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# Line 73 | Line 73 | Interfacial thermal conductance is extensively studied
73   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
74  
75   \section{Introduction}
76 < Interfacial thermal conductance is extensively studied both
77 < experimentally and computationally\cite{cahill:793}, due to its
78 < importance in nanoscale science and technology. Reliability of
79 < nanoscale devices depends on their thermal transport
80 < properties. Unlike bulk homogeneous materials, nanoscale materials
81 < features significant presence of interfaces, and these interfaces
82 < could dominate the heat transfer behavior of these
83 < materials. Furthermore, these materials are generally heterogeneous,
84 < which challenges traditional research methods for homogeneous
85 < systems.
76 > Due to the importance of heat flow in nanotechnology, interfacial
77 > thermal conductance has been studied extensively both experimentally
78 > and computationally.\cite{cahill:793} Unlike bulk materials, nanoscale
79 > materials have a significant fraction of their atoms at interfaces,
80 > and the chemical details of these interfaces govern the heat transfer
81 > behavior. Furthermore, the interfaces are
82 > heterogeneous (e.g. solid - liquid), which provides a challenge to
83 > traditional methods developed for homogeneous systems.
84  
85 < Heat conductance of molecular and nano-scale interfaces will be
86 < affected by the chemical details of the surface. Experimentally,
87 < various interfaces have been investigated for their thermal
88 < conductance properties. Wang {\it et al.} studied heat transport
89 < through long-chain hydrocarbon monolayers on gold substrate at
90 < individual molecular level\cite{Wang10082007}; Schmidt {\it et al.}
91 < studied the role of CTAB on thermal transport between gold nanorods
92 < and solvent\cite{doi:10.1021/jp8051888}; Juv\'e {\it et al.} studied
85 > Experimentally, various interfaces have been investigated for their
86 > thermal conductance. Cahill and coworkers studied nanoscale thermal
87 > transport from metal nanoparticle/fluid interfaces, to epitaxial
88 > TiN/single crystal oxides interfaces, to hydrophilic and hydrophobic
89 > interfaces between water and solids with different self-assembled
90 > monolayers.\cite{Wilson:2002uq,PhysRevB.67.054302,doi:10.1021/jp048375k,PhysRevLett.96.186101}
91 > Wang {\it et al.} studied heat transport through
92 > long-chain hydrocarbon monolayers on gold substrate at individual
93 > molecular level,\cite{Wang10082007} Schmidt {\it et al.} studied the
94 > role of CTAB on thermal transport between gold nanorods and
95 > solvent,\cite{doi:10.1021/jp8051888} and Juv\'e {\it et al.} studied
96   the cooling dynamics, which is controlled by thermal interface
97   resistence of glass-embedded metal
98 < nanoparticles\cite{PhysRevB.80.195406}. Although interfaces are
99 < commonly barriers for heat transport, Alper {\it et al.} suggested
100 < that specific ligands (capping agents) could completely eliminate this
101 < barrier ($G\rightarrow\infty$)\cite{doi:10.1021/la904855s}.
98 > nanoparticles.\cite{PhysRevB.80.195406} Although interfaces are
99 > normally considered barriers for heat transport, Alper {\it et al.}
100 > suggested that specific ligands (capping agents) could completely
101 > eliminate this barrier
102 > ($G\rightarrow\infty$).\cite{doi:10.1021/la904855s}
103  
104   Theoretical and computational models have also been used to study the
105   interfacial thermal transport in order to gain an understanding of
# Line 105 | Line 107 | atoms)\cite{hase:2010,hase:2011}. However, ensemble av
107   employed Non-Equilibrium Molecular Dynamics (NEMD) simulations to
108   study thermal transport from hot Au(111) substrate to a self-assembled
109   monolayer of alkylthiol with relatively long chain (8-20 carbon
110 < atoms)\cite{hase:2010,hase:2011}. However, ensemble averaged
110 > atoms).\cite{hase:2010,hase:2011} However, ensemble averaged
111   measurements for heat conductance of interfaces between the capping
112 < monolayer on Au and a solvent phase has yet to be studied.
113 < The comparatively low thermal flux through interfaces is
114 < difficult to measure with Equilibrium MD or forward NEMD simulation
115 < methods. Therefore, the Reverse NEMD (RNEMD) methods would have the
116 < advantage of having this difficult to measure flux known when studying
117 < the thermal transport across interfaces, given that the simulation
118 < methods being able to effectively apply an unphysical flux in
119 < non-homogeneous systems.
112 > monolayer on Au and a solvent phase have yet to be studied with their
113 > approach. The comparatively low thermal flux through interfaces is
114 > difficult to measure with Equilibrium
115 > MD\cite{doi:10.1080/0026897031000068578} or forward NEMD simulation
116 > methods. Therefore, the Reverse NEMD (RNEMD)
117 > methods\cite{MullerPlathe:1997xw,kuang:164101} would have the
118 > advantage of applying this difficult to measure flux (while measuring
119 > the resulting gradient), given that the simulation methods being able
120 > to effectively apply an unphysical flux in non-homogeneous systems.
121 > Garde and coworkers\cite{garde:nl2005,garde:PhysRevLett2009} applied
122 > this approach to various liquid interfaces and studied how thermal
123 > conductance (or resistance) is dependent on chemistry details of
124 > interfaces, e.g. hydrophobic and hydrophilic aqueous interfaces.
125  
126 < Recently, we have developed the Non-Isotropic Velocity Scaling (NIVS)
126 > Recently, we have developed a Non-Isotropic Velocity Scaling (NIVS)
127   algorithm for RNEMD simulations\cite{kuang:164101}. This algorithm
128   retains the desirable features of RNEMD (conservation of linear
129   momentum and total energy, compatibility with periodic boundary
# Line 131 | Line 138 | underlying mechanism for this phenomena was investigat
138   properties. Different models were used for both the capping agent and
139   the solvent force field parameters. Using the NIVS algorithm, the
140   thermal transport across these interfaces was studied and the
141 < underlying mechanism for this phenomena was investigated.
141 > underlying mechanism for the phenomena was investigated.
142  
136 [MAY ADD WHY STUDY AU-THIOL SURFACE; CITE SHAOYI JIANG]
137
143   \section{Methodology}
144   \subsection{Imposd-Flux Methods in MD Simulations}
145 < For systems with low interfacial conductivity one must have a method
146 < capable of generating relatively small fluxes, compared to those
147 < required for bulk conductivity. This requirement makes the calculation
148 < even more difficult for those slowly-converging equilibrium
149 < methods\cite{Viscardy:2007lq}.
150 < Forward methods impose gradient, but in interfacial conditions it is
151 < not clear what behavior to impose at the boundary...
152 < Imposed-flux reverse non-equilibrium
153 < methods\cite{MullerPlathe:1997xw} have the flux set {\it a priori} and
154 < the thermal response becomes easier to
155 < measure than the flux. Although M\"{u}ller-Plathe's original momentum
156 < swapping approach can be used for exchanging energy between particles
157 < of different identity, the kinetic energy transfer efficiency is
158 < affected by the mass difference between the particles, which limits
159 < its application on heterogeneous interfacial systems.
145 > Steady state MD simulations have an advantage in that not many
146 > trajectories are needed to study the relationship between thermal flux
147 > and thermal gradients. For systems with low interfacial conductance,
148 > one must have a method capable of generating or measuring relatively
149 > small fluxes, compared to those required for bulk conductivity. This
150 > requirement makes the calculation even more difficult for
151 > slowly-converging equilibrium methods.\cite{Viscardy:2007lq} Forward
152 > NEMD methods impose a gradient (and measure a flux), but at interfaces
153 > it is not clear what behavior should be imposed at the boundaries
154 > between materials.  Imposed-flux reverse non-equilibrium
155 > methods\cite{MullerPlathe:1997xw} set the flux {\it a priori} and
156 > the thermal response becomes an easy-to-measure quantity.  Although
157 > M\"{u}ller-Plathe's original momentum swapping approach can be used
158 > for exchanging energy between particles of different identity, the
159 > kinetic energy transfer efficiency is affected by the mass difference
160 > between the particles, which limits its application on heterogeneous
161 > interfacial systems.
162  
163 < The non-isotropic velocity scaling (NIVS)\cite{kuang:164101} approach to
164 < non-equilibrium MD simulations is able to impose a wide range of
163 > The non-isotropic velocity scaling (NIVS) \cite{kuang:164101} approach
164 > to non-equilibrium MD simulations is able to impose a wide range of
165   kinetic energy fluxes without obvious perturbation to the velocity
166   distributions of the simulated systems. Furthermore, this approach has
167   the advantage in heterogeneous interfaces in that kinetic energy flux
# Line 171 | Line 178 | momenta and energy and does not depend on an external
178   for computing thermal conductivities. The NIVS algorithm conserves
179   momenta and energy and does not depend on an external thermostat.
180  
181 < \subsection{Defining Interfacial Thermal Conductivity $G$}
182 < For interfaces with a relatively low interfacial conductance, the bulk
183 < regions on either side of an interface rapidly come to a state in
184 < which the two phases have relatively homogeneous (but distinct)
185 < temperatures. The interfacial thermal conductivity $G$ can therefore
186 < be approximated as:
181 > \subsection{Defining Interfacial Thermal Conductivity ($G$)}
182 >
183 > For an interface with relatively low interfacial conductance, and a
184 > thermal flux between two distinct bulk regions, the regions on either
185 > side of the interface rapidly come to a state in which the two phases
186 > have relatively homogeneous (but distinct) temperatures. The
187 > interfacial thermal conductivity $G$ can therefore be approximated as:
188   \begin{equation}
189 < G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
189 >  G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
190      \langle T_\mathrm{cold}\rangle \right)}
191   \label{lowG}
192   \end{equation}
193 < where ${E_{total}}$ is the imposed non-physical kinetic energy
194 < transfer and ${\langle T_\mathrm{hot}\rangle}$ and ${\langle
195 <  T_\mathrm{cold}\rangle}$ are the average observed temperature of the
196 < two separated phases.
193 > where ${E_{total}}$ is the total imposed non-physical kinetic energy
194 > transfer during the simulation and ${\langle T_\mathrm{hot}\rangle}$
195 > and ${\langle T_\mathrm{cold}\rangle}$ are the average observed
196 > temperature of the two separated phases.  For an applied flux $J_z$
197 > operating over a simulation time $t$ on a periodically-replicated slab
198 > of dimensions $L_x \times L_y$, $E_{total} = J_z *(t)*(2 L_x L_y)$.
199  
200   When the interfacial conductance is {\it not} small, there are two
201 < ways to define $G$.
202 <
203 < One way is to assume the temperature is discrete on the two sides of
204 < the interface. $G$ can be calculated using the applied thermal flux
205 < $J$ and the maximum temperature difference measured along the thermal
206 < gradient max($\Delta T$), which occurs at the Gibbs deviding surface
207 < (Figure \ref{demoPic}):
201 > ways to define $G$. One common way is to assume the temperature is
202 > discrete on the two sides of the interface. $G$ can be calculated
203 > using the applied thermal flux $J$ and the maximum temperature
204 > difference measured along the thermal gradient max($\Delta T$), which
205 > occurs at the Gibbs deviding surface (Figure \ref{demoPic}). This is
206 > known as the Kapitza conductance, which is the inverse of the Kapitza
207 > resistance.
208   \begin{equation}
209 < G=\frac{J}{\Delta T}
209 >  G=\frac{J}{\Delta T}
210   \label{discreteG}
211   \end{equation}
212  
# Line 216 | Line 226 | the magnitude of thermal conductivity $\lambda$ change
226  
227   The other approach is to assume a continuous temperature profile along
228   the thermal gradient axis (e.g. $z$) and define $G$ at the point where
229 < the magnitude of thermal conductivity $\lambda$ change reach its
229 > the magnitude of thermal conductivity ($\lambda$) change reaches its
230   maximum, given that $\lambda$ is well-defined throughout the space:
231   \begin{equation}
232   G^\prime = \Big|\frac{\partial\lambda}{\partial z}\Big|
# Line 227 | Line 237 | With the temperature profile obtained from simulations
237   \label{derivativeG}
238   \end{equation}
239  
240 < With the temperature profile obtained from simulations, one is able to
240 > With temperature profiles obtained from simulation, one is able to
241   approximate the first and second derivatives of $T$ with finite
242 < difference methods and thus calculate $G^\prime$.
242 > difference methods and calculate $G^\prime$. In what follows, both
243 > definitions have been used, and are compared in the results.
244  
245 < In what follows, both definitions have been used for calculation and
246 < are compared in the results.
247 <
248 < To compare the above definitions ($G$ and $G^\prime$), we have modeled
249 < a metal slab with its (111) surfaces perpendicular to the $z$-axis of
239 < our simulation cells. Both with and without capping agents on the
240 < surfaces, the metal slab is solvated with simple organic solvents, as
245 > To investigate the interfacial conductivity at metal / solvent
246 > interfaces, we have modeled a metal slab with its (111) surfaces
247 > perpendicular to the $z$-axis of our simulation cells. The metal slab
248 > has been prepared both with and without capping agents on the exposed
249 > surface, and has been solvated with simple organic solvents, as
250   illustrated in Figure \ref{gradT}.
251  
252   With the simulation cell described above, we are able to equilibrate
253   the system and impose an unphysical thermal flux between the liquid
254   and the metal phase using the NIVS algorithm. By periodically applying
255 < the unphysical flux, we are able to obtain a temperature profile and
256 < its spatial derivatives. These quantities enable the evaluation of the
257 < interfacial thermal conductance of a surface. Figure \ref{gradT} is an
258 < example how those applied thermal fluxes can be used to obtain the 1st
250 < and 2nd derivatives of the temperature profile.
255 > the unphysical flux, we obtained a temperature profile and its spatial
256 > derivatives. Figure \ref{gradT} shows how an applied thermal flux can
257 > be used to obtain the 1st and 2nd derivatives of the temperature
258 > profile.
259  
260   \begin{figure}
261   \includegraphics[width=\linewidth]{gradT}
# Line 261 | Line 269 | OpenMD\cite{Meineke:2005gd,openmd}, and was used for o
269   \section{Computational Details}
270   \subsection{Simulation Protocol}
271   The NIVS algorithm has been implemented in our MD simulation code,
272 < OpenMD\cite{Meineke:2005gd,openmd}, and was used for our
273 < simulations. Different metal slab thickness (layer numbers of Au) were
274 < simulated. Metal slabs were first equilibrated under atmospheric
275 < pressure (1 atm) and a desired temperature (e.g. 200K). After
276 < equilibration, butanethiol capping agents were placed at three-fold
277 < sites on the Au(111) surfaces. The maximum butanethiol capacity on Au
278 < surface is $1/3$ of the total number of surface Au
279 < atoms\cite{vlugt:cpc2007154}[CITE CHEM REV].
280 < A series of different coverages was
281 < investigated in order to study the relation between coverage and
282 < interfacial conductance.
272 > OpenMD\cite{Meineke:2005gd,openmd}, and was used for our simulations.
273 > Metal slabs of 6 or 11 layers of Au atoms were first equilibrated
274 > under atmospheric pressure (1 atm) and 200K. After equilibration,
275 > butanethiol capping agents were placed at three-fold hollow sites on
276 > the Au(111) surfaces. These sites are either {\it fcc} or {\it
277 >  hcp} sites, although Hase {\it et al.} found that they are
278 > equivalent in a heat transfer process,\cite{hase:2010} so we did not
279 > distinguish between these sites in our study. The maximum butanethiol
280 > capacity on Au surface is $1/3$ of the total number of surface Au
281 > atoms, and the packing forms a $(\sqrt{3}\times\sqrt{3})R30^\circ$
282 > structure\cite{doi:10.1021/ja00008a001,doi:10.1021/cr9801317}. A
283 > series of lower coverages was also prepared by eliminating
284 > butanethiols from the higher coverage surface in a regular manner. The
285 > lower coverages were prepared in order to study the relation between
286 > coverage and interfacial conductance.
287  
288   The capping agent molecules were allowed to migrate during the
289   simulations. They distributed themselves uniformly and sampled a
290   number of three-fold sites throughout out study. Therefore, the
291 < initial configuration would not noticeably affect the sampling of a
291 > initial configuration does not noticeably affect the sampling of a
292   variety of configurations of the same coverage, and the final
293   conductance measurement would be an average effect of these
294 < configurations explored in the simulations. [MAY NEED SNAPSHOTS]
294 > configurations explored in the simulations.
295  
296 < After the modified Au-butanethiol surface systems were equilibrated
297 < under canonical ensemble, organic solvent molecules were packed in the
298 < previously empty part of the simulation cells\cite{packmol}. Two
296 > After the modified Au-butanethiol surface systems were equilibrated in
297 > the canonical (NVT) ensemble, organic solvent molecules were packed in
298 > the previously empty part of the simulation cells.\cite{packmol} Two
299   solvents were investigated, one which has little vibrational overlap
300 < with the alkanethiol and a planar shape (toluene), and one which has
301 < similar vibrational frequencies and chain-like shape ({\it n}-hexane).
300 > with the alkanethiol and which has a planar shape (toluene), and one
301 > which has similar vibrational frequencies to the capping agent and
302 > chain-like shape ({\it n}-hexane).
303  
304 < The space filled by solvent molecules, i.e. the gap between
305 < periodically repeated Au-butanethiol surfaces should be carefully
306 < chosen. A very long length scale for the thermal gradient axis ($z$)
294 < may cause excessively hot or cold temperatures in the middle of the
304 > The simulation cells were not particularly extensive along the
305 > $z$-axis, as a very long length scale for the thermal gradient may
306 > cause excessively hot or cold temperatures in the middle of the
307   solvent region and lead to undesired phenomena such as solvent boiling
308   or freezing when a thermal flux is applied. Conversely, too few
309   solvent molecules would change the normal behavior of the liquid
310   phase. Therefore, our $N_{solvent}$ values were chosen to ensure that
311 < these extreme cases did not happen to our simulations. And the
312 < corresponding spacing is usually $35[?] \sim 75$\AA.
311 > these extreme cases did not happen to our simulations. The spacing
312 > between periodic images of the gold interfaces is $45 \sim 75$\AA.
313  
314   The initial configurations generated are further equilibrated with the
315 < $x$ and $y$ dimensions fixed, only allowing length scale change in $z$
316 < dimension. This is to ensure that the equilibration of liquid phase
317 < does not affect the metal crystal structure in $x$ and $y$ dimensions.
318 < To investigate this effect, comparisons were made with simulations
319 < allowed to change $L_x$ and $L_y$ during NPT equilibration, and the
320 < results are shown in later sections. After ensuring the liquid phase
321 < reaches equilibrium at atmospheric pressure (1 atm), further
322 < equilibration are followed under NVT and then NVE ensembles.
315 > $x$ and $y$ dimensions fixed, only allowing the $z$-length scale to
316 > change. This is to ensure that the equilibration of liquid phase does
317 > not affect the metal's crystalline structure. Comparisons were made
318 > with simulations that allowed changes of $L_x$ and $L_y$ during NPT
319 > equilibration. No substantial changes in the box geometry were noticed
320 > in these simulations. After ensuring the liquid phase reaches
321 > equilibrium at atmospheric pressure (1 atm), further equilibration was
322 > carried out under canonical (NVT) and microcanonical (NVE) ensembles.
323  
324 < After the systems reach equilibrium, NIVS is implemented to impose a
325 < periodic unphysical thermal flux between the metal and the liquid
326 < phase. Most of our simulations are under an average temperature of
327 < $\sim$200K. Therefore, this flux usually comes from the metal to the
324 > After the systems reach equilibrium, NIVS was used to impose an
325 > unphysical thermal flux between the metal and the liquid phases. Most
326 > of our simulations were done under an average temperature of
327 > $\sim$200K. Therefore, thermal flux usually came from the metal to the
328   liquid so that the liquid has a higher temperature and would not
329 < freeze due to excessively low temperature. This induced temperature
330 < gradient is stablized and the simulation cell is devided evenly into
331 < N slabs along the $z$-axis and the temperatures of each slab are
332 < recorded. When the slab width $d$ of each slab is the same, the
333 < derivatives of $T$ with respect to slab number $n$ can be directly
334 < used for $G^\prime$ calculations:
335 < \begin{equation}
336 < G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
329 > freeze due to lowered temperatures. After this induced temperature
330 > gradient had stablized, the temperature profile of the simulation cell
331 > was recorded. To do this, the simulation cell is devided evenly into
332 > $N$ slabs along the $z$-axis. The average temperatures of each slab
333 > are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
334 > the same, the derivatives of $T$ with respect to slab number $n$ can
335 > be directly used for $G^\prime$ calculations: \begin{equation}
336 >  G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
337           \Big/\left(\frac{\partial T}{\partial z}\right)^2
338           = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
339           \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
# Line 330 | Line 342 | G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}
342   \label{derivativeG2}
343   \end{equation}
344  
345 + All of the above simulation procedures use a time step of 1 fs. Each
346 + equilibration stage took a minimum of 100 ps, although in some cases,
347 + longer equilibration stages were utilized.
348 +
349   \subsection{Force Field Parameters}
350 < Our simulations include various components. Figure \ref{demoMol}
351 < demonstrates the sites defined for both United-Atom and All-Atom
352 < models of the organic solvent and capping agent molecules in our
353 < simulations. Force field parameter descriptions are needed for
350 > Our simulations include a number of chemically distinct components.
351 > Figure \ref{demoMol} demonstrates the sites defined for both
352 > United-Atom and All-Atom models of the organic solvent and capping
353 > agents in our simulations. Force field parameters are needed for
354   interactions both between the same type of particles and between
355   particles of different species.
356  
# Line 344 | Line 360 | particles of different species.
360    these simulations. The chemically-distinct sites (a-e) are expanded
361    in terms of constituent atoms for both United Atom (UA) and All Atom
362    (AA) force fields.  Most parameters are from
363 <  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} (UA) and
364 <  \protect\cite{OPLSAA} (AA).  Cross-interactions with the Au atoms are given
365 <  in Table \ref{MnM}.}
363 >  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes,TraPPE-UA.thiols}
364 >  (UA) and \protect\cite{OPLSAA} (AA). Cross-interactions with the Au
365 >  atoms are given in Table \ref{MnM}.}
366   \label{demoMol}
367   \end{figure}
368  
369   The Au-Au interactions in metal lattice slab is described by the
370 < quantum Sutton-Chen (QSC) formulation\cite{PhysRevB.59.3527}. The QSC
370 > quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
371   potentials include zero-point quantum corrections and are
372   reparametrized for accurate surface energies compared to the
373 < Sutton-Chen potentials\cite{Chen90}.
373 > Sutton-Chen potentials.\cite{Chen90}
374  
375 < For both solvent molecules, straight chain {\it n}-hexane and aromatic
376 < toluene, United-Atom (UA) and All-Atom (AA) models are used
377 < respectively. The TraPPE-UA
375 > For the two solvent molecules, {\it n}-hexane and toluene, two
376 > different atomistic models were utilized. Both solvents were modeled
377 > using United-Atom (UA) and All-Atom (AA) models. The TraPPE-UA
378   parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
379   for our UA solvent molecules. In these models, sites are located at
380   the carbon centers for alkyl groups. Bonding interactions, including
381   bond stretches and bends and torsions, were used for intra-molecular
382 < sites not separated by more than 3 bonds. Otherwise, for non-bonded
383 < interactions, Lennard-Jones potentials are used. [MORE CITATION?]
382 > sites closer than 3 bonds. For non-bonded interactions, Lennard-Jones
383 > potentials are used.
384  
385 < By eliminating explicit hydrogen atoms, these models are simple and
386 < computationally efficient, while maintains good accuracy. However, the
387 < TraPPE-UA for alkanes is known to predict a lower boiling point than
388 < experimental values. Considering that after an unphysical thermal flux
389 < is applied to a system, the temperature of ``hot'' area in the liquid
390 < phase would be significantly higher than the average, to prevent over
391 < heating and boiling of the liquid phase, the average temperature in
392 < our simulations should be much lower than the liquid boiling point.
385 > By eliminating explicit hydrogen atoms, the TraPPE-UA models are
386 > simple and computationally efficient, while maintaining good accuracy.
387 > However, the TraPPE-UA model for alkanes is known to predict a slighly
388 > lower boiling point than experimental values. This is one of the
389 > reasons we used a lower average temperature (200K) for our
390 > simulations. If heat is transferred to the liquid phase during the
391 > NIVS simulation, the liquid in the hot slab can actually be
392 > substantially warmer than the mean temperature in the simulation. The
393 > lower mean temperatures therefore prevent solvent boiling.
394  
395 < For UA-toluene model, the non-bonded potentials between
396 < inter-molecular sites have a similar Lennard-Jones formulation. For
397 < intra-molecular interactions, considering the stiffness of the benzene
398 < ring, rigid body constraints are applied for further computational
399 < efficiency. All bonds in the benzene ring and between the ring and the
383 < methyl group remain rigid during the progress of simulations.
395 > For UA-toluene, the non-bonded potentials between intermolecular sites
396 > have a similar Lennard-Jones formulation. The toluene molecules were
397 > treated as a single rigid body, so there was no need for
398 > intramolecular interactions (including bonds, bends, or torsions) in
399 > this solvent model.
400  
401   Besides the TraPPE-UA models, AA models for both organic solvents are
402 < included in our studies as well. For hexane, the OPLS-AA\cite{OPLSAA}
403 < force field is used. Additional explicit hydrogen sites were
402 > included in our studies as well. The OPLS-AA\cite{OPLSAA} force fields
403 > were used. For hexane, additional explicit hydrogen sites were
404   included. Besides bonding and non-bonded site-site interactions,
405   partial charges and the electrostatic interactions were added to each
406 < CT and HC site. For toluene, the United Force Field developed by
407 < Rapp\'{e} {\it et al.}\cite{doi:10.1021/ja00051a040} is
408 < adopted. Without the rigid body constraints, bonding interactions were
393 < included. For the aromatic ring, improper torsions (inversions) were
394 < added as an extra potential for maintaining the planar shape.
395 < [MORE CITATION?]
406 > CT and HC site. For toluene, a flexible model for the toluene molecule
407 > was utilized which included bond, bend, torsion, and inversion
408 > potentials to enforce ring planarity.
409  
410 < The capping agent in our simulations, the butanethiol molecules can
411 < either use UA or AA model. The TraPPE-UA force fields includes
410 > The butanethiol capping agent in our simulations, were also modeled
411 > with both UA and AA model. The TraPPE-UA force field includes
412   parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
413   UA butanethiol model in our simulations. The OPLS-AA also provides
414   parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
415 < surfaces do not have the hydrogen atom bonded to sulfur. To adapt this
416 < change and derive suitable parameters for butanethiol adsorbed on
417 < Au(111) surfaces, we adopt the S parameters from Luedtke and
418 < Landman\cite{landman:1998} and modify parameters for its neighbor C
419 < atom for charge balance in the molecule. Note that the model choice
420 < (UA or AA) of capping agent can be different from the
421 < solvent. Regardless of model choice, the force field parameters for
422 < interactions between capping agent and solvent can be derived using
410 < Lorentz-Berthelot Mixing Rule:
415 > surfaces do not have the hydrogen atom bonded to sulfur. To derive
416 > suitable parameters for butanethiol adsorbed on Au(111) surfaces, we
417 > adopt the S parameters from Luedtke and Landman\cite{landman:1998} and
418 > modify the parameters for the CTS atom to maintain charge neutrality
419 > in the molecule.  Note that the model choice (UA or AA) for the capping
420 > agent can be different from the solvent. Regardless of model choice,
421 > the force field parameters for interactions between capping agent and
422 > solvent can be derived using Lorentz-Berthelot Mixing Rule:
423   \begin{eqnarray}
424 < \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
425 < \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
424 >  \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
425 >  \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
426   \end{eqnarray}
427  
428 < To describe the interactions between metal Au and non-metal capping
429 < agent and solvent particles, we refer to an adsorption study of alkyl
430 < thiols on gold surfaces by Vlugt {\it et
431 <  al.}\cite{vlugt:cpc2007154} They fitted an effective Lennard-Jones
432 < form of potential parameters for the interaction between Au and
433 < pseudo-atoms CH$_x$ and S based on a well-established and widely-used
434 < effective potential of Hautman and Klein\cite{hautman:4994} for the
435 < Au(111) surface. As our simulations require the gold lattice slab to
436 < be non-rigid so that it could accommodate kinetic energy for thermal
425 < transport study purpose, the pair-wise form of potentials is
426 < preferred.
428 > To describe the interactions between metal (Au) and non-metal atoms,
429 > we refer to an adsorption study of alkyl thiols on gold surfaces by
430 > Vlugt {\it et al.}\cite{vlugt:cpc2007154} They fitted an effective
431 > Lennard-Jones form of potential parameters for the interaction between
432 > Au and pseudo-atoms CH$_x$ and S based on a well-established and
433 > widely-used effective potential of Hautman and Klein for the Au(111)
434 > surface.\cite{hautman:4994} As our simulations require the gold slab
435 > to be flexible to accommodate thermal excitation, the pair-wise form
436 > of potentials they developed was used for our study.
437  
438 < Besides, the potentials developed from {\it ab initio} calculations by
439 < Leng {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
440 < interactions between Au and aromatic C/H atoms in toluene. A set of
441 < pseudo Lennard-Jones parameters were provided for Au in their force
442 < fields. By using the Mixing Rule, this can be used to derive pair-wise
443 < potentials for non-bonded interactions between Au and non-metal sites.
438 > The potentials developed from {\it ab initio} calculations by Leng
439 > {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
440 > interactions between Au and aromatic C/H atoms in toluene. However,
441 > the Lennard-Jones parameters between Au and other types of particles,
442 > (e.g. AA alkanes) have not yet been established. For these
443 > interactions, the Lorentz-Berthelot mixing rule can be used to derive
444 > effective single-atom LJ parameters for the metal using the fit values
445 > for toluene. These are then used to construct reasonable mixing
446 > parameters for the interactions between the gold and other atoms.
447 > Table \ref{MnM} summarizes the ``metal/non-metal'' parameters used in
448 > our simulations.
449  
435 However, the Lennard-Jones parameters between Au and other types of
436 particles, such as All-Atom normal alkanes in our simulations are not
437 yet well-established. For these interactions, we attempt to derive
438 their parameters using the Mixing Rule. To do this, Au pseudo
439 Lennard-Jones parameters for ``Metal-non-Metal'' (MnM) interactions
440 were first extracted from the Au-CH$_x$ parameters by applying the
441 Mixing Rule reversely. Table \ref{MnM} summarizes these ``MnM''
442 parameters in our simulations.
443
450   \begin{table*}
451    \begin{minipage}{\linewidth}
452      \begin{center}
# Line 476 | Line 482 | parameters in our simulations.
482    \end{minipage}
483   \end{table*}
484  
479 \subsection{Vibrational Spectrum}
485  
486 < [MAY ADD EQN'S]
487 < To obtain these
488 < spectra, one first runs a simulation in the NVE ensemble and collects
489 < snapshots of configurations; these configurations are used to compute
490 < the velocity auto-correlation functions, which is used to construct a
491 < power spectrum via a Fourier transform.
486 > \section{Results}
487 > There are many factors contributing to the measured interfacial
488 > conductance; some of these factors are physically motivated
489 > (e.g. coverage of the surface by the capping agent coverage and
490 > solvent identity), while some are governed by parameters of the
491 > methodology (e.g. applied flux and the formulas used to obtain the
492 > conductance). In this section we discuss the major physical and
493 > calculational effects on the computed conductivity.
494  
495 < \section{Results and Discussions}
489 < [MAY HAVE A BRIEF SUMMARY]
490 < \subsection{How Simulation Parameters Affects $G$}
491 < [MAY NOT PUT AT FIRST]
492 < We have varied our protocol or other parameters of the simulations in
493 < order to investigate how these factors would affect the measurement of
494 < $G$'s. It turned out that while some of these parameters would not
495 < affect the results substantially, some other changes to the
496 < simulations would have a significant impact on the measurement
497 < results.
495 > \subsection{Effects due to capping agent coverage}
496  
497 < In some of our simulations, we allowed $L_x$ and $L_y$ to change
498 < during equilibrating the liquid phase. Due to the stiffness of the
499 < crystalline Au structure, $L_x$ and $L_y$ would not change noticeably
500 < after equilibration. Although $L_z$ could fluctuate $\sim$1\% after a
501 < system is fully equilibrated in the NPT ensemble, this fluctuation, as
502 < well as those of $L_x$ and $L_y$ (which is significantly smaller),
505 < would not be magnified on the calculated $G$'s, as shown in Table
506 < \ref{AuThiolHexaneUA}. This insensivity to $L_i$ fluctuations allows
507 < reliable measurement of $G$'s without the necessity of extremely
508 < cautious equilibration process.
497 > A series of different initial conditions with a range of surface
498 > coverages was prepared and solvated with various with both of the
499 > solvent molecules. These systems were then equilibrated and their
500 > interfacial thermal conductivity was measured with the NIVS
501 > algorithm. Figure \ref{coverage} demonstrates the trend of conductance
502 > with respect to surface coverage.
503  
504 < As stated in our computational details, the spacing filled with
505 < solvent molecules can be chosen within a range. This allows some
506 < change of solvent molecule numbers for the same Au-butanethiol
507 < surfaces. We did this study on our Au-butanethiol/hexane
508 < simulations. Nevertheless, the results obtained from systems of
509 < different $N_{hexane}$ did not indicate that the measurement of $G$ is
510 < susceptible to this parameter. For computational efficiency concern,
517 < smaller system size would be preferable, given that the liquid phase
518 < structure is not affected.
504 > \begin{figure}
505 > \includegraphics[width=\linewidth]{coverage}
506 > \caption{Comparison of interfacial thermal conductivity ($G$) values
507 >  for the Au-butanethiol/solvent interface with various UA models and
508 >  different capping agent coverages at $\langle T\rangle\sim$200K.}
509 > \label{coverage}
510 > \end{figure}
511  
512 < Our NIVS algorithm allows change of unphysical thermal flux both in
513 < direction and in quantity. This feature extends our investigation of
514 < interfacial thermal conductance. However, the magnitude of this
515 < thermal flux is not arbitary if one aims to obtain a stable and
516 < reliable thermal gradient. A temperature profile would be
517 < substantially affected by noise when $|J_z|$ has a much too low
518 < magnitude; while an excessively large $|J_z|$ that overwhelms the
519 < conductance capacity of the interface would prevent a thermal gradient
520 < to reach a stablized steady state. NIVS has the advantage of allowing
521 < $J$ to vary in a wide range such that the optimal flux range for $G$
522 < measurement can generally be simulated by the algorithm. Within the
523 < optimal range, we were able to study how $G$ would change according to
524 < the thermal flux across the interface. For our simulations, we denote
525 < $J_z$ to be positive when the physical thermal flux is from the liquid
526 < to metal, and negative vice versa. The $G$'s measured under different
527 < $J_z$ is listed in Table \ref{AuThiolHexaneUA} and
528 < \ref{AuThiolToluene}. These results do not suggest that $G$ is
529 < dependent on $J_z$ within this flux range. The linear response of flux
530 < to thermal gradient simplifies our investigations in that we can rely
531 < on $G$ measurement with only a couple $J_z$'s and do not need to test
532 < a large series of fluxes.
512 > In partially covered surfaces, the derivative definition for
513 > $G^\prime$ (Eq. \ref{derivativeG}) becomes difficult to apply, as the
514 > location of maximum change of $\lambda$ becomes washed out.  The
515 > discrete definition (Eq. \ref{discreteG}) is easier to apply, as the
516 > Gibbs dividing surface is still well-defined. Therefore, $G$ (not
517 > $G^\prime$) was used in this section.
518 >
519 > From Figure \ref{coverage}, one can see the significance of the
520 > presence of capping agents. When even a small fraction of the Au(111)
521 > surface sites are covered with butanethiols, the conductivity exhibits
522 > an enhancement by at least a factor of 3.  Cappping agents are clearly
523 > playing a major role in thermal transport at metal / organic solvent
524 > surfaces.
525 >
526 > We note a non-monotonic behavior in the interfacial conductance as a
527 > function of surface coverage. The maximum conductance (largest $G$)
528 > happens when the surfaces are about 75\% covered with butanethiol
529 > caps.  The reason for this behavior is not entirely clear.  One
530 > explanation is that incomplete butanethiol coverage allows small gaps
531 > between butanethiols to form. These gaps can be filled by transient
532 > solvent molecules.  These solvent molecules couple very strongly with
533 > the hot capping agent molecules near the surface, and can then carry
534 > away (diffusively) the excess thermal energy from the surface.
535 >
536 > There appears to be a competition between the conduction of the
537 > thermal energy away from the surface by the capping agents (enhanced
538 > by greater coverage) and the coupling of the capping agents with the
539 > solvent (enhanced by interdigitation at lower coverages).  This
540 > competition would lead to the non-monotonic coverage behavior observed
541 > here.
542 >
543 > Results for rigid body toluene solvent, as well as the UA hexane, are
544 > within the ranges expected from prior experimental
545 > work.\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406} This suggests
546 > that explicit hydrogen atoms might not be required for modeling
547 > thermal transport in these systems.  C-H vibrational modes do not see
548 > significant excited state population at low temperatures, and are not
549 > likely to carry lower frequency excitations from the solid layer into
550 > the bulk liquid.
551 >
552 > The toluene solvent does not exhibit the same behavior as hexane in
553 > that $G$ remains at approximately the same magnitude when the capping
554 > coverage increases from 25\% to 75\%.  Toluene, as a rigid planar
555 > molecule, cannot occupy the relatively small gaps between the capping
556 > agents as easily as the chain-like {\it n}-hexane.  The effect of
557 > solvent coupling to the capping agent is therefore weaker in toluene
558 > except at the very lowest coverage levels.  This effect counters the
559 > coverage-dependent conduction of heat away from the metal surface,
560 > leading to a much flatter $G$ vs. coverage trend than is observed in
561 > {\it n}-hexane.
562 >
563 > \subsection{Effects due to Solvent \& Solvent Models}
564 > In addition to UA solvent and capping agent models, AA models have
565 > also been included in our simulations.  In most of this work, the same
566 > (UA or AA) model for solvent and capping agent was used, but it is
567 > also possible to utilize different models for different components.
568 > We have also included isotopic substitutions (Hydrogen to Deuterium)
569 > to decrease the explicit vibrational overlap between solvent and
570 > capping agent. Table \ref{modelTest} summarizes the results of these
571 > studies.
572 >
573 > \begin{table*}
574 >  \begin{minipage}{\linewidth}
575 >    \begin{center}
576 >      
577 >      \caption{Computed interfacial thermal conductance ($G$ and
578 >        $G^\prime$) values for interfaces using various models for
579 >        solvent and capping agent (or without capping agent) at
580 >        $\langle T\rangle\sim$200K. (D stands for deuterated solvent
581 >        or capping agent molecules; ``Avg.'' denotes results that are
582 >        averages of simulations under different applied thermal flux values $(J_z)$. Error
583 >        estimates are indicated in parentheses.)}
584 >      
585 >      \begin{tabular}{llccc}
586 >        \hline\hline
587 >        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
588 >        (or bare surface) & model & (GW/m$^2$) &
589 >        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
590 >        \hline
591 >        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
592 >              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
593 >              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
594 >              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
595 >              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
596 >        \hline
597 >        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
598 >              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
599 >              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
600 >              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
601 >              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
602 >        \hline
603 >        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
604 >              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
605 >              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
606 >        \hline
607 >        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
608 >              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
609 >              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
610 >              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
611 >        \hline\hline
612 >      \end{tabular}
613 >      \label{modelTest}
614 >    \end{center}
615 >  \end{minipage}
616 > \end{table*}
617 >
618 > To facilitate direct comparison between force fields, systems with the
619 > same capping agent and solvent were prepared with the same length
620 > scales for the simulation cells.
621 >
622 > On bare metal / solvent surfaces, different force field models for
623 > hexane yield similar results for both $G$ and $G^\prime$, and these
624 > two definitions agree with each other very well. This is primarily an
625 > indicator of weak interactions between the metal and the solvent, and
626 > is a typical case for acoustic impedance mismatch between these two
627 > phases.  
628 >
629 > For the fully-covered surfaces, the choice of force field for the
630 > capping agent and solvent has a large impact on the calulated values
631 > of $G$ and $G^\prime$.  The AA thiol to AA solvent conductivities are
632 > much larger than their UA to UA counterparts, and these values exceed
633 > the experimental estimates by a large measure.  The AA force field
634 > allows significant energy to go into C-H (or C-D) stretching modes,
635 > and since these modes are high frequency, this non-quantum behavior is
636 > likely responsible for the overestimate of the conductivity.  Compared
637 > to the AA model, the UA model yields more reasonable conductivity
638 > values with much higher computational efficiency.
639 >
640 > \subsubsection{Are electronic excitations in the metal important?}
641 > Because they lack electronic excitations, the QSC and related embedded
642 > atom method (EAM) models for gold are known to predict unreasonably
643 > low values for bulk conductivity
644 > ($\lambda$).\cite{kuang:164101,ISI:000207079300006,Clancy:1992} If the
645 > conductance between the phases ($G$) is governed primarily by phonon
646 > excitation (and not electronic degrees of freedom), one would expect a
647 > classical model to capture most of the interfacial thermal
648 > conductance.  Our results for $G$ and $G^\prime$ indicate that this is
649 > indeed the case, and suggest that the modeling of interfacial thermal
650 > transport depends primarily on the description of the interactions
651 > between the various components at the interface.  When the metal is
652 > chemically capped, the primary barrier to thermal conductivity appears
653 > to be the interface between the capping agent and the surrounding
654 > solvent, so the excitations in the metal have little impact on the
655 > value of $G$.
656  
657 + \subsection{Effects due to methodology and simulation parameters}
658 +
659 + We have varied the parameters of the simulations in order to
660 + investigate how these factors would affect the computation of $G$.  Of
661 + particular interest are: 1) the length scale for the applied thermal
662 + gradient (modified by increasing the amount of solvent in the system),
663 + 2) the sign and magnitude of the applied thermal flux, 3) the average
664 + temperature of the simulation (which alters the solvent density during
665 + equilibration), and 4) the definition of the interfacial conductance
666 + (Eqs. (\ref{discreteG}) or (\ref{derivativeG})) used in the
667 + calculation.
668 +
669 + Systems of different lengths were prepared by altering the number of
670 + solvent molecules and extending the length of the box along the $z$
671 + axis to accomodate the extra solvent.  Equilibration at the same
672 + temperature and pressure conditions led to nearly identical surface
673 + areas ($L_x$ and $L_y$) available to the metal and capping agent,
674 + while the extra solvent served mainly to lengthen the axis that was
675 + used to apply the thermal flux.  For a given value of the applied
676 + flux, the different $z$ length scale has only a weak effect on the
677 + computed conductivities (Table \ref{AuThiolHexaneUA}).
678 +
679 + \subsubsection{Effects of applied flux}
680 + The NIVS algorithm allows changes in both the sign and magnitude of
681 + the applied flux.  It is possible to reverse the direction of heat
682 + flow simply by changing the sign of the flux, and thermal gradients
683 + which would be difficult to obtain experimentally ($5$ K/\AA) can be
684 + easily simulated.  However, the magnitude of the applied flux is not
685 + arbitary if one aims to obtain a stable and reliable thermal gradient.
686 + A temperature gradient can be lost in the noise if $|J_z|$ is too
687 + small, and excessive $|J_z|$ values can cause phase transitions if the
688 + extremes of the simulation cell become widely separated in
689 + temperature.  Also, if $|J_z|$ is too large for the bulk conductivity
690 + of the materials, the thermal gradient will never reach a stable
691 + state.  
692 +
693 + Within a reasonable range of $J_z$ values, we were able to study how
694 + $G$ changes as a function of this flux.  In what follows, we use
695 + positive $J_z$ values to denote the case where energy is being
696 + transferred by the method from the metal phase and into the liquid.
697 + The resulting gradient therefore has a higher temperature in the
698 + liquid phase.  Negative flux values reverse this transfer, and result
699 + in higher temperature metal phases.  The conductance measured under
700 + different applied $J_z$ values is listed in Tables
701 + \ref{AuThiolHexaneUA} and \ref{AuThiolToluene}. These results do not
702 + indicate that $G$ depends strongly on $J_z$ within this flux
703 + range. The linear response of flux to thermal gradient simplifies our
704 + investigations in that we can rely on $G$ measurement with only a
705 + small number $J_z$ values.  
706 +
707   \begin{table*}
708    \begin{minipage}{\linewidth}
709      \begin{center}
# Line 550 | Line 715 | a large series of fluxes.
715        
716        \begin{tabular}{ccccccc}
717          \hline\hline
718 <        $\langle T\rangle$ & $N_{hexane}$ & Fixed & $\rho_{hexane}$ &
718 >        $\langle T\rangle$ & $N_{hexane}$  & $\rho_{hexane}$ &
719          $J_z$ & $G$ & $G^\prime$ \\
720 <        (K) & & $L_x$ \& $L_y$? & (g/cm$^3$) & (GW/m$^2$) &
720 >        (K) &  & (g/cm$^3$) & (GW/m$^2$) &
721          \multicolumn{2}{c}{(MW/m$^2$/K)} \\
722          \hline
723 <        200 & 266 & No  & 0.672 & -0.96 & 102(3)    & 80.0(0.8) \\
724 <            & 200 & Yes & 0.694 &  1.92 & 129(11)   & 87.3(0.3) \\
725 <            &     & Yes & 0.672 &  1.93 & 131(16)   & 78(13)    \\
726 <            &     & No  & 0.688 &  0.96 & 125(16)   & 90.2(15)  \\
727 <            &     &     &       &  1.91 & 139(10)   & 101(10)   \\
728 <            &     &     &       &  2.83 & 141(6)    & 89.9(9.8) \\
564 <            & 166 & Yes & 0.679 &  0.97 & 115(19)   & 69(18)    \\
565 <            &     &     &       &  1.94 & 125(9)    & 87.1(0.2) \\
566 <            &     & No  & 0.681 &  0.97 & 141(30)   & 78(22)    \\
567 <            &     &     &       &  1.92 & 138(4)    & 98.9(9.5) \\
723 >        200 & 266 &  0.672 & -0.96 & 102(3)    & 80.0(0.8) \\
724 >            & 200 &  0.688 &  0.96 & 125(16)   & 90.2(15)  \\
725 >            &     &        &  1.91 & 139(10)   & 101(10)   \\
726 >            &     &        &  2.83 & 141(6)    & 89.9(9.8) \\
727 >            & 166 &  0.681 &  0.97 & 141(30)   & 78(22)    \\
728 >            &     &        &  1.92 & 138(4)    & 98.9(9.5) \\
729          \hline
730 <        250 & 200 & No  & 0.560 &  0.96 & 75(10)    & 61.8(7.3) \\
731 <            &     &     &       & -0.95 & 49.4(0.3) & 45.7(2.1) \\
732 <            & 166 & Yes & 0.570 &  0.98 & 79.0(3.5) & 62.9(3.0) \\
733 <            &     & No  & 0.569 &  0.97 & 80.3(0.6) & 67(11)    \\
734 <            &     &     &       &  1.44 & 76.2(5.0) & 64.8(3.8) \\
735 <            &     &     &       & -0.95 & 56.4(2.5) & 54.4(1.1) \\
575 <            &     &     &       & -1.85 & 47.8(1.1) & 53.5(1.5) \\
730 >        250 & 200 &  0.560 &  0.96 & 75(10)    & 61.8(7.3) \\
731 >            &     &        & -0.95 & 49.4(0.3) & 45.7(2.1) \\
732 >            & 166 &  0.569 &  0.97 & 80.3(0.6) & 67(11)    \\
733 >            &     &        &  1.44 & 76.2(5.0) & 64.8(3.8) \\
734 >            &     &        & -0.95 & 56.4(2.5) & 54.4(1.1) \\
735 >            &     &        & -1.85 & 47.8(1.1) & 53.5(1.5) \\
736          \hline\hline
737        \end{tabular}
738        \label{AuThiolHexaneUA}
# Line 580 | Line 740 | Furthermore, we also attempted to increase system aver
740    \end{minipage}
741   \end{table*}
742  
743 < Furthermore, we also attempted to increase system average temperatures
744 < to above 200K. These simulations are first equilibrated in the NPT
745 < ensemble under normal pressure. As stated above, the TraPPE-UA model
746 < for hexane tends to predict a lower boiling point. In our simulations,
747 < hexane had diffculty to remain in liquid phase when NPT equilibration
748 < temperature is higher than 250K. Additionally, the equilibrated liquid
749 < hexane density under 250K becomes lower than experimental value. This
750 < expanded liquid phase leads to lower contact between hexane and
751 < butanethiol as well.[MAY NEED SLAB DENSITY FIGURE]
592 < And this reduced contact would
593 < probably be accountable for a lower interfacial thermal conductance,
594 < as shown in Table \ref{AuThiolHexaneUA}.
743 > The sign of $J_z$ is a different matter, however, as this can alter
744 > the temperature on the two sides of the interface. The average
745 > temperature values reported are for the entire system, and not for the
746 > liquid phase, so at a given $\langle T \rangle$, the system with
747 > positive $J_z$ has a warmer liquid phase.  This means that if the
748 > liquid carries thermal energy via convective transport, {\it positive}
749 > $J_z$ values will result in increased molecular motion on the liquid
750 > side of the interface, and this will increase the measured
751 > conductivity.
752  
753 < A similar study for TraPPE-UA toluene agrees with the above result as
597 < well. Having a higher boiling point, toluene tends to remain liquid in
598 < our simulations even equilibrated under 300K in NPT
599 < ensembles. Furthermore, the expansion of the toluene liquid phase is
600 < not as significant as that of the hexane. This prevents severe
601 < decrease of liquid-capping agent contact and the results (Table
602 < \ref{AuThiolToluene}) show only a slightly decreased interface
603 < conductance. Therefore, solvent-capping agent contact should play an
604 < important role in the thermal transport process across the interface
605 < in that higher degree of contact could yield increased conductance.
753 > \subsubsection{Effects due to average temperature}
754  
755 + We also studied the effect of average system temperature on the
756 + interfacial conductance.  The simulations are first equilibrated in
757 + the NPT ensemble at 1 atm.  The TraPPE-UA model for hexane tends to
758 + predict a lower boiling point (and liquid state density) than
759 + experiments.  This lower-density liquid phase leads to reduced contact
760 + between the hexane and butanethiol, and this accounts for our
761 + observation of lower conductance at higher temperatures as shown in
762 + Table \ref{AuThiolHexaneUA}.  In raising the average temperature from
763 + 200K to 250K, the density drop of ~20\% in the solvent phase leads to
764 + a ~65\% drop in the conductance.
765 +
766 + Similar behavior is observed in the TraPPE-UA model for toluene,
767 + although this model has better agreement with the experimental
768 + densities of toluene.  The expansion of the toluene liquid phase is
769 + not as significant as that of the hexane (8.3\% over 100K), and this
770 + limits the effect to ~20\% drop in thermal conductivity  (Table
771 + \ref{AuThiolToluene}).
772 +
773 + Although we have not mapped out the behavior at a large number of
774 + temperatures, is clear that there will be a strong temperature
775 + dependence in the interfacial conductance when the physical properties
776 + of one side of the interface (notably the density) change rapidly as a
777 + function of temperature.
778 +
779   \begin{table*}
780    \begin{minipage}{\linewidth}
781      \begin{center}
# Line 630 | Line 802 | Besides lower interfacial thermal conductance, surface
802    \end{minipage}
803   \end{table*}
804  
805 < Besides lower interfacial thermal conductance, surfaces in relatively
806 < high temperatures are susceptible to reconstructions, when
807 < butanethiols have a full coverage on the Au(111) surface. These
808 < reconstructions include surface Au atoms migrated outward to the S
809 < atom layer, and butanethiol molecules embedded into the original
810 < surface Au layer. The driving force for this behavior is the strong
811 < Au-S interactions in our simulations. And these reconstructions lead
812 < to higher ratio of Au-S attraction and thus is energetically
813 < favorable. Furthermore, this phenomenon agrees with experimental
814 < results\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}. Vlugt
815 < {\it et al.} had kept their Au(111) slab rigid so that their
816 < simulations can reach 300K without surface reconstructions. Without
817 < this practice, simulating 100\% thiol covered interfaces under higher
818 < temperatures could hardly avoid surface reconstructions. However, our
819 < measurement is based on assuming homogeneity on $x$ and $y$ dimensions
820 < so that measurement of $T$ at particular $z$ would be an effective
821 < average of the particles of the same type. Since surface
650 < reconstructions could eliminate the original $x$ and $y$ dimensional
651 < homogeneity, measurement of $G$ is more difficult to conduct under
652 < higher temperatures. Therefore, most of our measurements are
653 < undertaken at $\langle T\rangle\sim$200K.
805 > Besides the lower interfacial thermal conductance, surfaces at
806 > relatively high temperatures are susceptible to reconstructions,
807 > particularly when butanethiols fully cover the Au(111) surface. These
808 > reconstructions include surface Au atoms which migrate outward to the
809 > S atom layer, and butanethiol molecules which embed into the surface
810 > Au layer. The driving force for this behavior is the strong Au-S
811 > interactions which are modeled here with a deep Lennard-Jones
812 > potential. This phenomenon agrees with reconstructions that have beeen
813 > experimentally
814 > observed.\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}.  Vlugt
815 > {\it et al.} kept their Au(111) slab rigid so that their simulations
816 > could reach 300K without surface
817 > reconstructions.\cite{vlugt:cpc2007154} Since surface reconstructions
818 > blur the interface, the measurement of $G$ becomes more difficult to
819 > conduct at higher temperatures.  For this reason, most of our
820 > measurements are undertaken at $\langle T\rangle\sim$200K where
821 > reconstruction is minimized.
822  
823   However, when the surface is not completely covered by butanethiols,
824 < the simulated system is more resistent to the reconstruction
825 < above. Our Au-butanethiol/toluene system had the Au(111) surfaces 90\%
826 < covered by butanethiols, but did not see this above phenomena even at
827 < $\langle T\rangle\sim$300K. The empty three-fold sites not occupied by
828 < capping agents could help prevent surface reconstruction in that they
829 < provide other means of capping agent relaxation. It is observed that
830 < butanethiols can migrate to their neighbor empty sites during a
831 < simulation. Therefore, we were able to obtain $G$'s for these
664 < interfaces even at a relatively high temperature without being
665 < affected by surface reconstructions.
824 > the simulated system appears to be more resistent to the
825 > reconstruction. O ur Au / butanethiol / toluene system had the Au(111)
826 > surfaces 90\% covered by butanethiols, but did not see this above
827 > phenomena even at $\langle T\rangle\sim$300K.  That said, we did
828 > observe butanethiols migrating to neighboring three-fold sites during
829 > a simulation.  Since the interface persisted in these simulations,
830 > were able to obtain $G$'s for these interfaces even at a relatively
831 > high temperature without being affected by surface reconstructions.
832  
833 < \subsection{Influence of Capping Agent Coverage on $G$}
668 < To investigate the influence of butanethiol coverage on interfacial
669 < thermal conductance, a series of different coverage Au-butanethiol
670 < surfaces is prepared and solvated with various organic
671 < molecules. These systems are then equilibrated and their interfacial
672 < thermal conductivity are measured with our NIVS algorithm. Figure
673 < \ref{coverage} demonstrates the trend of conductance change with
674 < respect to different coverages of butanethiol. To study the isotope
675 < effect in interfacial thermal conductance, deuterated UA-hexane is
676 < included as well.
833 > \section{Discussion}
834  
835 < It turned out that with partial covered butanethiol on the Au(111)
836 < surface, the derivative definition for $G^\prime$
837 < (Eq. \ref{derivativeG}) was difficult to apply, due to the difficulty
838 < in locating the maximum of change of $\lambda$. Instead, the discrete
839 < definition (Eq. \ref{discreteG}) is easier to apply, as the Gibbs
840 < deviding surface can still be well-defined. Therefore, $G$ (not
684 < $G^\prime$) was used for this section.
835 > The primary result of this work is that the capping agent acts as an
836 > efficient thermal coupler between solid and solvent phases.  One of
837 > the ways the capping agent can carry out this role is to down-shift
838 > between the phonon vibrations in the solid (which carry the heat from
839 > the gold) and the molecular vibrations in the liquid (which carry some
840 > of the heat in the solvent).
841  
842 < From Figure \ref{coverage}, one can see the significance of the
843 < presence of capping agents. Even when a fraction of the Au(111)
844 < surface sites are covered with butanethiols, the conductivity would
845 < see an enhancement by at least a factor of 3. This indicates the
846 < important role cappping agent is playing for thermal transport
847 < phenomena on metal / organic solvent surfaces.
842 > To investigate the mechanism of interfacial thermal conductance, the
843 > vibrational power spectrum was computed. Power spectra were taken for
844 > individual components in different simulations. To obtain these
845 > spectra, simulations were run after equilibration in the
846 > microcanonical (NVE) ensemble and without a thermal
847 > gradient. Snapshots of configurations were collected at a frequency
848 > that is higher than that of the fastest vibrations occuring in the
849 > simulations. With these configurations, the velocity auto-correlation
850 > functions can be computed:
851 > \begin{equation}
852 > C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
853 > \label{vCorr}
854 > \end{equation}
855 > The power spectrum is constructed via a Fourier transform of the
856 > symmetrized velocity autocorrelation function,
857 > \begin{equation}
858 >  \hat{f}(\omega) =
859 >  \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
860 > \label{fourier}
861 > \end{equation}
862  
863 < Interestingly, as one could observe from our results, the maximum
864 < conductance enhancement (largest $G$) happens while the surfaces are
865 < about 75\% covered with butanethiols. This again indicates that
866 < solvent-capping agent contact has an important role of the thermal
867 < transport process. Slightly lower butanethiol coverage allows small
868 < gaps between butanethiols to form. And these gaps could be filled with
869 < solvent molecules, which acts like ``heat conductors'' on the
870 < surface. The higher degree of interaction between these solvent
871 < molecules and capping agents increases the enhancement effect and thus
872 < produces a higher $G$ than densely packed butanethiol arrays. However,
873 < once this maximum conductance enhancement is reached, $G$ decreases
704 < when butanethiol coverage continues to decrease. Each capping agent
705 < molecule reaches its maximum capacity for thermal
706 < conductance. Therefore, even higher solvent-capping agent contact
707 < would not offset this effect. Eventually, when butanethiol coverage
708 < continues to decrease, solvent-capping agent contact actually
709 < decreases with the disappearing of butanethiol molecules. In this
710 < case, $G$ decrease could not be offset but instead accelerated. [NEED
711 < SNAPSHOT SHOWING THE PHENOMENA / SLAB DENSITY ANALYSIS]
863 > \subsection{The role of specific vibrations}
864 > The vibrational spectra for gold slabs in different environments are
865 > shown as in Figure \ref{specAu}. Regardless of the presence of
866 > solvent, the gold surfaces which are covered by butanethiol molecules
867 > exhibit an additional peak observed at a frequency of
868 > $\sim$170cm$^{-1}$.  We attribute this peak to the S-Au bonding
869 > vibration. This vibration enables efficient thermal coupling of the
870 > surface Au layer to the capping agents. Therefore, in our simulations,
871 > the Au / S interfaces do not appear to be the primary barrier to
872 > thermal transport when compared with the butanethiol / solvent
873 > interfaces.
874  
875 < A comparison of the results obtained from differenet organic solvents
876 < can also provide useful information of the interfacial thermal
877 < transport process. The deuterated hexane (UA) results do not appear to
878 < be much different from those of normal hexane (UA), given that
879 < butanethiol (UA) is non-deuterated for both solvents. These UA model
880 < studies, even though eliminating C-H vibration samplings, still have
881 < C-C vibrational frequencies different from each other. However, these
720 < differences in the infrared range do not seem to produce an observable
721 < difference for the results of $G$. [MAY NEED SPECTRA FIGURE]
875 > \begin{figure}
876 > \includegraphics[width=\linewidth]{vibration}
877 > \caption{Vibrational power spectra for gold in different solvent
878 >  environments.  The presence of the butanethiol capping molecules
879 >  adds a vibrational peak at $\sim$170cm$^{-1}$.}
880 > \label{specAu}
881 > \end{figure}
882  
883 < Furthermore, results for rigid body toluene solvent, as well as other
884 < UA-hexane solvents, are reasonable within the general experimental
885 < ranges[CITATIONS]. This suggests that explicit hydrogen might not be a
726 < required factor for modeling thermal transport phenomena of systems
727 < such as Au-thiol/organic solvent.
883 > Also in this figure, we show the vibrational power spectrum for the
884 > bound butanethiol molecules, which also exhibits the same
885 > $\sim$170cm$^{-1}$ peak.
886  
887 < However, results for Au-butanethiol/toluene do not show an identical
888 < trend with those for Au-butanethiol/hexane in that $G$ remains at
889 < approximately the same magnitue when butanethiol coverage differs from
890 < 25\% to 75\%. This might be rooted in the molecule shape difference
891 < for planar toluene and chain-like {\it n}-hexane. Due to this
892 < difference, toluene molecules have more difficulty in occupying
893 < relatively small gaps among capping agents when their coverage is not
894 < too low. Therefore, the solvent-capping agent contact may keep
895 < increasing until the capping agent coverage reaches a relatively low
896 < level. This becomes an offset for decreasing butanethiol molecules on
897 < its effect to the process of interfacial thermal transport. Thus, one
740 < can see a plateau of $G$ vs. butanethiol coverage in our results.
887 > \subsection{Overlap of power spectra}
888 > A comparison of the results obtained from the two different organic
889 > solvents can also provide useful information of the interfacial
890 > thermal transport process.  In particular, the vibrational overlap
891 > between the butanethiol and the organic solvents suggests a highly
892 > efficient thermal exchange between these components.  Very high
893 > thermal conductivity was observed when AA models were used and C-H
894 > vibrations were treated classically.  The presence of extra degrees of
895 > freedom in the AA force field yields higher heat exchange rates
896 > between the two phases and results in a much higher conductivity than
897 > in the UA force field.
898  
899 + The similarity in the vibrational modes available to solvent and
900 + capping agent can be reduced by deuterating one of the two components
901 + (Fig. \ref{aahxntln}).  Once either the hexanes or the butanethiols
902 + are deuterated, one can observe a significantly lower $G$ and
903 + $G^\prime$ values (Table \ref{modelTest}).
904 +
905   \begin{figure}
906 < \includegraphics[width=\linewidth]{coverage}
907 < \caption{Comparison of interfacial thermal conductivity ($G$) values
908 <  for the Au-butanethiol/solvent interface with various UA models and
909 <  different capping agent coverages at $\langle T\rangle\sim$200K
910 <  using certain energy flux respectively.}
911 < \label{coverage}
906 > \includegraphics[width=\linewidth]{aahxntln}
907 > \caption{Spectra obtained for all-atom (AA) Au / butanethiol / solvent
908 >  systems. When butanethiol is deuterated (lower left), its
909 >  vibrational overlap with hexane decreases significantly.  Since
910 >  aromatic molecules and the butanethiol are vibrationally dissimilar,
911 >  the change is not as dramatic when toluene is the solvent (right).}
912 > \label{aahxntln}
913   \end{figure}
914  
915 < \subsection{Influence of Chosen Molecule Model on $G$}
752 < In addition to UA solvent/capping agent models, AA models are included
753 < in our simulations as well. Besides simulations of the same (UA or AA)
754 < model for solvent and capping agent, different models can be applied
755 < to different components. Furthermore, regardless of models chosen,
756 < either the solvent or the capping agent can be deuterated, similar to
757 < the previous section. Table \ref{modelTest} summarizes the results of
758 < these studies.
759 <
760 < \begin{table*}
761 <  \begin{minipage}{\linewidth}
762 <    \begin{center}
763 <      
764 <      \caption{Computed interfacial thermal conductivity ($G$ and
765 <        $G^\prime$) values for interfaces using various models for
766 <        solvent and capping agent (or without capping agent) at
767 <        $\langle T\rangle\sim$200K. (D stands for deuterated solvent
768 <        or capping agent molecules; ``Avg.'' denotes results that are
769 <        averages of simulations under different $J_z$'s. Error
770 <        estimates indicated in parenthesis.)}
771 <      
772 <      \begin{tabular}{llccc}
773 <        \hline\hline
774 <        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
775 <        (or bare surface) & model & (GW/m$^2$) &
776 <        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
777 <        \hline
778 <        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
779 <              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
780 <              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
781 <              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
782 <              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
783 <        \hline
784 <        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
785 <              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
786 <              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
787 <              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
788 <              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
789 <        \hline
790 <        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
791 <              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
792 <              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
793 <        \hline
794 <        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
795 <              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
796 <              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
797 <              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
798 <        \hline\hline
799 <      \end{tabular}
800 <      \label{modelTest}
801 <    \end{center}
802 <  \end{minipage}
803 < \end{table*}
804 <
805 < To facilitate direct comparison, the same system with differnt models
806 < for different components uses the same length scale for their
807 < simulation cells. Without the presence of capping agent, using
808 < different models for hexane yields similar results for both $G$ and
809 < $G^\prime$, and these two definitions agree with eath other very
810 < well. This indicates very weak interaction between the metal and the
811 < solvent, and is a typical case for acoustic impedance mismatch between
812 < these two phases.
813 <
814 < As for Au(111) surfaces completely covered by butanethiols, the choice
815 < of models for capping agent and solvent could impact the measurement
816 < of $G$ and $G^\prime$ quite significantly. For Au-butanethiol/hexane
817 < interfaces, using AA model for both butanethiol and hexane yields
818 < substantially higher conductivity values than using UA model for at
819 < least one component of the solvent and capping agent, which exceeds
820 < the general range of experimental measurement results. This is
821 < probably due to the classically treated C-H vibrations in the AA
822 < model, which should not be appreciably populated at normal
823 < temperatures. In comparison, once either the hexanes or the
824 < butanethiols are deuterated, one can see a significantly lower $G$ and
825 < $G^\prime$. In either of these cases, the C-H(D) vibrational overlap
826 < between the solvent and the capping agent is removed.
827 < [MAY NEED SPECTRA FIGURE] Conclusively, the
828 < improperly treated C-H vibration in the AA model produced
829 < over-predicted results accordingly. Compared to the AA model, the UA
830 < model yields more reasonable results with higher computational
831 < efficiency.
832 <
833 < However, for Au-butanethiol/toluene interfaces, having the AA
915 > For the Au / butanethiol / toluene interfaces, having the AA
916   butanethiol deuterated did not yield a significant change in the
917 < measurement results. Compared to the C-H vibrational overlap between
918 < hexane and butanethiol, both of which have alkyl chains, that overlap
919 < between toluene and butanethiol is not so significant and thus does
920 < not have as much contribution to the ``Intramolecular Vibration
839 < Redistribution''[CITE HASE]. Conversely, extra degrees of freedom such
840 < as the C-H vibrations could yield higher heat exchange rate between
841 < these two phases and result in a much higher conductivity.
917 > measured conductance. Compared to the C-H vibrational overlap between
918 > hexane and butanethiol, both of which have alkyl chains, the overlap
919 > between toluene and butanethiol is not as significant and thus does
920 > not contribute as much to the heat exchange process.
921  
922 < Although the QSC model for Au is known to predict an overly low value
923 < for bulk metal gold conductivity\cite{kuang:164101}, our computational
924 < results for $G$ and $G^\prime$ do not seem to be affected by this
925 < drawback of the model for metal. Instead, our results suggest that the
926 < modeling of interfacial thermal transport behavior relies mainly on
927 < the accuracy of the interaction descriptions between components
928 < occupying the interfaces.
922 > Previous observations of Zhang {\it et al.}\cite{hase:2010} indicate
923 > that the {\it intra}molecular heat transport due to alkylthiols is
924 > highly efficient.  Combining our observations with those of Zhang {\it
925 >  et al.}, it appears that butanethiol acts as a channel to expedite
926 > heat flow from the gold surface and into the alkyl chain.  The
927 > acoustic impedance mismatch between the metal and the liquid phase can
928 > therefore be effectively reduced with the presence of suitable capping
929 > agents.
930  
931 < \subsection{Role of Capping Agent in Interfacial Thermal Conductance}
932 < To investigate the mechanism of this interfacial thermal conductance,
933 < the vibrational spectra of various gold systems were obtained and are
934 < shown as in Fig. \ref{vibration}.
935 < [MAY RELATE TO HASE'S]
936 < The gold surfaces covered by butanethiol molecules, compared to bare
937 < gold surfaces, exhibit an additional peak observed at the frequency of
938 < $\sim$170cm$^{-1}$, which is attributed to the S-Au bonding
859 < vibration. This vibration enables efficient thermal transport from
860 < surface Au layer to the capping agents.
861 < [MAY PUT IN OTHER SECTION] Simultaneously, as shown in
862 < the lower panel of Fig. \ref{vibration}, the large overlap of the
863 < vibration spectra of butanethiol and hexane in the All-Atom model,
864 < including the C-H vibration, also suggests high thermal exchange
865 < efficiency. The combination of these two effects produces the drastic
866 < interfacial thermal conductance enhancement in the All-Atom model.
931 > Deuterated models in the UA force field did not decouple the thermal
932 > transport as well as in the AA force field.  The UA models, even
933 > though they have eliminated the high frequency C-H vibrational
934 > overlap, still have significant overlap in the lower-frequency
935 > portions of the infrared spectra (Figure \ref{uahxnua}).  Deuterating
936 > the UA models did not decouple the low frequency region enough to
937 > produce an observable difference for the results of $G$ (Table
938 > \ref{modelTest}).
939  
868 [NEED SEPARATE FIGURE. MAY NEED TO CONVERT TO JPEG]
940   \begin{figure}
941 < \includegraphics[width=\linewidth]{vibration}
942 < \caption{Vibrational spectra obtained for gold in different
943 <  environments.}
944 < \label{vibration}
941 > \includegraphics[width=\linewidth]{uahxnua}
942 > \caption{Vibrational spectra obtained for normal (upper) and
943 >  deuterated (lower) hexane in Au-butanethiol/hexane
944 >  systems. Butanethiol spectra are shown as reference. Both hexane and
945 >  butanethiol were using United-Atom models.}
946 > \label{uahxnua}
947   \end{figure}
948  
876 [MAY ADD COMPARISON OF G AND G', AU SLAB WIDTHS, ETC]
877 % The results show that the two definitions used for $G$ yield
878 % comparable values, though $G^\prime$ tends to be smaller.
879
949   \section{Conclusions}
950 < The NIVS algorithm we developed has been applied to simulations of
951 < Au-butanethiol surfaces with organic solvents. This algorithm allows
952 < effective unphysical thermal flux transferred between the metal and
953 < the liquid phase. With the flux applied, we were able to measure the
954 < corresponding thermal gradient and to obtain interfacial thermal
955 < conductivities. Our simulations have seen significant conductance
956 < enhancement with the presence of capping agent, compared to the bare
957 < gold / liquid interfaces. The acoustic impedance mismatch between the
958 < metal and the liquid phase is effectively eliminated by proper capping
950 > The NIVS algorithm has been applied to simulations of
951 > butanethiol-capped Au(111) surfaces in the presence of organic
952 > solvents. This algorithm allows the application of unphysical thermal
953 > flux to transfer heat between the metal and the liquid phase. With the
954 > flux applied, we were able to measure the corresponding thermal
955 > gradients and to obtain interfacial thermal conductivities. Under
956 > steady states, 2-3 ns trajectory simulations are sufficient for
957 > computation of this quantity.
958 >
959 > Our simulations have seen significant conductance enhancement in the
960 > presence of capping agent, compared with the bare gold / liquid
961 > interfaces. The acoustic impedance mismatch between the metal and the
962 > liquid phase is effectively eliminated by a chemically-bonded capping
963   agent. Furthermore, the coverage precentage of the capping agent plays
964 < an important role in the interfacial thermal transport process.
964 > an important role in the interfacial thermal transport
965 > process. Moderately low coverages allow higher contact between capping
966 > agent and solvent, and thus could further enhance the heat transfer
967 > process, giving a non-monotonic behavior of conductance with
968 > increasing coverage.
969  
970 < Our measurement results, particularly of the UA models, agree with
971 < available experimental data. This indicates that our force field
895 < parameters have a nice description of the interactions between the
896 < particles at the interfaces. AA models tend to overestimate the
970 > Our results, particularly using the UA models, agree well with
971 > available experimental data.  The AA models tend to overestimate the
972   interfacial thermal conductance in that the classically treated C-H
973 < vibration would be overly sampled. Compared to the AA models, the UA
974 < models have higher computational efficiency with satisfactory
975 < accuracy, and thus are preferable in interfacial thermal transport
976 < modelings.
973 > vibrations become too easily populated. Compared to the AA models, the
974 > UA models have higher computational efficiency with satisfactory
975 > accuracy, and thus are preferable in modeling interfacial thermal
976 > transport.
977  
978 < Vlugt {\it et al.} has investigated the surface thiol structures for
979 < nanocrystal gold and pointed out that they differs from those of the
980 < Au(111) surface\cite{vlugt:cpc2007154}. This difference might lead to
981 < change of interfacial thermal transport behavior as well. To
982 < investigate this problem, an effective means to introduce thermal flux
983 < and measure the corresponding thermal gradient is desirable for
984 < simulating structures with spherical symmetry.
978 > Of the two definitions for $G$, the discrete form
979 > (Eq. \ref{discreteG}) was easier to use and gives out relatively
980 > consistent results, while the derivative form (Eq. \ref{derivativeG})
981 > is not as versatile. Although $G^\prime$ gives out comparable results
982 > and follows similar trend with $G$ when measuring close to fully
983 > covered or bare surfaces, the spatial resolution of $T$ profile
984 > required for the use of a derivative form is limited by the number of
985 > bins and the sampling required to obtain thermal gradient information.
986  
987 + Vlugt {\it et al.} have investigated the surface thiol structures for
988 + nanocrystalline gold and pointed out that they differ from those of
989 + the Au(111) surface.\cite{landman:1998,vlugt:cpc2007154} This
990 + difference could also cause differences in the interfacial thermal
991 + transport behavior. To investigate this problem, one would need an
992 + effective method for applying thermal gradients in non-planar
993 + (i.e. spherical) geometries.
994  
995   \section{Acknowledgments}
996   Support for this project was provided by the National Science
997   Foundation under grant CHE-0848243. Computational time was provided by
998   the Center for Research Computing (CRC) at the University of Notre
999 < Dame. \newpage
999 > Dame.
1000 > \newpage
1001  
1002   \bibliography{interfacial}
1003  

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