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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  
# 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 convective heat transport 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
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 arbitary identity, and
179 < the flux quantity is not restricted by particle mass difference.
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   The other approach is to assume a continuous temperature profile along
238   the thermal gradient axis (e.g. $z$) and define $G$ at the point where
239 < the magnitude of thermal conductivity $\lambda$ change reach its
239 > the magnitude of thermal conductivity ($\lambda$) change reaches its
240   maximum, given that $\lambda$ is well-defined throughout the space:
241   \begin{equation}
242   G^\prime = \Big|\frac{\partial\lambda}{\partial z}\Big|
# Line 173 | Line 247 | With the temperature profile obtained from simulations
247   \label{derivativeG}
248   \end{equation}
249  
250 < With the temperature profile obtained from simulations, one is able to
250 > With temperature profiles obtained from simulation, one is able to
251   approximate the first and second derivatives of $T$ with finite
252 < difference method and thus calculate $G^\prime$.
252 > difference methods and calculate $G^\prime$. In what follows, both
253 > definitions have been used, and are compared in the results.
254  
255 < In what follows, both definitions are used for calculation and comparison.
255 > To investigate the interfacial conductivity at metal / solvent
256 > interfaces, we have modeled a metal slab with its (111) surfaces
257 > perpendicular to the $z$-axis of our simulation cells. The metal slab
258 > has been prepared both with and without capping agents on the exposed
259 > surface, and has been solvated with simple organic solvents, as
260 > illustrated in Figure \ref{gradT}.
261  
262 < [IMPOSE G DEFINITION INTO OUR SYSTEMS]
263 < To facilitate the use of the above definitions in calculating $G$ and
264 < $G^\prime$, we have a metal slab with its (111) surfaces perpendicular
265 < to the $z$-axis of our simulation cells. With or withour capping
266 < agents on the surfaces, the metal slab is solvated with organic
267 < solvents, as illustrated in Figure \ref{demoPic}.
262 > With the simulation cell described above, we are able to equilibrate
263 > the system and impose an unphysical thermal flux between the liquid
264 > and the metal phase using the NIVS algorithm. By periodically applying
265 > the unphysical flux, we obtained a temperature profile and its spatial
266 > derivatives. Figure \ref{gradT} shows how an applied thermal flux can
267 > be used to obtain the 1st and 2nd derivatives of the temperature
268 > profile.
269  
270   \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}
271   \includegraphics[width=\linewidth]{gradT}
272 < \caption{The 1st and 2nd derivatives of temperature profile can be
273 <  obtained with finite difference approximation.}
272 > \caption{A sample of Au (111) / butanethiol / hexane interfacial
273 >  system with the temperature profile after a kinetic energy flux has
274 >  been imposed.  Note that the largest temperature jump in the thermal
275 >  profile (corresponding to the lowest interfacial conductance) is at
276 >  the interface between the butanethiol molecules (blue) and the
277 >  solvent (grey).  First and second derivatives of the temperature
278 >  profile are obtained using a finite difference approximation (lower
279 >  panel).}
280   \label{gradT}
281   \end{figure}
282  
283   \section{Computational Details}
284 < \subsection{System Geometry}
285 < In our simulations, Au is used to construct a metal slab with bare
286 < (111) surface perpendicular to the $z$-axis. Different slab thickness
287 < (layer numbers of Au) are simulated. This metal slab is first
288 < equilibrated under normal pressure (1 atm) and a desired
289 < temperature. After equilibration, butanethiol is used as the capping
290 < agent molecule to cover the bare Au (111) surfaces evenly. The sulfur
291 < atoms in the butanethiol molecules would occupy the three-fold sites
292 < of the surfaces, and the maximal butanethiol capacity on Au surface is
293 < $1/3$ of the total number of surface Au atoms[CITATION]. A series of
294 < different coverage surfaces is investigated in order to study the
295 < relation between coverage and conductance.
296 <
297 < [COVERAGE DISCRIPTION] However, since the interactions between surface
298 < Au and butanethiol is non-bonded, the capping agent molecules are
299 < allowed to migrate to an empty neighbor three-fold site during a
300 < simulation. Therefore, the initial configuration would not severely
232 < affect the sampling of a variety of configurations of the same
233 < coverage, and the final conductance measurement would be an average
234 < effect of these configurations explored in the simulations. [MAY NEED FIGURES]
284 > \subsection{Simulation Protocol}
285 > The NIVS algorithm has been implemented in our MD simulation code,
286 > OpenMD\cite{Meineke:2005gd,openmd}, and was used for our simulations.
287 > Metal slabs of 6 or 11 layers of Au atoms were first equilibrated
288 > under atmospheric pressure (1 atm) and 200K. After equilibration,
289 > butanethiol capping agents were placed at three-fold hollow sites on
290 > the Au(111) surfaces. These sites are either {\it fcc} or {\it
291 >  hcp} sites, although Hase {\it et al.} found that they are
292 > equivalent in a heat transfer process,\cite{hase:2010} so we did not
293 > distinguish between these sites in our study. The maximum butanethiol
294 > capacity on Au surface is $1/3$ of the total number of surface Au
295 > atoms, and the packing forms a $(\sqrt{3}\times\sqrt{3})R30^\circ$
296 > structure\cite{doi:10.1021/ja00008a001,doi:10.1021/cr9801317}. A
297 > series of lower coverages was also prepared by eliminating
298 > butanethiols from the higher coverage surface in a regular manner. The
299 > lower coverages were prepared in order to study the relation between
300 > coverage and interfacial conductance.
301  
302 < After the modified Au-butanethiol surface systems are equilibrated
303 < under canonical ensemble, Packmol\cite{packmol} is used to pack
304 < organic solvent molecules in the previously vacuum part of the
305 < simulation cells, which guarantees that short range repulsive
306 < interactions do not disrupt the simulations. Two solvents are
307 < investigated, one which has little vibrational overlap with the
308 < 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.
302 > The capping agent molecules were allowed to migrate during the
303 > simulations. They distributed themselves uniformly and sampled a
304 > number of three-fold sites throughout out study. Therefore, the
305 > initial configuration does not noticeably affect the sampling of a
306 > variety of configurations of the same coverage, and the final
307 > conductance measurement would be an average effect of these
308 > configurations explored in the simulations.
309  
310 < The initial configurations generated by Packmol are further
311 < equilibrated with the $x$ and $y$ dimensions fixed, only allowing
312 < length scale change in $z$ dimension. This is to ensure that the
313 < equilibration of liquid phase does not affect the metal crystal
314 < structure in $x$ and $y$ dimensions. Further equilibration are run
315 < under NVT and then NVE ensembles.
310 > After the modified Au-butanethiol surface systems were equilibrated in
311 > the canonical (NVT) ensemble, organic solvent molecules were packed in
312 > the previously empty part of the simulation cells.\cite{packmol} Two
313 > solvents were investigated, one which has little vibrational overlap
314 > with the alkanethiol and which has a planar shape (toluene), and one
315 > which has similar vibrational frequencies to the capping agent and
316 > chain-like shape ({\it n}-hexane).
317  
318 < After the systems reach equilibrium, NIVS is implemented to impose a
319 < periodic unphysical thermal flux between the metal and the liquid
320 < phase. Most of our simulations are under an average temperature of
321 < $\sim$200K. Therefore, this flux usually comes from the metal to the
318 > The simulation cells were not particularly extensive along the
319 > $z$-axis, as a very long length scale for the thermal gradient may
320 > cause excessively hot or cold temperatures in the middle of the
321 > solvent region and lead to undesired phenomena such as solvent boiling
322 > or freezing when a thermal flux is applied. Conversely, too few
323 > solvent molecules would change the normal behavior of the liquid
324 > phase. Therefore, our $N_{solvent}$ values were chosen to ensure that
325 > these extreme cases did not happen to our simulations. The spacing
326 > between periodic images of the gold interfaces is $45 \sim 75$\AA in
327 > our simulations.
328 >
329 > The initial configurations generated are further equilibrated with the
330 > $x$ and $y$ dimensions fixed, only allowing the $z$-length scale to
331 > change. This is to ensure that the equilibration of liquid phase does
332 > not affect the metal's crystalline structure. Comparisons were made
333 > with simulations that allowed changes of $L_x$ and $L_y$ during NPT
334 > equilibration. No substantial changes in the box geometry were noticed
335 > in these simulations. After ensuring the liquid phase reaches
336 > equilibrium at atmospheric pressure (1 atm), further equilibration was
337 > carried out under canonical (NVT) and microcanonical (NVE) ensembles.
338 >
339 > After the systems reach equilibrium, NIVS was used to impose an
340 > unphysical thermal flux between the metal and the liquid phases. Most
341 > of our simulations were done under an average temperature of
342 > $\sim$200K. Therefore, thermal flux usually came from the metal to the
343   liquid so that the liquid has a higher temperature and would not
344 < freeze due to excessively low temperature. This induced temperature
345 < gradient is stablized and the simulation cell is devided evenly into
346 < N slabs along the $z$-axis and the temperatures of each slab are
347 < recorded. When the slab width $d$ of each slab is the same, the
348 < derivatives of $T$ with respect to slab number $n$ can be directly
349 < used for $G^\prime$ calculations:
350 < \begin{equation}
351 < G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
344 > freeze due to lowered temperatures. After this induced temperature
345 > gradient had stabilized, the temperature profile of the simulation cell
346 > was recorded. To do this, the simulation cell is divided evenly into
347 > $N$ slabs along the $z$-axis. The average temperatures of each slab
348 > are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
349 > the same, the derivatives of $T$ with respect to slab number $n$ can
350 > be directly used for $G^\prime$ calculations: \begin{equation}
351 >  G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
352           \Big/\left(\frac{\partial T}{\partial z}\right)^2
353           = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
354           \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
# Line 276 | Line 357 | G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}
357   \label{derivativeG2}
358   \end{equation}
359  
360 + All of the above simulation procedures use a time step of 1 fs. Each
361 + equilibration stage took a minimum of 100 ps, although in some cases,
362 + longer equilibration stages were utilized.
363 +
364   \subsection{Force Field Parameters}
365 < Our simulations include various components. Therefore, force field
366 < parameter descriptions are needed for interactions both between the
367 < same type of particles and between particles of different species.
365 > Our simulations include a number of chemically distinct components.
366 > Figure \ref{demoMol} demonstrates the sites defined for both
367 > United-Atom and All-Atom models of the organic solvent and capping
368 > agents in our simulations. Force field parameters are needed for
369 > interactions both between the same type of particles and between
370 > particles of different species.
371  
372 + \begin{figure}
373 + \includegraphics[width=\linewidth]{structures}
374 + \caption{Structures of the capping agent and solvents utilized in
375 +  these simulations. The chemically-distinct sites (a-e) are expanded
376 +  in terms of constituent atoms for both United Atom (UA) and All Atom
377 +  (AA) force fields.  Most parameters are from References
378 +  \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes,TraPPE-UA.thiols}
379 +  (UA) and \protect\cite{OPLSAA} (AA). Cross-interactions with the Au
380 +  atoms are given in Table \ref{MnM}.}
381 + \label{demoMol}
382 + \end{figure}
383 +
384   The Au-Au interactions in metal lattice slab is described by the
385   quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
386   potentials include zero-point quantum corrections and are
387   reparametrized for accurate surface energies compared to the
388 < Sutton-Chen potentials\cite{Chen90}.
388 > Sutton-Chen potentials.\cite{Chen90}
389  
390 < For both solvent molecules, straight chain {\it n}-hexane and aromatic
391 < toluene, United-Atom (UA) and All-Atom (AA) models are used
392 < respectively. The TraPPE-UA
390 > For the two solvent molecules, {\it n}-hexane and toluene, two
391 > different atomistic models were utilized. Both solvents were modeled
392 > using United-Atom (UA) and All-Atom (AA) models. The TraPPE-UA
393   parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
394 < for our UA solvent molecules. In these models, pseudo-atoms are
395 < located at the carbon centers for alkyl groups. By eliminating
396 < explicit hydrogen atoms, these models are simple and computationally
397 < efficient, while maintains good accuracy. However, the TraPPE-UA for
398 < alkanes is known to predict a lower boiling point than experimental
299 < values. Considering that after an unphysical thermal flux is applied
300 < to a system, the temperature of ``hot'' area in the liquid phase would be
301 < significantly higher than the average, to prevent over heating and
302 < boiling of the liquid phase, the average temperature in our
303 < simulations should be much lower than the liquid boiling point. [NEED MORE DISCUSSION]
304 < For UA-toluene model, rigid body constraints are applied, so that the
305 < benzene ring and the methyl-C(aromatic) bond are kept rigid. This
306 < would save computational time.[MORE DETAILS NEEDED]
394 > for our UA solvent molecules. In these models, sites are located at
395 > the carbon centers for alkyl groups. Bonding interactions, including
396 > bond stretches and bends and torsions, were used for intra-molecular
397 > sites closer than 3 bonds. For non-bonded interactions, Lennard-Jones
398 > potentials are used.
399  
400 < Besides the TraPPE-UA models, AA models for both organic solvents are
401 < included in our studies as well. For hexane, the OPLS
402 < all-atom\cite{OPLSAA} force field is used. [MORE DETAILS]
403 < For toluene, the United Force Field developed by Rapp\'{e} {\it et
404 <  al.}\cite{doi:10.1021/ja00051a040} is adopted.[MORE DETAILS]
400 > By eliminating explicit hydrogen atoms, the TraPPE-UA models are
401 > simple and computationally efficient, while maintaining good accuracy.
402 > However, the TraPPE-UA model for alkanes is known to predict a slightly
403 > lower boiling point than experimental values. This is one of the
404 > reasons we used a lower average temperature (200K) for our
405 > simulations. If heat is transferred to the liquid phase during the
406 > NIVS simulation, the liquid in the hot slab can actually be
407 > substantially warmer than the mean temperature in the simulation. The
408 > lower mean temperatures therefore prevent solvent boiling.
409  
410 < The capping agent in our simulations, the butanethiol molecules can
411 < either use UA or AA model. The TraPPE-UA force fields includes
412 < parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used in
413 < our simulations corresponding to our TraPPE-UA models for solvent.
414 < and All-Atom models [NEED CITATIONS]
319 < However, the model choice (UA or AA) of capping agent can be different
320 < from the solvent. Regardless of model choice, the force field
321 < parameters for interactions between capping agent and solvent can be
322 < derived using Lorentz-Berthelot Mixing Rule.
410 > For UA-toluene, the non-bonded potentials between intermolecular sites
411 > have a similar Lennard-Jones formulation. The toluene molecules were
412 > treated as a single rigid body, so there was no need for
413 > intramolecular interactions (including bonds, bends, or torsions) in
414 > this solvent model.
415  
416 < To describe the interactions between metal Au and non-metal capping
417 < agent and solvent, we refer to Vlugt\cite{vlugt:cpc2007154} and derive
418 < other interactions which are not yet finely parametrized. [can add
419 < hautman and klein's paper here and more discussion; need to put
420 < aromatic-metal interaction approximation here]\cite{doi:10.1021/jp034405s}
416 > Besides the TraPPE-UA models, AA models for both organic solvents are
417 > included in our studies as well. The OPLS-AA\cite{OPLSAA} force fields
418 > were used. For hexane, additional explicit hydrogen sites were
419 > included. Besides bonding and non-bonded site-site interactions,
420 > partial charges and the electrostatic interactions were added to each
421 > CT and HC site. For toluene, a flexible model for the toluene molecule
422 > was utilized which included bond, bend, torsion, and inversion
423 > potentials to enforce ring planarity.
424  
425 < [TABULATED FORCE FIELD PARAMETERS NEEDED]
425 > The butanethiol capping agent in our simulations, were also modeled
426 > with both UA and AA model. The TraPPE-UA force field includes
427 > parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
428 > UA butanethiol model in our simulations. The OPLS-AA also provides
429 > parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
430 > surfaces do not have the hydrogen atom bonded to sulfur. To derive
431 > suitable parameters for butanethiol adsorbed on Au(111) surfaces, we
432 > adopt the S parameters from Luedtke and Landman\cite{landman:1998} and
433 > modify the parameters for the CTS atom to maintain charge neutrality
434 > in the molecule.  Note that the model choice (UA or AA) for the capping
435 > agent can be different from the solvent. Regardless of model choice,
436 > the force field parameters for interactions between capping agent and
437 > solvent can be derived using Lorentz-Berthelot Mixing Rule:
438 > \begin{eqnarray}
439 >  \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
440 >  \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
441 > \end{eqnarray}
442  
443 + To describe the interactions between metal (Au) and non-metal atoms,
444 + we refer to an adsorption study of alkyl thiols on gold surfaces by
445 + Vlugt {\it et al.}\cite{vlugt:cpc2007154} They fitted an effective
446 + Lennard-Jones form of potential parameters for the interaction between
447 + Au and pseudo-atoms CH$_x$ and S based on a well-established and
448 + widely-used effective potential of Hautman and Klein for the Au(111)
449 + surface.\cite{hautman:4994} As our simulations require the gold slab
450 + to be flexible to accommodate thermal excitation, the pair-wise form
451 + of potentials they developed was used for our study.
452  
453 < [SURFACE RECONSTRUCTION PREVENTS SIMULATION TEMP TO GO HIGHER]
453 > The potentials developed from {\it ab initio} calculations by Leng
454 > {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
455 > interactions between Au and aromatic C/H atoms in toluene. However,
456 > the Lennard-Jones parameters between Au and other types of particles,
457 > (e.g. AA alkanes) have not yet been established. For these
458 > interactions, the Lorentz-Berthelot mixing rule can be used to derive
459 > effective single-atom LJ parameters for the metal using the fit values
460 > for toluene. These are then used to construct reasonable mixing
461 > parameters for the interactions between the gold and other atoms.
462 > Table \ref{MnM} summarizes the ``metal/non-metal'' parameters used in
463 > our simulations.
464  
335
336 \section{Results}
337 [REARRANGEMENT NEEDED]
338 \subsection{Toluene Solvent}
339
340 The results (Table \ref{AuThiolToluene}) show a
341 significant conductance enhancement compared to the gold/water
342 interface without capping agent and agree with available experimental
343 data. This indicates that the metal-metal potential, though not
344 predicting an accurate bulk metal thermal conductivity, does not
345 greatly interfere with the simulation of the thermal conductance
346 behavior across a non-metal interface. The solvent model is not
347 particularly volatile, so the simulation cell does not expand
348 significantly under higher temperature. We did not observe a
349 significant conductance decrease when the temperature was increased to
350 300K. The results show that the two definitions used for $G$ yield
351 comparable values, though $G^\prime$ tends to be smaller.
352
465   \begin{table*}
466    \begin{minipage}{\linewidth}
467      \begin{center}
468 <      \caption{Computed interfacial thermal conductivity ($G$ and
469 <        $G^\prime$) values for the Au/butanethiol/toluene interface at
470 <        different temperatures using a range of energy fluxes.}
471 <      
360 <      \begin{tabular}{cccc}
468 >      \caption{Non-bonded interaction parameters (including cross
469 >        interactions with Au atoms) for both force fields used in this
470 >        work.}      
471 >      \begin{tabular}{lllllll}
472          \hline\hline
473 <        $\langle T\rangle$ & $J_z$ & $G$ & $G^\prime$ \\
474 <        (K) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
473 >        & Site  & $\sigma_{ii}$ & $\epsilon_{ii}$ & $q_i$ &
474 >        $\sigma_{Au-i}$ & $\epsilon_{Au-i}$ \\
475 >        & & (\AA) & (kcal/mol) & ($e$) & (\AA) & (kcal/mol) \\
476          \hline
477 <        200 & 1.86 & 180 & 135 \\
478 <            & 2.15 & 204 & 113 \\
479 <            & 3.93 & 175 & 114 \\
480 <        300 & 1.91 & 143 & 125 \\
481 <            & 4.19 & 134 & 113 \\
477 >        United Atom (UA)
478 >        &CH3  & 3.75  & 0.1947  & -      & 3.54   & 0.2146  \\
479 >        &CH2  & 3.95  & 0.0914  & -      & 3.54   & 0.1749  \\
480 >        &CHar & 3.695 & 0.1003  & -      & 3.4625 & 0.1680  \\
481 >        &CRar & 3.88  & 0.04173 & -      & 3.555  & 0.1604  \\
482 >        \hline
483 >        All Atom (AA)
484 >        &CT3  & 3.50  & 0.066   & -0.18  & 3.365  & 0.1373  \\
485 >        &CT2  & 3.50  & 0.066   & -0.12  & 3.365  & 0.1373  \\
486 >        &CTT  & 3.50  & 0.066   & -0.065 & 3.365  & 0.1373  \\
487 >        &HC   & 2.50  & 0.030   &  0.06  & 2.865  & 0.09256 \\
488 >        &CA   & 3.55  & 0.070   & -0.115 & 3.173  & 0.0640  \\
489 >        &HA   & 2.42  & 0.030   &  0.115 & 2.746  & 0.0414  \\
490 >        \hline
491 >        Both UA and AA
492 >        & S   & 4.45  & 0.25    & -      & 2.40   & 8.465   \\
493          \hline\hline
494        \end{tabular}
495 <      \label{AuThiolToluene}
495 >      \label{MnM}
496      \end{center}
497    \end{minipage}
498   \end{table*}
499  
377 \subsection{Hexane Solvent}
500  
501 < Using the united-atom model, different coverages of capping agent,
502 < temperatures of simulations and numbers of solvent molecules were all
503 < investigated and Table \ref{AuThiolHexaneUA} shows the results of
504 < these computations. The number of hexane molecules in our simulations
505 < does not affect the calculations significantly. However, a very long
506 < length scale for the thermal gradient axis ($z$) may cause excessively
507 < hot or cold temperatures in the middle of the solvent region and lead
508 < to undesired phenomena such as solvent boiling or freezing, while too
387 < few solvent molecules would change the normal behavior of the liquid
388 < phase. Our $N_{hexane}$ values were chosen to ensure that these
389 < extreme cases did not happen to our simulations.
501 > \section{Results}
502 > There are many factors contributing to the measured interfacial
503 > conductance; some of these factors are physically motivated
504 > (e.g. coverage of the surface by the capping agent coverage and
505 > solvent identity), while some are governed by parameters of the
506 > methodology (e.g. applied flux and the formulas used to obtain the
507 > conductance). In this section we discuss the major physical and
508 > calculational effects on the computed conductivity.
509  
510 < Table \ref{AuThiolHexaneUA} enables direct comparison between
392 < different coverages of capping agent, when other system parameters are
393 < held constant. With high coverage of butanethiol on the gold surface,
394 < the interfacial thermal conductance is enhanced
395 < significantly. Interestingly, a slightly lower butanethiol coverage
396 < leads to a moderately higher conductivity. This is probably due to
397 < more solvent/capping agent contact when butanethiol molecules are
398 < not densely packed, which enhances the interactions between the two
399 < phases and lowers the thermal transfer barrier of this interface.
400 < % [COMPARE TO AU/WATER IN PAPER]
510 > \subsection{Effects due to capping agent coverage}
511  
512 < It is also noted that the overall simulation temperature is another
513 < factor that affects the interfacial thermal conductance. One
514 < possibility of this effect may be rooted in the decrease in density of
515 < the liquid phase. We observed that when the average temperature
516 < increases from 200K to 250K, the bulk hexane density becomes lower
517 < than experimental value, as the system is equilibrated under NPT
408 < ensemble. This leads to lower contact between solvent and capping
409 < agent, and thus lower conductivity.
512 > A series of different initial conditions with a range of surface
513 > coverages was prepared and solvated with various with both of the
514 > solvent molecules. These systems were then equilibrated and their
515 > interfacial thermal conductivity was measured with the NIVS
516 > algorithm. Figure \ref{coverage} demonstrates the trend of conductance
517 > with respect to surface coverage.
518  
519 < Conductivity values are more difficult to obtain under higher
520 < temperatures. This is because the Au surface tends to undergo
521 < reconstructions in relatively high temperatures. Surface Au atoms can
522 < migrate outward to reach higher Au-S contact; and capping agent
523 < molecules can be embedded into the surface Au layer due to the same
524 < driving force. This phenomenon agrees with experimental
525 < results\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}. A surface
526 < fully covered in capping agent is more susceptible to reconstruction,
419 < possibly because fully coverage prevents other means of capping agent
420 < relaxation, such as migration to an empty neighbor three-fold site.
519 > \begin{figure}
520 > \includegraphics[width=\linewidth]{coverage}
521 > \caption{The interfacial thermal conductivity ($G$) has a
522 >  non-monotonic dependence on the degree of surface capping.  This
523 >  data is for the Au(111) / butanethiol / solvent interface with
524 >  various UA force fields at $\langle T\rangle \sim $200K.}
525 > \label{coverage}
526 > \end{figure}
527  
528 < %MAY ADD MORE DATA TO TABLE
528 > In partially covered surfaces, the derivative definition for
529 > $G^\prime$ (Eq. \ref{derivativeG}) becomes difficult to apply, as the
530 > location of maximum change of $\lambda$ becomes washed out.  The
531 > discrete definition (Eq. \ref{discreteG}) is easier to apply, as the
532 > Gibbs dividing surface is still well-defined. Therefore, $G$ (not
533 > $G^\prime$) was used in this section.
534 >
535 > From Figure \ref{coverage}, one can see the significance of the
536 > presence of capping agents. When even a small fraction of the Au(111)
537 > surface sites are covered with butanethiols, the conductivity exhibits
538 > an enhancement by at least a factor of 3.  Capping agents are clearly
539 > playing a major role in thermal transport at metal / organic solvent
540 > surfaces.
541 >
542 > We note a non-monotonic behavior in the interfacial conductance as a
543 > function of surface coverage. The maximum conductance (largest $G$)
544 > happens when the surfaces are about 75\% covered with butanethiol
545 > caps.  The reason for this behavior is not entirely clear.  One
546 > explanation is that incomplete butanethiol coverage allows small gaps
547 > between butanethiols to form. These gaps can be filled by transient
548 > solvent molecules.  These solvent molecules couple very strongly with
549 > the hot capping agent molecules near the surface, and can then carry
550 > away (diffusively) the excess thermal energy from the surface.
551 >
552 > There appears to be a competition between the conduction of the
553 > thermal energy away from the surface by the capping agents (enhanced
554 > by greater coverage) and the coupling of the capping agents with the
555 > solvent (enhanced by interdigitation at lower coverages).  This
556 > competition would lead to the non-monotonic coverage behavior observed
557 > here.
558 >
559 > Results for rigid body toluene solvent, as well as the UA hexane, are
560 > within the ranges expected from prior experimental
561 > work.\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406} This suggests
562 > that explicit hydrogen atoms might not be required for modeling
563 > thermal transport in these systems.  C-H vibrational modes do not see
564 > significant excited state population at low temperatures, and are not
565 > likely to carry lower frequency excitations from the solid layer into
566 > the bulk liquid.
567 >
568 > The toluene solvent does not exhibit the same behavior as hexane in
569 > that $G$ remains at approximately the same magnitude when the capping
570 > coverage increases from 25\% to 75\%.  Toluene, as a rigid planar
571 > molecule, cannot occupy the relatively small gaps between the capping
572 > agents as easily as the chain-like {\it n}-hexane.  The effect of
573 > solvent coupling to the capping agent is therefore weaker in toluene
574 > except at the very lowest coverage levels.  This effect counters the
575 > coverage-dependent conduction of heat away from the metal surface,
576 > leading to a much flatter $G$ vs. coverage trend than is observed in
577 > {\it n}-hexane.
578 >
579 > \subsection{Effects due to Solvent \& Solvent Models}
580 > In addition to UA solvent and capping agent models, AA models have
581 > also been included in our simulations.  In most of this work, the same
582 > (UA or AA) model for solvent and capping agent was used, but it is
583 > also possible to utilize different models for different components.
584 > We have also included isotopic substitutions (Hydrogen to Deuterium)
585 > to decrease the explicit vibrational overlap between solvent and
586 > capping agent. Table \ref{modelTest} summarizes the results of these
587 > studies.
588 >
589   \begin{table*}
590    \begin{minipage}{\linewidth}
591      \begin{center}
426      \caption{Computed interfacial thermal conductivity ($G$ and
427        $G^\prime$) values for the Au/butanethiol/hexane interface
428        with united-atom model and different capping agent coverage
429        and solvent molecule numbers at different temperatures using a
430        range of energy fluxes.}
592        
593 <      \begin{tabular}{cccccc}
593 >      \caption{Computed interfacial thermal conductance ($G$ and
594 >        $G^\prime$) values for interfaces using various models for
595 >        solvent and capping agent (or without capping agent) at
596 >        $\langle T\rangle\sim$200K.  Here ``D'' stands for deuterated
597 >        solvent or capping agent molecules; ``Avg.'' denotes results
598 >        that are averages of simulations under different applied
599 >        thermal flux $(J_z)$ values. Error estimates are indicated in
600 >        parentheses.}
601 >      
602 >      \begin{tabular}{llccc}
603          \hline\hline
604 <        Thiol & $\langle T\rangle$ & & $J_z$ & $G$ & $G^\prime$ \\
605 <        coverage (\%) & (K) & $N_{hexane}$ & (GW/m$^2$) &
604 >        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
605 >        (or bare surface) & model & (GW/m$^2$) &
606          \multicolumn{2}{c}{(MW/m$^2$/K)} \\
607          \hline
608 <        0.0   & 200 & 200 & 0.96 & 43.3 & 42.7 \\
609 <              &     &     & 1.91 & 45.7 & 42.9 \\
610 <              &     & 166 & 0.96 & 43.1 & 53.4 \\
611 <        88.9  & 200 & 166 & 1.94 & 172  & 108  \\
612 <        100.0 & 250 & 200 & 0.96 & 81.8 & 67.0 \\
613 <              &     & 166 & 0.98 & 79.0 & 62.9 \\
614 <              &     &     & 1.44 & 76.2 & 64.8 \\
615 <              & 200 & 200 & 1.92 & 129  & 87.3 \\
616 <              &     &     & 1.93 & 131  & 77.5 \\
617 <              &     & 166 & 0.97 & 115  & 69.3 \\
618 <              &     &     & 1.94 & 125  & 87.1 \\
608 >        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
609 >              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
610 >              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
611 >              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
612 >              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
613 >        \hline
614 >        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
615 >              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
616 >              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
617 >              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
618 >              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
619 >        \hline
620 >        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
621 >              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
622 >              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
623 >        \hline
624 >        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
625 >              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
626 >              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
627 >              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
628          \hline\hline
629        \end{tabular}
630 <      \label{AuThiolHexaneUA}
630 >      \label{modelTest}
631      \end{center}
632    \end{minipage}
633   \end{table*}
634  
635 < For the all-atom model, the liquid hexane phase was not stable under NPT
636 < conditions. Therefore, the simulation length scale parameters are
637 < adopted from previous equilibration results of the united-atom model
459 < at 200K. Table \ref{AuThiolHexaneAA} shows the results of these
460 < simulations. The conductivity values calculated with full capping
461 < agent coverage are substantially larger than observed in the
462 < united-atom model, and is even higher than predicted by
463 < experiments. It is possible that our parameters for metal-non-metal
464 < particle interactions lead to an overestimate of the interfacial
465 < thermal conductivity, although the active C-H vibrations in the
466 < all-atom model (which should not be appreciably populated at normal
467 < temperatures) could also account for this high conductivity. The major
468 < thermal transfer barrier of Au/butanethiol/hexane interface is between
469 < the liquid phase and the capping agent, so extra degrees of freedom
470 < such as the C-H vibrations could enhance heat exchange between these
471 < two phases and result in a much higher conductivity.
635 > To facilitate direct comparison between force fields, systems with the
636 > same capping agent and solvent were prepared with the same length
637 > scales for the simulation cells.
638  
639 + On bare metal / solvent surfaces, different force field models for
640 + hexane yield similar results for both $G$ and $G^\prime$, and these
641 + two definitions agree with each other very well. This is primarily an
642 + indicator of weak interactions between the metal and the solvent, and
643 + is a typical case for acoustic impedance mismatch between these two
644 + phases.  
645 +
646 + For the fully-covered surfaces, the choice of force field for the
647 + capping agent and solvent has a large impact on the calculated values
648 + of $G$ and $G^\prime$.  The AA thiol to AA solvent conductivities are
649 + much larger than their UA to UA counterparts, and these values exceed
650 + the experimental estimates by a large measure.  The AA force field
651 + allows significant energy to go into C-H (or C-D) stretching modes,
652 + and since these modes are high frequency, this non-quantum behavior is
653 + likely responsible for the overestimate of the conductivity.  Compared
654 + to the AA model, the UA model yields more reasonable conductivity
655 + values with much higher computational efficiency.
656 +
657 + \subsubsection{Are electronic excitations in the metal important?}
658 + Because they lack electronic excitations, the QSC and related embedded
659 + atom method (EAM) models for gold are known to predict unreasonably
660 + low values for bulk conductivity
661 + ($\lambda$).\cite{kuang:164101,ISI:000207079300006,Clancy:1992} If the
662 + conductance between the phases ($G$) is governed primarily by phonon
663 + excitation (and not electronic degrees of freedom), one would expect a
664 + classical model to capture most of the interfacial thermal
665 + conductance.  Our results for $G$ and $G^\prime$ indicate that this is
666 + indeed the case, and suggest that the modeling of interfacial thermal
667 + transport depends primarily on the description of the interactions
668 + between the various components at the interface.  When the metal is
669 + chemically capped, the primary barrier to thermal conductivity appears
670 + to be the interface between the capping agent and the surrounding
671 + solvent, so the excitations in the metal have little impact on the
672 + value of $G$.
673 +
674 + \subsection{Effects due to methodology and simulation parameters}
675 +
676 + We have varied the parameters of the simulations in order to
677 + investigate how these factors would affect the computation of $G$.  Of
678 + particular interest are: 1) the length scale for the applied thermal
679 + gradient (modified by increasing the amount of solvent in the system),
680 + 2) the sign and magnitude of the applied thermal flux, 3) the average
681 + temperature of the simulation (which alters the solvent density during
682 + equilibration), and 4) the definition of the interfacial conductance
683 + (Eqs. (\ref{discreteG}) or (\ref{derivativeG})) used in the
684 + calculation.
685 +
686 + Systems of different lengths were prepared by altering the number of
687 + solvent molecules and extending the length of the box along the $z$
688 + axis to accomodate the extra solvent.  Equilibration at the same
689 + temperature and pressure conditions led to nearly identical surface
690 + areas ($L_x$ and $L_y$) available to the metal and capping agent,
691 + while the extra solvent served mainly to lengthen the axis that was
692 + used to apply the thermal flux.  For a given value of the applied
693 + flux, the different $z$ length scale has only a weak effect on the
694 + computed conductivities (Table \ref{AuThiolHexaneUA}).
695 +
696 + \subsubsection{Effects of applied flux}
697 + The NIVS algorithm allows changes in both the sign and magnitude of
698 + the applied flux.  It is possible to reverse the direction of heat
699 + flow simply by changing the sign of the flux, and thermal gradients
700 + which would be difficult to obtain experimentally ($5$ K/\AA) can be
701 + easily simulated.  However, the magnitude of the applied flux is not
702 + arbitrary if one aims to obtain a stable and reliable thermal gradient.
703 + A temperature gradient can be lost in the noise if $|J_z|$ is too
704 + small, and excessive $|J_z|$ values can cause phase transitions if the
705 + extremes of the simulation cell become widely separated in
706 + temperature.  Also, if $|J_z|$ is too large for the bulk conductivity
707 + of the materials, the thermal gradient will never reach a stable
708 + state.  
709 +
710 + Within a reasonable range of $J_z$ values, we were able to study how
711 + $G$ changes as a function of this flux.  In what follows, we use
712 + positive $J_z$ values to denote the case where energy is being
713 + transferred by the method from the metal phase and into the liquid.
714 + The resulting gradient therefore has a higher temperature in the
715 + liquid phase.  Negative flux values reverse this transfer, and result
716 + in higher temperature metal phases.  The conductance measured under
717 + different applied $J_z$ values is listed in Tables
718 + \ref{AuThiolHexaneUA} and \ref{AuThiolToluene}. These results do not
719 + indicate that $G$ depends strongly on $J_z$ within this flux
720 + range. The linear response of flux to thermal gradient simplifies our
721 + investigations in that we can rely on $G$ measurement with only a
722 + small number $J_z$ values.  
723 +
724   \begin{table*}
725    \begin{minipage}{\linewidth}
726      \begin{center}
727 +      \caption{In the hexane-solvated interfaces, the system size has
728 +        little effect on the calculated values for interfacial
729 +        conductance ($G$ and $G^\prime$), but the direction of heat
730 +        flow (i.e. the sign of $J_z$) can alter the average
731 +        temperature of the liquid phase and this can alter the
732 +        computed conductivity.}
733        
734 <      \caption{Computed interfacial thermal conductivity ($G$ and
478 <        $G^\prime$) values for the Au/butanethiol/hexane interface
479 <        with all-atom model and different capping agent coverage at
480 <        200K using a range of energy fluxes.}
481 <      
482 <      \begin{tabular}{cccc}
734 >      \begin{tabular}{ccccccc}
735          \hline\hline
736 <        Thiol & $J_z$ & $G$ & $G^\prime$ \\
737 <        coverage (\%) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
736 >        $\langle T\rangle$ & $N_{hexane}$  & $\rho_{hexane}$ &
737 >        $J_z$ & $G$ & $G^\prime$ \\
738 >        (K) &  & (g/cm$^3$) & (GW/m$^2$) &
739 >        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
740          \hline
741 <        0.0   & 0.95 & 28.5 & 27.2 \\
742 <              & 1.88 & 30.3 & 28.9 \\
743 <        100.0 & 2.87 & 551  & 294  \\
744 <              & 3.81 & 494  & 193  \\
741 >        200 & 266 &  0.672 & -0.96 & 102(3)    & 80.0(0.8) \\
742 >            & 200 &  0.688 &  0.96 & 125(16)   & 90.2(15)  \\
743 >            &     &        &  1.91 & 139(10)   & 101(10)   \\
744 >            &     &        &  2.83 & 141(6)    & 89.9(9.8) \\
745 >            & 166 &  0.681 &  0.97 & 141(30)   & 78(22)    \\
746 >            &     &        &  1.92 & 138(4)    & 98.9(9.5) \\
747 >        \hline
748 >        250 & 200 &  0.560 &  0.96 & 75(10)    & 61.8(7.3) \\
749 >            &     &        & -0.95 & 49.4(0.3) & 45.7(2.1) \\
750 >            & 166 &  0.569 &  0.97 & 80.3(0.6) & 67(11)    \\
751 >            &     &        &  1.44 & 76.2(5.0) & 64.8(3.8) \\
752 >            &     &        & -0.95 & 56.4(2.5) & 54.4(1.1) \\
753 >            &     &        & -1.85 & 47.8(1.1) & 53.5(1.5) \\
754          \hline\hline
755        \end{tabular}
756 <      \label{AuThiolHexaneAA}
756 >      \label{AuThiolHexaneUA}
757      \end{center}
758    \end{minipage}
759   \end{table*}
760  
761 < %subsubsection{Vibrational spectrum study on conductance mechanism}
762 < To investigate the mechanism of this interfacial thermal conductance,
763 < the vibrational spectra of various gold systems were obtained and are
764 < shown as in the upper panel of Fig. \ref{vibration}. To obtain these
765 < spectra, one first runs a simulation in the NVE ensemble and collects
766 < snapshots of configurations; these configurations are used to compute
767 < the velocity auto-correlation functions, which is used to construct a
768 < power spectrum via a Fourier transform. The gold surfaces covered by
769 < butanethiol molecules exhibit an additional peak observed at a
507 < frequency of $\sim$170cm$^{-1}$, which is attributed to the vibration
508 < of the S-Au bond. This vibration enables efficient thermal transport
509 < from surface Au atoms to the capping agents. Simultaneously, as shown
510 < in the lower panel of Fig. \ref{vibration}, the large overlap of the
511 < vibration spectra of butanethiol and hexane in the all-atom model,
512 < including the C-H vibration, also suggests high thermal exchange
513 < efficiency. The combination of these two effects produces the drastic
514 < interfacial thermal conductance enhancement in the all-atom model.
761 > The sign of $J_z$ is a different matter, however, as this can alter
762 > the temperature on the two sides of the interface. The average
763 > temperature values reported are for the entire system, and not for the
764 > liquid phase, so at a given $\langle T \rangle$, the system with
765 > positive $J_z$ has a warmer liquid phase.  This means that if the
766 > liquid carries thermal energy via convective transport, {\it positive}
767 > $J_z$ values will result in increased molecular motion on the liquid
768 > side of the interface, and this will increase the measured
769 > conductivity.
770  
771 + \subsubsection{Effects due to average temperature}
772 +
773 + We also studied the effect of average system temperature on the
774 + interfacial conductance.  The simulations are first equilibrated in
775 + the NPT ensemble at 1 atm.  The TraPPE-UA model for hexane tends to
776 + predict a lower boiling point (and liquid state density) than
777 + experiments.  This lower-density liquid phase leads to reduced contact
778 + between the hexane and butanethiol, and this accounts for our
779 + observation of lower conductance at higher temperatures as shown in
780 + Table \ref{AuThiolHexaneUA}.  In raising the average temperature from
781 + 200K to 250K, the density drop of $\sim$20\% in the solvent phase
782 + leads to a $\sim$40\% drop in the conductance.
783 +
784 + Similar behavior is observed in the TraPPE-UA model for toluene,
785 + although this model has better agreement with the experimental
786 + densities of toluene.  The expansion of the toluene liquid phase is
787 + not as significant as that of the hexane (8.3\% over 100K), and this
788 + limits the effect to $\sim$20\% drop in thermal conductivity  (Table
789 + \ref{AuThiolToluene}).
790 +
791 + Although we have not mapped out the behavior at a large number of
792 + temperatures, is clear that there will be a strong temperature
793 + dependence in the interfacial conductance when the physical properties
794 + of one side of the interface (notably the density) change rapidly as a
795 + function of temperature.
796 +
797 + \begin{table*}
798 +  \begin{minipage}{\linewidth}
799 +    \begin{center}
800 +      \caption{When toluene is the solvent, the interfacial thermal
801 +        conductivity is less sensitive to temperature, but again, the
802 +        direction of the heat flow can alter the solvent temperature
803 +        and can change the computed conductance values.}
804 +      
805 +      \begin{tabular}{ccccc}
806 +        \hline\hline
807 +        $\langle T\rangle$ & $\rho_{toluene}$ & $J_z$ & $G$ & $G^\prime$ \\
808 +        (K) & (g/cm$^3$) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
809 +        \hline
810 +        200 & 0.933 &  2.15 & 204(12) & 113(12) \\
811 +            &       & -1.86 & 180(3)  & 135(21) \\
812 +            &       & -3.93 & 176(5)  & 113(12) \\
813 +        \hline
814 +        300 & 0.855 & -1.91 & 143(5)  & 125(2)  \\
815 +            &       & -4.19 & 135(9)  & 113(12) \\
816 +        \hline\hline
817 +      \end{tabular}
818 +      \label{AuThiolToluene}
819 +    \end{center}
820 +  \end{minipage}
821 + \end{table*}
822 +
823 + Besides the lower interfacial thermal conductance, surfaces at
824 + relatively high temperatures are susceptible to reconstructions,
825 + particularly when butanethiols fully cover the Au(111) surface. These
826 + reconstructions include surface Au atoms which migrate outward to the
827 + S atom layer, and butanethiol molecules which embed into the surface
828 + Au layer. The driving force for this behavior is the strong Au-S
829 + interactions which are modeled here with a deep Lennard-Jones
830 + potential. This phenomenon agrees with reconstructions that have been
831 + experimentally
832 + observed.\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}.  Vlugt
833 + {\it et al.} kept their Au(111) slab rigid so that their simulations
834 + could reach 300K without surface
835 + reconstructions.\cite{vlugt:cpc2007154} Since surface reconstructions
836 + blur the interface, the measurement of $G$ becomes more difficult to
837 + conduct at higher temperatures.  For this reason, most of our
838 + measurements are undertaken at $\langle T\rangle\sim$200K where
839 + reconstruction is minimized.
840 +
841 + However, when the surface is not completely covered by butanethiols,
842 + the simulated system appears to be more resistent to the
843 + reconstruction. Our Au / butanethiol / toluene system had the Au(111)
844 + surfaces 90\% covered by butanethiols, but did not see this above
845 + phenomena even at $\langle T\rangle\sim$300K.  That said, we did
846 + observe butanethiols migrating to neighboring three-fold sites during
847 + a simulation.  Since the interface persisted in these simulations, we
848 + were able to obtain $G$'s for these interfaces even at a relatively
849 + high temperature without being affected by surface reconstructions.
850 +
851 + \section{Discussion}
852 +
853 + The primary result of this work is that the capping agent acts as an
854 + efficient thermal coupler between solid and solvent phases.  One of
855 + the ways the capping agent can carry out this role is to down-shift
856 + between the phonon vibrations in the solid (which carry the heat from
857 + the gold) and the molecular vibrations in the liquid (which carry some
858 + of the heat in the solvent).
859 +
860 + To investigate the mechanism of interfacial thermal conductance, the
861 + vibrational power spectrum was computed. Power spectra were taken for
862 + individual components in different simulations. To obtain these
863 + spectra, simulations were run after equilibration in the
864 + microcanonical (NVE) ensemble and without a thermal
865 + gradient. Snapshots of configurations were collected at a frequency
866 + that is higher than that of the fastest vibrations occurring in the
867 + simulations. With these configurations, the velocity auto-correlation
868 + functions can be computed:
869 + \begin{equation}
870 + C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
871 + \label{vCorr}
872 + \end{equation}
873 + The power spectrum is constructed via a Fourier transform of the
874 + symmetrized velocity autocorrelation function,
875 + \begin{equation}
876 +  \hat{f}(\omega) =
877 +  \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
878 + \label{fourier}
879 + \end{equation}
880 +
881 + \subsection{The role of specific vibrations}
882 + The vibrational spectra for gold slabs in different environments are
883 + shown as in Figure \ref{specAu}. Regardless of the presence of
884 + solvent, the gold surfaces which are covered by butanethiol molecules
885 + exhibit an additional peak observed at a frequency of
886 + $\sim$165cm$^{-1}$.  We attribute this peak to the S-Au bonding
887 + vibration. This vibration enables efficient thermal coupling of the
888 + surface Au layer to the capping agents. Therefore, in our simulations,
889 + the Au / S interfaces do not appear to be the primary barrier to
890 + thermal transport when compared with the butanethiol / solvent
891 + interfaces. {\bf This confirms the results from Luo {\it et
892 +    al.}\cite{Luo20101}, which reported $G$ for Au-SAM junctions
893 +  generally twice larger than what we have computed for the
894 +  thiol-liquid interfaces.}
895 +
896   \begin{figure}
897   \includegraphics[width=\linewidth]{vibration}
898 < \caption{Vibrational spectra obtained for gold in different
899 <  environments (upper panel) and for Au/thiol/hexane simulation in
900 <  all-atom model (lower panel).}
901 < \label{vibration}
898 > \caption{The vibrational power spectrum for thiol-capped gold has an
899 >  additional vibrational peak at $\sim $165cm$^{-1}$.  Bare gold
900 >  surfaces (both with and without a solvent over-layer) are missing
901 >  this peak.   A similar peak at  $\sim $165cm$^{-1}$ also appears in
902 >  the vibrational power spectrum for the butanethiol capping agents.}
903 > \label{specAu}
904   \end{figure}
523 % 600dpi, letter size. too large?
905  
906 + Also in this figure, we show the vibrational power spectrum for the
907 + bound butanethiol molecules, which also exhibits the same
908 + $\sim$165cm$^{-1}$ peak.
909  
910 + \subsection{Overlap of power spectra}
911 + A comparison of the results obtained from the two different organic
912 + solvents can also provide useful information of the interfacial
913 + thermal transport process.  In particular, the vibrational overlap
914 + between the butanethiol and the organic solvents suggests a highly
915 + efficient thermal exchange between these components.  Very high
916 + thermal conductivity was observed when AA models were used and C-H
917 + vibrations were treated classically.  The presence of extra degrees of
918 + freedom in the AA force field yields higher heat exchange rates
919 + between the two phases and results in a much higher conductivity than
920 + in the UA force field. {\bf Due to the classical models used, this
921 +  even includes those high frequency modes which should be unpopulated
922 +  at our relatively low temperatures. This artifact causes high
923 +  frequency vibrations accountable for thermal transport in classical
924 +  MD simulations.}
925 +
926 + The similarity in the vibrational modes available to solvent and
927 + capping agent can be reduced by deuterating one of the two components
928 + (Fig. \ref{aahxntln}).  Once either the hexanes or the butanethiols
929 + are deuterated, one can observe a significantly lower $G$ and
930 + $G^\prime$ values (Table \ref{modelTest}).
931 +
932 + \begin{figure}
933 + \includegraphics[width=\linewidth]{aahxntln}
934 + \caption{Spectra obtained for all-atom (AA) Au / butanethiol / solvent
935 +  systems. When butanethiol is deuterated (lower left), its
936 +  vibrational overlap with hexane decreases significantly.  Since
937 +  aromatic molecules and the butanethiol are vibrationally dissimilar,
938 +  the change is not as dramatic when toluene is the solvent (right).}
939 + \label{aahxntln}
940 + \end{figure}
941 +
942 + For the Au / butanethiol / toluene interfaces, having the AA
943 + butanethiol deuterated did not yield a significant change in the
944 + measured conductance. Compared to the C-H vibrational overlap between
945 + hexane and butanethiol, both of which have alkyl chains, the overlap
946 + between toluene and butanethiol is not as significant and thus does
947 + not contribute as much to the heat exchange process.
948 +
949 + Previous observations of Zhang {\it et al.}\cite{hase:2010} indicate
950 + that the {\it intra}molecular heat transport due to alkylthiols is
951 + highly efficient.  Combining our observations with those of Zhang {\it
952 +  et al.}, it appears that butanethiol acts as a channel to expedite
953 + heat flow from the gold surface and into the alkyl chain.  The
954 + acoustic impedance mismatch between the metal and the liquid phase can
955 + therefore be effectively reduced with the presence of suitable capping
956 + agents.
957 +
958 + Deuterated models in the UA force field did not decouple the thermal
959 + transport as well as in the AA force field.  The UA models, even
960 + though they have eliminated the high frequency C-H vibrational
961 + overlap, still have significant overlap in the lower-frequency
962 + portions of the infrared spectra (Figure \ref{uahxnua}).  Deuterating
963 + the UA models did not decouple the low frequency region enough to
964 + produce an observable difference for the results of $G$ (Table
965 + \ref{modelTest}).
966 +
967 + \begin{figure}
968 + \includegraphics[width=\linewidth]{uahxnua}
969 + \caption{Vibrational power spectra for UA models for the butanethiol
970 +  and hexane solvent (upper panel) show the high degree of overlap
971 +  between these two molecules, particularly at lower frequencies.
972 +  Deuterating a UA model for the solvent (lower panel) does not
973 +  decouple the two spectra to the same degree as in the AA force
974 +  field (see Fig \ref{aahxntln}).}
975 + \label{uahxnua}
976 + \end{figure}
977 +
978 + \section{Conclusions}
979 + The NIVS algorithm has been applied to simulations of
980 + butanethiol-capped Au(111) surfaces in the presence of organic
981 + solvents. This algorithm allows the application of unphysical thermal
982 + flux to transfer heat between the metal and the liquid phase. With the
983 + flux applied, we were able to measure the corresponding thermal
984 + gradients and to obtain interfacial thermal conductivities. Under
985 + steady states, 2-3 ns trajectory simulations are sufficient for
986 + computation of this quantity.
987 +
988 + Our simulations have seen significant conductance enhancement in the
989 + presence of capping agent, compared with the bare gold / liquid
990 + interfaces. The acoustic impedance mismatch between the metal and the
991 + liquid phase is effectively eliminated by a chemically-bonded capping
992 + agent. Furthermore, the coverage percentage of the capping agent plays
993 + an important role in the interfacial thermal transport
994 + process. Moderately low coverages allow higher contact between capping
995 + agent and solvent, and thus could further enhance the heat transfer
996 + process, giving a non-monotonic behavior of conductance with
997 + increasing coverage.
998 +
999 + Our results, particularly using the UA models, agree well with
1000 + available experimental data.  The AA models tend to overestimate the
1001 + interfacial thermal conductance in that the classically treated C-H
1002 + vibrations become too easily populated. Compared to the AA models, the
1003 + UA models have higher computational efficiency with satisfactory
1004 + accuracy, and thus are preferable in modeling interfacial thermal
1005 + transport.
1006 +
1007 + Of the two definitions for $G$, the discrete form
1008 + (Eq. \ref{discreteG}) was easier to use and gives out relatively
1009 + consistent results, while the derivative form (Eq. \ref{derivativeG})
1010 + is not as versatile. Although $G^\prime$ gives out comparable results
1011 + and follows similar trend with $G$ when measuring close to fully
1012 + covered or bare surfaces, the spatial resolution of $T$ profile
1013 + required for the use of a derivative form is limited by the number of
1014 + bins and the sampling required to obtain thermal gradient information.
1015 +
1016 + Vlugt {\it et al.} have investigated the surface thiol structures for
1017 + nanocrystalline gold and pointed out that they differ from those of
1018 + the Au(111) surface.\cite{landman:1998,vlugt:cpc2007154} This
1019 + difference could also cause differences in the interfacial thermal
1020 + transport behavior. To investigate this problem, one would need an
1021 + effective method for applying thermal gradients in non-planar
1022 + (i.e. spherical) geometries.
1023 +
1024   \section{Acknowledgments}
1025   Support for this project was provided by the National Science
1026   Foundation under grant CHE-0848243. Computational time was provided by
1027   the Center for Research Computing (CRC) at the University of Notre
1028 < Dame.  \newpage
1028 > Dame.
1029  
1030 + \section{Supporting Information}
1031 + This information is available free of charge via the Internet at
1032 + http://pubs.acs.org.
1033 +
1034 + \newpage
1035 +
1036   \bibliography{interfacial}
1037  
1038   \end{doublespace}

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