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

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