trunk/COonPt/firstTry.tex

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%%
%Introduction
%       Experimental observations
%       Previous work on Pt, CO, etc.
%
%Simulation Methodology
%       FF (fits and parameters)
%       MD (setup, equilibration, collection)
%
%       Analysis of trajectories!!!
%Discussion
%       CO preferences for specific locales
%       CO-CO interactions
%       Differences between Au & Pt
%       Causes of 2_layer reordering in Pt
%Summary
%%

%Title
\title{Molecular Dynamics simulations of the surface reconstructions
  of Pt(557) and Au(557) under exposure to CO}

\author{Joseph R. Michalka, Patrick W. McIntyre and J. Daniel
Gezelter\footnote{Corresponding author. \ Electronic mail: gezelter@nd.edu} \\
Department of Chemistry and Biochemistry,\\
University of Notre Dame\\
Notre Dame, Indiana 46556}

%Date
\date{Mar 4, 2013}

%authors

% make the title
\maketitle

\begin{doublespace}

\begin{abstract}
We examine potential surface reconstructions of Pt and Au(557)
under various CO coverages using molecular dynamics in order
to explore possible mechanisms for any observed reconstructions and their dynamics.
The metal-CO interactions were parameterized as part of this 
work so that an efficient large-scale treatment of this system could be
undertaken. The relative binding strengths of the metal-CO 
interactions were found to play a large role with regards to 
step-edge stability and adatom diffusion. A small correlation 
between coverage and the size of the diffusion constant was 
also determined. An in-depth examination of the energetics of CO
adsorbed to the surface provides results that appear sufficient to explain the 
reconstructions observed on the Pt systems and the corresponding lack  
on the Au systems.
\end{abstract}

\newpage


\section{Introduction}
% Importance: catalytically active metals are important
%       Sub: Knowledge of how their surface structure affects their ability to catalytically facilitate certain reactions is growing, but is more reactionary than predictive
%       Sub: Designing catalysis is the future, and will play an important role in numerous processes (ones that are currently seen to be impractical, or at least inefficient)
% Theory can explore temperatures and pressures which are difficult to work with in experiments
%       Sub: Also, easier to observe what is going on and provide reasons and explanations
%

Industrial catalysts usually consist of small particles that exhibit a
high concentration of steps, kink sites, and vacancies at the edges of
the facets.  These sites are thought to be the locations of catalytic
activity.\cite{ISI:000083038000001,ISI:000083924800001} There is now
significant evidence that solid surfaces are often structurally,
compositionally, and chemically modified by reactants under operating
conditions.\cite{Tao2008,Tao:2010,Tao2011} The coupling between
surface oxidation states and catalytic activity for CO oxidation on
Pt, for instance, is widely documented.\cite{Ertl08,Hendriksen:2002}
Despite the well-documented role of these effects on reactivity, the
ability to capture or predict them in atomistic models is somewhat
limited.  While these effects are perhaps unsurprising on the highly
disperse, multi-faceted nanoscale particles that characterize
industrial catalysts, they are manifest even on ordered, well-defined
surfaces. The Pt(557) surface, for example, exhibits substantial and
reversible restructuring under exposure to moderate pressures of
carbon monoxide.\cite{Tao:2010}

This work is an attempt to understand the mechanism and timescale for
surface restructuring using molecular simulations.  Since the dynamics
of the process are of particular interest, we employ classical force
fields that represent a compromise between chemical accuracy and the
computational efficiency necessary to simulate the process of interest.
Restructuring can occur as a result of specific interactions of the
catalyst with adsorbates. In this work, two metal systems exposed 
to carbon monoxide were examined. The Pt(557) surface has already been shown
to reconstruct under certain conditions. The Au(557) surface, because
of a weaker interaction with CO, is less likely to undergo this kind
of reconstruction.  MORE HERE ON PT AND AU PREVIOUS WORK.

%Platinum molecular dynamics
%gold molecular dynamics

\section{Simulation Methods}
The challenge in modeling any solid/gas interface problem is the
development of a sufficiently general yet computationally tractable
model of the chemical interactions between the surface atoms and
adsorbates.  Since the interfaces involved are quite large (10$^3$ -
10$^6$ atoms) and respond slowly to perturbations, {\it ab initio}
molecular dynamics
(AIMD),\cite{KRESSE:1993ve,KRESSE:1993qf,KRESSE:1994ul} Car-Parrinello
methods,\cite{CAR:1985bh,Izvekov:2000fv,Guidelli:2000fy} and quantum
mechanical potential energy surfaces remain out of reach.
Additionally, the ``bonds'' between metal atoms at a surface are
typically not well represented in terms of classical pairwise
interactions in the same way that bonds in a molecular material are,
nor are they captured by simple non-directional interactions like the
Coulomb potential.  For this work, we have used classical molecular
dynamics with potential energy surfaces that are specifically tuned
for transition metals.  In particular, we used the EAM potential for
Au-Au and Pt-Pt interactions\cite{EAM}, while modeling the CO using a rigid
three-site model developed by Straub and Karplus for studying
photodissociation of CO from myoglobin.\cite{Straub} The Au-CO and
Pt-CO cross interactions were parameterized as part of this work.
  
\subsection{Metal-metal interactions}
Many of the potentials used for modeling transition metals are based
on a non-pairwise additive functional of the local electron
density. The embedded atom method (EAM) is perhaps the best known of
these
methods,\cite{Daw84,Foiles86,Johnson89,Daw89,Plimpton93,Voter95a,Lu97,Alemany98}
but other models like the Finnis-Sinclair\cite{Finnis84,Chen90} and
the quantum-corrected Sutton-Chen method\cite{QSC,Qi99} have simpler
parameter sets. The glue model of Ercolessi et al. is among the
fastest of these density functional approaches.\cite{Ercolessi88} In
all of these models, atoms are conceptualized as a positively charged
core with a radially-decaying valence electron distribution. To
calculate the energy for embedding the core at a particular location,
the electron density due to the valence electrons at all of the other
atomic sites is computed at atom $i$'s location,
\begin{equation*}
\bar{\rho}_i = \sum_{j\neq i} \rho_j(r_{ij})
\end{equation*}
Here, $\rho_j(r_{ij})$ is the function that describes the distance
dependence of the valence electron distribution of atom $j$. The
contribution to the potential that comes from placing atom $i$ at that
location is then
\begin{equation*}
V_i =  F[ \bar{\rho}_i ]  + \sum_{j \neq i} \phi_{ij}(r_{ij})
\end{equation*}
where $F[ \bar{\rho}_i ]$ is an energy embedding functional, and
$\phi_{ij}(r_{ij})$ is a pairwise term that is meant to represent the
repulsive overlap of the two positively charged cores.  

% The {\it modified} embedded atom method (MEAM) adds angular terms to
% the electron density functions and an angular screening factor to the
% pairwise interaction between two
% atoms.\cite{BASKES:1994fk,Lee:2000vn,Thijsse:2002ly,Timonova:2011ve}
% MEAM has become widely used to simulate systems in which angular
% interactions are important (e.g. silicon,\cite{Timonova:2011ve} bcc
% metals,\cite{Lee:2001qf} and also interfaces.\cite{Beurden:2002ys})
% MEAM presents significant additional computational costs, however.

The EAM, Finnis-Sinclair, and the Quantum Sutton-Chen (QSC) potentials
have all been widely used by the materials simulation community for
simulations of bulk and nanoparticle
properties,\cite{Chui:2003fk,Wang:2005qy,Medasani:2007uq}
melting,\cite{Belonoshko00,sankaranarayanan:155441,Sankaranarayanan:2005lr}
fracture,\cite{Shastry:1996qg,Shastry:1998dx} crack
propagation,\cite{BECQUART:1993rg} and alloying
dynamics.\cite{Shibata:2002hh} All of these potentials have their
strengths and weaknesses.  One of the strengths common to all of the
methods is the relatively large library of metals for which these
potentials have been
parameterized.\cite{Foiles86,PhysRevB.37.3924,Rifkin1992,mishin99:_inter,mishin01:cu,mishin02:b2nial,zope03:tial_ap,mishin05:phase_fe_ni}  

\subsection{Carbon Monoxide model}
Previous explanations for the surface rearrangements center on
the large linear quadrupole moment of carbon monoxide.  
We used a model first proposed by Karplus and Straub to study
the photodissociation of CO from myoglobin because it reproduces 
the quadrupole moment well.\cite{Straub} The Straub and
Karplus model, treats CO as a rigid three site molecule which places a massless M
site at the center of mass position along the CO bond.  The geometry used along
with the interaction parameters are reproduced in Table~\ref{tab:CO}. The effective 
dipole moment, calculated from the assigned charges, is still
small (0.35 D) while the linear quadrupole (-2.40 D~\AA) is close
to the experimental (-2.63 D~\AA)\cite{QuadrupoleCO} and quantum 
mechanical predictions (-2.46 D~\AA)\cite{QuadrupoleCOCalc}.
%CO Table
\begin{table}[H]
  \caption{Positions, Lennard-Jones parameters ($\sigma$ and
    $\epsilon$), and charges for the CO-CO 
    interactions borrowed from Ref.\bibpunct{}{}{,}{n}{}{,} \protect\cite{Straub}. Distances are in \AA, energies are 
    in kcal/mol, and charges are in atomic units.}
\centering
\begin{tabular}{| c | c | ccc |}
\hline
&  {\it z} & $\sigma$ & $\epsilon$ & q\\
\hline
\textbf{C} & -0.6457 &  0.0262  & 3.83   &   -0.75 \\
\textbf{O} &  0.4843 &   0.1591 &   3.12 &   -0.85 \\
\textbf{M} & 0.0 & -  &  -  &    1.6 \\
\hline
\end{tabular}
\label{tab:CO}
\end{table}

\subsection{Cross-Interactions between the metals and carbon monoxide}

Since the adsorption of CO onto a Pt surface has been the focus
of much experimental \cite{Yeo, Hopster:1978, Ertl:1977, Kelemen:1979}
and theoretical work
\cite{Beurden:2002ys,Pons:1986,Deshlahra:2009,Feibelman:2001,Mason:2004}
there is a significant amount of data on adsorption energies for CO on
clean metal surfaces. Parameters reported by Korzeniewski {\it et
  al.}\cite{Pons:1986} were a starting point for our fits, which were
modified to ensure that the Pt-CO interaction favored the atop binding
position on Pt(111). These parameters are reproduced in Table~\ref{tab:co_parameters}
This resulted in binding energies that are slightly higher
than the experimentally-reported values as shown in Table~\ref{tab:co_energies}. Following Korzeniewski
et al.,\cite{Pons:1986} the Pt-C interaction was fit to a deep
Lennard-Jones interaction to mimic strong, but short-ranged partial
binding between the Pt $d$ orbitals and the $\pi^*$ orbital on CO. The
Pt-O interaction was parameterized to a Morse potential at a larger
minimum distance, ($r_o$).  This was chosen so that the C would be preferred
over O as the binder to the surface. In most cases, this parameterization contributes a weak
repulsion which favors the atop site.  The resulting potential-energy
surface suitably recovers the calculated Pt-C separation length
(1.6~\AA)\cite{Beurden:2002ys} and affinity for the atop binding
position.\cite{Deshlahra:2012, Hopster:1978}

%where did you actually get the functionals for citation?
%scf calculations, so initial relaxation was of the four layers, but two layers weren't kept fixed, I don't think
%same cutoff for slab and slab + CO ? seems low, although feibelmen had values around there...
The Au-C and Au-O cross-interactions were also fit using Lennard-Jones and
Morse potentials, respectively, to reproduce Au-CO binding energies.
The limited experimental data for CO adsorption on Au lead us to refine our fits against DFT.
Adsorption energies were obtained from gas-surface DFT calculations with a
periodic supercell plane-wave basis approach, as implemented in the
{\sc Quantum ESPRESSO} package.\cite{QE-2009} Electron cores are
described with the projector augmented-wave (PAW)
method,\cite{PhysRevB.50.17953,PhysRevB.59.1758} with plane waves
included to an energy cutoff of 20 Ry. Electronic energies are
computed with the PBE implementation of the generalized gradient
approximation (GGA) for gold, carbon, and oxygen that was constructed
by Rappe, Rabe, Kaxiras, and Joannopoulos.\cite{Perdew_GGA,RRKJ_PP}
In testing the Au-CO interaction, Au(111) supercells were constructed of four layers of 4
Au x 2 Au surface planes and separated from vertical images by six
layers of vacuum space. The surface atoms were all allowed to relax 
before CO was added to the system. Electronic relaxations were 
performed until the energy difference between subsequent steps 
was less than $10^{-8}$ Ry.   Nonspin-polarized supercell calculations 
were performed with a 4~x~4~x~4 Monkhorst-Pack {\bf k}-point sampling of the first Brillouin
zone.\cite{Monkhorst:1976,PhysRevB.13.5188} The relaxed gold slab was
then used in numerous single point calculations with CO at various
heights (and angles relative to the surface) to allow fitting of the
empirical force field.

%Hint at future work
The parameters employed for the metal-CO cross-interactions in this work 
are shown in Table~\ref{co_parameters} and the binding energies on the 
(111) surfaces are displayed in Table~\ref{co_energies}.  Charge transfer
and polarization are neglected in this model, although these effects are likely to
affect binding energies and binding site preferences, and will be added in
a future work.\cite{Deshlahra:2012,StreitzMintmire:1994}

%Table  of Parameters
%Pt Parameter Set 9
%Au Parameter Set 35
\begin{table}[H]
  \caption{Best fit parameters for metal-CO cross-interactions. Metal-C
    interactions are modeled with Lennard-Jones potential, while the
    metal-O interactions were fit to Morse
    potentials.  Distances are given in \AA~and energies in kcal/mol. }
\centering
\begin{tabular}{| c | cc | c | ccc |}
\hline
 &  $\sigma$ & $\epsilon$ & & $r$ & $D$ & $\gamma$ (\AA$^{-1}$) \\
\hline
\textbf{Pt-C} & 1.3 & 15  & \textbf{Pt-O} & 3.8 & 3.0 & 1 \\
\textbf{Au-C} & 1.9 & 6.5  & \textbf{Au-O} & 3.8 & 0.37 & 0.9\\

\hline
\end{tabular}
\label{tab:co_parameters}
\end{table}

%Table of energies
\begin{table}[H]
  \caption{Adsorption energies for CO on M(111) at the atop site using the potentials
    described in this work.  All values are in eV.}
\centering
\begin{tabular}{| c | cc |}
  \hline
  & Calculated & Experimental \\
  \hline
  \multirow{2}{*}{\textbf{Pt-CO}} & \multirow{2}{*}{-1.9} & -1.4 \bibpunct{}{}{,}{n}{}{,}
  (Ref. \protect\cite{Kelemen:1979}) \\
 & &  -1.9 \bibpunct{}{}{,}{n}{}{,} (Ref. \protect\cite{Yeo}) \\ \hline
  \textbf{Au-CO} & -0.39 & -0.40 \bibpunct{}{}{,}{n}{}{,}  (Ref. \protect\cite{TPD_Gold}) \\
  \hline
\end{tabular}
\label{tab:co_energies}
\end{table}

\subsection{Pt(557) and Au(557) metal interfaces}

Our model systems are composed of 3888 Pt atoms and 3384 Au atoms in a
FCC crystal that have been cut along the (557) plane so that they are
periodic in the {\it x} and {\it y} directions, and have been oriented
to expose two aligned (557) cuts along the extended {\it
  z}-axis.  Simulations of the bare metal interfaces at temperatures
ranging from 300~K to 1200~K were performed to observe the relative
stability of the surfaces without a CO overlayer.  

The different bulk (and surface) melting temperatures (1337~K for Au
and 2045~K for Pt) suggest that any possible reconstruction may happen at
different temperatures for the two metals.  The bare Au and Pt surfaces were
initially run in the canonical (NVT) ensemble at 800~K and 1000~K
respectively for 100 ps. These temperatures were chosen because the 
surfaces were relatively stable at these temperatures when no CO was 
present, but experienced additional instability upon addition of CO in the time
frames we were examining. Each surface was exposed to a range of CO
that was initially placed in the vacuum region.  Upon full adsorption,
these amounts correspond to 0\%, 5\%, 25\%, 33\%, and 50\% surface
coverage. Higher coverages were tried, but the CO-CO repulsion was preventing
a higher amount of adsorption.  Because of the difference in binding energies, the Pt
systems very rarely had CO that was not bound to the surface, while
the Au surfaces often had a significant CO population in the gas
phase.  These systems were allowed to reach thermal equilibrium (over
5 ns) before being run in the microcanonical (NVE) ensemble for
data collection. All of the systems examined had at least 40 ns in the
data collection stage, although simulation times for some of the
systems exceeded 200ns.  All simulations were run using the open
source molecular dynamics package, OpenMD.\cite{Ewald,OOPSE}

% Just results, leave discussion for discussion section
% structure
%       Pt: step wandering, double layers, no triangular motifs
%       Au: step wandering, no double layers
% dynamics
%       diffusion
%       time scale, formation, breakage
\section{Results}
\subsection{Structural remodeling}
Tao et al. showed experimentally that the Pt(557) surface 
undergoes two separate reconstructions upon CO 
adsorption.\cite{Tao:2010} The first involves a doubling of 
the step height and plateau length. Similar behavior has been 
seen to occur on numerous surfaces at varying conditions (Ni 977, Si 111, etc).
\cite{Williams:1994,Williams:1991,Pearl} Of the two systems 
we examined, the Pt system showed a larger amount of 
reconstruction when compared to the Au system. The amount 
of reconstruction appears to be correlated to the amount of CO 
adsorbed upon the surface.  We believe this is related to the 
effect that adsorbate coverage has on edge breakup and surface 
diffusion of adatoms. While both systems displayed step-edge 
wandering, only the Pt surface underwent the doubling seen by 
Tao et al., within the time scales we were modeling. Specifically, 
only the 50~\% coverage Pt system was observed to have a 
step-edge undergo a complete doubling in the time scales we 
were able to monitor. This event encouraged us to allow that 
specific system to run for much longer periods during which two 
more double layers were created. The other systems, not displaying 
any large scale changes of interest, were all stopped after running 
for 40 ns in the microcanonical ensemble. Despite no observation 
of double layer formation, the other Pt systems tended to show 
more cumulative lateral movement of the step-edges when 
compared to the Au systems. The 50\% Pt system is highlighted 
in Figure \ref{fig:reconstruct} at various times along the simulation 
showing the evolution of the system.

The second reconstruction on the Pt(557) surface observed by 
Tao involved the formation of triangular clusters that stretched 
across the plateau between two step-edges. Neither system, within 
our simulated time scales, experiences this reconstruction. A constructed 
system in which the triangular motifs were constructed on the surface 
will be explored in future work and is shown in the supporting information.

\subsection{Dynamics}
While atomistic-like simulations of stepped surfaces have been 
performed before, they tend to be performed using Monte Carlo 
techniques\cite{Williams:1991,Williams:1994}. This allows them 
to efficiently sample the equilibrium thermodynamic landscape 
but at the expense of ignoring the dynamics of the system. Previous
work by Pearl and Sibener\cite{Pearl}, using STM, has been able to 
visualize the coalescing of steps of Ni(977). The time scale of the image 
acquisition, $\sim$70 s/image provides an upper bounds for the time 
required for the doubling to actually occur. Statistical treatments of step-edges
are adept at analyzing such systems. However, in a system where 
the number of steps is limited, examining the individual atoms that make 
up the steps can provide useful information as well. 


\subsubsection{Transport of surface metal atoms}
%forcedSystems/stepSeparation
The movement or wandering of a step-edge is a cooperative effect 
arising from the individual movements, primarily through surface 
diffusion, of the atoms making up the step. An ideal metal surface 
displaying a low index facet, (111) or (100) is unlikely to experience 
much surface diffusion because of the large energetic barrier that must 
be overcome to lift an atom out of the surface. The presence of step-edges 
on higher-index surfaces provide a source for mobile metal atoms. 
Breaking away from the step-edge on a clean surface still imposes an 
energetic penalty around $\sim$~40 kcal/mole, but is much less than lifting 
the same metal atom out from the surface,  \textgreater~60 kcal/mole, and 
the penalty lowers even further when CO is present in sufficient quantities 
on the surface. For certain tested distributions of CO, the penalty was lowered 
to $\sim$~20 kcal/mole. Once an adatom exists on the surface, its barrier for 
diffusion is negligible ( \textless~4 kcal/mole) and is well able to explore the 
terrace before potentially rejoining its original step-edge or becoming a part 
of a different edge. Atoms traversing separate terraces is a more difficult 
process, but can be overcome through a joining and lifting stage which is 
examined in the discussion section. By tracking the mobility of individual 
metal atoms on the Pt and Au surfaces we were able to determine the relative 
diffusion rates and how varying coverages of CO affected the rates. Close 
observation of the mobile metal atoms showed that they were typically in 
equilibrium with the step-edges, constantly breaking apart and rejoining. 
At times their motion was concerted and two or more adatoms would be 
observed moving together across the surfaces. The primary challenge in 
quantifying the overall surface mobility was in defining ``mobile" vs. ``static" atoms.

A particle was considered mobile once it had traveled more than 2~\AA~
between saved configurations of the system (10-100 ps). An atom that was 
truly mobile would typically travel much greater than this, but the 2~\AA~ cutoff 
was to prevent the in-place vibrational movement of non-surface atoms from 
being included in the analysis. Diffusion on  a surface is strongly affected by 
local structures and in this work the presence of single and double layer 
step-edges causes the diffusion parallel to the step-edges to be different 
from the diffusion perpendicular to these edges. This led us to compute 
those diffusions separately as seen in Figure \ref{fig:diff}.

\subsubsection{Double layer formation}
The increased amounts of diffusion on Pt at the higher CO coverages appears
to play a primary role in the formation of double layers, although this conclusion
does not explain the 33\% coverage Pt system. On the 50\% system, three
separate layers were formed over the extended run time of this system. As 
mentioned earlier, previous experimental work has given some insight into the 
upper bounds of the time required for enough atoms to move around to allow two 
steps to coalesce\cite{Williams:1991,Pearl}. As seen in Figure \ref{fig:reconstruct},
the first appearance of a double layer, a nodal site, appears at 19 ns into the 
simulation. Within 12 ns, nearly half of the step has formed the double layer and 
by 86 ns, a smooth complete layer has formed. The double layer is ``complete" by 
37 ns but is a bit rough. From the appearance of the first node to the initial doubling 
of the layers ignoring their roughness took $\sim$~20 ns. Another ~40 ns was 
necessary for the layer to completely straighten. The other two layers in this 
simulation form over a period of 22 ns and 42 ns respectively. Comparing this to 
the upper bounds of the image scan, it is likely that aspects of this reconstruction 
occur very quickly.

%Evolution of surface
\begin{figure}[H]
\includegraphics[width=\linewidth]{ProgressionOfDoubleLayerFormation_yellowCircle.png}
\caption{The Pt(557) / 50\% CO system at a sequence of times after
  initial exposure to the CO: (a) 258 ps, (b) 19 ns, (c) 31.2 ns, and
  (d) 86.1 ns. Disruption of the (557) step-edges occurs quickly.  The
  doubling of the layers appears only after two adjacent step-edges
  touch.  The circled spot in (b) nucleated the growth of the double
  step observed in the later configurations.}
  \label{fig:reconstruct}
\end{figure}

\begin{figure}[H]
\includegraphics[width=\linewidth]{DiffusionComparison_errorXY_remade.pdf}
\caption{Diffusion constants for mobile surface atoms along directions
  parallel ($\mathbf{D}_{\parallel}$) and perpendicular
  ($\mathbf{D}_{\perp}$) to the (557) step-edges as a function of CO
  surface coverage.  Diffusion parallel to the step-edge is higher
  than that perpendicular to the edge because of the lower energy
  barrier associated with traversing along the edge as compared to
  completely breaking away. Additionally, the observed
  maximum and subsequent decrease for the Pt system suggests that the
  CO self-interactions are playing a significant role with regards to
  movement of the Pt atoms around and across the surface. }
\label{fig:diff}
\end{figure}


%Discussion
\section{Discussion}
In this paper we have shown that we were able to accurately model the initial reconstruction of the
Pt(557) surface upon CO adsorption as shown by Tao et al. \cite{Tao:2010}. More importantly, we
were able to observe the dynamic processes necessary for this reconstruction. 

\subsection{Mechanism for restructuring}
Comparing the results from simulation to those reported previously by 
Tao et al.\cite{Tao:2010} the similarities in the Pt-CO system are quite 
strong. As shown in Figure \ref{fig:reconstruct}, the simulated Pt 
system under a CO atmosphere will restructure by doubling the terrace 
heights. The restructuring occurs slowly, one to two Pt atoms at a time. 
Looking at individual configurations of the system, the adatoms either 
break away from the step-edge and stay on the lower terrace or they lift 
up onto the higher terrace. Once ``free'' they will diffuse on the terrace 
until reaching another step-edge or coming back to their original edge.  
This combination of growth and decay of the step-edges is in a state of 
dynamic equilibrium. However, once two previously separated edges 
meet as shown in Figure 1.B, this meeting point tends to act as a focus 
or growth point for the rest of the edge to meet up, akin to that of a zipper. 
From the handful of cases where a double layer was formed during the 
simulation, measuring from the initial appearance of a growth point, the 
double layer tends to be fully formed within $\sim$~35 ns.

A number of possible mechanisms exist to explain the role of adsorbed 
CO in restructuring the Pt surface. Quadrupolar repulsion between adjacent 
CO molecules adsorbed on the surface is one likely possibility.  However, 
the quadrupole-quadrupole interaction is short-ranged and is attractive for 
some orientations.  If the CO molecules are ``locked'' in a specific orientation 
relative to each other, through atop adsorption perhaps, this explanation 
gains some weight.  The energetic repulsion between two CO located a 
distance of 2.77~\AA~apart (nearest-neighbor distance of Pt) with both in 
a  vertical orientation is 8.62 kcal/mole. Moving the CO apart to the second 
nearest-neighbor distance of 4.8~\AA~or 5.54~\AA~drops the repulsion to 
nearly 0 kcal/mole. Allowing the CO's to leave a purely vertical orientation 
also quickly drops the repulsion, a minimum is reached at $\sim$24 degrees 
of 6.2 kcal/mole. As mentioned above, the energy barrier for surface diffusion 
of a Pt adatom is only 4 kcal/mole. So this repulsion between CO can help 
increase the surface diffusion. However, the residence time of CO was 
examined and while the majority of the CO is on or near the surface throughout 
the run, it is extremely mobile. This mobility suggests that the CO are more 
likely to shift their positions without necessarily dragging the Pt along with them.

Another possible and more likely mechanism for the restructuring is in the
destabilization of strong Pt-Pt interactions by CO adsorbed on surface
Pt atoms.  This would then have the effect of increasing surface mobility
of these atoms.  To test this hypothesis, numerous configurations of 
CO in varying quantities were arranged on the higher and lower plateaus
around a step on a otherwise clean Pt(557) surface. One representative
configuration is displayed in Figure \ref{fig:lambda}. Single or concerted movement
of Pt atoms was then examined to determine possible barriers. Because
the movement was forced along a pre-defined reaction coordinate that may differ
from the true minimum of this path, only the beginning and ending energies
are displayed in Table \ref{tab:energies}. These values suggest that the presence of CO at suitable
locations can lead to lowered barriers for Pt breaking apart from the step-edge.
Additionally, as highlighted in Figure \ref{fig:lambda}, the presence of CO makes the
burrowing and lifting of adatoms favorable, whereas without CO, the process is neutral
in terms of energetics.

%lambda progression of Pt -> shoving its way into the step
\begin{figure}[H]
\includegraphics[width=\linewidth]{lambdaProgression_atopCO.png}
\caption{A model system of the Pt(557) surface was used as the framework
 for exploring energy barriers along a reaction coordinate. Various numbers,
 placements, and rotations of CO were examined as they affect Pt movement.
 The coordinate displayed in this Figure was a representative run. As shown
 in Table \ref{tab:rxcoord}, relative to the energy of the system at 0\%, there
 is a slight decrease upon insertion of the Pt atom into the step-edge along
 with the resultant lifting of the other Pt atom when CO is present at certain positions.}
\label{fig:lambda}
\end{figure}


\subsection{Diffusion}
As shown in the results section, the diffusion parallel to the step-edge tends to be 
much larger than that perpendicular to the step-edge, likely because of the dynamic 
equilibrium that is established between the step-edge and adatom interface. The coverage 
of CO also appears to play a slight role in relative rates of diffusion, as shown in Figure \ref{fig:diff}.
The 
Thus, the bottleneck of the double layer formation appears to be the initial formation
of this growth point, which seems to be somewhat of a stochastic event. Once it 
appears, parallel diffusion, along the now slightly angled step-edge, will allow for 
a faster formation of the double layer than if the entire process were dependent on 
only perpendicular diffusion across the plateaus. Thus, the larger $D_{\perp}$, the 
more likely a growth point is to be formed. 
\\


%breaking of the double layer upon removal of CO
\begin{figure}[H]
\includegraphics[width=\linewidth]{doubleLayerBreaking_greenBlue_whiteLetters.png}
%:
\caption{(A)  0 ps, (B) 100 ps, (C) 1 ns, after the removal of CO. The presence of the CO
 helped maintain the stability of the double layer and upon removal the two layers break
 and begin separating. The separation is not a simple pulling apart however, rather 
 there is a mixing of the lower and upper atoms at the edge.}
\label{fig:breaking}
\end{figure}


%Peaks!
\begin{figure}[H]
\includegraphics[width=\linewidth]{doublePeaks_noCO.png}
\caption{At the initial formation of this double layer  ( $\sim$ 37 ns) there is a degree
 of roughness inherent to the edge. The next $\sim$ 40 ns show the edge with 
 aspects of waviness and by 80 ns the double layer is completely formed and smooth. }
\label{fig:peaks}
\end{figure}


%Don't think I need this
%clean surface...
%\begin{figure}[H]
%\includegraphics[width=\linewidth]{557_300K_cleanPDF.pdf}
%\caption{}

%\end{figure}
%\label{fig:clean}


\section{Conclusion}
In this work we have shown the reconstruction of the Pt(557) crystalline surface upon adsorption of CO in < $\mu s$. Only the highest coverage Pt system showed this initial reconstruction similar to that seen previously. The strong interaction between Pt and CO and the limited interaction between Au and CO helps explain the differences between the two systems. 

%Things I am not ready to remove yet

%Table of Diffusion Constants
%Add gold?M
% \begin{table}[H]
%   \caption{}
%   \centering
% \begin{tabular}{| c | cc | cc | }
%   \hline
%   &\multicolumn{2}{c|}{\textbf{Platinum}}&\multicolumn{2}{c|}{\textbf{Gold}} \\
%   \hline
%   \textbf{Surface Coverage} & $\mathbf{D}_{\parallel}$ & $\mathbf{D}_{\perp}$ & $\mathbf{D}_{\parallel}$ & $\mathbf{D}_{\perp}$  \\
%   \hline
%   50\% & 4.32(2) & 1.185(8)  & 1.72(2) & 0.455(6) \\
%   33\% & 5.18(3)  & 1.999(5)  & 1.95(2) & 0.337(4)   \\
%   25\% & 5.01(2)  & 1.574(4)  & 1.26(3) & 0.377(6) \\
%   5\%   & 3.61(2)  & 0.355(2)  & 1.84(3)  & 0.169(4)  \\
%   0\%   & 3.27(2)  & 0.147(4)  & 1.50(2)  & 0.194(2)   \\
%   \hline 
% \end{tabular}
% \end{table}

\section{Acknowledgments}
Support for this project was provided by the National Science
Foundation under grant CHE-0848243 and by the Center for Sustainable
Energy at Notre Dame (cSEND). Computational time was provided by the
Center for Research Computing (CRC) at the University of Notre Dame.

\newpage
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\end{document}
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