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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.}
93 < studied the role of CTAB on thermal transport between gold nanorods
94 < 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. Wang {\it et al.} studied heat transport through
87 > long-chain hydrocarbon monolayers on gold substrate at individual
88 > molecular level,\cite{Wang10082007} Schmidt {\it et al.} studied the
89 > role of CTAB on thermal transport between gold nanorods and
90 > solvent,\cite{doi:10.1021/jp8051888} and Juv\'e {\it et al.} studied
91   the cooling dynamics, which is controlled by thermal interface
92   resistence of glass-embedded metal
93 < nanoparticles\cite{PhysRevB.80.195406}. Although interfaces are
94 < commonly barriers for heat transport, Alper {\it et al.} suggested
95 < that specific ligands (capping agents) could completely eliminate this
96 < barrier ($G\rightarrow\infty$)\cite{doi:10.1021/la904855s}.
93 > nanoparticles.\cite{PhysRevB.80.195406} Although interfaces are
94 > normally considered barriers for heat transport, Alper {\it et al.}
95 > suggested that specific ligands (capping agents) could completely
96 > eliminate this barrier
97 > ($G\rightarrow\infty$).\cite{doi:10.1021/la904855s}
98  
99   Theoretical and computational models have also been used to study the
100   interfacial thermal transport in order to gain an understanding of
# Line 105 | Line 102 | atoms)\cite{hase:2010,hase:2011}. However, ensemble av
102   employed Non-Equilibrium Molecular Dynamics (NEMD) simulations to
103   study thermal transport from hot Au(111) substrate to a self-assembled
104   monolayer of alkylthiol with relatively long chain (8-20 carbon
105 < atoms)\cite{hase:2010,hase:2011}. However, ensemble averaged
105 > atoms).\cite{hase:2010,hase:2011} However, ensemble averaged
106   measurements for heat conductance of interfaces between the capping
107 < monolayer on Au and a solvent phase has yet to be studied.
108 < The comparatively low thermal flux through interfaces is
107 > monolayer on Au and a solvent phase have yet to be studied with their
108 > approach. The comparatively low thermal flux through interfaces is
109   difficult to measure with Equilibrium MD or forward NEMD simulation
110 < methods. Therefore, the Reverse NEMD (RNEMD) methods would have the
111 < advantage of having this difficult to measure flux known when studying
112 < the thermal transport across interfaces, given that the simulation
113 < methods being able to effectively apply an unphysical flux in
114 < non-homogeneous systems.
110 > methods. Therefore, the Reverse NEMD (RNEMD)
111 > methods\cite{MullerPlathe:1997xw,kuang:164101} would have the
112 > advantage of applying this difficult to measure flux (while measuring
113 > the resulting gradient), given that the simulation methods being able
114 > to effectively apply an unphysical flux in non-homogeneous systems.
115 > Garde and coworkers\cite{garde:nl2005,garde:PhysRevLett2009} applied
116 > this approach to various liquid interfaces and studied how thermal
117 > conductance (or resistance) is dependent on chemistry details of
118 > interfaces, e.g. hydrophobic and hydrophilic aqueous interfaces.
119  
120 < Recently, we have developed the Non-Isotropic Velocity Scaling (NIVS)
120 > Recently, we have developed a Non-Isotropic Velocity Scaling (NIVS)
121   algorithm for RNEMD simulations\cite{kuang:164101}. This algorithm
122   retains the desirable features of RNEMD (conservation of linear
123   momentum and total energy, compatibility with periodic boundary
# Line 131 | Line 132 | underlying mechanism for this phenomena was investigat
132   properties. Different models were used for both the capping agent and
133   the solvent force field parameters. Using the NIVS algorithm, the
134   thermal transport across these interfaces was studied and the
135 < underlying mechanism for this phenomena was investigated.
135 > underlying mechanism for the phenomena was investigated.
136  
136 [MAY ADD WHY STUDY AU-THIOL SURFACE; CITE SHAOYI JIANG]
137
137   \section{Methodology}
138   \subsection{Imposd-Flux Methods in MD Simulations}
139 < For systems with low interfacial conductivity one must have a method
140 < capable of generating relatively small fluxes, compared to those
141 < required for bulk conductivity. This requirement makes the calculation
142 < even more difficult for those slowly-converging equilibrium
143 < methods\cite{Viscardy:2007lq}.
144 < Forward methods impose gradient, but in interfacail conditions it is
145 < not clear what behavior to impose at the boundary...
146 < Imposed-flux reverse non-equilibrium
147 < methods\cite{MullerPlathe:1997xw} have the flux set {\it a priori} and
148 < the thermal response becomes easier to
149 < measure than the flux. Although M\"{u}ller-Plathe's original momentum
150 < swapping approach can be used for exchanging energy between particles
151 < of different identity, the kinetic energy transfer efficiency is
152 < affected by the mass difference between the particles, which limits
153 < its application on heterogeneous interfacial systems.
139 > Steady state MD simulations have an advantage in that not many
140 > trajectories are needed to study the relationship between thermal flux
141 > and thermal gradients. For systems with low interfacial conductance,
142 > one must have a method capable of generating or measuring relatively
143 > small fluxes, compared to those required for bulk conductivity. This
144 > requirement makes the calculation even more difficult for
145 > slowly-converging equilibrium methods.\cite{Viscardy:2007lq} Forward
146 > NEMD methods impose a gradient (and measure a flux), but at interfaces
147 > it is not clear what behavior should be imposed at the boundaries
148 > between materials.  Imposed-flux reverse non-equilibrium
149 > methods\cite{MullerPlathe:1997xw} set the flux {\it a priori} and
150 > the thermal response becomes an easy-to-measure quantity.  Although
151 > M\"{u}ller-Plathe's original momentum swapping approach can be used
152 > for exchanging energy between particles of different identity, the
153 > kinetic energy transfer efficiency is affected by the mass difference
154 > between the particles, which limits its application on heterogeneous
155 > interfacial systems.
156  
157 < The non-isotropic velocity scaling (NIVS)\cite{kuang:164101} approach to
158 < non-equilibrium MD simulations is able to impose a wide range of
157 > The non-isotropic velocity scaling (NIVS) \cite{kuang:164101} approach
158 > to non-equilibrium MD simulations is able to impose a wide range of
159   kinetic energy fluxes without obvious perturbation to the velocity
160   distributions of the simulated systems. Furthermore, this approach has
161   the advantage in heterogeneous interfaces in that kinetic energy flux
# Line 171 | Line 172 | momenta and energy and does not depend on an external
172   for computing thermal conductivities. The NIVS algorithm conserves
173   momenta and energy and does not depend on an external thermostat.
174  
175 < \subsection{Defining Interfacial Thermal Conductivity $G$}
176 < For interfaces with a relatively low interfacial conductance, the bulk
177 < regions on either side of an interface rapidly come to a state in
178 < which the two phases have relatively homogeneous (but distinct)
179 < temperatures. The interfacial thermal conductivity $G$ can therefore
180 < be approximated as:
175 > \subsection{Defining Interfacial Thermal Conductivity ($G$)}
176 >
177 > For an interface with relatively low interfacial conductance, and a
178 > thermal flux between two distinct bulk regions, the regions on either
179 > side of the interface rapidly come to a state in which the two phases
180 > have relatively homogeneous (but distinct) temperatures. The
181 > interfacial thermal conductivity $G$ can therefore be approximated as:
182   \begin{equation}
183 < G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
183 >  G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_\mathrm{hot}\rangle -
184      \langle T_\mathrm{cold}\rangle \right)}
185   \label{lowG}
186   \end{equation}
187 < where ${E_{total}}$ is the imposed non-physical kinetic energy
188 < transfer and ${\langle T_\mathrm{hot}\rangle}$ and ${\langle
189 <  T_\mathrm{cold}\rangle}$ are the average observed temperature of the
190 < two separated phases.
187 > where ${E_{total}}$ is the total imposed non-physical kinetic energy
188 > transfer during the simulation and ${\langle T_\mathrm{hot}\rangle}$
189 > and ${\langle T_\mathrm{cold}\rangle}$ are the average observed
190 > temperature of the two separated phases.
191  
192   When the interfacial conductance is {\it not} small, there are two
193 < ways to define $G$.
194 <
195 < One way is to assume the temperature is discrete on the two sides of
196 < the interface. $G$ can be calculated using the applied thermal flux
197 < $J$ and the maximum temperature difference measured along the thermal
196 < gradient max($\Delta T$), which occurs at the Gibbs deviding surface
197 < (Figure \ref{demoPic}):
193 > ways to define $G$. One common way is to assume the temperature is
194 > discrete on the two sides of the interface. $G$ can be calculated
195 > using the applied thermal flux $J$ and the maximum temperature
196 > difference measured along the thermal gradient max($\Delta T$), which
197 > occurs at the Gibbs deviding surface (Figure \ref{demoPic}):
198   \begin{equation}
199 < G=\frac{J}{\Delta T}
199 >  G=\frac{J}{\Delta T}
200   \label{discreteG}
201   \end{equation}
202  
# Line 216 | Line 216 | the magnitude of thermal conductivity $\lambda$ change
216  
217   The other approach is to assume a continuous temperature profile along
218   the thermal gradient axis (e.g. $z$) and define $G$ at the point where
219 < the magnitude of thermal conductivity $\lambda$ change reach its
219 > the magnitude of thermal conductivity ($\lambda$) change reaches its
220   maximum, given that $\lambda$ is well-defined throughout the space:
221   \begin{equation}
222   G^\prime = \Big|\frac{\partial\lambda}{\partial z}\Big|
# Line 227 | Line 227 | With the temperature profile obtained from simulations
227   \label{derivativeG}
228   \end{equation}
229  
230 < With the temperature profile obtained from simulations, one is able to
230 > With temperature profiles obtained from simulation, one is able to
231   approximate the first and second derivatives of $T$ with finite
232 < difference methods and thus calculate $G^\prime$.
232 > difference methods and calculate $G^\prime$. In what follows, both
233 > definitions have been used, and are compared in the results.
234  
235 < In what follows, both definitions have been used for calculation and
236 < are compared in the results.
237 <
238 < To compare the above definitions ($G$ and $G^\prime$), we have modeled
239 < a metal slab with its (111) surfaces perpendicular to the $z$-axis of
240 < our simulation cells. Both with and without capping agents on the
240 < surfaces, the metal slab is solvated with simple organic solvents, as
241 < illustrated in Figure \ref{demoPic}.
235 > To investigate the interfacial conductivity at metal / solvent
236 > interfaces, we have modeled a metal slab with its (111) surfaces
237 > perpendicular to the $z$-axis of our simulation cells. The metal slab
238 > has been prepared both with and without capping agents on the exposed
239 > surface, and has been solvated with simple organic solvents, as
240 > illustrated in Figure \ref{gradT}.
241  
242   With the simulation cell described above, we are able to equilibrate
243   the system and impose an unphysical thermal flux between the liquid
244   and the metal phase using the NIVS algorithm. By periodically applying
245 < the unphysical flux, we are able to obtain a temperature profile and
246 < its spatial derivatives. These quantities enable the evaluation of the
247 < interfacial thermal conductance of a surface. Figure \ref{gradT} is an
248 < example how those applied thermal fluxes can be used to obtain the 1st
250 < and 2nd derivatives of the temperature profile.
245 > the unphysical flux, we obtained a temperature profile and its spatial
246 > derivatives. Figure \ref{gradT} shows how an applied thermal flux can
247 > be used to obtain the 1st and 2nd derivatives of the temperature
248 > profile.
249  
250   \begin{figure}
251   \includegraphics[width=\linewidth]{gradT}
# Line 261 | Line 259 | OpenMD\cite{Meineke:2005gd,openmd}, and was used for o
259   \section{Computational Details}
260   \subsection{Simulation Protocol}
261   The NIVS algorithm has been implemented in our MD simulation code,
262 < OpenMD\cite{Meineke:2005gd,openmd}, and was used for our
263 < simulations. Different slab thickness (layer numbers of Au) were
264 < simulated. Metal slabs were first equilibrated under atmospheric
265 < pressure (1 atm) and a desired temperature (e.g. 200K). After
266 < equilibration, butanethiol capping agents were placed at three-fold
267 < sites on the Au(111) surfaces. The maximum butanethiol capacity on Au
268 < surface is $1/3$ of the total number of surface Au
269 < atoms\cite{vlugt:cpc2007154}. A series of different coverages was
270 < investigated in order to study the relation between coverage and
271 < interfacial conductance.
262 > OpenMD\cite{Meineke:2005gd,openmd}, and was used for our simulations.
263 > Metal slabs of 6 or 11 layers of Au atoms were first equilibrated
264 > under atmospheric pressure (1 atm) and 200K. After equilibration,
265 > butanethiol capping agents were placed at three-fold hollow sites on
266 > the Au(111) surfaces. These sites are either {\it fcc} or {\it
267 >  hcp} sites, although Hase {\it et al.} found that they are
268 > equivalent in a heat transfer process,\cite{hase:2010} so we did not
269 > distinguish between these sites in our study. The maximum butanethiol
270 > capacity on Au surface is $1/3$ of the total number of surface Au
271 > atoms, and the packing forms a $(\sqrt{3}\times\sqrt{3})R30^\circ$
272 > structure\cite{doi:10.1021/ja00008a001,doi:10.1021/cr9801317}. A
273 > series of lower coverages was also prepared by eliminating
274 > butanethiols from the higher coverage surface in a regular manner. The
275 > lower coverages were prepared in order to study the relation between
276 > coverage and interfacial conductance.
277  
278   The capping agent molecules were allowed to migrate during the
279   simulations. They distributed themselves uniformly and sampled a
280   number of three-fold sites throughout out study. Therefore, the
281 < initial configuration would not noticeably affect the sampling of a
281 > initial configuration does not noticeably affect the sampling of a
282   variety of configurations of the same coverage, and the final
283   conductance measurement would be an average effect of these
284 < configurations explored in the simulations. [MAY NEED FIGURES]
284 > configurations explored in the simulations.
285  
286 < After the modified Au-butanethiol surface systems were equilibrated
287 < under canonical ensemble, organic solvent molecules were packed in the
288 < previously empty part of the simulation cells\cite{packmol}. Two
286 > After the modified Au-butanethiol surface systems were equilibrated in
287 > the canonical (NVT) ensemble, organic solvent molecules were packed in
288 > the previously empty part of the simulation cells.\cite{packmol} Two
289   solvents were investigated, one which has little vibrational overlap
290 < with the alkanethiol and a planar shape (toluene), and one which has
291 < similar vibrational frequencies and chain-like shape ({\it n}-hexane).
290 > with the alkanethiol and which has a planar shape (toluene), and one
291 > which has similar vibrational frequencies to the capping agent and
292 > chain-like shape ({\it n}-hexane).
293  
294 < The space filled by solvent molecules, i.e. the gap between
295 < periodically repeated Au-butanethiol surfaces should be carefully
296 < chosen. A very long length scale for the thermal gradient axis ($z$)
293 < may cause excessively hot or cold temperatures in the middle of the
294 > The simulation cells were not particularly extensive along the
295 > $z$-axis, as a very long length scale for the thermal gradient may
296 > cause excessively hot or cold temperatures in the middle of the
297   solvent region and lead to undesired phenomena such as solvent boiling
298   or freezing when a thermal flux is applied. Conversely, too few
299   solvent molecules would change the normal behavior of the liquid
300   phase. Therefore, our $N_{solvent}$ values were chosen to ensure that
301 < these extreme cases did not happen to our simulations. And the
302 < corresponding spacing is usually $35 \sim 75$\AA.
301 > these extreme cases did not happen to our simulations. The spacing
302 > between periodic images of the gold interfaces is $45 \sim 75$\AA.
303  
304 < The initial configurations generated by Packmol are further
305 < equilibrated with the $x$ and $y$ dimensions fixed, only allowing
306 < length scale change in $z$ dimension. This is to ensure that the
307 < equilibration of liquid phase does not affect the metal crystal
308 < structure in $x$ and $y$ dimensions. Further equilibration are run
309 < under NVT and then NVE ensembles.
304 > The initial configurations generated are further equilibrated with the
305 > $x$ and $y$ dimensions fixed, only allowing the $z$-length scale to
306 > change. This is to ensure that the equilibration of liquid phase does
307 > not affect the metal's crystalline structure. Comparisons were made
308 > with simulations that allowed changes of $L_x$ and $L_y$ during NPT
309 > equilibration. No substantial changes in the box geometry were noticed
310 > in these simulations. After ensuring the liquid phase reaches
311 > equilibrium at atmospheric pressure (1 atm), further equilibration was
312 > carried out under canonical (NVT) and microcanonical (NVE) ensembles.
313  
314 < After the systems reach equilibrium, NIVS is implemented to impose a
315 < periodic unphysical thermal flux between the metal and the liquid
316 < phase. Most of our simulations are under an average temperature of
317 < $\sim$200K. Therefore, this flux usually comes from the metal to the
314 > After the systems reach equilibrium, NIVS was used to impose an
315 > unphysical thermal flux between the metal and the liquid phases. Most
316 > of our simulations were done under an average temperature of
317 > $\sim$200K. Therefore, thermal flux usually came from the metal to the
318   liquid so that the liquid has a higher temperature and would not
319 < freeze due to excessively low temperature. This induced temperature
320 < gradient is stablized and the simulation cell is devided evenly into
321 < N slabs along the $z$-axis and the temperatures of each slab are
322 < recorded. When the slab width $d$ of each slab is the same, the
323 < derivatives of $T$ with respect to slab number $n$ can be directly
324 < used for $G^\prime$ calculations:
325 < \begin{equation}
326 < G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
319 > freeze due to lowered temperatures. After this induced temperature
320 > gradient had stablized, the temperature profile of the simulation cell
321 > was recorded. To do this, the simulation cell is devided evenly into
322 > $N$ slabs along the $z$-axis. The average temperatures of each slab
323 > are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
324 > the same, the derivatives of $T$ with respect to slab number $n$ can
325 > be directly used for $G^\prime$ calculations: \begin{equation}
326 >  G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
327           \Big/\left(\frac{\partial T}{\partial z}\right)^2
328           = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
329           \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
# Line 326 | Line 332 | G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}
332   \label{derivativeG2}
333   \end{equation}
334  
335 + All of the above simulation procedures use a time step of 1 fs. Each
336 + equilibration stage took a minimum of 100 ps, although in some cases,
337 + longer equilibration stages were utilized.
338 +
339   \subsection{Force Field Parameters}
340 < Our simulations include various components. Figure \ref{demoMol}
341 < demonstrates the sites defined for both United-Atom and All-Atom
342 < models of the organic solvent and capping agent molecules in our
343 < simulations. Force field parameter descriptions are needed for
340 > Our simulations include a number of chemically distinct components.
341 > Figure \ref{demoMol} demonstrates the sites defined for both
342 > United-Atom and All-Atom models of the organic solvent and capping
343 > agents in our simulations. Force field parameters are needed for
344   interactions both between the same type of particles and between
345   particles of different species.
346  
# Line 340 | Line 350 | particles of different species.
350    these simulations. The chemically-distinct sites (a-e) are expanded
351    in terms of constituent atoms for both United Atom (UA) and All Atom
352    (AA) force fields.  Most parameters are from
353 <  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} (UA) and
344 <  \protect\cite{OPLSAA} (AA).  Cross-interactions with the Au atoms are given
345 <  in Table \ref{MnM}.}
353 >  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes,TraPPE-UA.thiols} (UA) and \protect\cite{OPLSAA} (AA). Cross-interactions with the Au atoms are given in Table \ref{MnM}.}
354   \label{demoMol}
355   \end{figure}
356  
357   The Au-Au interactions in metal lattice slab is described by the
358 < quantum Sutton-Chen (QSC) formulation\cite{PhysRevB.59.3527}. The QSC
358 > quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
359   potentials include zero-point quantum corrections and are
360   reparametrized for accurate surface energies compared to the
361 < Sutton-Chen potentials\cite{Chen90}.
361 > Sutton-Chen potentials.\cite{Chen90}
362  
363 < For both solvent molecules, straight chain {\it n}-hexane and aromatic
364 < toluene, United-Atom (UA) and All-Atom (AA) models are used
365 < respectively. The TraPPE-UA
363 > For the two solvent molecules, {\it n}-hexane and toluene, two
364 > different atomistic models were utilized. Both solvents were modeled
365 > using United-Atom (UA) and All-Atom (AA) models. The TraPPE-UA
366   parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
367   for our UA solvent molecules. In these models, sites are located at
368   the carbon centers for alkyl groups. Bonding interactions, including
369   bond stretches and bends and torsions, were used for intra-molecular
370 < sites not separated by more than 3 bonds. Otherwise, for non-bonded
371 < interactions, Lennard-Jones potentials are used. [MORE CITATION?]
370 > sites closer than 3 bonds. For non-bonded interactions, Lennard-Jones
371 > potentials are used.
372  
373 < By eliminating explicit hydrogen atoms, these models are simple and
374 < computationally efficient, while maintains good accuracy. However, the
375 < TraPPE-UA for alkanes is known to predict a lower boiling point than
376 < experimental values. Considering that after an unphysical thermal flux
377 < is applied to a system, the temperature of ``hot'' area in the liquid
378 < phase would be significantly higher than the average, to prevent over
379 < heating and boiling of the liquid phase, the average temperature in
380 < our simulations should be much lower than the liquid boiling point.
373 > By eliminating explicit hydrogen atoms, the TraPPE-UA models are
374 > simple and computationally efficient, while maintaining good accuracy.
375 > However, the TraPPE-UA model for alkanes is known to predict a slighly
376 > lower boiling point than experimental values. This is one of the
377 > reasons we used a lower average temperature (200K) for our
378 > simulations. If heat is transferred to the liquid phase during the
379 > NIVS simulation, the liquid in the hot slab can actually be
380 > substantially warmer than the mean temperature in the simulation. The
381 > lower mean temperatures therefore prevent solvent boiling.
382  
383 < For UA-toluene model, the non-bonded potentials between
384 < inter-molecular sites have a similar Lennard-Jones formulation. For
385 < intra-molecular interactions, considering the stiffness of the benzene
386 < ring, rigid body constraints are applied for further computational
387 < efficiency. All bonds in the benzene ring and between the ring and the
379 < methyl group remain rigid during the progress of simulations.
383 > For UA-toluene, the non-bonded potentials between intermolecular sites
384 > have a similar Lennard-Jones formulation. The toluene molecules were
385 > treated as a single rigid body, so there was no need for
386 > intramolecular interactions (including bonds, bends, or torsions) in
387 > this solvent model.
388  
389   Besides the TraPPE-UA models, AA models for both organic solvents are
390 < included in our studies as well. For hexane, the OPLS-AA\cite{OPLSAA}
391 < force field is used. Additional explicit hydrogen sites were
390 > included in our studies as well. The OPLS-AA\cite{OPLSAA} force fields
391 > were used. For hexane, additional explicit hydrogen sites were
392   included. Besides bonding and non-bonded site-site interactions,
393   partial charges and the electrostatic interactions were added to each
394 < CT and HC site. For toluene, the United Force Field developed by
395 < Rapp\'{e} {\it et al.}\cite{doi:10.1021/ja00051a040} is
396 < adopted. Without the rigid body constraints, bonding interactions were
389 < included. For the aromatic ring, improper torsions (inversions) were
390 < added as an extra potential for maintaining the planar shape.
391 < [MORE CITATION?]
394 > CT and HC site. For toluene, a flexible model for the toluene molecule
395 > was utilized which included bond, bend, torsion, and inversion
396 > potentials to enforce ring planarity.
397  
398 < The capping agent in our simulations, the butanethiol molecules can
399 < either use UA or AA model. The TraPPE-UA force fields includes
398 > The butanethiol capping agent in our simulations, were also modeled
399 > with both UA and AA model. The TraPPE-UA force field includes
400   parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
401   UA butanethiol model in our simulations. The OPLS-AA also provides
402   parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
403 < surfaces do not have the hydrogen atom bonded to sulfur. To adapt this
404 < change and derive suitable parameters for butanethiol adsorbed on
405 < Au(111) surfaces, we adopt the S parameters from Luedtke and
406 < Landman\cite{landman:1998} and modify parameters for its neighbor C
407 < atom for charge balance in the molecule. Note that the model choice
408 < (UA or AA) of capping agent can be different from the
409 < solvent. Regardless of model choice, the force field parameters for
410 < interactions between capping agent and solvent can be derived using
406 < Lorentz-Berthelot Mixing Rule:
403 > surfaces do not have the hydrogen atom bonded to sulfur. To derive
404 > suitable parameters for butanethiol adsorbed on Au(111) surfaces, we
405 > adopt the S parameters from Luedtke and Landman\cite{landman:1998} and
406 > modify the parameters for the CTS atom to maintain charge neutrality
407 > in the molecule.  Note that the model choice (UA or AA) for the capping
408 > agent can be different from the solvent. Regardless of model choice,
409 > the force field parameters for interactions between capping agent and
410 > solvent can be derived using Lorentz-Berthelot Mixing Rule:
411   \begin{eqnarray}
412 < \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
413 < \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
412 >  \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
413 >  \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
414   \end{eqnarray}
415  
416 < To describe the interactions between metal Au and non-metal capping
417 < agent and solvent particles, we refer to an adsorption study of alkyl
418 < thiols on gold surfaces by Vlugt {\it et
419 <  al.}\cite{vlugt:cpc2007154} They fitted an effective Lennard-Jones
420 < form of potential parameters for the interaction between Au and
421 < pseudo-atoms CH$_x$ and S based on a well-established and widely-used
422 < effective potential of Hautman and Klein\cite{hautman:4994} for the
423 < Au(111) surface. As our simulations require the gold lattice slab to
424 < be non-rigid so that it could accommodate kinetic energy for thermal
421 < transport study purpose, the pair-wise form of potentials is
422 < preferred.
423 <
424 < Besides, the potentials developed from {\it ab initio} calculations by
425 < Leng {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
426 < interactions between Au and aromatic C/H atoms in toluene. A set of
427 < pseudo Lennard-Jones parameters were provided for Au in their force
428 < fields. By using the Mixing Rule, this can be used to derive pair-wise
429 < potentials for non-bonded interactions between Au and non-metal sites.
416 > To describe the interactions between metal (Au) and non-metal atoms,
417 > we refer to an adsorption study of alkyl thiols on gold surfaces by
418 > Vlugt {\it et al.}\cite{vlugt:cpc2007154} They fitted an effective
419 > Lennard-Jones form of potential parameters for the interaction between
420 > Au and pseudo-atoms CH$_x$ and S based on a well-established and
421 > widely-used effective potential of Hautman and Klein for the Au(111)
422 > surface.\cite{hautman:4994} As our simulations require the gold slab
423 > to be flexible to accommodate thermal excitation, the pair-wise form
424 > of potentials they developed was used for our study.
425  
426 < However, the Lennard-Jones parameters between Au and other types of
427 < particles, such as All-Atom normal alkanes in our simulations are not
428 < yet well-established. For these interactions, we attempt to derive
429 < their parameters using the Mixing Rule. To do this, Au pseudo
430 < Lennard-Jones parameters for ``Metal-non-Metal'' (MnM) interactions
431 < were first extracted from the Au-CH$_x$ parameters by applying the
432 < Mixing Rule reversely. Table \ref{MnM} summarizes these ``MnM''
433 < parameters in our simulations.
426 > The potentials developed from {\it ab initio} calculations by Leng
427 > {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
428 > interactions between Au and aromatic C/H atoms in toluene. However,
429 > the Lennard-Jones parameters between Au and other types of particles,
430 > (e.g. AA alkanes) have not yet been established. For these
431 > interactions, the Lorentz-Berthelot mixing rule can be used to derive
432 > effective single-atom LJ parameters for the metal using the fit values
433 > for toluene. These are then used to construct reasonable mixing
434 > parameters for the interactions between the gold and other atoms.
435 > Table \ref{MnM} summarizes the ``metal/non-metal'' parameters used in
436 > our simulations.
437  
438   \begin{table*}
439    \begin{minipage}{\linewidth}
# Line 472 | Line 470 | parameters in our simulations.
470    \end{minipage}
471   \end{table*}
472  
473 + \subsection{Vibrational Power Spectrum}
474 +
475 + To investigate the mechanism of interfacial thermal conductance, the
476 + vibrational power spectrum was computed. Power spectra were taken for
477 + individual components in different simulations. To obtain these
478 + spectra, simulations were run after equilibration, in the NVE
479 + ensemble, and without a thermal gradient. Snapshots of configurations
480 + were collected at a frequency that is higher than that of the fastest
481 + vibrations occuring in the simulations. With these configurations, the
482 + velocity auto-correlation functions can be computed:
483 + \begin{equation}
484 + C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
485 + \label{vCorr}
486 + \end{equation}
487 + The power spectrum is constructed via a Fourier transform of the
488 + symmetrized velocity autocorrelation function,
489 + \begin{equation}
490 +  \hat{f}(\omega) =
491 +  \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
492 + \label{fourier}
493 + \end{equation}
494  
495   \section{Results and Discussions}
496 < [MAY HAVE A BRIEF SUMMARY]
496 > In what follows, how the parameters and protocol of simulations would
497 > affect the measurement of $G$'s is first discussed. With a reliable
498 > protocol and set of parameters, the influence of capping agent
499 > coverage on thermal conductance is investigated. Besides, different
500 > force field models for both solvents and selected deuterated models
501 > were tested and compared. Finally, a summary of the role of capping
502 > agent in the interfacial thermal transport process is given.
503 >
504   \subsection{How Simulation Parameters Affects $G$}
479 [MAY NOT PUT AT FIRST]
505   We have varied our protocol or other parameters of the simulations in
506   order to investigate how these factors would affect the measurement of
507   $G$'s. It turned out that while some of these parameters would not
# Line 663 | Line 688 | It turned out that with partial covered butanethiol on
688   effect in interfacial thermal conductance, deuterated UA-hexane is
689   included as well.
690  
691 + \begin{figure}
692 + \includegraphics[width=\linewidth]{coverage}
693 + \caption{Comparison of interfacial thermal conductivity ($G$) values
694 +  for the Au-butanethiol/solvent interface with various UA models and
695 +  different capping agent coverages at $\langle T\rangle\sim$200K
696 +  using certain energy flux respectively.}
697 + \label{coverage}
698 + \end{figure}
699 +
700   It turned out that with partial covered butanethiol on the Au(111)
701   surface, the derivative definition for $G^\prime$
702   (Eq. \ref{derivativeG}) was difficult to apply, due to the difficulty
# Line 695 | Line 729 | case, $G$ decrease could not be offset but instead acc
729   would not offset this effect. Eventually, when butanethiol coverage
730   continues to decrease, solvent-capping agent contact actually
731   decreases with the disappearing of butanethiol molecules. In this
732 < case, $G$ decrease could not be offset but instead accelerated. [NEED
733 < SNAPSHOT SHOWING THE PHENOMENA]
732 > case, $G$ decrease could not be offset but instead accelerated. [MAY NEED
733 > SNAPSHOT SHOWING THE PHENOMENA / SLAB DENSITY ANALYSIS]
734  
735   A comparison of the results obtained from differenet organic solvents
736   can also provide useful information of the interfacial thermal
# Line 706 | Line 740 | difference for the results of $G$. [MAY NEED SPECTRA F
740   studies, even though eliminating C-H vibration samplings, still have
741   C-C vibrational frequencies different from each other. However, these
742   differences in the infrared range do not seem to produce an observable
743 < difference for the results of $G$. [MAY NEED SPECTRA FIGURE]
743 > difference for the results of $G$ (Figure \ref{uahxnua}).
744  
745 + \begin{figure}
746 + \includegraphics[width=\linewidth]{uahxnua}
747 + \caption{Vibrational spectra obtained for normal (upper) and
748 +  deuterated (lower) hexane in Au-butanethiol/hexane
749 +  systems. Butanethiol spectra are shown as reference. Both hexane and
750 +  butanethiol were using United-Atom models.}
751 + \label{uahxnua}
752 + \end{figure}
753 +
754   Furthermore, results for rigid body toluene solvent, as well as other
755   UA-hexane solvents, are reasonable within the general experimental
756 < ranges[CITATIONS]. This suggests that explicit hydrogen might not be a
757 < required factor for modeling thermal transport phenomena of systems
758 < such as Au-thiol/organic solvent.
756 > ranges\cite{Wilson:2002uq,cahill:793,PhysRevB.80.195406}. This
757 > suggests that explicit hydrogen might not be a required factor for
758 > modeling thermal transport phenomena of systems such as
759 > Au-thiol/organic solvent.
760  
761   However, results for Au-butanethiol/toluene do not show an identical
762   trend with those for Au-butanethiol/hexane in that $G$ remains at
# Line 727 | Line 771 | can see a plateau of $G$ vs. butanethiol coverage in o
771   its effect to the process of interfacial thermal transport. Thus, one
772   can see a plateau of $G$ vs. butanethiol coverage in our results.
773  
730 \begin{figure}
731 \includegraphics[width=\linewidth]{coverage}
732 \caption{Comparison of interfacial thermal conductivity ($G$) values
733  for the Au-butanethiol/solvent interface with various UA models and
734  different capping agent coverages at $\langle T\rangle\sim$200K
735  using certain energy flux respectively.}
736 \label{coverage}
737 \end{figure}
738
774   \subsection{Influence of Chosen Molecule Model on $G$}
740 [MAY COMBINE W MECHANISM STUDY]
741
775   In addition to UA solvent/capping agent models, AA models are included
776   in our simulations as well. Besides simulations of the same (UA or AA)
777   model for solvent and capping agent, different models can be applied
# Line 813 | Line 846 | between the solvent and the capping agent is removed.
846   temperatures. In comparison, once either the hexanes or the
847   butanethiols are deuterated, one can see a significantly lower $G$ and
848   $G^\prime$. In either of these cases, the C-H(D) vibrational overlap
849 < between the solvent and the capping agent is removed.
850 < [MAY NEED SPECTRA FIGURE] Conclusively, the
851 < improperly treated C-H vibration in the AA model produced
852 < over-predicted results accordingly. Compared to the AA model, the UA
853 < model yields more reasonable results with higher computational
821 < efficiency.
849 > between the solvent and the capping agent is removed (Figure
850 > \ref{aahxntln}). Conclusively, the improperly treated C-H vibration in
851 > the AA model produced over-predicted results accordingly. Compared to
852 > the AA model, the UA model yields more reasonable results with higher
853 > computational efficiency.
854  
855 + \begin{figure}
856 + \includegraphics[width=\linewidth]{aahxntln}
857 + \caption{Spectra obtained for All-Atom model Au-butanethil/solvent
858 +  systems. When butanethiol is deuterated (lower left), its
859 +  vibrational overlap with hexane would decrease significantly,
860 +  compared with normal butanethiol (upper left). However, this
861 +  dramatic change does not apply to toluene as much (right).}
862 + \label{aahxntln}
863 + \end{figure}
864 +
865   However, for Au-butanethiol/toluene interfaces, having the AA
866   butanethiol deuterated did not yield a significant change in the
867   measurement results. Compared to the C-H vibrational overlap between
868   hexane and butanethiol, both of which have alkyl chains, that overlap
869   between toluene and butanethiol is not so significant and thus does
870 < not have as much contribution to the ``Intramolecular Vibration
871 < Redistribution''[CITE HASE]. Conversely, extra degrees of freedom such
872 < as the C-H vibrations could yield higher heat exchange rate between
873 < these two phases and result in a much higher conductivity.
870 > not have as much contribution to the heat exchange
871 > process. Conversely, extra degrees of freedom such as the C-H
872 > vibrations could yield higher heat exchange rate between these two
873 > phases and result in a much higher conductivity.
874  
875   Although the QSC model for Au is known to predict an overly low value
876   for bulk metal gold conductivity\cite{kuang:164101}, our computational
# Line 838 | Line 880 | occupying the interfaces.
880   the accuracy of the interaction descriptions between components
881   occupying the interfaces.
882  
883 < \subsection{Mechanism of Interfacial Thermal Conductance Enhancement
884 <  by Capping Agent}
885 < [OR: Vibrational Spectrum Study on Conductance Mechanism]
883 > \subsection{Role of Capping Agent in Interfacial Thermal Conductance}
884 > The vibrational spectra for gold slabs in different environments are
885 > shown as in Figure \ref{specAu}. Regardless of the presence of
886 > solvent, the gold surfaces covered by butanethiol molecules, compared
887 > to bare gold surfaces, exhibit an additional peak observed at the
888 > frequency of $\sim$170cm$^{-1}$, which is attributed to the S-Au
889 > bonding vibration. This vibration enables efficient thermal transport
890 > from surface Au layer to the capping agents. Therefore, in our
891 > simulations, the Au/S interfaces do not appear major heat barriers
892 > compared to the butanethiol / solvent interfaces.
893  
894 < [MAY INTRODUCE PROTOCOL IN METHODOLOGY/COMPUTATIONAL DETAIL, EQN'S]
894 > Simultaneously, the vibrational overlap between butanethiol and
895 > organic solvents suggests higher thermal exchange efficiency between
896 > these two components. Even exessively high heat transport was observed
897 > when All-Atom models were used and C-H vibrations were treated
898 > classically. Compared to metal and organic liquid phase, the heat
899 > transfer efficiency between butanethiol and organic solvents is closer
900 > to that within bulk liquid phase.
901  
902 < To investigate the mechanism of this interfacial thermal conductance,
903 < the vibrational spectra of various gold systems were obtained and are
904 < shown as in the upper panel of Fig. \ref{vibration}. To obtain these
905 < spectra, one first runs a simulation in the NVE ensemble and collects
906 < snapshots of configurations; these configurations are used to compute
907 < the velocity auto-correlation functions, which is used to construct a
908 < power spectrum via a Fourier transform.
902 > Furthermore, our observation validated previous
903 > results\cite{hase:2010} that the intramolecular heat transport of
904 > alkylthiols is highly effecient. As a combinational effects of these
905 > phenomena, butanethiol acts as a channel to expedite thermal transport
906 > process. The acoustic impedance mismatch between the metal and the
907 > liquid phase can be effectively reduced with the presence of suitable
908 > capping agents.
909  
855 [MAY RELATE TO HASE'S]
856 The gold surfaces covered by butanethiol molecules, compared to bare
857 gold surfaces, exhibit an additional peak observed at the frequency of
858 $\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.
867
868 [NEED SEPARATE FIGURE. MAY NEED TO CONVERT TO JPEG]
910   \begin{figure}
911   \includegraphics[width=\linewidth]{vibration}
912   \caption{Vibrational spectra obtained for gold in different
913    environments.}
914 < \label{vibration}
914 > \label{specAu}
915   \end{figure}
916  
917 < [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.
917 > [MAY ADD COMPARISON OF AU SLAB WIDTHS]
918  
919   \section{Conclusions}
920   The NIVS algorithm we developed has been applied to simulations of
# Line 883 | Line 922 | conductivities. Our simulations have seen significant
922   effective unphysical thermal flux transferred between the metal and
923   the liquid phase. With the flux applied, we were able to measure the
924   corresponding thermal gradient and to obtain interfacial thermal
925 < conductivities. Our simulations have seen significant conductance
926 < enhancement with the presence of capping agent, compared to the bare
927 < gold / liquid interfaces. The acoustic impedance mismatch between the
928 < metal and the liquid phase is effectively eliminated by proper capping
925 > conductivities. Under steady states, single trajectory simulation
926 > would be enough for accurate measurement. This would be advantageous
927 > compared to transient state simulations, which need multiple
928 > trajectories to produce reliable average results.
929 >
930 > Our simulations have seen significant conductance enhancement with the
931 > presence of capping agent, compared to the bare gold / liquid
932 > interfaces. The acoustic impedance mismatch between the metal and the
933 > liquid phase is effectively eliminated by proper capping
934   agent. Furthermore, the coverage precentage of the capping agent plays
935 < an important role in the interfacial thermal transport process.
935 > an important role in the interfacial thermal transport
936 > process. Moderately lower coverages allow higher contact between
937 > capping agent and solvent, and thus could further enhance the heat
938 > transfer process.
939  
940   Our measurement results, particularly of the UA models, agree with
941   available experimental data. This indicates that our force field
# Line 898 | Line 945 | modelings.
945   vibration would be overly sampled. Compared to the AA models, the UA
946   models have higher computational efficiency with satisfactory
947   accuracy, and thus are preferable in interfacial thermal transport
948 < modelings.
948 > modelings. Of the two definitions for $G$, the discrete form
949 > (Eq. \ref{discreteG}) was easier to use and gives out relatively
950 > consistent results, while the derivative form (Eq. \ref{derivativeG})
951 > is not as versatile. Although $G^\prime$ gives out comparable results
952 > and follows similar trend with $G$ when measuring close to fully
953 > covered or bare surfaces, the spatial resolution of $T$ profile is
954 > limited for accurate computation of derivatives data.
955  
956   Vlugt {\it et al.} has investigated the surface thiol structures for
957   nanocrystal gold and pointed out that they differs from those of the
958 < Au(111) surface\cite{vlugt:cpc2007154}. This difference might lead to
959 < change of interfacial thermal transport behavior as well. To
960 < investigate this problem, an effective means to introduce thermal flux
961 < and measure the corresponding thermal gradient is desirable for
962 < simulating structures with spherical symmetry.
958 > Au(111) surface\cite{landman:1998,vlugt:cpc2007154}. This difference
959 > might lead to change of interfacial thermal transport behavior as
960 > well. To investigate this problem, an effective means to introduce
961 > thermal flux and measure the corresponding thermal gradient is
962 > desirable for simulating structures with spherical symmetry.
963  
911
964   \section{Acknowledgments}
965   Support for this project was provided by the National Science
966   Foundation under grant CHE-0848243. Computational time was provided by

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