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# Line 23 | Line 23
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 45 | Line 45 | We have developed a Non-Isotropic Velocity Scaling alg
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.
48 > With the Non-Isotropic Velocity Scaling algorithm (NIVS) we have
49 > developed, an unphysical thermal flux can be effectively set up even
50 > for non-homogeneous systems like interfaces in non-equilibrium
51 > molecular dynamics simulations. In this work, this algorithm is
52 > applied for simulating thermal conductance at metal / organic solvent
53 > interfaces with various coverages of butanethiol capping
54 > agents. Different solvents and force field models were tested. Our
55 > results suggest that the United-Atom models are able to provide an
56 > estimate of the interfacial thermal conductivity comparable to
57 > experiments in our simulations with satisfactory computational
58 > efficiency. From our results, the acoustic impedance mismatch between
59 > metal and liquid phase is effectively reduced by the capping
60 > agents, and thus leads to interfacial thermal conductance
61 > enhancement. Furthermore, this effect is closely related to the
62 > capping agent coverage on the metal surfaces and the type of solvent
63 > molecules, and is affected by the models used in the simulations.
64  
65   \end{abstract}
66  
# Line 71 | Line 73 | leads to higher interfacial thermal transfer efficienc
73   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
74  
75   \section{Introduction}
74 [BACKGROUND FOR INTERFACIAL THERMAL CONDUCTANCE PROBLEM]
76   Interfacial thermal conductance is extensively studied both
77 < experimentally and computationally, and systems with interfaces
78 < present are generally heterogeneous. Although interfaces are commonly
79 < barriers to heat transfer, it has been
80 < reported\cite{doi:10.1021/la904855s} that under specific circustances,
81 < e.g. with certain capping agents present on the surface, interfacial
82 < conductance can be significantly enhanced. However, heat conductance
83 < of molecular and nano-scale interfaces will be affected by the
84 < chemical details of the surface and is challenging to
85 < 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.
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.
86  
87 + Heat conductance of molecular and nano-scale interfaces will be
88 + affected by the chemical details of the surface. Experimentally,
89 + various interfaces have been investigated for their thermal
90 + conductance properties. Wang {\it et al.} studied heat transport
91 + through long-chain hydrocarbon monolayers on gold substrate at
92 + 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
95 + the cooling dynamics, which is controlled by thermal interface
96 + resistence of glass-embedded metal
97 + nanoparticles\cite{PhysRevB.80.195406}. Although interfaces are
98 + commonly barriers for heat transport, Alper {\it et al.} suggested
99 + that specific ligands (capping agents) could completely eliminate this
100 + barrier ($G\rightarrow\infty$)\cite{doi:10.1021/la904855s}.
101 +
102 + Theoretical and computational models have also been used to study the
103 + interfacial thermal transport in order to gain an understanding of
104 + this phenomena at the molecular level. Recently, Hase and coworkers
105 + employed Non-Equilibrium Molecular Dynamics (NEMD) simulations to
106 + study thermal transport from hot Au(111) substrate to a self-assembled
107 + monolayer of alkylthiol with relatively long chain (8-20 carbon
108 + atoms)\cite{hase:2010,hase:2011}. However, ensemble averaged
109 + measurements for heat conductance of interfaces between the capping
110 + monolayer on Au and a solvent phase has yet to be studied.
111 + The comparatively low thermal flux through interfaces is
112 + difficult to measure with Equilibrium MD or forward NEMD simulation
113 + methods. Therefore, the Reverse NEMD (RNEMD) methods would have the
114 + advantage of having this difficult to measure flux known when studying
115 + the thermal transport across interfaces, given that the simulation
116 + methods being able to effectively apply an unphysical flux in
117 + non-homogeneous systems.
118 +
119   Recently, we have developed the Non-Isotropic Velocity Scaling (NIVS)
120   algorithm for RNEMD simulations\cite{kuang:164101}. This algorithm
121   retains the desirable features of RNEMD (conservation of linear
122   momentum and total energy, compatibility with periodic boundary
123   conditions) while establishing true thermal distributions in each of
124 < the two slabs. Furthermore, it allows more effective thermal exchange
125 < between particles of different identities, and thus enables extensive
126 < study of interfacial conductance.
124 > the two slabs. Furthermore, it allows effective thermal exchange
125 > between particles of different identities, and thus makes the study of
126 > interfacial conductance much simpler.
127  
128 + The work presented here deals with the Au(111) surface covered to
129 + varying degrees by butanethiol, a capping agent with short carbon
130 + chain, and solvated with organic solvents of different molecular
131 + properties. Different models were used for both the capping agent and
132 + the solvent force field parameters. Using the NIVS algorithm, the
133 + thermal transport across these interfaces was studied and the
134 + underlying mechanism for this phenomena was investigated.
135 +
136 + [MAY ADD WHY STUDY AU-THIOL SURFACE; CITE SHAOYI JIANG]
137 +
138   \section{Methodology}
139 < \subsection{Algorithm}
140 < [BACKGROUND FOR MD METHODS]
141 < There have been many algorithms for computing thermal conductivity
142 < using molecular dynamics simulations. However, interfacial conductance
143 < is at least an order of magnitude smaller. This would make the
144 < calculation even more difficult for those slowly-converging
145 < equilibrium methods. Imposed-flux non-equilibrium
139 > \subsection{Imposd-Flux Methods in MD Simulations}
140 > For systems with low interfacial conductivity one must have a method
141 > capable of generating relatively small fluxes, compared to those
142 > required for bulk conductivity. This requirement makes the calculation
143 > even more difficult for those slowly-converging equilibrium
144 > methods\cite{Viscardy:2007lq}.
145 > Forward methods impose gradient, but in interfacail conditions it is
146 > not clear what behavior to impose at the boundary...
147 > Imposed-flux reverse non-equilibrium
148   methods\cite{MullerPlathe:1997xw} have the flux set {\it a priori} and
149 < the response of temperature or momentum gradients are easier to
150 < measure than the flux, if unknown, and thus, is a preferable way to
151 < the forward NEMD methods. Although the momentum swapping approach for
152 < flux-imposing can be used for exchanging energy between particles of
153 < different identity, the kinetic energy transfer efficiency is affected
154 < by the mass difference between the particles, which limits its
113 < application on heterogeneous interfacial systems.
149 > the thermal response becomes easier to
150 > measure than the flux. Although M\"{u}ller-Plathe's original momentum
151 > swapping approach can be used for exchanging energy between particles
152 > of different identity, the kinetic energy transfer efficiency is
153 > affected by the mass difference between the particles, which limits
154 > its application on heterogeneous interfacial systems.
155  
156 < The non-isotropic velocity scaling (NIVS)\cite{kuang:164101} approach in
157 < non-equilibrium MD simulations is able to impose relatively large
158 < kinetic energy flux without obvious perturbation to the velocity
159 < distribution of the simulated systems. Furthermore, this approach has
156 > The non-isotropic velocity scaling (NIVS)\cite{kuang:164101} approach to
157 > non-equilibrium MD simulations is able to impose a wide range of
158 > kinetic energy fluxes without obvious perturbation to the velocity
159 > distributions of the simulated systems. Furthermore, this approach has
160   the advantage in heterogeneous interfaces in that kinetic energy flux
161   can be applied between regions of particles of arbitary identity, and
162 < the flux quantity is not restricted by particle mass difference.
162 > the flux will not be restricted by difference in particle mass.
163  
164   The NIVS algorithm scales the velocity vectors in two separate regions
165   of a simulation system with respective diagonal scaling matricies. To
166   determine these scaling factors in the matricies, a set of equations
167   including linear momentum conservation and kinetic energy conservation
168 < constraints and target momentum/energy flux satisfaction is
169 < solved. With the scaling operation applied to the system in a set
170 < frequency, corresponding momentum/temperature gradients can be built,
171 < which can be used for computing transportation properties and other
172 < applications related to momentum/temperature gradients. The NIVS
132 < algorithm conserves momenta and energy and does not depend on an
133 < external thermostat.
168 > constraints and target energy flux satisfaction is solved. With the
169 > scaling operation applied to the system in a set frequency, bulk
170 > temperature gradients can be easily established, and these can be used
171 > for computing thermal conductivities. The NIVS algorithm conserves
172 > momenta and energy and does not depend on an external thermostat.
173  
174   \subsection{Defining Interfacial Thermal Conductivity $G$}
175   For interfaces with a relatively low interfacial conductance, the bulk
# Line 148 | Line 187 | When the interfacial conductance is {\it not} small, t
187    T_\mathrm{cold}\rangle}$ are the average observed temperature of the
188   two separated phases.
189  
190 < When the interfacial conductance is {\it not} small, two ways can be
191 < used to define $G$.
190 > When the interfacial conductance is {\it not} small, there are two
191 > ways to define $G$.
192  
193 < One way is to assume the temperature is discretely different on two
194 < sides of the interface, $G$ can be calculated with the thermal flux
195 < applied $J$ and the maximum temperature difference measured along the
196 < thermal gradient max($\Delta T$), which occurs at the interface, as:
193 > One way is to assume the temperature is discrete on the two sides of
194 > the interface. $G$ can be calculated using the applied thermal flux
195 > $J$ and the maximum temperature difference measured along the thermal
196 > gradient max($\Delta T$), which occurs at the Gibbs deviding surface,
197 > as:
198   \begin{equation}
199   G=\frac{J}{\Delta T}
200   \label{discreteG}
# Line 175 | Line 215 | difference method and thus calculate $G^\prime$.
215  
216   With the temperature profile obtained from simulations, one is able to
217   approximate the first and second derivatives of $T$ with finite
218 < difference method and thus calculate $G^\prime$.
218 > difference methods and thus calculate $G^\prime$.
219 >
220 > In what follows, both definitions have been used for calculation and
221 > are compared in the results.
222  
223 < In what follows, both definitions are used for calculation and comparison.
223 > To compare the above definitions ($G$ and $G^\prime$), we have modeled
224 > a metal slab with its (111) surfaces perpendicular to the $z$-axis of
225 > our simulation cells. Both with and withour capping agents on the
226 > surfaces, the metal slab is solvated with simple organic solvents, as
227 > illustrated in Figure \ref{demoPic}.
228  
182 [IMPOSE G DEFINITION INTO OUR SYSTEMS]
183 To facilitate the use of the above definitions in calculating $G$ and
184 $G^\prime$, we have a metal slab with its (111) surfaces perpendicular
185 to the $z$-axis of our simulation cells. With or withour capping
186 agents on the surfaces, the metal slab is solvated with organic
187 solvents, as illustrated in Figure \ref{demoPic}.
188
229   \begin{figure}
230 < \includegraphics[width=\linewidth]{demoPic}
231 < \caption{A sample showing how a metal slab has its (111) surface
232 <  covered by capping agent molecules and solvated by hexane.}
230 > \includegraphics[width=\linewidth]{method}
231 > \caption{Interfacial conductance can be calculated by applying an
232 >  (unphysical) kinetic energy flux between two slabs, one located
233 >  within the metal and another on the edge of the periodic box.  The
234 >  system responds by forming a thermal response or a gradient.  In
235 >  bulk liquids, this gradient typically has a single slope, but in
236 >  interfacial systems, there are distinct thermal conductivity
237 >  domains.  The interfacial conductance, $G$ is found by measuring the
238 >  temperature gap at the Gibbs dividing surface, or by using second
239 >  derivatives of the thermal profile.}
240   \label{demoPic}
241   \end{figure}
242  
243 < With a simulation cell setup following the above manner, one is able
244 < to equilibrate the system and impose an unphysical thermal flux
245 < between the liquid and the metal phase with the NIVS algorithm. Under
246 < a stablized thermal gradient induced by periodically applying the
247 < unphysical flux, one is able to obtain a temperature profile and the
248 < physical thermal flux corresponding to it, which equals to the
249 < unphysical flux applied by NIVS. These data enables the evaluation of
250 < 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.
243 > With the simulation cell described above, we are able to equilibrate
244 > the system and impose an unphysical thermal flux between the liquid
245 > and the metal phase using the NIVS algorithm. By periodically applying
246 > the unphysical flux, we are able to obtain a temperature profile and
247 > its spatial derivatives. These quantities enable the evaluation of the
248 > interfacial thermal conductance of a surface. Figure \ref{gradT} is an
249 > example how those applied thermal fluxes can be used to obtain the 1st
250 > and 2nd derivatives of the temperature profile.
251  
252   \begin{figure}
253   \includegraphics[width=\linewidth]{gradT}
# Line 211 | Line 256 | obtain the 1st and 2nd derivatives of the temperature
256   \label{gradT}
257   \end{figure}
258  
214 [MAY INCLUDE POWER SPECTRUM PROTOCOL]
215
259   \section{Computational Details}
260   \subsection{Simulation Protocol}
261 < In our simulations, Au is used to construct a metal slab with bare
262 < (111) surface perpendicular to the $z$-axis. Different slab thickness
263 < (layer numbers of Au) are simulated. This metal slab is first
264 < equilibrated under normal pressure (1 atm) and a desired
265 < temperature. After equilibration, butanethiol is used as the capping
266 < agent molecule to cover the bare Au (111) surfaces evenly. The sulfur
267 < atoms in the butanethiol molecules would occupy the three-fold sites
268 < of the surfaces, and the maximal butanethiol capacity on Au surface is
269 < $1/3$ of the total number of surface Au atoms[CITATION]. A series of
270 < different coverage surfaces is investigated in order to study the
271 < relation between coverage and conductance.
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.
272  
273 < [COVERAGE DISCRIPTION] However, since the interactions between surface
274 < Au and butanethiol is non-bonded, the capping agent molecules are
275 < allowed to migrate to an empty neighbor three-fold site during a
276 < simulation. Therefore, the initial configuration would not severely
277 < affect the sampling of a variety of configurations of the same
278 < coverage, and the final conductance measurement would be an average
279 < effect of these configurations explored in the simulations. [MAY NEED FIGURES]
273 > The capping agent molecules were allowed to migrate during the
274 > simulations. They distributed themselves uniformly and sampled a
275 > number of three-fold sites throughout out study. Therefore, the
276 > initial configuration would not noticeably affect the sampling of a
277 > variety of configurations of the same coverage, and the final
278 > conductance measurement would be an average effect of these
279 > configurations explored in the simulations. [MAY NEED FIGURES]
280  
281 < After the modified Au-butanethiol surface systems are equilibrated
282 < under canonical ensemble, Packmol\cite{packmol} is used to pack
283 < organic solvent molecules in the previously vacuum part of the
284 < simulation cells, which guarantees that short range repulsive
285 < interactions do not disrupt the simulations. Two solvents are
286 < investigated, one which has little vibrational overlap with the
244 < alkanethiol and plane-like shape (toluene), and one which has similar
245 < vibrational frequencies and chain-like shape ({\it n}-hexane). [MAY
246 < EXPLAIN WHY WE CHOOSE THEM]
281 > After the modified Au-butanethiol surface systems were equilibrated
282 > under canonical ensemble, organic solvent molecules were packed in the
283 > previously empty part of the simulation cells\cite{packmol}. Two
284 > solvents were investigated, one which has little vibrational overlap
285 > with the alkanethiol and a planar shape (toluene), and one which has
286 > similar vibrational frequencies and chain-like shape ({\it n}-hexane).
287  
288 < The spacing filled by solvent molecules, i.e. the gap between
288 > The space filled by solvent molecules, i.e. the gap between
289   periodically repeated Au-butanethiol surfaces should be carefully
290   chosen. A very long length scale for the thermal gradient axis ($z$)
291   may cause excessively hot or cold temperatures in the middle of the
# Line 290 | Line 330 | quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.5
330   same type of particles and between particles of different species.
331  
332   The Au-Au interactions in metal lattice slab is described by the
333 < quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The QSC
333 > quantum Sutton-Chen (QSC) formulation\cite{PhysRevB.59.3527}. The QSC
334   potentials include zero-point quantum corrections and are
335   reparametrized for accurate surface energies compared to the
336   Sutton-Chen potentials\cite{Chen90}.
337  
338 < Figure [REF] demonstrates how we name our pseudo-atoms of the
339 < molecules in our simulations.
300 < [FIGURE FOR MOLECULE NOMENCLATURE]
338 > Figure \ref{demoMol} demonstrates how we name our pseudo-atoms of the
339 > organic solvent molecules in our simulations.
340  
341 + \begin{figure}
342 + \includegraphics[width=\linewidth]{structures}
343 + \caption{Structures of the capping agent and solvents utilized in
344 +  these simulations. The chemically-distinct sites (a-e) are expanded
345 +  in terms of constituent atoms for both United Atom (UA) and All Atom
346 +  (AA) force fields.  Most parameters are from
347 +  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} (UA) and
348 +  \protect\cite{OPLSAA} (AA).  Cross-interactions with the Au atoms are given
349 +  in Table \ref{MnM}.}
350 + \label{demoMol}
351 + \end{figure}
352 +
353   For both solvent molecules, straight chain {\it n}-hexane and aromatic
354   toluene, United-Atom (UA) and All-Atom (AA) models are used
355   respectively. The TraPPE-UA
# Line 330 | Line 381 | Au(111) surfaces, we adopt the S parameters from [CITA
381   parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
382   surfaces do not have the hydrogen atom bonded to sulfur. To adapt this
383   change and derive suitable parameters for butanethiol adsorbed on
384 < Au(111) surfaces, we adopt the S parameters from [CITATION CF VLUGT]
385 < and modify parameters for its neighbor C atom for charge balance in
386 < the molecule. Note that the model choice (UA or AA) of capping agent
387 < can be different from the solvent. Regardless of model choice, the
388 < force field parameters for interactions between capping agent and
389 < solvent can be derived using Lorentz-Berthelot Mixing Rule:
384 > Au(111) surfaces, we adopt the S parameters from Luedtke and
385 > Landman\cite{landman:1998} and modify parameters for its neighbor C
386 > atom for charge balance in the molecule. Note that the model choice
387 > (UA or AA) of capping agent can be different from the
388 > solvent. Regardless of model choice, the force field parameters for
389 > interactions between capping agent and solvent can be derived using
390 > Lorentz-Berthelot Mixing Rule:
391 > \begin{eqnarray}
392 > \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
393 > \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
394 > \end{eqnarray}
395  
340
396   To describe the interactions between metal Au and non-metal capping
397   agent and solvent particles, we refer to an adsorption study of alkyl
398   thiols on gold surfaces by Vlugt {\it et
399    al.}\cite{vlugt:cpc2007154} They fitted an effective Lennard-Jones
400   form of potential parameters for the interaction between Au and
401   pseudo-atoms CH$_x$ and S based on a well-established and widely-used
402 < effective potential of Hautman and Klein[CITATION] for the Au(111)
403 < surface. As our simulations require the gold lattice slab to be
404 < non-rigid so that it could accommodate kinetic energy for thermal
402 > effective potential of Hautman and Klein\cite{hautman:4994} for the
403 > Au(111) surface. As our simulations require the gold lattice slab to
404 > be non-rigid so that it could accommodate kinetic energy for thermal
405   transport study purpose, the pair-wise form of potentials is
406   preferred.
407  
# Line 365 | Line 420 | parameters in our simulations.
420   \begin{table*}
421    \begin{minipage}{\linewidth}
422      \begin{center}
423 <      \caption{Lennard-Jones parameters for Au-non-Metal
424 <        interactions in our simulations.}
425 <      
426 <      \begin{tabular}{ccc}
423 >      \caption{Non-bonded interaction parameters (including cross
424 >        interactions with Au atoms) for both force fields used in this
425 >        work.}      
426 >      \begin{tabular}{lllllll}
427          \hline\hline
428 <        Non-metal & $\sigma$/\AA & $\epsilon$/kcal/mol \\
428 >        & Site  & $\sigma_{ii}$ & $\epsilon_{ii}$ & $q_i$ &
429 >        $\sigma_{Au-i}$ & $\epsilon_{Au-i}$ \\
430 >        & & (\AA) & (kcal/mol) & ($e$) & (\AA) & (kcal/mol) \\
431          \hline
432 <        S    & 2.40   & 8.465   \\
433 <        CH3  & 3.54   & 0.2146  \\
434 <        CH2  & 3.54   & 0.1749  \\
435 <        CT3  & 3.365  & 0.1373  \\
436 <        CT2  & 3.365  & 0.1373  \\
437 <        CTT  & 3.365  & 0.1373  \\
438 <        HC   & 2.865  & 0.09256 \\
439 <        CHar & 3.4625 & 0.1680  \\
440 <        CRar & 3.555  & 0.1604  \\
441 <        CA   & 3.173  & 0.0640  \\
442 <        HA   & 2.746  & 0.0414  \\
432 >        United Atom (UA)
433 >        &CH3  & 3.75  & 0.1947  & -      & 3.54   & 0.2146  \\
434 >        &CH2  & 3.95  & 0.0914  & -      & 3.54   & 0.1749  \\
435 >        &CHar & 3.695 & 0.1003  & -      & 3.4625 & 0.1680  \\
436 >        &CRar & 3.88  & 0.04173 & -      & 3.555  & 0.1604  \\
437 >        \hline
438 >        All Atom (AA)
439 >        &CT3  & 3.50  & 0.066   & -0.18  & 3.365  & 0.1373  \\
440 >        &CT2  & 3.50  & 0.066   & -0.12  & 3.365  & 0.1373  \\
441 >        &CTT  & 3.50  & 0.066   & -0.065 & 3.365  & 0.1373  \\
442 >        &HC   & 2.50  & 0.030   &  0.06  & 2.865  & 0.09256 \\
443 >        &CA   & 3.55  & 0.070   & -0.115 & 3.173  & 0.0640  \\
444 >        &HA   & 2.42  & 0.030   &  0.115 & 2.746  & 0.0414  \\
445 >        \hline
446 >        Both UA and AA & S    & 4.45  & 0.25    & -      & 2.40   & 8.465   \\
447          \hline\hline
448        \end{tabular}
449        \label{MnM}
# Line 452 | Line 513 | couple $J_z$'s and do not need to test a large series
513          interfaces with UA model and different hexane molecule numbers
514          at different temperatures using a range of energy fluxes.}
515        
516 <      \begin{tabular}{cccccccc}
516 >      \begin{tabular}{ccccccc}
517          \hline\hline
518 <        $\langle T\rangle$ & & $L_x$ & $L_y$ & $L_z$ & $J_z$ &
519 <        $G$ & $G^\prime$ \\
520 <        (K) & $N_{hexane}$ & \multicolumn{3}{c}\AA & (GW/m$^2$) &
518 >        $\langle T\rangle$ & $N_{hexane}$ & Fixed & $\rho_{hexane}$ &
519 >        $J_z$ & $G$ & $G^\prime$ \\
520 >        (K) & & $L_x$ \& $L_y$? & (g/cm$^3$) & (GW/m$^2$) &
521          \multicolumn{2}{c}{(MW/m$^2$/K)} \\
522          \hline
523 <        200 & 266 & 29.86 & 25.80 & 113.1 & -0.96 &
524 <        102()  & 80.0() \\
525 <            & 200 & 29.84 & 25.81 &  93.9 &  1.92 &
526 <        129()  & 87.3() \\
527 <            &     & 29.84 & 25.81 &  95.3 &  1.93 &
528 <        131()  & 77.5() \\
529 <            & 166 & 29.84 & 25.81 &  85.7 &  0.97 &
530 <        115()  & 69.3() \\
531 <            &     &       &       &       &  1.94 &
532 <        125()  & 87.1() \\
533 <        250 & 200 & 29.84 & 25.87 & 106.8 &  0.96 &
534 <        81.8() & 67.0() \\
535 <            & 166 & 29.87 & 25.84 &  94.8 &  0.98 &
536 <        79.0() & 62.9() \\
537 <            &     & 29.84 & 25.85 &  95.0 &  1.44 &
538 <        76.2() & 64.8() \\
523 >        200 & 266 & No  & 0.672 & -0.96 & 102()  & 80.0() \\
524 >            & 200 & Yes & 0.694 &  1.92 & 129()  & 87.3() \\
525 >            &     & Yes & 0.672 &  1.93 & 131()  & 77.5() \\
526 >            &     & No  & 0.688 &  0.96 & 125()  & 90.2() \\
527 >            &     &     &       &  1.91 & 139()  & 101()  \\
528 >            &     &     &       &  2.83 & 141()  & 89.9() \\
529 >            & 166 & Yes & 0.679 &  0.97 & 115()  & 69.3() \\
530 >            &     &     &       &  1.94 & 125()  & 87.1() \\
531 >            &     & No  & 0.681 &  0.97 & 141()  & 77.7() \\
532 >            &     &     &       &  1.92 & 138()  & 98.9() \\
533 >        \hline
534 >        250 & 200 & No  & 0.560 &  0.96 & 74.8() & 61.8() \\
535 >            &     &     &       & -0.95 & 49.4() & 45.7() \\
536 >            & 166 & Yes & 0.570 &  0.98 & 79.0() & 62.9() \\
537 >            &     & No  & 0.569 &  0.97 & 80.3() & 67.1() \\
538 >            &     &     &       &  1.44 & 76.2() & 64.8() \\
539 >            &     &     &       & -0.95 & 56.4() & 54.4() \\
540 >            &     &     &       & -1.85 & 47.8() & 53.5() \\
541          \hline\hline
542        \end{tabular}
543        \label{AuThiolHexaneUA}
# Line 505 | Line 568 | in that higher degree of contact could yield increased
568   important role in the thermal transport process across the interface
569   in that higher degree of contact could yield increased conductance.
570  
571 < [ADD Lxyz AND ERROR ESTIMATE TO TABLE]
571 > [ADD ERROR ESTIMATE TO TABLE]
572   \begin{table*}
573    \begin{minipage}{\linewidth}
574      \begin{center}
# Line 514 | Line 577 | in that higher degree of contact could yield increased
577          interface at different temperatures using a range of energy
578          fluxes.}
579        
580 <      \begin{tabular}{cccc}
580 >      \begin{tabular}{ccccc}
581          \hline\hline
582 <        $\langle T\rangle$ & $J_z$ & $G$ & $G^\prime$ \\
583 <        (K) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
582 >        $\langle T\rangle$ & $\rho_{toluene}$ & $J_z$ & $G$ & $G^\prime$ \\
583 >        (K) & (g/cm$^3$) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
584          \hline
585 <        200 & -1.86 & 180() & 135() \\
586 <            &  2.15 & 204() & 113() \\
587 <            & -3.93 & 175() & 114() \\
588 <        300 & -1.91 & 143() & 125() \\
589 <            & -4.19 & 134() & 113() \\
585 >        200 & 0.933 & -1.86 & 180() & 135() \\
586 >            &       &  2.15 & 204() & 113() \\
587 >            &       & -3.93 & 175() & 114() \\
588 >        \hline
589 >        300 & 0.855 & -1.91 & 143() & 125() \\
590 >            &       & -4.19 & 134() & 113() \\
591          \hline\hline
592        \end{tabular}
593        \label{AuThiolToluene}
# Line 551 | Line 615 | undertaken at $<T>\sim$200K.
615   reconstructions could eliminate the original $x$ and $y$ dimensional
616   homogeneity, measurement of $G$ is more difficult to conduct under
617   higher temperatures. Therefore, most of our measurements are
618 < undertaken at $<T>\sim$200K.
618 > undertaken at $\langle T\rangle\sim$200K.
619  
620   However, when the surface is not completely covered by butanethiols,
621   the simulated system is more resistent to the reconstruction
622   above. Our Au-butanethiol/toluene system did not see this phenomena
623 < even at $<T>\sim$300K. The Au(111) surfaces have a 90\% coverage of
624 < butanethiols and have empty three-fold sites. These empty sites could
625 < help prevent surface reconstruction in that they provide other means
626 < of capping agent relaxation. It is observed that butanethiols can
627 < migrate to their neighbor empty sites during a simulation. Therefore,
628 < we were able to obtain $G$'s for these interfaces even at a relatively
629 < high temperature without being affected by surface reconstructions.
623 > even at $\langle T\rangle\sim$300K. The Au(111) surfaces have a 90\%
624 > coverage of butanethiols and have empty three-fold sites. These empty
625 > sites could help prevent surface reconstruction in that they provide
626 > other means of capping agent relaxation. It is observed that
627 > butanethiols can migrate to their neighbor empty sites during a
628 > simulation. Therefore, we were able to obtain $G$'s for these
629 > interfaces even at a relatively high temperature without being
630 > affected by surface reconstructions.
631  
632   \subsection{Influence of Capping Agent Coverage on $G$}
633   To investigate the influence of butanethiol coverage on interfacial
# Line 616 | Line 681 | differences in the IR range do not seem to produce an
681   butanethiol (UA) is non-deuterated for both solvents. These UA model
682   studies, even though eliminating C-H vibration samplings, still have
683   C-C vibrational frequencies different from each other. However, these
684 < differences in the IR range do not seem to produce an observable
684 > differences in the infrared range do not seem to produce an observable
685   difference for the results of $G$. [MAY NEED FIGURE]
686  
687   Furthermore, results for rigid body toluene solvent, as well as other
# Line 638 | Line 703 | can see a plateau of $G$ vs. butanethiol coverage in o
703   its effect to the process of interfacial thermal transport. Thus, one
704   can see a plateau of $G$ vs. butanethiol coverage in our results.
705  
706 < [NEED ERROR ESTIMATE, MAY ALSO PUT J HERE]
707 < \begin{table*}
708 <  \begin{minipage}{\linewidth}
709 <    \begin{center}
710 <      \caption{Computed interfacial thermal conductivity ($G$ in
711 <        MW/m$^2$/K) values for the Au-butanethiol/solvent interface
712 <        with various UA models and different capping agent coverages
713 <        at $<T>\sim$200K using certain energy flux respectively.}
649 <      
650 <      \begin{tabular}{cccc}
651 <        \hline\hline
652 <        Thiol & & & \\
653 <        coverage (\%) & hexane & hexane-D & toluene \\
654 <        \hline
655 <        0.0   & 46.5 & 43.9 & 70.1 \\
656 <        25.0  & 151  & 153  & 249  \\
657 <        50.0  & 172  & 182  & 214  \\
658 <        75.0  & 242  & 229  & 244  \\
659 <        88.9  & 178  & -    & -    \\
660 <        100.0 & 137  & 153  & 187  \\
661 <        \hline\hline
662 <      \end{tabular}
663 <      \label{tlnUhxnUhxnD}
664 <    \end{center}
665 <  \end{minipage}
666 < \end{table*}
706 > \begin{figure}
707 > \includegraphics[width=\linewidth]{coverage}
708 > \caption{Comparison of interfacial thermal conductivity ($G$) values
709 >  for the Au-butanethiol/solvent interface with various UA models and
710 >  different capping agent coverages at $\langle T\rangle\sim$200K
711 >  using certain energy flux respectively.}
712 > \label{coverage}
713 > \end{figure}
714  
715   \subsection{Influence of Chosen Molecule Model on $G$}
716   [MAY COMBINE W MECHANISM STUDY]
717  
718 < For the all-atom model, the liquid hexane phase was not stable under NPT
719 < conditions. Therefore, the simulation length scale parameters are
720 < adopted from previous equilibration results of the united-atom model
721 < at 200K. Table \ref{AuThiolHexaneAA} shows the results of these
722 < simulations. The conductivity values calculated with full capping
723 < agent coverage are substantially larger than observed in the
724 < united-atom model, and is even higher than predicted by
678 < experiments. It is possible that our parameters for metal-non-metal
679 < particle interactions lead to an overestimate of the interfacial
680 < thermal conductivity, although the active C-H vibrations in the
681 < all-atom model (which should not be appreciably populated at normal
682 < temperatures) could also account for this high conductivity. The major
683 < thermal transfer barrier of Au/butanethiol/hexane interface is between
684 < the liquid phase and the capping agent, so extra degrees of freedom
685 < such as the C-H vibrations could enhance heat exchange between these
686 < two phases and result in a much higher conductivity.
718 > In addition to UA solvent/capping agent models, AA models are included
719 > in our simulations as well. Besides simulations of the same (UA or AA)
720 > model for solvent and capping agent, different models can be applied
721 > to different components. Furthermore, regardless of models chosen,
722 > either the solvent or the capping agent can be deuterated, similar to
723 > the previous section. Table \ref{modelTest} summarizes the results of
724 > these studies.
725  
726   \begin{table*}
727    \begin{minipage}{\linewidth}
728      \begin{center}
729        
730        \caption{Computed interfacial thermal conductivity ($G$ and
731 <        $G^\prime$) values for the Au/butanethiol/hexane interface
732 <        with all-atom model and different capping agent coverage at
733 <        200K using a range of energy fluxes.}
731 >        $G^\prime$) values for interfaces using various models for
732 >        solvent and capping agent (or without capping agent) at
733 >        $\langle T\rangle\sim$200K. (D stands for deuterated solvent
734 >        or capping agent molecules; ``Avg.'' denotes results that are
735 >        averages of simulations under different $J_z$'s. Error
736 >        estimates indicated in parenthesis.)}
737        
738 <      \begin{tabular}{cccc}
738 >      \begin{tabular}{llccc}
739          \hline\hline
740 <        Thiol & $J_z$ & $G$ & $G^\prime$ \\
741 <        coverage (\%) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
740 >        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
741 >        (or bare surface) & model & (GW/m$^2$) &
742 >        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
743          \hline
744 <        0.0   & 0.95 & 28.5 & 27.2 \\
745 <              & 1.88 & 30.3 & 28.9 \\
746 <        100.0 & 2.87 & 551  & 294  \\
747 <              & 3.81 & 494  & 193  \\
744 >        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
745 >              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
746 >              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
747 >              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
748 >              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
749 >        \hline
750 >        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
751 >              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
752 >              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
753 >              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
754 >              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
755 >        \hline
756 >        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
757 >              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
758 >              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
759 >        \hline
760 >        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
761 >              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
762 >              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
763 >              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
764          \hline\hline
765        \end{tabular}
766 <      \label{AuThiolHexaneAA}
766 >      \label{modelTest}
767      \end{center}
768    \end{minipage}
769   \end{table*}
770  
771 + To facilitate direct comparison, the same system with differnt models
772 + for different components uses the same length scale for their
773 + simulation cells. Without the presence of capping agent, using
774 + different models for hexane yields similar results for both $G$ and
775 + $G^\prime$, and these two definitions agree with eath other very
776 + well. This indicates very weak interaction between the metal and the
777 + solvent, and is a typical case for acoustic impedance mismatch between
778 + these two phases.
779  
780 < significant conductance enhancement compared to the gold/water
781 < interface without capping agent and agree with available experimental
782 < data. This indicates that the metal-metal potential, though not
783 < predicting an accurate bulk metal thermal conductivity, does not
784 < greatly interfere with the simulation of the thermal conductance
785 < behavior across a non-metal interface.
780 > As for Au(111) surfaces completely covered by butanethiols, the choice
781 > of models for capping agent and solvent could impact the measurement
782 > of $G$ and $G^\prime$ quite significantly. For Au-butanethiol/hexane
783 > interfaces, using AA model for both butanethiol and hexane yields
784 > substantially higher conductivity values than using UA model for at
785 > least one component of the solvent and capping agent, which exceeds
786 > the upper bond of experimental value range. This is probably due to
787 > the classically treated C-H vibrations in the AA model, which should
788 > not be appreciably populated at normal temperatures. In comparison,
789 > once either the hexanes or the butanethiols are deuterated, one can
790 > see a significantly lower $G$ and $G^\prime$. In either of these
791 > cases, the C-H(D) vibrational overlap between the solvent and the
792 > capping agent is removed. [MAY NEED FIGURE] Conclusively, the
793 > improperly treated C-H vibration in the AA model produced
794 > over-predicted results accordingly. Compared to the AA model, the UA
795 > model yields more reasonable results with higher computational
796 > efficiency.
797  
798 < % The results show that the two definitions used for $G$ yield
799 < % comparable values, though $G^\prime$ tends to be smaller.
798 > However, for Au-butanethiol/toluene interfaces, having the AA
799 > butanethiol deuterated did not yield a significant change in the
800 > measurement results. Compared to the C-H vibrational overlap between
801 > hexane and butanethiol, both of which have alkyl chains, that overlap
802 > between toluene and butanethiol is not so significant and thus does
803 > not have as much contribution to the ``Intramolecular Vibration
804 > Redistribution''[CITE HASE]. Conversely, extra degrees of freedom such
805 > as the C-H vibrations could yield higher heat exchange rate between
806 > these two phases and result in a much higher conductivity.
807  
808 + Although the QSC model for Au is known to predict an overly low value
809 + for bulk metal gold conductivity\cite{kuang:164101}, our computational
810 + results for $G$ and $G^\prime$ do not seem to be affected by this
811 + drawback of the model for metal. Instead, our results suggest that the
812 + modeling of interfacial thermal transport behavior relies mainly on
813 + the accuracy of the interaction descriptions between components
814 + occupying the interfaces.
815 +
816   \subsection{Mechanism of Interfacial Thermal Conductance Enhancement
817    by Capping Agent}
818 < [MAY INTRODUCE PROTOCOL IN METHODOLOGY/COMPUTATIONAL DETAIL]
818 > %OR\subsection{Vibrational spectrum study on conductance mechanism}
819  
820 + [MAY INTRODUCE PROTOCOL IN METHODOLOGY/COMPUTATIONAL DETAIL, EQN'S]
821  
729 %subsubsection{Vibrational spectrum study on conductance mechanism}
822   To investigate the mechanism of this interfacial thermal conductance,
823   the vibrational spectra of various gold systems were obtained and are
824   shown as in the upper panel of Fig. \ref{vibration}. To obtain these
825   spectra, one first runs a simulation in the NVE ensemble and collects
826   snapshots of configurations; these configurations are used to compute
827   the velocity auto-correlation functions, which is used to construct a
828 < power spectrum via a Fourier transform. The gold surfaces covered by
737 < butanethiol molecules exhibit an additional peak observed at a
738 < frequency of $\sim$170cm$^{-1}$, which is attributed to the vibration
739 < of the S-Au bond. This vibration enables efficient thermal transport
740 < from surface Au atoms to the capping agents. Simultaneously, as shown
741 < in the lower panel of Fig. \ref{vibration}, the large overlap of the
742 < vibration spectra of butanethiol and hexane in the all-atom model,
743 < including the C-H vibration, also suggests high thermal exchange
744 < efficiency. The combination of these two effects produces the drastic
745 < interfacial thermal conductance enhancement in the all-atom model.
828 > power spectrum via a Fourier transform.
829  
830 + [MAY RELATE TO HASE'S]
831 + The gold surfaces covered by
832 + butanethiol molecules, compared to bare gold surfaces, exhibit an
833 + additional peak observed at a frequency of $\sim$170cm$^{-1}$, which
834 + is attributed to the vibration of the S-Au bond. This vibration
835 + enables efficient thermal transport from surface Au atoms to the
836 + capping agents. Simultaneously, as shown in the lower panel of
837 + Fig. \ref{vibration}, the large overlap of the vibration spectra of
838 + butanethiol and hexane in the all-atom model, including the C-H
839 + vibration, also suggests high thermal exchange efficiency. The
840 + combination of these two effects produces the drastic interfacial
841 + thermal conductance enhancement in the all-atom model.
842 +
843 + [REDO. MAY NEED TO CONVERT TO JPEG]
844   \begin{figure}
845   \includegraphics[width=\linewidth]{vibration}
846   \caption{Vibrational spectra obtained for gold in different
# Line 751 | Line 848 | interfacial thermal conductance enhancement in the all
848    all-atom model (lower panel).}
849   \label{vibration}
850   \end{figure}
754 % MAY NEED TO CONVERT TO JPEG
851  
852 + [COMPARISON OF TWO G'S; AU SLAB WIDTHS; ETC]
853 + % The results show that the two definitions used for $G$ yield
854 + % comparable values, though $G^\prime$ tends to be smaller.
855 +
856   \section{Conclusions}
857 + The NIVS algorithm we developed has been applied to simulations of
858 + Au-butanethiol surfaces with organic solvents. This algorithm allows
859 + effective unphysical thermal flux transferred between the metal and
860 + the liquid phase. With the flux applied, we were able to measure the
861 + corresponding thermal gradient and to obtain interfacial thermal
862 + conductivities. Our simulations have seen significant conductance
863 + enhancement with the presence of capping agent, compared to the bare
864 + gold/liquid interfaces. The acoustic impedance mismatch between the
865 + metal and the liquid phase is effectively eliminated by proper capping
866 + agent. Furthermore, the coverage precentage of the capping agent plays
867 + an important role in the interfacial thermal transport process.
868  
869 + Our measurement results, particularly of the UA models, agree with
870 + available experimental data. This indicates that our force field
871 + parameters have a nice description of the interactions between the
872 + particles at the interfaces. AA models tend to overestimate the
873 + interfacial thermal conductance in that the classically treated C-H
874 + vibration would be overly sampled. Compared to the AA models, the UA
875 + models have higher computational efficiency with satisfactory
876 + accuracy, and thus are preferable in interfacial thermal transport
877 + modelings.
878  
879 < [NECESSITY TO STUDY THERMAL CONDUCTANCE IN NANOCRYSTAL STRUCTURE]\cite{vlugt:cpc2007154}
879 > Vlugt {\it et al.} has investigated the surface thiol structures for
880 > nanocrystal gold and pointed out that they differs from those of the
881 > Au(111) surface\cite{vlugt:cpc2007154}. This difference might lead to
882 > change of interfacial thermal transport behavior as well. To
883 > investigate this problem, an effective means to introduce thermal flux
884 > and measure the corresponding thermal gradient is desirable for
885 > simulating structures with spherical symmetry.
886  
887 +
888   \section{Acknowledgments}
889   Support for this project was provided by the National Science
890   Foundation under grant CHE-0848243. Computational time was provided by

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