {\it
97 >  et al.} studied the cooling dynamics, which is controlled by thermal
98 > interface resistance of glass-embedded metal
99 > nanoparticles.\cite{PhysRevB.80.195406} Although interfaces are
100 > normally considered barriers for heat transport, Alper {\it et al.}
101 > suggested that specific ligands (capping agents) could completely
102 > eliminate this barrier
103 > ($G\rightarrow\infty$).\cite{doi:10.1021/la904855s}
104 >
105 > Theoretical and computational models have also been used to study the
106 > interfacial thermal transport in order to gain an understanding of
107 > this phenomena at the molecular level. Recently, Hase and coworkers
108 > employed Non-Equilibrium Molecular Dynamics (NEMD) simulations to
109 > study thermal transport from hot Au(111) substrate to a self-assembled
110 > monolayer of alkylthiol with relatively long chain (8-20 carbon
111 > atoms).\cite{hase:2010,hase:2011} However, ensemble averaged
112 > measurements for heat conductance of interfaces between the capping
113 > monolayer on Au and a solvent phase have yet to be studied with their
114 > approach. The comparatively low thermal flux through interfaces is
115 > difficult to measure with Equilibrium
116 > MD\cite{doi:10.1080/0026897031000068578} or forward NEMD simulation
117 > methods. Therefore, the Reverse NEMD (RNEMD)
118 > methods\cite{MullerPlathe:1997xw,kuang:164101} would be advantageous
119 > in that they {\it apply} the difficult to measure quantity (flux),
120 > while {\it measuring} the easily-computed quantity (the thermal
121 > gradient).  This is particularly true for inhomogeneous interfaces
122 > where it would not be clear how to apply a gradient {\it a priori}.
123 > Garde and coworkers\cite{garde:nl2005,garde:PhysRevLett2009} applied
124 > this approach to various liquid interfaces and studied how thermal
125 > conductance (or resistance) is dependent on chemical details of a
126 > number of hydrophobic and hydrophilic aqueous interfaces. {\bf And
127 >  Luo {\it et al.} studied the thermal conductance of Au-SAM-Au
128 >  junctions using the same approach, with comparison to a constant
129 >  temperature difference method\cite{Luo20101}. While this latter
130 >  approach establishes more thermal distributions compared to the
131 >  former RNEMD methods, it does not guarantee momentum or kinetic
132 >  energy conservations.}
133 >
134 > Recently, we have developed a Non-Isotropic Velocity Scaling (NIVS)
135   algorithm for RNEMD simulations\cite{kuang:164101}. This algorithm
136   retains the desirable features of RNEMD (conservation of linear
137   momentum and total energy, compatibility with periodic boundary
138   conditions) while establishing true thermal distributions in each of
139 < the two slabs. Furthermore, it allows more effective thermal exchange
140 < between particles of different identities, and thus enables extensive
141 < study of interfacial conductance.
139 > the two slabs. Furthermore, it allows effective thermal exchange
140 > between particles of different identities, and thus makes the study of
141 > interfacial conductance much simpler.
142  
143 + The work presented here deals with the Au(111) surface covered to
144 + varying degrees by butanethiol, a capping agent with short carbon
145 + chain, and solvated with organic solvents of different molecular
146 + properties. {\bf To our knowledge, few previous MD inverstigations
147 +  have been found to address to these systems yet.} Different models
148 + were used for both the capping agent and the solvent force field
149 + parameters. Using the NIVS algorithm, the thermal transport across
150 + these interfaces was studied and the underlying mechanism for the
151 + phenomena was investigated.
152 +
153   \section{Methodology}
154 < \subsection{Algorithm}
155 < [BACKGROUND FOR MD METHODS]
156 < There have been many algorithms for computing thermal conductivity
157 < using molecular dynamics simulations. However, interfacial conductance
158 < is at least an order of magnitude smaller. This would make the
159 < calculation even more difficult for those slowly-converging
160 < equilibrium methods. Imposed-flux non-equilibrium
161 < methods\cite{MullerPlathe:1997xw} have the flux set {\it a priori} and
162 < the response of temperature or momentum gradients are easier to
163 < measure than the flux, if unknown, and thus, is a preferable way to
164 < the forward NEMD methods. Although the momentum swapping approach for
165 < flux-imposing can be used for exchanging energy between particles of
166 < different identity, the kinetic energy transfer efficiency is affected
167 < by the mass difference between the particles, which limits its
168 < application on heterogeneous interfacial systems.
154 > \subsection{Imposed-Flux Methods in MD Simulations}
155 > Steady state MD simulations have an advantage in that not many
156 > trajectories are needed to study the relationship between thermal flux
157 > and thermal gradients. For systems with low interfacial conductance,
158 > one must have a method capable of generating or measuring relatively
159 > small fluxes, compared to those required for bulk conductivity. This
160 > requirement makes the calculation even more difficult for
161 > slowly-converging equilibrium methods.\cite{Viscardy:2007lq} Forward
162 > NEMD methods impose a gradient (and measure a flux), but at interfaces
163 > it is not clear what behavior should be imposed at the boundaries
164 > between materials.  Imposed-flux reverse non-equilibrium
165 > methods\cite{MullerPlathe:1997xw} set the flux {\it a priori} and
166 > the thermal response becomes an easy-to-measure quantity.  Although
167 > M\"{u}ller-Plathe's original momentum swapping approach can be used
168 > for exchanging energy between particles of different identity, the
169 > kinetic energy transfer efficiency is affected by the mass difference
170 > between the particles, which limits its application on heterogeneous
171 > interfacial systems.
172  
173 < The non-isotropic velocity scaling (NIVS)\cite{kuang:164101} approach in
174 < non-equilibrium MD simulations is able to impose relatively large
175 < kinetic energy flux without obvious perturbation to the velocity
176 < distribution of the simulated systems. Furthermore, this approach has
177 < the advantage in heterogeneous interfaces in that kinetic energy flux
178 < can be applied between regions of particles of arbitary identity, and
179 < the flux quantity is not restricted by particle mass difference.
173 > The non-isotropic velocity scaling (NIVS) \cite{kuang:164101} approach
174 > to non-equilibrium MD simulations is able to impose a wide range of
175 > kinetic energy fluxes without obvious perturbation to the velocity
176 > distributions of the simulated systems. Furthermore, this approach has
177 > the advantage in heterogeneous interfaces in that kinetic energy flux
178 > can be applied between regions of particles of arbitrary identity, and
179 > the flux will not be restricted by difference in particle mass.
180  
181   The NIVS algorithm scales the velocity vectors in two separate regions
182 < of a simulation system with respective diagonal scaling matricies. To
183 < determine these scaling factors in the matricies, a set of equations
182 > of a simulation system with respective diagonal scaling matrices. To
183 > determine these scaling factors in the matrices, a set of equations
184   including linear momentum conservation and kinetic energy conservation
185 < constraints and target momentum/energy flux satisfaction is
186 < solved. With the scaling operation applied to the system in a set
187 < frequency, corresponding momentum/temperature gradients can be built,
188 < which can be used for computing transportation properties and other
189 < applications related to momentum/temperature gradients. The NIVS
132 < algorithm conserves momenta and energy and does not depend on an
133 < external thermostat.
185 > constraints and target energy flux satisfaction is solved. With the
186 > scaling operation applied to the system in a set frequency, bulk
187 > temperature gradients can be easily established, and these can be used
188 > for computing thermal conductivities. The NIVS algorithm conserves
189 > momenta and energy and does not depend on an external thermostat.
190  
191 < \subsection{Defining Interfacial Thermal Conductivity $G$}
192 < For interfaces with a relatively low interfacial conductance, the bulk
193 < regions on either side of an interface rapidly come to a state in
194 < which the two phases have relatively homogeneous (but distinct)
195 < temperatures. The interfacial thermal conductivity $G$ can therefore
196 < be approximated as:
191 > \subsection{Defining Interfacial Thermal Conductivity ($G$)}
192 >
193 > For an interface with relatively low interfacial conductance, and a
194 > thermal flux between two distinct bulk regions, the regions on either
195 > side of the interface rapidly come to a state in which the two phases
196 > have relatively homogeneous (but distinct) temperatures. The
197 > interfacial thermal conductivity $G$ can therefore be approximated as:
198   \begin{equation}
199 < G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
199 >  G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
200      \langle T_\mathrm{cold}\rangle \right)}
201   \label{lowG}
202   \end{equation}
203 < where ${E_{total}}$ is the imposed non-physical kinetic energy
204 < transfer and ${\langle T_\mathrm{hot}\rangle}$ and ${\langle
205 <  T_\mathrm{cold}\rangle}$ are the average observed temperature of the
206 < two separated phases.
203 > where ${E_{total}}$ is the total imposed non-physical kinetic energy
204 > transfer during the simulation and ${\langle T_\mathrm{hot}\rangle}$
205 > and ${\langle T_\mathrm{cold}\rangle}$ are the average observed
206 > temperature of the two separated phases.  For an applied flux $J_z$
207 > operating over a simulation time $t$ on a periodically-replicated slab
208 > of dimensions $L_x \times L_y$, $E_{total} = J_z *(t)*(2 L_x L_y)$.
209  
210 < When the interfacial conductance is {\it not} small, two ways can be
211 < used to define $G$.
212 <
213 < One way is to assume the temperature is discretely different on two
214 < sides of the interface, $G$ can be calculated with the thermal flux
215 < applied $J$ and the maximum temperature difference measured along the
216 < thermal gradient max($\Delta T$), which occurs at the interface, as:
210 > When the interfacial conductance is {\it not} small, there are two
211 > ways to define $G$. One common way is to assume the temperature is
212 > discrete on the two sides of the interface. $G$ can be calculated
213 > using the applied thermal flux $J$ and the maximum temperature
214 > difference measured along the thermal gradient max($\Delta T$), which
215 > occurs at the Gibbs dividing surface (Figure \ref{demoPic}). This is
216 > known as the Kapitza conductance, which is the inverse of the Kapitza
217 > resistance.
218   \begin{equation}
219 < G=\frac{J}{\Delta T}
219 >  G=\frac{J}{\Delta T}
220   \label{discreteG}
221   \end{equation}
222  
223 + \begin{figure}
224 + \includegraphics[width=\linewidth]{method}
225 + \caption{Interfacial conductance can be calculated by applying an
226 +  (unphysical) kinetic energy flux between two slabs, one located
227 +  within the metal and another on the edge of the periodic box.  The
228 +  system responds by forming a thermal gradient.  In bulk liquids,
229 +  this gradient typically has a single slope, but in interfacial
230 +  systems, there are distinct thermal conductivity domains.  The
231 +  interfacial conductance, $G$ is found by measuring the temperature
232 +  gap at the Gibbs dividing surface, or by using second derivatives of
233 +  the thermal profile.}
234 + \label{demoPic}
235 + \end{figure}
236 +
237 + {\bf We attempt another approach by assuming that temperature is
238 +  continuous and differentiable throughout the space. Given that
239 +  $\lambda$ is also differentiable, $G$ can be defined as its
240 +  gradient. This quantity has the same unit as the commonly known $G$,
241 +  and the maximum of its magnitude denotes where thermal conductivity
242 +  has the largest change, i.e. the interface. And vector
243 +  $\nabla\lambda$ is normal to the interface. In a simplified
244 +  condition here, we have both $\vec{J}$ and the thermal gradient
245 +  paralell to the $z$ axis and yield the formula used in our
246 +  computations.}
247 + (original text)
248   The other approach is to assume a continuous temperature profile along
249   the thermal gradient axis (e.g. $z$) and define $G$ at the point where
250 < the magnitude of thermal conductivity $\lambda$ change reach its
250 > the magnitude of thermal conductivity ($\lambda$) change reaches its
251   maximum, given that $\lambda$ is well-defined throughout the space:
252   \begin{equation}
253   G^\prime = \Big|\frac{\partial\lambda}{\partial z}\Big|
# Line 173 | Line 258 | With the temperature profile obtained from simulations
258   \label{derivativeG}
259   \end{equation}
260  
261 < With the temperature profile obtained from simulations, one is able to
261 > With temperature profiles obtained from simulation, one is able to
262   approximate the first and second derivatives of $T$ with finite
263 < difference method and thus calculate $G^\prime$.
263 > difference methods and calculate $G^\prime$. In what follows, both
264 > definitions have been used, and are compared in the results.
265  
266 < In what follows, both definitions are used for calculation and comparison.
266 > To investigate the interfacial conductivity at metal / solvent
267 > interfaces, we have modeled a metal slab with its (111) surfaces
268 > perpendicular to the $z$-axis of our simulation cells. The metal slab
269 > has been prepared both with and without capping agents on the exposed
270 > surface, and has been solvated with simple organic solvents, as
271 > illustrated in Figure \ref{gradT}.
272  
273 < [IMPOSE G DEFINITION INTO OUR SYSTEMS]
274 < To facilitate the use of the above definitions in calculating $G$ and
275 < $G^\prime$, we have a metal slab with its (111) surfaces perpendicular
276 < to the $z$-axis of our simulation cells. With or withour capping
277 < agents on the surfaces, the metal slab is solvated with organic
278 < solvents, as illustrated in Figure \ref{demoPic}.
273 > With the simulation cell described above, we are able to equilibrate
274 > the system and impose an unphysical thermal flux between the liquid
275 > and the metal phase using the NIVS algorithm. By periodically applying
276 > the unphysical flux, we obtained a temperature profile and its spatial
277 > derivatives. Figure \ref{gradT} shows how an applied thermal flux can
278 > be used to obtain the 1st and 2nd derivatives of the temperature
279 > profile.
280  
281   \begin{figure}
190 \includegraphics[width=\linewidth]{demoPic}
191 \caption{A sample showing how a metal slab has its (111) surface
192  covered by capping agent molecules and solvated by hexane.}
193 \label{demoPic}
194 \end{figure}
195
196 With a simulation cell setup following the above manner, one is able
197 to equilibrate the system and impose an unphysical thermal flux
198 between the liquid and the metal phase with the NIVS algorithm. Under
199 a stablized thermal gradient induced by periodically applying the
200 unphysical flux, one is able to obtain a temperature profile and the
201 physical thermal flux corresponding to it, which equals to the
202 unphysical flux applied by NIVS. These data enables the evaluation of
203 the interfacial thermal conductance of a surface. Figure \ref{gradT}
204 is an example how those stablized thermal gradient can be used to
205 obtain the 1st and 2nd derivatives of the temperature profile.
206
207 \begin{figure}
282   \includegraphics[width=\linewidth]{gradT}
283 < \caption{The 1st and 2nd derivatives of temperature profile can be
284 <  obtained with finite difference approximation.}
283 > \caption{A sample of Au (111) / butanethiol / hexane interfacial
284 >  system with the temperature profile after a kinetic energy flux has
285 >  been imposed.  Note that the largest temperature jump in the thermal
286 >  profile (corresponding to the lowest interfacial conductance) is at
287 >  the interface between the butanethiol molecules (blue) and the
288 >  solvent (grey).  First and second derivatives of the temperature
289 >  profile are obtained using a finite difference approximation (lower
290 >  panel).}
291   \label{gradT}
292   \end{figure}
293  
294   \section{Computational Details}
295 < \subsection{System Geometry}
296 < In our simulations, Au is used to construct a metal slab with bare
297 < (111) surface perpendicular to the $z$-axis. Different slab thickness
298 < (layer numbers of Au) are simulated. This metal slab is first
299 < equilibrated under normal pressure (1 atm) and a desired
300 < temperature. After equilibration, butanethiol is used as the capping
301 < agent molecule to cover the bare Au (111) surfaces evenly. The sulfur
302 < atoms in the butanethiol molecules would occupy the three-fold sites
303 < of the surfaces, and the maximal butanethiol capacity on Au surface is
304 < $1/3$ of the total number of surface Au atoms[CITATION]. A series of
305 < different coverage surfaces is investigated in order to study the
306 < relation between coverage and conductance.
295 > \subsection{Simulation Protocol}
296 > The NIVS algorithm has been implemented in our MD simulation code,
297 > OpenMD\cite{Meineke:2005gd,openmd}, and was used for our simulations.
298 > Metal slabs of 6 or 11 layers of Au atoms were first equilibrated
299 > under atmospheric pressure (1 atm) and 200K. After equilibration,
300 > butanethiol capping agents were placed at three-fold hollow sites on
301 > the Au(111) surfaces. These sites are either {\it fcc} or {\it
302 >  hcp} sites, although Hase {\it et al.} found that they are
303 > equivalent in a heat transfer process,\cite{hase:2010} so we did not
304 > distinguish between these sites in our study. The maximum butanethiol
305 > capacity on Au surface is $1/3$ of the total number of surface Au
306 > atoms, and the packing forms a $(\sqrt{3}\times\sqrt{3})R30^\circ$
307 > structure\cite{doi:10.1021/ja00008a001,doi:10.1021/cr9801317}. A
308 > series of lower coverages was also prepared by eliminating
309 > butanethiols from the higher coverage surface in a regular manner. The
310 > lower coverages were prepared in order to study the relation between
311 > coverage and interfacial conductance.
312  
313 < [COVERAGE DISCRIPTION] However, since the interactions between surface
314 < Au and butanethiol is non-bonded, the capping agent molecules are
315 < allowed to migrate to an empty neighbor three-fold site during a
316 < simulation. Therefore, the initial configuration would not severely
317 < affect the sampling of a variety of configurations of the same
318 < coverage, and the final conductance measurement would be an average
319 < effect of these configurations explored in the simulations. [MAY NEED FIGURES]
313 > The capping agent molecules were allowed to migrate during the
314 > simulations. They distributed themselves uniformly and sampled a
315 > number of three-fold sites throughout out study. Therefore, the
316 > initial configuration does not noticeably affect the sampling of a
317 > variety of configurations of the same coverage, and the final
318 > conductance measurement would be an average effect of these
319 > configurations explored in the simulations.
320  
321 < After the modified Au-butanethiol surface systems are equilibrated
322 < under canonical ensemble, Packmol\cite{packmol} is used to pack
323 < organic solvent molecules in the previously vacuum part of the
324 < simulation cells, which guarantees that short range repulsive
325 < interactions do not disrupt the simulations. Two solvents are
326 < investigated, one which has little vibrational overlap with the
327 < alkanethiol and plane-like shape (toluene), and one which has similar
243 < vibrational frequencies and chain-like shape ({\it n}-hexane). The
244 < spacing filled by solvent molecules, i.e. the gap between periodically
245 < repeated Au-butanethiol surfaces should be carefully chosen so that it
246 < would not be too short to affect the liquid phase structure, nor too
247 < long, leading to over cooling (freezing) or heating (boiling) when a
248 < thermal flux is applied. In our simulations, this spacing is usually
249 < $35 \sim 60$\AA.
321 > After the modified Au-butanethiol surface systems were equilibrated in
322 > the canonical (NVT) ensemble, organic solvent molecules were packed in
323 > the previously empty part of the simulation cells.\cite{packmol} Two
324 > solvents were investigated, one which has little vibrational overlap
325 > with the alkanethiol and which has a planar shape (toluene), and one
326 > which has similar vibrational frequencies to the capping agent and
327 > chain-like shape ({\it n}-hexane).
328  
329 < The initial configurations generated by Packmol are further
330 < equilibrated with the $x$ and $y$ dimensions fixed, only allowing
331 < length scale change in $z$ dimension. This is to ensure that the
332 < equilibration of liquid phase does not affect the metal crystal
333 < structure in $x$ and $y$ dimensions. Further equilibration are run
334 < under NVT and then NVE ensembles.
329 > The simulation cells were not particularly extensive along the
330 > $z$-axis, as a very long length scale for the thermal gradient may
331 > cause excessively hot or cold temperatures in the middle of the
332 > solvent region and lead to undesired phenomena such as solvent boiling
333 > or freezing when a thermal flux is applied. Conversely, too few
334 > solvent molecules would change the normal behavior of the liquid
335 > phase. Therefore, our $N_{solvent}$ values were chosen to ensure that
336 > these extreme cases did not happen to our simulations. The spacing
337 > between periodic images of the gold interfaces is $45 \sim 75$\AA in
338 > our simulations.
339  
340 < After the systems reach equilibrium, NIVS is implemented to impose a
341 < periodic unphysical thermal flux between the metal and the liquid
342 < phase. Most of our simulations are under an average temperature of
343 < $\sim$200K. Therefore, this flux usually comes from the metal to the
340 > The initial configurations generated are further equilibrated with the
341 > $x$ and $y$ dimensions fixed, only allowing the $z$-length scale to
342 > change. This is to ensure that the equilibration of liquid phase does
343 > not affect the metal's crystalline structure. Comparisons were made
344 > with simulations that allowed changes of $L_x$ and $L_y$ during NPT
345 > equilibration. No substantial changes in the box geometry were noticed
346 > in these simulations. After ensuring the liquid phase reaches
347 > equilibrium at atmospheric pressure (1 atm), further equilibration was
348 > carried out under canonical (NVT) and microcanonical (NVE) ensembles.
349 >
350 > After the systems reach equilibrium, NIVS was used to impose an
351 > unphysical thermal flux between the metal and the liquid phases. Most
352 > of our simulations were done under an average temperature of
353 > $\sim$200K. Therefore, thermal flux usually came from the metal to the
354   liquid so that the liquid has a higher temperature and would not
355 < freeze due to excessively low temperature. This induced temperature
356 < gradient is stablized and the simulation cell is devided evenly into
357 < N slabs along the $z$-axis and the temperatures of each slab are
358 < recorded. When the slab width $d$ of each slab is the same, the
359 < derivatives of $T$ with respect to slab number $n$ can be directly
360 < used for $G^\prime$ calculations:
361 < \begin{equation}
362 < G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
355 > freeze due to lowered temperatures. After this induced temperature
356 > gradient had stabilized, the temperature profile of the simulation cell
357 > was recorded. To do this, the simulation cell is divided evenly into
358 > $N$ slabs along the $z$-axis. The average temperatures of each slab
359 > are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
360 > the same, the derivatives of $T$ with respect to slab number $n$ can
361 > be directly used for $G^\prime$ calculations: \begin{equation}
362 >  G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
363           \Big/\left(\frac{\partial T}{\partial z}\right)^2
364           = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
365           \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
# Line 276 | Line 368 | G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}
368   \label{derivativeG2}
369   \end{equation}
370  
371 + All of the above simulation procedures use a time step of 1 fs. Each
372 + equilibration stage took a minimum of 100 ps, although in some cases,
373 + longer equilibration stages were utilized.
374 +
375   \subsection{Force Field Parameters}
376 < Our simulations include various components. Therefore, force field
377 < parameter descriptions are needed for interactions both between the
378 < same type of particles and between particles of different species.
376 > Our simulations include a number of chemically distinct components.
377 > Figure \ref{demoMol} demonstrates the sites defined for both
378 > United-Atom and All-Atom models of the organic solvent and capping
379 > agents in our simulations. Force field parameters are needed for
380 > interactions both between the same type of particles and between
381 > particles of different species.
382  
383 + \begin{figure}
384 + \includegraphics[width=\linewidth]{structures}
385 + \caption{Structures of the capping agent and solvents utilized in
386 +  these simulations. The chemically-distinct sites (a-e) are expanded
387 +  in terms of constituent atoms for both United Atom (UA) and All Atom
388 +  (AA) force fields.  Most parameters are from References
389 +  \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes,TraPPE-UA.thiols}
390 +  (UA) and \protect\cite{OPLSAA} (AA). Cross-interactions with the Au
391 +  atoms are given in Table \ref{MnM}.}
392 + \label{demoMol}
393 + \end{figure}
394 +
395   The Au-Au interactions in metal lattice slab is described by the
396   quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
397   potentials include zero-point quantum corrections and are
398   reparametrized for accurate surface energies compared to the
399 < Sutton-Chen potentials\cite{Chen90}.
399 > Sutton-Chen potentials.\cite{Chen90}
400  
401 < For both solvent molecules, straight chain {\it n}-hexane and aromatic
402 < toluene, United-Atom (UA) and All-Atom (AA) models are used
403 < respectively. The TraPPE-UA
401 > For the two solvent molecules, {\it n}-hexane and toluene, two
402 > different atomistic models were utilized. Both solvents were modeled
403 > using United-Atom (UA) and All-Atom (AA) models. The TraPPE-UA
404   parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
405 < for our UA solvent molecules. In these models, pseudo-atoms are
406 < located at the carbon centers for alkyl groups. By eliminating
407 < explicit hydrogen atoms, these models are simple and computationally
408 < efficient, while maintains good accuracy. [LOW BOILING POINT IS A
409 < KNOWN PROBLEM FOR TRAPPE-UA ALKANES, NEED MORE DISCUSSION]
299 < for
300 < toluene,  force fields are
301 < used with rigid body constraints applied.[MORE DETAILS NEEDED]
405 > for our UA solvent molecules. In these models, sites are located at
406 > the carbon centers for alkyl groups. Bonding interactions, including
407 > bond stretches and bends and torsions, were used for intra-molecular
408 > sites closer than 3 bonds. For non-bonded interactions, Lennard-Jones
409 > potentials are used.
410  
411 < Besides the TraPPE-UA models, AA models are included in our studies as
412 < well. For hexane, the OPLS all-atom\cite{OPLSAA} force field is
413 < used. [MORE DETAILS]
414 < For toluene,
411 > By eliminating explicit hydrogen atoms, the TraPPE-UA models are
412 > simple and computationally efficient, while maintaining good accuracy.
413 > However, the TraPPE-UA model for alkanes is known to predict a slightly
414 > lower boiling point than experimental values. This is one of the
415 > reasons we used a lower average temperature (200K) for our
416 > simulations. If heat is transferred to the liquid phase during the
417 > NIVS simulation, the liquid in the hot slab can actually be
418 > substantially warmer than the mean temperature in the simulation. The
419 > lower mean temperatures therefore prevent solvent boiling.
420  
421 < Buatnethiol molecules are used as capping agent for some of our
422 < simulations. United-Atom\cite{TraPPE-UA.thiols} and All-Atom models
423 < are respectively used corresponding to the force field type of
424 < solvent.
421 > For UA-toluene, the non-bonded potentials between intermolecular sites
422 > have a similar Lennard-Jones formulation. The toluene molecules were
423 > treated as a single rigid body, so there was no need for
424 > intramolecular interactions (including bonds, bends, or torsions) in
425 > this solvent model.
426  
427 < To describe the interactions between metal Au and non-metal capping
428 < agent and solvent, we refer to Vlugt\cite{vlugt:cpc2007154} and derive
429 < other interactions which are not parametrized in their work. (can add
430 < hautman and klein's paper here and more discussion; need to put
431 < aromatic-metal interaction approximation here)
427 > Besides the TraPPE-UA models, AA models for both organic solvents are
428 > included in our studies as well. The OPLS-AA\cite{OPLSAA} force fields
429 > were used. For hexane, additional explicit hydrogen sites were
430 > included. Besides bonding and non-bonded site-site interactions,
431 > partial charges and the electrostatic interactions were added to each
432 > CT and HC site. For toluene, a flexible model for the toluene molecule
433 > was utilized which included bond, bend, torsion, and inversion
434 > potentials to enforce ring planarity.
435  
436 < [TABULATED FORCE FIELD PARAMETERS NEEDED]
436 > The butanethiol capping agent in our simulations, were also modeled
437 > with both UA and AA model. The TraPPE-UA force field includes
438 > parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
439 > UA butanethiol model in our simulations. The OPLS-AA also provides
440 > parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
441 > surfaces do not have the hydrogen atom bonded to sulfur. To derive
442 > suitable parameters for butanethiol adsorbed on Au(111) surfaces, we
443 > adopt the S parameters from Luedtke and Landman\cite{landman:1998} and
444 > modify the parameters for the CTS atom to maintain charge neutrality
445 > in the molecule.  Note that the model choice (UA or AA) for the capping
446 > agent can be different from the solvent. Regardless of model choice,
447 > the force field parameters for interactions between capping agent and
448 > solvent can be derived using Lorentz-Berthelot Mixing Rule:
449 > \begin{eqnarray}
450 >  \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
451 >  \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
452 > \end{eqnarray}
453  
454 < \section{Results}
455 < \subsection{Toluene Solvent}
454 > To describe the interactions between metal (Au) and non-metal atoms,
455 > we refer to an adsorption study of alkyl thiols on gold surfaces by
456 > Vlugt {\it et al.}\cite{vlugt:cpc2007154} They fitted an effective
457 > Lennard-Jones form of potential parameters for the interaction between
458 > Au and pseudo-atoms CH$_x$ and S based on a well-established and
459 > widely-used effective potential of Hautman and Klein for the Au(111)
460 > surface.\cite{hautman:4994} As our simulations require the gold slab
461 > to be flexible to accommodate thermal excitation, the pair-wise form
462 > of potentials they developed was used for our study.
463  
464 < The results (Table \ref{AuThiolToluene}) show a
465 < significant conductance enhancement compared to the gold/water
466 < interface without capping agent and agree with available experimental
467 < data. This indicates that the metal-metal potential, though not
468 < predicting an accurate bulk metal thermal conductivity, does not
469 < greatly interfere with the simulation of the thermal conductance
470 < behavior across a non-metal interface. The solvent model is not
471 < particularly volatile, so the simulation cell does not expand
472 < significantly under higher temperature. We did not observe a
473 < significant conductance decrease when the temperature was increased to
474 < 300K. The results show that the two definitions used for $G$ yield
335 < comparable values, though $G^\prime$ tends to be smaller.
464 > The potentials developed from {\it ab initio} calculations by Leng
465 > {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
466 > interactions between Au and aromatic C/H atoms in toluene. However,
467 > the Lennard-Jones parameters between Au and other types of particles,
468 > (e.g. AA alkanes) have not yet been established. For these
469 > interactions, the Lorentz-Berthelot mixing rule can be used to derive
470 > effective single-atom LJ parameters for the metal using the fit values
471 > for toluene. These are then used to construct reasonable mixing
472 > parameters for the interactions between the gold and other atoms.
473 > Table \ref{MnM} summarizes the ``metal/non-metal'' parameters used in
474 > our simulations.
475  
476   \begin{table*}
477    \begin{minipage}{\linewidth}
478      \begin{center}
479 <      \caption{Computed interfacial thermal conductivity ($G$ and
480 <        $G^\prime$) values for the Au/butanethiol/toluene interface at
481 <        different temperatures using a range of energy fluxes.}
482 <      
344 <      \begin{tabular}{cccc}
479 >      \caption{Non-bonded interaction parameters (including cross
480 >        interactions with Au atoms) for both force fields used in this
481 >        work.}      
482 >      \begin{tabular}{lllllll}
483          \hline\hline
484 <        $\langle T\rangle$ & $J_z$ & $G$ & $G^\prime$ \\
485 <        (K) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
484 >        & Site  & $\sigma_{ii}$ & $\epsilon_{ii}$ & $q_i$ &
485 >        $\sigma_{Au-i}$ & $\epsilon_{Au-i}$ \\
486 >        & & (\AA) & (kcal/mol) & ($e$) & (\AA) & (kcal/mol) \\
487          \hline
488 <        200 & 1.86 & 180 & 135 \\
489 <            & 2.15 & 204 & 113 \\
490 <            & 3.93 & 175 & 114 \\
491 <        300 & 1.91 & 143 & 125 \\
492 <            & 4.19 & 134 & 113 \\
488 >        United Atom (UA)
489 >        &CH3  & 3.75  & 0.1947  & -      & 3.54   & 0.2146  \\
490 >        &CH2  & 3.95  & 0.0914  & -      & 3.54   & 0.1749  \\
491 >        &CHar & 3.695 & 0.1003  & -      & 3.4625 & 0.1680  \\
492 >        &CRar & 3.88  & 0.04173 & -      & 3.555  & 0.1604  \\
493 >        \hline
494 >        All Atom (AA)
495 >        &CT3  & 3.50  & 0.066   & -0.18  & 3.365  & 0.1373  \\
496 >        &CT2  & 3.50  & 0.066   & -0.12  & 3.365  & 0.1373  \\
497 >        &CTT  & 3.50  & 0.066   & -0.065 & 3.365  & 0.1373  \\
498 >        &HC   & 2.50  & 0.030   &  0.06  & 2.865  & 0.09256 \\
499 >        &CA   & 3.55  & 0.070   & -0.115 & 3.173  & 0.0640  \\
500 >        &HA   & 2.42  & 0.030   &  0.115 & 2.746  & 0.0414  \\
501 >        \hline
502 >        Both UA and AA
503 >        & S   & 4.45  & 0.25    & -      & 2.40   & 8.465   \\
504          \hline\hline
505        \end{tabular}
506 <      \label{AuThiolToluene}
506 >      \label{MnM}
507      \end{center}
508    \end{minipage}
509   \end{table*}
510  
361 \subsection{Hexane Solvent}
511  
512 < Using the united-atom model, different coverages of capping agent,
513 < temperatures of simulations and numbers of solvent molecules were all
514 < investigated and Table \ref{AuThiolHexaneUA} shows the results of
515 < these computations. The number of hexane molecules in our simulations
516 < does not affect the calculations significantly. However, a very long
517 < length scale for the thermal gradient axis ($z$) may cause excessively
518 < hot or cold temperatures in the middle of the solvent region and lead
519 < to undesired phenomena such as solvent boiling or freezing, while too
371 < few solvent molecules would change the normal behavior of the liquid
372 < phase. Our $N_{hexane}$ values were chosen to ensure that these
373 < extreme cases did not happen to our simulations.
512 > \section{Results}
513 > There are many factors contributing to the measured interfacial
514 > conductance; some of these factors are physically motivated
515 > (e.g. coverage of the surface by the capping agent coverage and
516 > solvent identity), while some are governed by parameters of the
517 > methodology (e.g. applied flux and the formulas used to obtain the
518 > conductance). In this section we discuss the major physical and
519 > calculational effects on the computed conductivity.
520  
521 < Table \ref{AuThiolHexaneUA} enables direct comparison between
376 < different coverages of capping agent, when other system parameters are
377 < held constant. With high coverage of butanethiol on the gold surface,
378 < the interfacial thermal conductance is enhanced
379 < significantly. Interestingly, a slightly lower butanethiol coverage
380 < leads to a moderately higher conductivity. This is probably due to
381 < more solvent/capping agent contact when butanethiol molecules are
382 < not densely packed, which enhances the interactions between the two
383 < phases and lowers the thermal transfer barrier of this interface.
384 < % [COMPARE TO AU/WATER IN PAPER]
521 > \subsection{Effects due to capping agent coverage}
522  
523 < It is also noted that the overall simulation temperature is another
524 < factor that affects the interfacial thermal conductance. One
525 < possibility of this effect may be rooted in the decrease in density of
526 < the liquid phase. We observed that when the average temperature
527 < increases from 200K to 250K, the bulk hexane density becomes lower
528 < than experimental value, as the system is equilibrated under NPT
392 < ensemble. This leads to lower contact between solvent and capping
393 < agent, and thus lower conductivity.
523 > A series of different initial conditions with a range of surface
524 > coverages was prepared and solvated with various with both of the
525 > solvent molecules. These systems were then equilibrated and their
526 > interfacial thermal conductivity was measured with the NIVS
527 > algorithm. Figure \ref{coverage} demonstrates the trend of conductance
528 > with respect to surface coverage.
529  
530 < Conductivity values are more difficult to obtain under higher
531 < temperatures. This is because the Au surface tends to undergo
532 < reconstructions in relatively high temperatures. Surface Au atoms can
533 < migrate outward to reach higher Au-S contact; and capping agent
534 < molecules can be embedded into the surface Au layer due to the same
535 < driving force. This phenomenon agrees with experimental
536 < results\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}. A surface
537 < fully covered in capping agent is more susceptible to reconstruction,
403 < possibly because fully coverage prevents other means of capping agent
404 < relaxation, such as migration to an empty neighbor three-fold site.
530 > \begin{figure}
531 > \includegraphics[width=\linewidth]{coverage}
532 > \caption{The interfacial thermal conductivity ($G$) has a
533 >  non-monotonic dependence on the degree of surface capping.  This
534 >  data is for the Au(111) / butanethiol / solvent interface with
535 >  various UA force fields at $\langle T\rangle \sim $200K.}
536 > \label{coverage}
537 > \end{figure}
538  
539 < %MAY ADD MORE DATA TO TABLE
539 > In partially covered surfaces, the derivative definition for
540 > $G^\prime$ (Eq. \ref{derivativeG}) becomes difficult to apply, as the
541 > location of maximum change of $\lambda$ becomes washed out.  The
542 > discrete definition (Eq. \ref{discreteG}) is easier to apply, as the
543 > Gibbs dividing surface is still well-defined. Therefore, $G$ (not
544 > $G^\prime$) was used in this section.
545 >
546 > From Figure \ref{coverage}, one can see the significance of the
547 > presence of capping agents. When even a small fraction of the Au(111)
548 > surface sites are covered with butanethiols, the conductivity exhibits
549 > an enhancement by at least a factor of 3.  Capping agents are clearly
550 > playing a major role in thermal transport at metal / organic solvent
551 > surfaces.
552 >
553 > We note a non-monotonic behavior in the interfacial conductance as a
554 > function of surface coverage. The maximum conductance (largest $G$)
555 > happens when the surfaces are about 75\% covered with butanethiol
556 > caps.  The reason for this behavior is not entirely clear.  One
557 > explanation is that incomplete butanethiol coverage allows small gaps
558 > between butanethiols to form. These gaps can be filled by transient
559 > solvent molecules.  These solvent molecules couple very strongly with
560 > the hot capping agent molecules near the surface, and can then carry
561 > away (diffusively) the excess thermal energy from the surface.
562 >
563 > There appears to be a competition between the conduction of the
564 > thermal energy away from the surface by the capping agents (enhanced
565 > by greater coverage) and the coupling of the capping agents with the
566 > solvent (enhanced by interdigitation at lower coverages).  This
567 > competition would lead to the non-monotonic coverage behavior observed
568 > here.
569 >
570 > Results for rigid body toluene solvent, as well as the UA hexane, are
571 > within the ranges expected from prior experimental
572 > work.\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406} This suggests
573 > that explicit hydrogen atoms might not be required for modeling
574 > thermal transport in these systems.  C-H vibrational modes do not see
575 > significant excited state population at low temperatures, and are not
576 > likely to carry lower frequency excitations from the solid layer into
577 > the bulk liquid.
578 >
579 > The toluene solvent does not exhibit the same behavior as hexane in
580 > that $G$ remains at approximately the same magnitude when the capping
581 > coverage increases from 25\% to 75\%.  Toluene, as a rigid planar
582 > molecule, cannot occupy the relatively small gaps between the capping
583 > agents as easily as the chain-like {\it n}-hexane.  The effect of
584 > solvent coupling to the capping agent is therefore weaker in toluene
585 > except at the very lowest coverage levels.  This effect counters the
586 > coverage-dependent conduction of heat away from the metal surface,
587 > leading to a much flatter $G$ vs. coverage trend than is observed in
588 > {\it n}-hexane.
589 >
590 > \subsection{Effects due to Solvent \& Solvent Models}
591 > In addition to UA solvent and capping agent models, AA models have
592 > also been included in our simulations.  In most of this work, the same
593 > (UA or AA) model for solvent and capping agent was used, but it is
594 > also possible to utilize different models for different components.
595 > We have also included isotopic substitutions (Hydrogen to Deuterium)
596 > to decrease the explicit vibrational overlap between solvent and
597 > capping agent. Table \ref{modelTest} summarizes the results of these
598 > studies.
599 >
600 > {\bf MAY NOT NEED $J_z$ IN TABLE}
601   \begin{table*}
602    \begin{minipage}{\linewidth}
603      \begin{center}
410      \caption{Computed interfacial thermal conductivity ($G$ and
411        $G^\prime$) values for the Au/butanethiol/hexane interface
412        with united-atom model and different capping agent coverage
413        and solvent molecule numbers at different temperatures using a
414        range of energy fluxes.}
604        
605 <      \begin{tabular}{cccccc}
605 >      \caption{Computed interfacial thermal conductance ($G$ and
606 >        $G^\prime$) values for interfaces using various models for
607 >        solvent and capping agent (or without capping agent) at
608 >        $\langle T\rangle\sim$200K.  Here ``D'' stands for deuterated
609 >        solvent or capping agent molecules; ``Avg.'' denotes results
610 >        that are averages of simulations under different applied
611 >        thermal flux $(J_z)$ values. Error estimates are indicated in
612 >        parentheses.}
613 >      
614 >      \begin{tabular}{llccc}
615          \hline\hline
616 <        Thiol & $\langle T\rangle$ & & $J_z$ & $G$ & $G^\prime$ \\
617 <        coverage (\%) & (K) & $N_{hexane}$ & (GW/m$^2$) &
616 >        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
617 >        (or bare surface) & model & (GW/m$^2$) &
618          \multicolumn{2}{c}{(MW/m$^2$/K)} \\
619          \hline
620 <        0.0   & 200 & 200 & 0.96 & 43.3 & 42.7 \\
621 <              &     &     & 1.91 & 45.7 & 42.9 \\
622 <              &     & 166 & 0.96 & 43.1 & 53.4 \\
623 <        88.9  & 200 & 166 & 1.94 & 172  & 108  \\
624 <        100.0 & 250 & 200 & 0.96 & 81.8 & 67.0 \\
625 <              &     & 166 & 0.98 & 79.0 & 62.9 \\
626 <              &     &     & 1.44 & 76.2 & 64.8 \\
627 <              & 200 & 200 & 1.92 & 129  & 87.3 \\
628 <              &     &     & 1.93 & 131  & 77.5 \\
629 <              &     & 166 & 0.97 & 115  & 69.3 \\
630 <              &     &     & 1.94 & 125  & 87.1 \\
620 >        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
621 >              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
622 >              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
623 >              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
624 >              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
625 >        \hline
626 >        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
627 >              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
628 >              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
629 >              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
630 >              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
631 >        \hline
632 >        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
633 >              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
634 >              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
635 >        \hline
636 >        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
637 >              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
638 >              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
639 >              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
640          \hline\hline
641        \end{tabular}
642 <      \label{AuThiolHexaneUA}
642 >      \label{modelTest}
643      \end{center}
644    \end{minipage}
645   \end{table*}
646  
647 < For the all-atom model, the liquid hexane phase was not stable under NPT
648 < conditions. Therefore, the simulation length scale parameters are
649 < adopted from previous equilibration results of the united-atom model
443 < at 200K. Table \ref{AuThiolHexaneAA} shows the results of these
444 < simulations. The conductivity values calculated with full capping
445 < agent coverage are substantially larger than observed in the
446 < united-atom model, and is even higher than predicted by
447 < experiments. It is possible that our parameters for metal-non-metal
448 < particle interactions lead to an overestimate of the interfacial
449 < thermal conductivity, although the active C-H vibrations in the
450 < all-atom model (which should not be appreciably populated at normal
451 < temperatures) could also account for this high conductivity. The major
452 < thermal transfer barrier of Au/butanethiol/hexane interface is between
453 < the liquid phase and the capping agent, so extra degrees of freedom
454 < such as the C-H vibrations could enhance heat exchange between these
455 < two phases and result in a much higher conductivity.
647 > To facilitate direct comparison between force fields, systems with the
648 > same capping agent and solvent were prepared with the same length
649 > scales for the simulation cells.
650  
651 + On bare metal / solvent surfaces, different force field models for
652 + hexane yield similar results for both $G$ and $G^\prime$, and these
653 + two definitions agree with each other very well. This is primarily an
654 + indicator of weak interactions between the metal and the solvent, and
655 + is a typical case for acoustic impedance mismatch between these two
656 + phases.  
657 +
658 + For the fully-covered surfaces, the choice of force field for the
659 + capping agent and solvent has a large impact on the calculated values
660 + of $G$ and $G^\prime$.  The AA thiol to AA solvent conductivities are
661 + much larger than their UA to UA counterparts, and these values exceed
662 + the experimental estimates by a large measure.  The AA force field
663 + allows significant energy to go into C-H (or C-D) stretching modes,
664 + and since these modes are high frequency, this non-quantum behavior is
665 + likely responsible for the overestimate of the conductivity.  Compared
666 + to the AA model, the UA model yields more reasonable conductivity
667 + values with much higher computational efficiency.
668 +
669 + \subsubsection{Are electronic excitations in the metal important?}
670 + Because they lack electronic excitations, the QSC and related embedded
671 + atom method (EAM) models for gold are known to predict unreasonably
672 + low values for bulk conductivity
673 + ($\lambda$).\cite{kuang:164101,ISI:000207079300006,Clancy:1992} If the
674 + conductance between the phases ($G$) is governed primarily by phonon
675 + excitation (and not electronic degrees of freedom), one would expect a
676 + classical model to capture most of the interfacial thermal
677 + conductance.  Our results for $G$ and $G^\prime$ indicate that this is
678 + indeed the case, and suggest that the modeling of interfacial thermal
679 + transport depends primarily on the description of the interactions
680 + between the various components at the interface.  When the metal is
681 + chemically capped, the primary barrier to thermal conductivity appears
682 + to be the interface between the capping agent and the surrounding
683 + solvent, so the excitations in the metal have little impact on the
684 + value of $G$.
685 +
686 + \subsection{Effects due to methodology and simulation parameters}
687 +
688 + We have varied the parameters of the simulations in order to
689 + investigate how these factors would affect the computation of $G$.  Of
690 + particular interest are: 1) the length scale for the applied thermal
691 + gradient (modified by increasing the amount of solvent in the system),
692 + 2) the sign and magnitude of the applied thermal flux, 3) the average
693 + temperature of the simulation (which alters the solvent density during
694 + equilibration), and 4) the definition of the interfacial conductance
695 + (Eqs. (\ref{discreteG}) or (\ref{derivativeG})) used in the
696 + calculation.
697 +
698 + Systems of different lengths were prepared by altering the number of
699 + solvent molecules and extending the length of the box along the $z$
700 + axis to accomodate the extra solvent.  Equilibration at the same
701 + temperature and pressure conditions led to nearly identical surface
702 + areas ($L_x$ and $L_y$) available to the metal and capping agent,
703 + while the extra solvent served mainly to lengthen the axis that was
704 + used to apply the thermal flux.  For a given value of the applied
705 + flux, the different $z$ length scale has only a weak effect on the
706 + computed conductivities (Table \ref{AuThiolHexaneUA}).
707 +
708 + \subsubsection{Effects of applied flux}
709 + The NIVS algorithm allows changes in both the sign and magnitude of
710 + the applied flux.  It is possible to reverse the direction of heat
711 + flow simply by changing the sign of the flux, and thermal gradients
712 + which would be difficult to obtain experimentally ($5$ K/\AA) can be
713 + easily simulated.  However, the magnitude of the applied flux is not
714 + arbitrary if one aims to obtain a stable and reliable thermal gradient.
715 + A temperature gradient can be lost in the noise if $|J_z|$ is too
716 + small, and excessive $|J_z|$ values can cause phase transitions if the
717 + extremes of the simulation cell become widely separated in
718 + temperature.  Also, if $|J_z|$ is too large for the bulk conductivity
719 + of the materials, the thermal gradient will never reach a stable
720 + state.  
721 +
722 + Within a reasonable range of $J_z$ values, we were able to study how
723 + $G$ changes as a function of this flux.  In what follows, we use
724 + positive $J_z$ values to denote the case where energy is being
725 + transferred by the method from the metal phase and into the liquid.
726 + The resulting gradient therefore has a higher temperature in the
727 + liquid phase.  Negative flux values reverse this transfer, and result
728 + in higher temperature metal phases.  The conductance measured under
729 + different applied $J_z$ values is listed in Tables
730 + \ref{AuThiolHexaneUA} and \ref{AuThiolToluene}. These results do not
731 + indicate that $G$ depends strongly on $J_z$ within this flux
732 + range. The linear response of flux to thermal gradient simplifies our
733 + investigations in that we can rely on $G$ measurement with only a
734 + small number $J_z$ values.  
735 +
736 + {\bf MAY MOVE TO SUPPORT INFO}
737   \begin{table*}
738    \begin{minipage}{\linewidth}
739      \begin{center}
740 +      \caption{In the hexane-solvated interfaces, the system size has
741 +        little effect on the calculated values for interfacial
742 +        conductance ($G$ and $G^\prime$), but the direction of heat
743 +        flow (i.e. the sign of $J_z$) can alter the average
744 +        temperature of the liquid phase and this can alter the
745 +        computed conductivity.}
746        
747 <      \caption{Computed interfacial thermal conductivity ($G$ and
462 <        $G^\prime$) values for the Au/butanethiol/hexane interface
463 <        with all-atom model and different capping agent coverage at
464 <        200K using a range of energy fluxes.}
465 <      
466 <      \begin{tabular}{cccc}
747 >      \begin{tabular}{ccccccc}
748          \hline\hline
749 <        Thiol & $J_z$ & $G$ & $G^\prime$ \\
750 <        coverage (\%) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
749 >        $\langle T\rangle$ & $N_{hexane}$  & $\rho_{hexane}$ &
750 >        $J_z$ & $G$ & $G^\prime$ \\
751 >        (K) &  & (g/cm$^3$) & (GW/m$^2$) &
752 >        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
753          \hline
754 <        0.0   & 0.95 & 28.5 & 27.2 \\
755 <              & 1.88 & 30.3 & 28.9 \\
756 <        100.0 & 2.87 & 551  & 294  \\
757 <              & 3.81 & 494  & 193  \\
754 >        200 & 266 &  0.672 & -0.96 & 102(3)    & 80.0(0.8) \\
755 >            & 200 &  0.688 &  0.96 & 125(16)   & 90.2(15)  \\
756 >            &     &        &  1.91 & 139(10)   & 101(10)   \\
757 >            &     &        &  2.83 & 141(6)    & 89.9(9.8) \\
758 >            & 166 &  0.681 &  0.97 & 141(30)   & 78(22)    \\
759 >            &     &        &  1.92 & 138(4)    & 98.9(9.5) \\
760 >        \hline
761 >        250 & 200 &  0.560 &  0.96 & 75(10)    & 61.8(7.3) \\
762 >            &     &        & -0.95 & 49.4(0.3) & 45.7(2.1) \\
763 >            & 166 &  0.569 &  0.97 & 80.3(0.6) & 67(11)    \\
764 >            &     &        &  1.44 & 76.2(5.0) & 64.8(3.8) \\
765 >            &     &        & -0.95 & 56.4(2.5) & 54.4(1.1) \\
766 >            &     &        & -1.85 & 47.8(1.1) & 53.5(1.5) \\
767          \hline\hline
768        \end{tabular}
769 <      \label{AuThiolHexaneAA}
769 >      \label{AuThiolHexaneUA}
770      \end{center}
771    \end{minipage}
772   \end{table*}
773  
774 < %subsubsection{Vibrational spectrum study on conductance mechanism}
775 < To investigate the mechanism of this interfacial thermal conductance,
776 < the vibrational spectra of various gold systems were obtained and are
777 < shown as in the upper panel of Fig. \ref{vibration}. To obtain these
778 < spectra, one first runs a simulation in the NVE ensemble and collects
779 < snapshots of configurations; these configurations are used to compute
780 < the velocity auto-correlation functions, which is used to construct a
781 < power spectrum via a Fourier transform. The gold surfaces covered by
782 < butanethiol molecules exhibit an additional peak observed at a
491 < frequency of $\sim$170cm$^{-1}$, which is attributed to the vibration
492 < of the S-Au bond. This vibration enables efficient thermal transport
493 < from surface Au atoms to the capping agents. Simultaneously, as shown
494 < in the lower panel of Fig. \ref{vibration}, the large overlap of the
495 < vibration spectra of butanethiol and hexane in the all-atom model,
496 < including the C-H vibration, also suggests high thermal exchange
497 < efficiency. The combination of these two effects produces the drastic
498 < interfacial thermal conductance enhancement in the all-atom model.
774 > The sign of $J_z$ is a different matter, however, as this can alter
775 > the temperature on the two sides of the interface. The average
776 > temperature values reported are for the entire system, and not for the
777 > liquid phase, so at a given $\langle T \rangle$, the system with
778 > positive $J_z$ has a warmer liquid phase.  This means that if the
779 > liquid carries thermal energy via diffusive transport, {\it positive}
780 > $J_z$ values will result in increased molecular motion on the liquid
781 > side of the interface, and this will increase the measured
782 > conductivity.
783  
784 + \subsubsection{Effects due to average temperature}
785 +
786 + We also studied the effect of average system temperature on the
787 + interfacial conductance.  The simulations are first equilibrated in
788 + the NPT ensemble at 1 atm.  The TraPPE-UA model for hexane tends to
789 + predict a lower boiling point (and liquid state density) than
790 + experiments.  This lower-density liquid phase leads to reduced contact
791 + between the hexane and butanethiol, and this accounts for our
792 + observation of lower conductance at higher temperatures as shown in
793 + Table \ref{AuThiolHexaneUA}.  In raising the average temperature from
794 + 200K to 250K, the density drop of $\sim$20\% in the solvent phase
795 + leads to a $\sim$40\% drop in the conductance.
796 +
797 + Similar behavior is observed in the TraPPE-UA model for toluene,
798 + although this model has better agreement with the experimental
799 + densities of toluene.  The expansion of the toluene liquid phase is
800 + not as significant as that of the hexane (8.3\% over 100K), and this
801 + limits the effect to $\sim$20\% drop in thermal conductivity  (Table
802 + \ref{AuThiolToluene}).
803 +
804 + Although we have not mapped out the behavior at a large number of
805 + temperatures, is clear that there will be a strong temperature
806 + dependence in the interfacial conductance when the physical properties
807 + of one side of the interface (notably the density) change rapidly as a
808 + function of temperature.
809 +
810 + {\bf MAY MOVE TO SUPPORT INFO}
811 + \begin{table*}
812 +  \begin{minipage}{\linewidth}
813 +    \begin{center}
814 +      \caption{When toluene is the solvent, the interfacial thermal
815 +        conductivity is less sensitive to temperature, but again, the
816 +        direction of the heat flow can alter the solvent temperature
817 +        and can change the computed conductance values.}
818 +      
819 +      \begin{tabular}{ccccc}
820 +        \hline\hline
821 +        $\langle T\rangle$ & $\rho_{toluene}$ & $J_z$ & $G$ & $G^\prime$ \\
822 +        (K) & (g/cm$^3$) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
823 +        \hline
824 +        200 & 0.933 &  2.15 & 204(12) & 113(12) \\
825 +            &       & -1.86 & 180(3)  & 135(21) \\
826 +            &       & -3.93 & 176(5)  & 113(12) \\
827 +        \hline
828 +        300 & 0.855 & -1.91 & 143(5)  & 125(2)  \\
829 +            &       & -4.19 & 135(9)  & 113(12) \\
830 +        \hline\hline
831 +      \end{tabular}
832 +      \label{AuThiolToluene}
833 +    \end{center}
834 +  \end{minipage}
835 + \end{table*}
836 +
837 + Besides the lower interfacial thermal conductance, surfaces at
838 + relatively high temperatures are susceptible to reconstructions,
839 + particularly when butanethiols fully cover the Au(111) surface. These
840 + reconstructions include surface Au atoms which migrate outward to the
841 + S atom layer, and butanethiol molecules which embed into the surface
842 + Au layer. The driving force for this behavior is the strong Au-S
843 + interactions which are modeled here with a deep Lennard-Jones
844 + potential. This phenomenon agrees with reconstructions that have been
845 + experimentally
846 + observed.\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}.  Vlugt
847 + {\it et al.} kept their Au(111) slab rigid so that their simulations
848 + could reach 300K without surface
849 + reconstructions.\cite{vlugt:cpc2007154} Since surface reconstructions
850 + blur the interface, the measurement of $G$ becomes more difficult to
851 + conduct at higher temperatures.  For this reason, most of our
852 + measurements are undertaken at $\langle T\rangle\sim$200K where
853 + reconstruction is minimized.
854 +
855 + However, when the surface is not completely covered by butanethiols,
856 + the simulated system appears to be more resistent to the
857 + reconstruction. Our Au / butanethiol / toluene system had the Au(111)
858 + surfaces 90\% covered by butanethiols, but did not see this above
859 + phenomena even at $\langle T\rangle\sim$300K.  That said, we did
860 + observe butanethiols migrating to neighboring three-fold sites during
861 + a simulation.  Since the interface persisted in these simulations, we
862 + were able to obtain $G$'s for these interfaces even at a relatively
863 + high temperature without being affected by surface reconstructions.
864 +
865 + \section{Discussion}
866 +
867 + The primary result of this work is that the capping agent acts as an
868 + efficient thermal coupler between solid and solvent phases.  One of
869 + the ways the capping agent can carry out this role is to down-shift
870 + between the phonon vibrations in the solid (which carry the heat from
871 + the gold) and the molecular vibrations in the liquid (which carry some
872 + of the heat in the solvent).
873 +
874 + To investigate the mechanism of interfacial thermal conductance, the
875 + vibrational power spectrum was computed. Power spectra were taken for
876 + individual components in different simulations. To obtain these
877 + spectra, simulations were run after equilibration in the
878 + microcanonical (NVE) ensemble and without a thermal
879 + gradient. Snapshots of configurations were collected at a frequency
880 + that is higher than that of the fastest vibrations occurring in the
881 + simulations. With these configurations, the velocity auto-correlation
882 + functions can be computed:
883 + \begin{equation}
884 + C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
885 + \label{vCorr}
886 + \end{equation}
887 + The power spectrum is constructed via a Fourier transform of the
888 + symmetrized velocity autocorrelation function,
889 + \begin{equation}
890 +  \hat{f}(\omega) =
891 +  \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
892 + \label{fourier}
893 + \end{equation}
894 +
895 + \subsection{The role of specific vibrations}
896 + The vibrational spectra for gold slabs in different environments are
897 + shown as in Figure \ref{specAu}. Regardless of the presence of
898 + solvent, the gold surfaces which are covered by butanethiol molecules
899 + exhibit an additional peak observed at a frequency of
900 + $\sim$165cm$^{-1}$.  We attribute this peak to the S-Au bonding
901 + vibration. This vibration enables efficient thermal coupling of the
902 + surface Au layer to the capping agents. Therefore, in our simulations,
903 + the Au / S interfaces do not appear to be the primary barrier to
904 + thermal transport when compared with the butanethiol / solvent
905 + interfaces. {\bf This confirms the results from Luo {\it et
906 +    al.}\cite{Luo20101}, which reported $G$ for Au-SAM junctions
907 +  generally twice larger than what we have computed for the
908 +  thiol-liquid interfaces.}
909 +
910   \begin{figure}
911   \includegraphics[width=\linewidth]{vibration}
912 < \caption{Vibrational spectra obtained for gold in different
913 <  environments (upper panel) and for Au/thiol/hexane simulation in
914 <  all-atom model (lower panel).}
915 < \label{vibration}
912 > \caption{The vibrational power spectrum for thiol-capped gold has an
913 >  additional vibrational peak at $\sim $165cm$^{-1}$.  Bare gold
914 >  surfaces (both with and without a solvent over-layer) are missing
915 >  this peak.   A similar peak at  $\sim $165cm$^{-1}$ also appears in
916 >  the vibrational power spectrum for the butanethiol capping agents.}
917 > \label{specAu}
918   \end{figure}
507 % 600dpi, letter size. too large?
919  
920 + Also in this figure, we show the vibrational power spectrum for the
921 + bound butanethiol molecules, which also exhibits the same
922 + $\sim$165cm$^{-1}$ peak.
923  
924 + \subsection{Overlap of power spectra}
925 + A comparison of the results obtained from the two different organic
926 + solvents can also provide useful information of the interfacial
927 + thermal transport process.  In particular, the vibrational overlap
928 + between the butanethiol and the organic solvents suggests a highly
929 + efficient thermal exchange between these components.  Very high
930 + thermal conductivity was observed when AA models were used and C-H
931 + vibrations were treated classically.  The presence of extra degrees of
932 + freedom in the AA force field yields higher heat exchange rates
933 + between the two phases and results in a much higher conductivity than
934 + in the UA force field. {\bf Due to the classical models used, this
935 +  even includes those high frequency modes which should be unpopulated
936 +  at our relatively low temperatures. This artifact causes high
937 +  frequency vibrations accountable for thermal transport in classical
938 +  MD simulations.}
939 +
940 + The similarity in the vibrational modes available to solvent and
941 + capping agent can be reduced by deuterating one of the two components
942 + (Fig. \ref{aahxntln}).  Once either the hexanes or the butanethiols
943 + are deuterated, one can observe a significantly lower $G$ and
944 + $G^\prime$ values (Table \ref{modelTest}).
945 +
946 + \begin{figure}
947 + \includegraphics[width=\linewidth]{aahxntln}
948 + \caption{Spectra obtained for all-atom (AA) Au / butanethiol / solvent
949 +  systems. When butanethiol is deuterated (lower left), its
950 +  vibrational overlap with hexane decreases significantly.  Since
951 +  aromatic molecules and the butanethiol are vibrationally dissimilar,
952 +  the change is not as dramatic when toluene is the solvent (right).}
953 + \label{aahxntln}
954 + \end{figure}
955 +
956 + For the Au / butanethiol / toluene interfaces, having the AA
957 + butanethiol deuterated did not yield a significant change in the
958 + measured conductance. Compared to the C-H vibrational overlap between
959 + hexane and butanethiol, both of which have alkyl chains, the overlap
960 + between toluene and butanethiol is not as significant and thus does
961 + not contribute as much to the heat exchange process.
962 +
963 + Previous observations of Zhang {\it et al.}\cite{hase:2010} indicate
964 + that the {\it intra}molecular heat transport due to alkylthiols is
965 + highly efficient.  Combining our observations with those of Zhang {\it
966 +  et al.}, it appears that butanethiol acts as a channel to expedite
967 + heat flow from the gold surface and into the alkyl chain.  The
968 + acoustic impedance mismatch between the metal and the liquid phase can
969 + therefore be effectively reduced with the presence of suitable capping
970 + agents.
971 +
972 + Deuterated models in the UA force field did not decouple the thermal
973 + transport as well as in the AA force field.  The UA models, even
974 + though they have eliminated the high frequency C-H vibrational
975 + overlap, still have significant overlap in the lower-frequency
976 + portions of the infrared spectra (Figure \ref{uahxnua}).  Deuterating
977 + the UA models did not decouple the low frequency region enough to
978 + produce an observable difference for the results of $G$ (Table
979 + \ref{modelTest}).
980 +
981 + \begin{figure}
982 + \includegraphics[width=\linewidth]{uahxnua}
983 + \caption{Vibrational power spectra for UA models for the butanethiol
984 +  and hexane solvent (upper panel) show the high degree of overlap
985 +  between these two molecules, particularly at lower frequencies.
986 +  Deuterating a UA model for the solvent (lower panel) does not
987 +  decouple the two spectra to the same degree as in the AA force
988 +  field (see Fig \ref{aahxntln}).}
989 + \label{uahxnua}
990 + \end{figure}
991 +
992 + \section{Conclusions}
993 + The NIVS algorithm has been applied to simulations of
994 + butanethiol-capped Au(111) surfaces in the presence of organic
995 + solvents. This algorithm allows the application of unphysical thermal
996 + flux to transfer heat between the metal and the liquid phase. With the
997 + flux applied, we were able to measure the corresponding thermal
998 + gradients and to obtain interfacial thermal conductivities. Under
999 + steady states, 2-3 ns trajectory simulations are sufficient for
1000 + computation of this quantity.
1001 +
1002 + Our simulations have seen significant conductance enhancement in the
1003 + presence of capping agent, compared with the bare gold / liquid
1004 + interfaces. The acoustic impedance mismatch between the metal and the
1005 + liquid phase is effectively eliminated by a chemically-bonded capping
1006 + agent. Furthermore, the coverage percentage of the capping agent plays
1007 + an important role in the interfacial thermal transport
1008 + process. Moderately low coverages allow higher contact between capping
1009 + agent and solvent, and thus could further enhance the heat transfer
1010 + process, giving a non-monotonic behavior of conductance with
1011 + increasing coverage.
1012 +
1013 + Our results, particularly using the UA models, agree well with
1014 + available experimental data.  The AA models tend to overestimate the
1015 + interfacial thermal conductance in that the classically treated C-H
1016 + vibrations become too easily populated. Compared to the AA models, the
1017 + UA models have higher computational efficiency with satisfactory
1018 + accuracy, and thus are preferable in modeling interfacial thermal
1019 + transport.
1020 +
1021 + Of the two definitions for $G$, the discrete form
1022 + (Eq. \ref{discreteG}) was easier to use and gives out relatively
1023 + consistent results, while the derivative form (Eq. \ref{derivativeG})
1024 + is not as versatile. Although $G^\prime$ gives out comparable results
1025 + and follows similar trend with $G$ when measuring close to fully
1026 + covered or bare surfaces, the spatial resolution of $T$ profile
1027 + required for the use of a derivative form is limited by the number of
1028 + bins and the sampling required to obtain thermal gradient information.
1029 +
1030 + Vlugt {\it et al.} have investigated the surface thiol structures for
1031 + nanocrystalline gold and pointed out that they differ from those of
1032 + the Au(111) surface.\cite{landman:1998,vlugt:cpc2007154} This
1033 + difference could also cause differences in the interfacial thermal
1034 + transport behavior. To investigate this problem, one would need an
1035 + effective method for applying thermal gradients in non-planar
1036 + (i.e. spherical) geometries.
1037 +
1038   \section{Acknowledgments}
1039   Support for this project was provided by the National Science
1040   Foundation under grant CHE-0848243. Computational time was provided by
1041   the Center for Research Computing (CRC) at the University of Notre
1042 < Dame.  \newpage
1042 > Dame.
1043  
1044 + \section{Supporting Information}
1045 + This information is available free of charge via the Internet at
1046 + http://pubs.acs.org.
1047 +
1048 + \newpage
1049 +
1050   \bibliography{interfacial}
1051  
1052   \end{doublespace}

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