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Revision 3748 by skuang, Mon Jul 25 03:28:20 2011 UTC

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28  
29   \begin{document}
30  
# Line 44 | Line 44 | The abstract version 2
44   \begin{doublespace}
45  
46   \begin{abstract}
47 < The abstract version 2
47 >
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  
67   \newpage
# Line 56 | Line 73 | The abstract version 2
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.
86  
87 < The intro.
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 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 the phenomena was investigated.
135 >
136 > [MAY ADD WHY STUDY AU-THIOL SURFACE; CITE SHAOYI JIANG]
137 >
138 > \section{Methodology}
139 > \subsection{Imposd-Flux Methods in MD Simulations}
140 > [CF. CAHILL]
141 > For systems with low interfacial conductivity one must have a method
142 > capable of generating relatively small fluxes, compared to those
143 > required for bulk conductivity. This requirement makes the calculation
144 > even more difficult for those slowly-converging equilibrium
145 > methods\cite{Viscardy:2007lq}.
146 > Forward methods impose gradient, but in interfacial conditions it is
147 > not clear what behavior to impose at the boundary...
148 > Imposed-flux reverse non-equilibrium
149 > methods\cite{MullerPlathe:1997xw} have the flux set {\it a priori} and
150 > the thermal response becomes easier to
151 > measure than the flux. Although M\"{u}ller-Plathe's original momentum
152 > swapping approach can be used for exchanging energy between particles
153 > of different identity, the kinetic energy transfer efficiency is
154 > affected by the mass difference between the particles, which limits
155 > its application on heterogeneous 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
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
162 > can be applied between regions of particles of arbitary identity, and
163 > the flux will not be restricted by difference in particle mass.
164 >
165 > The NIVS algorithm scales the velocity vectors in two separate regions
166 > of a simulation system with respective diagonal scaling matricies. To
167 > determine these scaling factors in the matricies, a set of equations
168 > including linear momentum conservation and kinetic energy conservation
169 > constraints and target energy flux satisfaction is solved. With the
170 > scaling operation applied to the system in a set frequency, bulk
171 > temperature gradients can be easily established, and these can be used
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 > Given a system with thermal gradients and the corresponding thermal
177 > flux, for interfaces with a relatively low interfacial conductance,
178 > the bulk regions on either side of an interface rapidly come to a
179 > state in which the two phases have relatively homogeneous (but
180 > distinct) temperatures. The interfacial thermal conductivity $G$ can
181 > therefore be approximated as:
182 > \begin{equation}
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.
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
198 > gradient max($\Delta T$), which occurs at the Gibbs deviding surface
199 > (Figure \ref{demoPic}):
200 > \begin{equation}
201 > G=\frac{J}{\Delta T}
202 > \label{discreteG}
203 > \end{equation}
204 >
205 > \begin{figure}
206 > \includegraphics[width=\linewidth]{method}
207 > \caption{Interfacial conductance can be calculated by applying an
208 >  (unphysical) kinetic energy flux between two slabs, one located
209 >  within the metal and another on the edge of the periodic box.  The
210 >  system responds by forming a thermal response or a gradient.  In
211 >  bulk liquids, this gradient typically has a single slope, but in
212 >  interfacial systems, there are distinct thermal conductivity
213 >  domains.  The interfacial conductance, $G$ is found by measuring the
214 >  temperature gap at the Gibbs dividing surface, or by using second
215 >  derivatives of the thermal profile.}
216 > \label{demoPic}
217 > \end{figure}
218 >
219 > The other approach is to assume a continuous temperature profile along
220 > the thermal gradient axis (e.g. $z$) and define $G$ at the point where
221 > the magnitude of thermal conductivity $\lambda$ change reach its
222 > maximum, given that $\lambda$ is well-defined throughout the space:
223 > \begin{equation}
224 > G^\prime = \Big|\frac{\partial\lambda}{\partial z}\Big|
225 >         = \Big|\frac{\partial}{\partial z}\left(-J_z\Big/
226 >           \left(\frac{\partial T}{\partial z}\right)\right)\Big|
227 >         = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
228 >         \Big/\left(\frac{\partial T}{\partial z}\right)^2
229 > \label{derivativeG}
230 > \end{equation}
231 >
232 > With the temperature profile obtained from simulations, one is able to
233 > approximate the first and second derivatives of $T$ with finite
234 > difference methods and thus calculate $G^\prime$.
235 >
236 > In what follows, both definitions have been used for calculation and
237 > are compared in the results.
238 >
239 > To compare the above definitions ($G$ and $G^\prime$), we have modeled
240 > a metal slab with its (111) surfaces perpendicular to the $z$-axis of
241 > our simulation cells. Both with and without capping agents on the
242 > surfaces, the metal slab is solvated with simple organic solvents, as
243 > illustrated in Figure \ref{gradT}.
244 >
245 > With the simulation cell described above, we are able to equilibrate
246 > the system and impose an unphysical thermal flux between the liquid
247 > and the metal phase using the NIVS algorithm. By periodically applying
248 > the unphysical flux, we are able to obtain a temperature profile and
249 > its spatial derivatives. These quantities enable the evaluation of the
250 > interfacial thermal conductance of a surface. Figure \ref{gradT} is an
251 > example of how an applied thermal flux can be used to obtain the 1st
252 > and 2nd derivatives of the temperature profile.
253 >
254 > \begin{figure}
255 > \includegraphics[width=\linewidth]{gradT}
256 > \caption{A sample of Au-butanethiol/hexane interfacial system and the
257 >  temperature profile after a kinetic energy flux is imposed to
258 >  it. The 1st and 2nd derivatives of the temperature profile can be
259 >  obtained with finite difference approximation (lower panel).}
260 > \label{gradT}
261 > \end{figure}
262 >
263 > \section{Computational Details}
264 > \subsection{Simulation Protocol}
265 > The NIVS algorithm has been implemented in our MD simulation code,
266 > OpenMD\cite{Meineke:2005gd,openmd}, and was used for our
267 > simulations. Different metal slab thickness (layer numbers of Au) was
268 > simulated. Metal slabs were first equilibrated under atmospheric
269 > pressure (1 atm) and a desired temperature (e.g. 200K). After
270 > equilibration, butanethiol capping agents were placed at three-fold
271 > hollow sites on the Au(111) surfaces. These sites could be either a
272 > {\it fcc} or {\it hcp} site. However, Hase {\it et al.} found that
273 > they are equivalent in a heat transfer process\cite{hase:2010}, so
274 > they are not distinguished in our study. The maximum butanethiol
275 > capacity on Au surface is $1/3$ of the total number of surface Au
276 > atoms, and the packing forms a $(\sqrt{3}\times\sqrt{3})R30^\circ$
277 > structure[CITE PORTER].
278 > A series of different coverages was derived by evenly eliminating
279 > butanethiols on the surfaces, and was investigated in order to study
280 > the relation between coverage and interfacial conductance.
281 >
282 > The capping agent molecules were allowed to migrate during the
283 > simulations. They distributed themselves uniformly and sampled a
284 > number of three-fold sites throughout out study. Therefore, the
285 > initial configuration would not noticeably affect the sampling of a
286 > variety of configurations of the same coverage, and the final
287 > conductance measurement would be an average effect of these
288 > configurations explored in the simulations. [MAY NEED SNAPSHOTS]
289  
290 + After the modified Au-butanethiol surface systems were equilibrated
291 + under canonical ensemble, organic solvent molecules were packed in the
292 + previously empty part of the simulation cells\cite{packmol}. Two
293 + solvents were investigated, one which has little vibrational overlap
294 + with the alkanethiol and a planar shape (toluene), and one which has
295 + similar vibrational frequencies and chain-like shape ({\it n}-hexane).
296 +
297 + The space filled by solvent molecules, i.e. the gap between
298 + periodically repeated Au-butanethiol surfaces should be carefully
299 + chosen. A very long length scale for the thermal gradient axis ($z$)
300 + may cause excessively hot or cold temperatures in the middle of the
301 + solvent region and lead to undesired phenomena such as solvent boiling
302 + or freezing when a thermal flux is applied. Conversely, too few
303 + solvent molecules would change the normal behavior of the liquid
304 + phase. Therefore, our $N_{solvent}$ values were chosen to ensure that
305 + these extreme cases did not happen to our simulations. And the
306 + corresponding spacing is usually $35[DOUBLE CHECK] \sim 75$\AA.
307 +
308 + The initial configurations generated are further equilibrated with the
309 + $x$ and $y$ dimensions fixed, only allowing length scale change in $z$
310 + dimension. This is to ensure that the equilibration of liquid phase
311 + does not affect the metal crystal structure in $x$ and $y$ dimensions.
312 + To investigate this effect, comparisons were made with simulations
313 + that allow changes of $L_x$ and $L_y$ during NPT equilibration, and
314 + the results are shown in later sections. After ensuring the liquid
315 + phase reaches equilibrium at atmospheric pressure (1 atm), further
316 + equilibration are followed under NVT and then NVE ensembles.
317 +
318 + After the systems reach equilibrium, NIVS is implemented to impose a
319 + periodic unphysical thermal flux between the metal and the liquid
320 + phase. Most of our simulations are under an average temperature of
321 + $\sim$200K. Therefore, this flux usually comes from the metal to the
322 + liquid so that the liquid has a higher temperature and would not
323 + freeze due to excessively low temperature. After this induced
324 + temperature gradient is stablized, the temperature profile of the
325 + simulation cell is recorded. To do this, the simulation cell is
326 + devided evenly into $N$ slabs along the $z$-axis and $N$ is maximized
327 + for highest possible spatial resolution but not too many to have some
328 + slabs empty most of the time. The average temperatures of each slab
329 + are recorded for 1$\sim$2 ns. When the slab width $d$ of each slab is
330 + the same, the derivatives of $T$ with respect to slab number $n$ can
331 + be directly used for $G^\prime$ calculations:
332 + \begin{equation}
333 + G^\prime = |J_z|\Big|\frac{\partial^2 T}{\partial z^2}\Big|
334 +         \Big/\left(\frac{\partial T}{\partial z}\right)^2
335 +         = |J_z|\Big|\frac{1}{d^2}\frac{\partial^2 T}{\partial n^2}\Big|
336 +         \Big/\left(\frac{1}{d}\frac{\partial T}{\partial n}\right)^2
337 +         = |J_z|\Big|\frac{\partial^2 T}{\partial n^2}\Big|
338 +         \Big/\left(\frac{\partial T}{\partial n}\right)^2
339 + \label{derivativeG2}
340 + \end{equation}
341 +
342 + All of the above simulation procedures use a time step of 1 fs. And
343 + each equilibration / stabilization step usually takes 100 ps, or
344 + longer, if necessary.
345 +
346 + \subsection{Force Field Parameters}
347 + Our simulations include various components. Figure \ref{demoMol}
348 + demonstrates the sites defined for both United-Atom and All-Atom
349 + models of the organic solvent and capping agent molecules in our
350 + simulations. Force field parameter descriptions are needed for
351 + interactions both between the same type of particles and between
352 + particles of different species.
353 +
354 + \begin{figure}
355 + \includegraphics[width=\linewidth]{structures}
356 + \caption{Structures of the capping agent and solvents utilized in
357 +  these simulations. The chemically-distinct sites (a-e) are expanded
358 +  in terms of constituent atoms for both United Atom (UA) and All Atom
359 +  (AA) force fields.  Most parameters are from
360 +  Refs. \protect\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} (UA) and
361 +  \protect\cite{OPLSAA} (AA).  Cross-interactions with the Au atoms are given
362 +  in Table \ref{MnM}.}
363 + \label{demoMol}
364 + \end{figure}
365 +
366 + The Au-Au interactions in metal lattice slab is described by the
367 + quantum Sutton-Chen (QSC) formulation\cite{PhysRevB.59.3527}. The QSC
368 + potentials include zero-point quantum corrections and are
369 + reparametrized for accurate surface energies compared to the
370 + Sutton-Chen potentials\cite{Chen90}.
371 +
372 + For both solvent molecules, straight chain {\it n}-hexane and aromatic
373 + toluene, United-Atom (UA) and All-Atom (AA) models are used
374 + respectively. The TraPPE-UA
375 + parameters\cite{TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} are used
376 + for our UA solvent molecules. In these models, sites are located at
377 + the carbon centers for alkyl groups. Bonding interactions, including
378 + bond stretches and bends and torsions, were used for intra-molecular
379 + sites not separated by more than 3 bonds. Otherwise, for non-bonded
380 + interactions, Lennard-Jones potentials are used. [CHECK CITATION]
381 +
382 + By eliminating explicit hydrogen atoms, these models are simple and
383 + computationally efficient, while maintains good accuracy. However, the
384 + TraPPE-UA for alkanes is known to predict a lower boiling point than
385 + experimental values. Considering that after an unphysical thermal flux
386 + is applied to a system, the temperature of ``hot'' area in the liquid
387 + phase would be significantly higher than the average of the system, to
388 + prevent over heating and boiling of the liquid phase, the average
389 + temperature in our simulations should be much lower than the liquid
390 + boiling point.
391 +
392 + For UA-toluene model, the non-bonded potentials between
393 + inter-molecular sites have a similar Lennard-Jones formulation. For
394 + intra-molecular interactions, considering the stiffness of the benzene
395 + ring, rigid body constraints are applied for further computational
396 + efficiency. All bonds in the benzene ring and between the ring and the
397 + methyl group remain rigid during the progress of simulations.
398 +
399 + Besides the TraPPE-UA models, AA models for both organic solvents are
400 + included in our studies as well. For hexane, the OPLS-AA\cite{OPLSAA}
401 + force field is used. Additional explicit hydrogen sites were
402 + included. Besides bonding and non-bonded site-site interactions,
403 + partial charges and the electrostatic interactions were added to each
404 + CT and HC site. For toluene, the United Force Field developed by
405 + Rapp\'{e} {\it et al.}\cite{doi:10.1021/ja00051a040} is
406 + adopted. Without the rigid body constraints, bonding interactions were
407 + included. For the aromatic ring, improper torsions (inversions) were
408 + added as an extra potential for maintaining the planar shape.
409 + [CHECK CITATION]
410 +
411 + The capping agent in our simulations, the butanethiol molecules can
412 + either use UA or AA model. The TraPPE-UA force fields includes
413 + parameters for thiol molecules\cite{TraPPE-UA.thiols} and are used for
414 + UA butanethiol model in our simulations. The OPLS-AA also provides
415 + parameters for alkyl thiols. However, alkyl thiols adsorbed on Au(111)
416 + surfaces do not have the hydrogen atom bonded to sulfur. To adapt this
417 + change and derive suitable parameters for butanethiol adsorbed on
418 + Au(111) surfaces, we adopt the S parameters from Luedtke and
419 + Landman\cite{landman:1998}[CHECK CITATION]
420 + and modify parameters for its neighbor C
421 + atom for charge balance in the molecule. Note that the model choice
422 + (UA or AA) of capping agent can be different from the
423 + solvent. Regardless of model choice, the force field parameters for
424 + interactions between capping agent and solvent can be derived using
425 + Lorentz-Berthelot Mixing Rule:
426 + \begin{eqnarray}
427 + \sigma_{ij} & = & \frac{1}{2} \left(\sigma_{ii} + \sigma_{jj}\right) \\
428 + \epsilon_{ij} & = & \sqrt{\epsilon_{ii}\epsilon_{jj}}
429 + \end{eqnarray}
430 +
431 + To describe the interactions between metal Au and non-metal capping
432 + agent and solvent particles, we refer to an adsorption study of alkyl
433 + thiols on gold surfaces by Vlugt {\it et
434 +  al.}\cite{vlugt:cpc2007154} They fitted an effective Lennard-Jones
435 + form of potential parameters for the interaction between Au and
436 + pseudo-atoms CH$_x$ and S based on a well-established and widely-used
437 + effective potential of Hautman and Klein\cite{hautman:4994} for the
438 + Au(111) surface. As our simulations require the gold lattice slab to
439 + be non-rigid so that it could accommodate kinetic energy for thermal
440 + transport study purpose, the pair-wise form of potentials is
441 + preferred.
442 +
443 + Besides, the potentials developed from {\it ab initio} calculations by
444 + Leng {\it et al.}\cite{doi:10.1021/jp034405s} are adopted for the
445 + interactions between Au and aromatic C/H atoms in toluene. A set of
446 + pseudo Lennard-Jones parameters were provided for Au in their force
447 + fields. By using the Mixing Rule, this can be used to derive pair-wise
448 + potentials for non-bonded interactions between Au and non-metal sites.
449 +
450 + However, the Lennard-Jones parameters between Au and other types of
451 + particles, such as All-Atom normal alkanes in our simulations are not
452 + yet well-established. For these interactions, we attempt to derive
453 + their parameters using the Mixing Rule. To do this, Au pseudo
454 + Lennard-Jones parameters for ``Metal-non-Metal'' (MnM) interactions
455 + were first extracted from the Au-CH$_x$ parameters by applying the
456 + Mixing Rule reversely. Table \ref{MnM} summarizes these ``MnM''
457 + parameters in our simulations.
458 +
459 + \begin{table*}
460 +  \begin{minipage}{\linewidth}
461 +    \begin{center}
462 +      \caption{Non-bonded interaction parameters (including cross
463 +        interactions with Au atoms) for both force fields used in this
464 +        work.}      
465 +      \begin{tabular}{lllllll}
466 +        \hline\hline
467 +        & Site  & $\sigma_{ii}$ & $\epsilon_{ii}$ & $q_i$ &
468 +        $\sigma_{Au-i}$ & $\epsilon_{Au-i}$ \\
469 +        & & (\AA) & (kcal/mol) & ($e$) & (\AA) & (kcal/mol) \\
470 +        \hline
471 +        United Atom (UA)
472 +        &CH3  & 3.75  & 0.1947  & -      & 3.54   & 0.2146  \\
473 +        &CH2  & 3.95  & 0.0914  & -      & 3.54   & 0.1749  \\
474 +        &CHar & 3.695 & 0.1003  & -      & 3.4625 & 0.1680  \\
475 +        &CRar & 3.88  & 0.04173 & -      & 3.555  & 0.1604  \\
476 +        \hline
477 +        All Atom (AA)
478 +        &CT3  & 3.50  & 0.066   & -0.18  & 3.365  & 0.1373  \\
479 +        &CT2  & 3.50  & 0.066   & -0.12  & 3.365  & 0.1373  \\
480 +        &CTT  & 3.50  & 0.066   & -0.065 & 3.365  & 0.1373  \\
481 +        &HC   & 2.50  & 0.030   &  0.06  & 2.865  & 0.09256 \\
482 +        &CA   & 3.55  & 0.070   & -0.115 & 3.173  & 0.0640  \\
483 +        &HA   & 2.42  & 0.030   &  0.115 & 2.746  & 0.0414  \\
484 +        \hline
485 +        Both UA and AA
486 +        & S   & 4.45  & 0.25    & -      & 2.40   & 8.465   \\
487 +        \hline\hline
488 +      \end{tabular}
489 +      \label{MnM}
490 +    \end{center}
491 +  \end{minipage}
492 + \end{table*}
493 +
494 + \subsection{Vibrational Spectrum}
495 + To investigate the mechanism of interfacial thermal conductance, the
496 + vibrational spectrum is utilized as a complementary tool. Vibrational
497 + spectra were taken for individual components in different
498 + simulations. To obtain these spectra, simulations were run after
499 + equilibration, in the NVE ensemble. Snapshots of configurations were
500 + collected at a frequency that is higher than that of the fastest
501 + vibrations occuring in the simulations. With these configurations, the
502 + velocity auto-correlation functions can be computed:
503 + \begin{equation}
504 + C_A (t) = \langle\vec{v}_A (t)\cdot\vec{v}_A (0)\rangle
505 + \label{vCorr}
506 + \end{equation}
507 +
508 + Followed by Fourier transforms, the power spectrum can be constructed:
509 + \begin{equation}
510 + \hat{f}(\omega) = \int_{-\infty}^{\infty} C_A (t) e^{-2\pi it\omega}\,dt
511 + \label{fourier}
512 + \end{equation}
513 +
514 + \section{Results and Discussions}
515 + In what follows, how the parameters and protocol of simulations would
516 + affect the measurement of $G$'s is first discussed. With a reliable
517 + protocol and set of parameters, the influence of capping agent
518 + coverage on thermal conductance is investigated. Besides, different
519 + force field models for both solvents and selected deuterated models
520 + were tested and compared. Finally, a summary of the role of capping
521 + agent in the interfacial thermal transport process is given.
522 +
523 + \subsection{How Simulation Parameters Affects $G$}
524 + We have varied our protocol or other parameters of the simulations in
525 + order to investigate how these factors would affect the measurement of
526 + $G$'s. It turned out that while some of these parameters would not
527 + affect the results substantially, some other changes to the
528 + simulations would have a significant impact on the measurement
529 + results.
530 +
531 + In some of our simulations, we allowed $L_x$ and $L_y$ to change
532 + during equilibrating the liquid phase. Due to the stiffness of the
533 + crystalline Au structure, $L_x$ and $L_y$ would not change noticeably
534 + after equilibration. Although $L_z$ could fluctuate $\sim$1\% after a
535 + system is fully equilibrated in the NPT ensemble, this fluctuation, as
536 + well as those of $L_x$ and $L_y$ (which is significantly smaller),
537 + would not be magnified on the calculated $G$'s, as shown in Table
538 + \ref{AuThiolHexaneUA}. This insensivity to $L_i$ fluctuations allows
539 + reliable measurement of $G$'s without the necessity of extremely
540 + cautious equilibration process.
541 +
542 + As stated in our computational details, the spacing filled with
543 + solvent molecules can be chosen within a range. This allows some
544 + change of solvent molecule numbers for the same Au-butanethiol
545 + surfaces. We did this study on our Au-butanethiol/hexane
546 + simulations. Nevertheless, the results obtained from systems of
547 + different $N_{hexane}$ did not indicate that the measurement of $G$ is
548 + susceptible to this parameter. For computational efficiency concern,
549 + smaller system size would be preferable, given that the liquid phase
550 + structure is not affected.
551 +
552 + Our NIVS algorithm allows change of unphysical thermal flux both in
553 + direction and in quantity. This feature extends our investigation of
554 + interfacial thermal conductance. However, the magnitude of this
555 + thermal flux is not arbitary if one aims to obtain a stable and
556 + reliable thermal gradient. A temperature profile would be
557 + substantially affected by noise when $|J_z|$ has a much too low
558 + magnitude; while an excessively large $|J_z|$ that overwhelms the
559 + conductance capacity of the interface would prevent a thermal gradient
560 + to reach a stablized steady state. NIVS has the advantage of allowing
561 + $J$ to vary in a wide range such that the optimal flux range for $G$
562 + measurement can generally be simulated by the algorithm. Within the
563 + optimal range, we were able to study how $G$ would change according to
564 + the thermal flux across the interface. For our simulations, we denote
565 + $J_z$ to be positive when the physical thermal flux is from the liquid
566 + to metal, and negative vice versa. The $G$'s measured under different
567 + $J_z$ is listed in Table \ref{AuThiolHexaneUA} and
568 + \ref{AuThiolToluene}. These results do not suggest that $G$ is
569 + dependent on $J_z$ within this flux range. The linear response of flux
570 + to thermal gradient simplifies our investigations in that we can rely
571 + on $G$ measurement with only a couple $J_z$'s and do not need to test
572 + a large series of fluxes.
573 +
574 + \begin{table*}
575 +  \begin{minipage}{\linewidth}
576 +    \begin{center}
577 +      \caption{Computed interfacial thermal conductivity ($G$ and
578 +        $G^\prime$) values for the 100\% covered Au-butanethiol/hexane
579 +        interfaces with UA model and different hexane molecule numbers
580 +        at different temperatures using a range of energy
581 +        fluxes. Error estimates indicated in parenthesis.}
582 +      
583 +      \begin{tabular}{ccccccc}
584 +        \hline\hline
585 +        $\langle T\rangle$ & $N_{hexane}$ & Fixed & $\rho_{hexane}$ &
586 +        $J_z$ & $G$ & $G^\prime$ \\
587 +        (K) & & $L_x$ \& $L_y$? & (g/cm$^3$) & (GW/m$^2$) &
588 +        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
589 +        \hline
590 +        200 & 266 & No  & 0.672 & -0.96 & 102(3)    & 80.0(0.8) \\
591 +            & 200 & Yes & 0.694 &  1.92 & 129(11)   & 87.3(0.3) \\
592 +            &     & Yes & 0.672 &  1.93 & 131(16)   & 78(13)    \\
593 +            &     & No  & 0.688 &  0.96 & 125(16)   & 90.2(15)  \\
594 +            &     &     &       &  1.91 & 139(10)   & 101(10)   \\
595 +            &     &     &       &  2.83 & 141(6)    & 89.9(9.8) \\
596 +            & 166 & Yes & 0.679 &  0.97 & 115(19)   & 69(18)    \\
597 +            &     &     &       &  1.94 & 125(9)    & 87.1(0.2) \\
598 +            &     & No  & 0.681 &  0.97 & 141(30)   & 78(22)    \\
599 +            &     &     &       &  1.92 & 138(4)    & 98.9(9.5) \\
600 +        \hline
601 +        250 & 200 & No  & 0.560 &  0.96 & 75(10)    & 61.8(7.3) \\
602 +            &     &     &       & -0.95 & 49.4(0.3) & 45.7(2.1) \\
603 +            & 166 & Yes & 0.570 &  0.98 & 79.0(3.5) & 62.9(3.0) \\
604 +            &     & No  & 0.569 &  0.97 & 80.3(0.6) & 67(11)    \\
605 +            &     &     &       &  1.44 & 76.2(5.0) & 64.8(3.8) \\
606 +            &     &     &       & -0.95 & 56.4(2.5) & 54.4(1.1) \\
607 +            &     &     &       & -1.85 & 47.8(1.1) & 53.5(1.5) \\
608 +        \hline\hline
609 +      \end{tabular}
610 +      \label{AuThiolHexaneUA}
611 +    \end{center}
612 +  \end{minipage}
613 + \end{table*}
614 +
615 + Furthermore, we also attempted to increase system average temperatures
616 + to above 200K. These simulations are first equilibrated in the NPT
617 + ensemble under normal pressure. As stated above, the TraPPE-UA model
618 + for hexane tends to predict a lower boiling point. In our simulations,
619 + hexane had diffculty to remain in liquid phase when NPT equilibration
620 + temperature is higher than 250K. Additionally, the equilibrated liquid
621 + hexane density under 250K becomes lower than experimental value. This
622 + expanded liquid phase leads to lower contact between hexane and
623 + butanethiol as well.[MAY NEED SLAB DENSITY FIGURE]
624 + And this reduced contact would
625 + probably be accountable for a lower interfacial thermal conductance,
626 + as shown in Table \ref{AuThiolHexaneUA}.
627 +
628 + A similar study for TraPPE-UA toluene agrees with the above result as
629 + well. Having a higher boiling point, toluene tends to remain liquid in
630 + our simulations even equilibrated under 300K in NPT
631 + ensembles. Furthermore, the expansion of the toluene liquid phase is
632 + not as significant as that of the hexane. This prevents severe
633 + decrease of liquid-capping agent contact and the results (Table
634 + \ref{AuThiolToluene}) show only a slightly decreased interface
635 + conductance. Therefore, solvent-capping agent contact should play an
636 + important role in the thermal transport process across the interface
637 + in that higher degree of contact could yield increased conductance.
638 +
639 + \begin{table*}
640 +  \begin{minipage}{\linewidth}
641 +    \begin{center}
642 +      \caption{Computed interfacial thermal conductivity ($G$ and
643 +        $G^\prime$) values for a 90\% coverage Au-butanethiol/toluene
644 +        interface at different temperatures using a range of energy
645 +        fluxes. Error estimates indicated in parenthesis.}
646 +      
647 +      \begin{tabular}{ccccc}
648 +        \hline\hline
649 +        $\langle T\rangle$ & $\rho_{toluene}$ & $J_z$ & $G$ & $G^\prime$ \\
650 +        (K) & (g/cm$^3$) & (GW/m$^2$) & \multicolumn{2}{c}{(MW/m$^2$/K)} \\
651 +        \hline
652 +        200 & 0.933 &  2.15 & 204(12) & 113(12) \\
653 +            &       & -1.86 & 180(3)  & 135(21) \\
654 +            &       & -3.93 & 176(5)  & 113(12) \\
655 +        \hline
656 +        300 & 0.855 & -1.91 & 143(5)  & 125(2)  \\
657 +            &       & -4.19 & 135(9)  & 113(12) \\
658 +        \hline\hline
659 +      \end{tabular}
660 +      \label{AuThiolToluene}
661 +    \end{center}
662 +  \end{minipage}
663 + \end{table*}
664 +
665 + Besides lower interfacial thermal conductance, surfaces in relatively
666 + high temperatures are susceptible to reconstructions, when
667 + butanethiols have a full coverage on the Au(111) surface. These
668 + reconstructions include surface Au atoms migrated outward to the S
669 + atom layer, and butanethiol molecules embedded into the original
670 + surface Au layer. The driving force for this behavior is the strong
671 + Au-S interactions in our simulations. And these reconstructions lead
672 + to higher ratio of Au-S attraction and thus is energetically
673 + favorable. Furthermore, this phenomenon agrees with experimental
674 + results\cite{doi:10.1021/j100035a033,doi:10.1021/la026493y}. Vlugt
675 + {\it et al.} had kept their Au(111) slab rigid so that their
676 + simulations can reach 300K without surface reconstructions. Without
677 + this practice, simulating 100\% thiol covered interfaces under higher
678 + temperatures could hardly avoid surface reconstructions. However, our
679 + measurement is based on assuming homogeneity on $x$ and $y$ dimensions
680 + so that measurement of $T$ at particular $z$ would be an effective
681 + average of the particles of the same type. Since surface
682 + reconstructions could eliminate the original $x$ and $y$ dimensional
683 + homogeneity, measurement of $G$ is more difficult to conduct under
684 + higher temperatures. Therefore, most of our measurements are
685 + undertaken at $\langle T\rangle\sim$200K.
686 +
687 + However, when the surface is not completely covered by butanethiols,
688 + the simulated system is more resistent to the reconstruction
689 + above. Our Au-butanethiol/toluene system had the Au(111) surfaces 90\%
690 + covered by butanethiols, but did not see this above phenomena even at
691 + $\langle T\rangle\sim$300K. The empty three-fold sites not occupied by
692 + capping agents could help prevent surface reconstruction in that they
693 + provide other means of capping agent relaxation. It is observed that
694 + butanethiols can migrate to their neighbor empty sites during a
695 + simulation. Therefore, we were able to obtain $G$'s for these
696 + interfaces even at a relatively high temperature without being
697 + affected by surface reconstructions.
698 +
699 + \subsection{Influence of Capping Agent Coverage on $G$}
700 + To investigate the influence of butanethiol coverage on interfacial
701 + thermal conductance, a series of different coverage Au-butanethiol
702 + surfaces is prepared and solvated with various organic
703 + molecules. These systems are then equilibrated and their interfacial
704 + thermal conductivity are measured with our NIVS algorithm. Figure
705 + \ref{coverage} demonstrates the trend of conductance change with
706 + respect to different coverages of butanethiol. To study the isotope
707 + effect in interfacial thermal conductance, deuterated UA-hexane is
708 + included as well.
709 +
710 + \begin{figure}
711 + \includegraphics[width=\linewidth]{coverage}
712 + \caption{Comparison of interfacial thermal conductivity ($G$) values
713 +  for the Au-butanethiol/solvent interface with various UA models and
714 +  different capping agent coverages at $\langle T\rangle\sim$200K
715 +  using certain energy flux respectively.}
716 + \label{coverage}
717 + \end{figure}
718 +
719 + It turned out that with partial covered butanethiol on the Au(111)
720 + surface, the derivative definition for $G^\prime$
721 + (Eq. \ref{derivativeG}) was difficult to apply, due to the difficulty
722 + in locating the maximum of change of $\lambda$. Instead, the discrete
723 + definition (Eq. \ref{discreteG}) is easier to apply, as the Gibbs
724 + deviding surface can still be well-defined. Therefore, $G$ (not
725 + $G^\prime$) was used for this section.
726 +
727 + From Figure \ref{coverage}, one can see the significance of the
728 + presence of capping agents. Even when a fraction of the Au(111)
729 + surface sites are covered with butanethiols, the conductivity would
730 + see an enhancement by at least a factor of 3. This indicates the
731 + important role cappping agent is playing for thermal transport
732 + phenomena on metal / organic solvent surfaces.
733 +
734 + Interestingly, as one could observe from our results, the maximum
735 + conductance enhancement (largest $G$) happens while the surfaces are
736 + about 75\% covered with butanethiols. This again indicates that
737 + solvent-capping agent contact has an important role of the thermal
738 + transport process. Slightly lower butanethiol coverage allows small
739 + gaps between butanethiols to form. And these gaps could be filled with
740 + solvent molecules, which acts like ``heat conductors'' on the
741 + surface. The higher degree of interaction between these solvent
742 + molecules and capping agents increases the enhancement effect and thus
743 + produces a higher $G$ than densely packed butanethiol arrays. However,
744 + once this maximum conductance enhancement is reached, $G$ decreases
745 + when butanethiol coverage continues to decrease. Each capping agent
746 + molecule reaches its maximum capacity for thermal
747 + conductance. Therefore, even higher solvent-capping agent contact
748 + would not offset this effect. Eventually, when butanethiol coverage
749 + continues to decrease, solvent-capping agent contact actually
750 + decreases with the disappearing of butanethiol molecules. In this
751 + case, $G$ decrease could not be offset but instead accelerated. [NEED
752 + SNAPSHOT SHOWING THE PHENOMENA / SLAB DENSITY ANALYSIS]
753 +
754 + A comparison of the results obtained from differenet organic solvents
755 + can also provide useful information of the interfacial thermal
756 + transport process. The deuterated hexane (UA) results do not appear to
757 + be much different from those of normal hexane (UA), given that
758 + butanethiol (UA) is non-deuterated for both solvents. These UA model
759 + studies, even though eliminating C-H vibration samplings, still have
760 + C-C vibrational frequencies different from each other. However, these
761 + differences in the infrared range do not seem to produce an observable
762 + difference for the results of $G$ (Figure \ref{uahxnua}).
763 +
764 + \begin{figure}
765 + \includegraphics[width=\linewidth]{uahxnua}
766 + \caption{Vibrational spectra obtained for normal (upper) and
767 +  deuterated (lower) hexane in Au-butanethiol/hexane
768 +  systems. Butanethiol spectra are shown as reference. Both hexane and
769 +  butanethiol were using United-Atom models.}
770 + \label{uahxnua}
771 + \end{figure}
772 +
773 + Furthermore, results for rigid body toluene solvent, as well as other
774 + UA-hexane solvents, are reasonable within the general experimental
775 + ranges[CITATIONS]. This suggests that explicit hydrogen might not be a
776 + required factor for modeling thermal transport phenomena of systems
777 + such as Au-thiol/organic solvent.
778 +
779 + However, results for Au-butanethiol/toluene do not show an identical
780 + trend with those for Au-butanethiol/hexane in that $G$ remains at
781 + approximately the same magnitue when butanethiol coverage differs from
782 + 25\% to 75\%. This might be rooted in the molecule shape difference
783 + for planar toluene and chain-like {\it n}-hexane. Due to this
784 + difference, toluene molecules have more difficulty in occupying
785 + relatively small gaps among capping agents when their coverage is not
786 + too low. Therefore, the solvent-capping agent contact may keep
787 + increasing until the capping agent coverage reaches a relatively low
788 + level. This becomes an offset for decreasing butanethiol molecules on
789 + its effect to the process of interfacial thermal transport. Thus, one
790 + can see a plateau of $G$ vs. butanethiol coverage in our results.
791 +
792 + \subsection{Influence of Chosen Molecule Model on $G$}
793 + In addition to UA solvent/capping agent models, AA models are included
794 + in our simulations as well. Besides simulations of the same (UA or AA)
795 + model for solvent and capping agent, different models can be applied
796 + to different components. Furthermore, regardless of models chosen,
797 + either the solvent or the capping agent can be deuterated, similar to
798 + the previous section. Table \ref{modelTest} summarizes the results of
799 + these studies.
800 +
801 + \begin{table*}
802 +  \begin{minipage}{\linewidth}
803 +    \begin{center}
804 +      
805 +      \caption{Computed interfacial thermal conductivity ($G$ and
806 +        $G^\prime$) values for interfaces using various models for
807 +        solvent and capping agent (or without capping agent) at
808 +        $\langle T\rangle\sim$200K. (D stands for deuterated solvent
809 +        or capping agent molecules; ``Avg.'' denotes results that are
810 +        averages of simulations under different $J_z$'s. Error
811 +        estimates indicated in parenthesis.)}
812 +      
813 +      \begin{tabular}{llccc}
814 +        \hline\hline
815 +        Butanethiol model & Solvent & $J_z$ & $G$ & $G^\prime$ \\
816 +        (or bare surface) & model & (GW/m$^2$) &
817 +        \multicolumn{2}{c}{(MW/m$^2$/K)} \\
818 +        \hline
819 +        UA    & UA hexane    & Avg. & 131(9)    & 87(10)    \\
820 +              & UA hexane(D) & 1.95 & 153(5)    & 136(13)   \\
821 +              & AA hexane    & Avg. & 131(6)    & 122(10)   \\
822 +              & UA toluene   & 1.96 & 187(16)   & 151(11)   \\
823 +              & AA toluene   & 1.89 & 200(36)   & 149(53)   \\
824 +        \hline
825 +        AA    & UA hexane    & 1.94 & 116(9)    & 129(8)    \\
826 +              & AA hexane    & Avg. & 442(14)   & 356(31)   \\
827 +              & AA hexane(D) & 1.93 & 222(12)   & 234(54)   \\
828 +              & UA toluene   & 1.98 & 125(25)   & 97(60)    \\
829 +              & AA toluene   & 3.79 & 487(56)   & 290(42)   \\
830 +        \hline
831 +        AA(D) & UA hexane    & 1.94 & 158(25)   & 172(4)    \\
832 +              & AA hexane    & 1.92 & 243(29)   & 191(11)   \\
833 +              & AA toluene   & 1.93 & 364(36)   & 322(67)   \\
834 +        \hline
835 +        bare  & UA hexane    & Avg. & 46.5(3.2) & 49.4(4.5) \\
836 +              & UA hexane(D) & 0.98 & 43.9(4.6) & 43.0(2.0) \\
837 +              & AA hexane    & 0.96 & 31.0(1.4) & 29.4(1.3) \\
838 +              & UA toluene   & 1.99 & 70.1(1.3) & 65.8(0.5) \\
839 +        \hline\hline
840 +      \end{tabular}
841 +      \label{modelTest}
842 +    \end{center}
843 +  \end{minipage}
844 + \end{table*}
845 +
846 + To facilitate direct comparison, the same system with differnt models
847 + for different components uses the same length scale for their
848 + simulation cells. Without the presence of capping agent, using
849 + different models for hexane yields similar results for both $G$ and
850 + $G^\prime$, and these two definitions agree with eath other very
851 + well. This indicates very weak interaction between the metal and the
852 + solvent, and is a typical case for acoustic impedance mismatch between
853 + these two phases.
854 +
855 + As for Au(111) surfaces completely covered by butanethiols, the choice
856 + of models for capping agent and solvent could impact the measurement
857 + of $G$ and $G^\prime$ quite significantly. For Au-butanethiol/hexane
858 + interfaces, using AA model for both butanethiol and hexane yields
859 + substantially higher conductivity values than using UA model for at
860 + least one component of the solvent and capping agent, which exceeds
861 + the general range of experimental measurement results. This is
862 + probably due to the classically treated C-H vibrations in the AA
863 + model, which should not be appreciably populated at normal
864 + temperatures. In comparison, once either the hexanes or the
865 + butanethiols are deuterated, one can see a significantly lower $G$ and
866 + $G^\prime$. In either of these cases, the C-H(D) vibrational overlap
867 + between the solvent and the capping agent is removed (Figure
868 + \ref{aahxntln}). Conclusively, the improperly treated C-H vibration in
869 + the AA model produced over-predicted results accordingly. Compared to
870 + the AA model, the UA model yields more reasonable results with higher
871 + computational efficiency.
872 +
873 + \begin{figure}
874 + \includegraphics[width=\linewidth]{aahxntln}
875 + \caption{Spectra obtained for All-Atom model Au-butanethil/solvent
876 +  systems. When butanethiol is deuterated (lower left), its
877 +  vibrational overlap with hexane would decrease significantly,
878 +  compared with normal butanethiol (upper left). However, this
879 +  dramatic change does not apply to toluene as much (right).}
880 + \label{aahxntln}
881 + \end{figure}
882 +
883 + However, for Au-butanethiol/toluene interfaces, having the AA
884 + butanethiol deuterated did not yield a significant change in the
885 + measurement results. Compared to the C-H vibrational overlap between
886 + hexane and butanethiol, both of which have alkyl chains, that overlap
887 + between toluene and butanethiol is not so significant and thus does
888 + not have as much contribution to the ``Intramolecular Vibration
889 + Redistribution''[CITE HASE]. Conversely, extra degrees of freedom such
890 + as the C-H vibrations could yield higher heat exchange rate between
891 + these two phases and result in a much higher conductivity.
892 +
893 + Although the QSC model for Au is known to predict an overly low value
894 + for bulk metal gold conductivity\cite{kuang:164101}, our computational
895 + results for $G$ and $G^\prime$ do not seem to be affected by this
896 + drawback of the model for metal. Instead, our results suggest that the
897 + modeling of interfacial thermal transport behavior relies mainly on
898 + the accuracy of the interaction descriptions between components
899 + occupying the interfaces.
900 +
901 + \subsection{Role of Capping Agent in Interfacial Thermal Conductance}
902 + The vibrational spectra for gold slabs in different environments are
903 + shown as in Figure \ref{specAu}. Regardless of the presence of
904 + solvent, the gold surfaces covered by butanethiol molecules, compared
905 + to bare gold surfaces, exhibit an additional peak observed at the
906 + frequency of $\sim$170cm$^{-1}$, which is attributed to the S-Au
907 + bonding vibration. This vibration enables efficient thermal transport
908 + from surface Au layer to the capping agents. Therefore, in our
909 + simulations, the Au/S interfaces do not appear major heat barriers
910 + compared to the butanethiol / solvent interfaces.
911 +
912 + Simultaneously, the vibrational overlap between butanethiol and
913 + organic solvents suggests higher thermal exchange efficiency between
914 + these two components. Even exessively high heat transport was observed
915 + when All-Atom models were used and C-H vibrations were treated
916 + classically. Compared to metal and organic liquid phase, the heat
917 + transfer efficiency between butanethiol and organic solvents is closer
918 + to that within bulk liquid phase.
919 +
920 + As a combinational effects of the above two, butanethiol acts as a
921 + channel to expedite thermal transport process. The acoustic impedance
922 + mismatch between the metal and the liquid phase can be effectively
923 + reduced with the presence of suitable capping agents.
924 +
925 + \begin{figure}
926 + \includegraphics[width=\linewidth]{vibration}
927 + \caption{Vibrational spectra obtained for gold in different
928 +  environments.}
929 + \label{specAu}
930 + \end{figure}
931 +
932 + [MAY ADD COMPARISON OF AU SLAB WIDTHS]
933 +
934 + \section{Conclusions}
935 + The NIVS algorithm we developed has been applied to simulations of
936 + Au-butanethiol surfaces with organic solvents. This algorithm allows
937 + effective unphysical thermal flux transferred between the metal and
938 + the liquid phase. With the flux applied, we were able to measure the
939 + corresponding thermal gradient and to obtain interfacial thermal
940 + conductivities. Under steady states, single trajectory simulation
941 + would be enough for accurate measurement. This would be advantageous
942 + compared to transient state simulations, which need multiple
943 + trajectories to produce reliable average results.
944 +
945 + Our simulations have seen significant conductance enhancement with the
946 + presence of capping agent, compared to the bare gold / liquid
947 + interfaces. The acoustic impedance mismatch between the metal and the
948 + liquid phase is effectively eliminated by proper capping
949 + agent. Furthermore, the coverage precentage of the capping agent plays
950 + an important role in the interfacial thermal transport
951 + process. Moderately lower coverages allow higher contact between
952 + capping agent and solvent, and thus could further enhance the heat
953 + transfer process.
954 +
955 + Our measurement results, particularly of the UA models, agree with
956 + available experimental data. This indicates that our force field
957 + parameters have a nice description of the interactions between the
958 + particles at the interfaces. AA models tend to overestimate the
959 + interfacial thermal conductance in that the classically treated C-H
960 + vibration would be overly sampled. Compared to the AA models, the UA
961 + models have higher computational efficiency with satisfactory
962 + accuracy, and thus are preferable in interfacial thermal transport
963 + modelings. Of the two definitions for $G$, the discrete form
964 + (Eq. \ref{discreteG}) was easier to use and gives out relatively
965 + consistent results, while the derivative form (Eq. \ref{derivativeG})
966 + is not as versatile. Although $G^\prime$ gives out comparable results
967 + and follows similar trend with $G$ when measuring close to fully
968 + covered or bare surfaces, the spatial resolution of $T$ profile is
969 + limited for accurate computation of derivatives data.
970 +
971 + Vlugt {\it et al.} has investigated the surface thiol structures for
972 + nanocrystal gold and pointed out that they differs from those of the
973 + Au(111) surface\cite{vlugt:cpc2007154}. This difference might lead to
974 + change of interfacial thermal transport behavior as well. To
975 + investigate this problem, an effective means to introduce thermal flux
976 + and measure the corresponding thermal gradient is desirable for
977 + simulating structures with spherical symmetry.
978 +
979   \section{Acknowledgments}
980   Support for this project was provided by the National Science
981   Foundation under grant CHE-0848243. Computational time was provided by
982   the Center for Research Computing (CRC) at the University of Notre
983 < Dame.  \newpage
983 > Dame. \newpage
984  
985   \bibliography{interfacial}
986  

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