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1 \documentclass[11pt]{article}
2 \usepackage{amsmath}
3 \usepackage{amssymb}
4 \usepackage{times}
5 \usepackage{mathptm}
6 \usepackage{setspace}
7 \usepackage{endfloat}
8 \usepackage{caption}
9 %\usepackage{tabularx}
10 \usepackage{graphicx}
11 \usepackage{multirow}
12 %\usepackage{booktabs}
13 %\usepackage{bibentry}
14 %\usepackage{mathrsfs}
15 \usepackage[square, comma, sort&compress]{natbib}
16 \usepackage{url}
17 \pagestyle{plain} \pagenumbering{arabic} \oddsidemargin 0.0cm
18 \evensidemargin 0.0cm \topmargin -21pt \headsep 10pt \textheight
19 9.0in \textwidth 6.5in \brokenpenalty=10000
20
21 % double space list of tables and figures
22 %\AtBeginDelayedFloats{\renewcomand{\baselinestretch}{1.66}}
23 \setlength{\abovecaptionskip}{20 pt}
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25
26 \bibpunct{}{}{,}{s}{}{;}
27 \bibliographystyle{achemso}
28
29 \begin{document}
30
31
32 %%
33 %Introduction
34 % Experimental observations
35 % Previous work on Pt, CO, etc.
36 %
37 %Simulation Methodology
38 % FF (fits and parameters)
39 % MD (setup, equilibration, collection)
40 %
41 % Analysis of trajectories!!!
42 %Discussion
43 % CO preferences for specific locales
44 % CO-CO interactions
45 % Differences between Au & Pt
46 % Causes of 2_layer reordering in Pt
47 %Summary
48 %%
49
50 %Title
51 \title{Molecular Dynamics simulations of the surface reconstructions
52 of Pt(557) and Au(557) under exposure to CO}
53
54 \author{Joseph R. Michalka, Patrick W. McIntyre and J. Daniel
55 Gezelter\footnote{Corresponding author. \ Electronic mail: gezelter@nd.edu} \\
56 Department of Chemistry and Biochemistry,\\
57 University of Notre Dame\\
58 Notre Dame, Indiana 46556}
59
60 %Date
61 \date{Mar 5, 2013}
62
63 %authors
64
65 % make the title
66 \maketitle
67
68 \begin{doublespace}
69
70 \begin{abstract}
71 We examine surface reconstructions of Pt and Au(557) under
72 various CO coverages using molecular dynamics in order to
73 explore possible mechanisms for any observed reconstructions
74 and their dynamics. The metal-CO interactions were parameterized
75 as part of this work so that an efficient large-scale treatment of
76 this system could be undertaken. The large difference in binding
77 strengths of the metal-CO interactions was found to play a significant
78 role with regards to step-edge stability and adatom diffusion. A
79 small correlation between coverage and the diffusion constant
80 was also determined. The energetics of CO adsorbed to the surface
81 is sufficient to explain the reconstructions observed on the Pt
82 systems and the lack of reconstruction of the Au systems.
83
84 \end{abstract}
85
86 \newpage
87
88
89 \section{Introduction}
90 % Importance: catalytically active metals are important
91 % Sub: Knowledge of how their surface structure affects their ability to catalytically facilitate certain reactions is growing, but is more reactionary than predictive
92 % 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)
93 % Theory can explore temperatures and pressures which are difficult to work with in experiments
94 % Sub: Also, easier to observe what is going on and provide reasons and explanations
95 %
96
97 Industrial catalysts usually consist of small particles that exhibit a
98 high concentration of steps, kink sites, and vacancies at the edges of
99 the facets. These sites are thought to be the locations of catalytic
100 activity.\cite{ISI:000083038000001,ISI:000083924800001} There is now
101 significant evidence that solid surfaces are often structurally,
102 compositionally, and chemically modified by reactants under operating
103 conditions.\cite{Tao2008,Tao:2010,Tao2011} The coupling between
104 surface oxidation states and catalytic activity for CO oxidation on
105 Pt, for instance, is widely documented.\cite{Ertl08,Hendriksen:2002}
106 Despite the well-documented role of these effects on reactivity, the
107 ability to capture or predict them in atomistic models is somewhat
108 limited. While these effects are perhaps unsurprising on the highly
109 disperse, multi-faceted nanoscale particles that characterize
110 industrial catalysts, they are manifest even on ordered, well-defined
111 surfaces. The Pt(557) surface, for example, exhibits substantial and
112 reversible restructuring under exposure to moderate pressures of
113 carbon monoxide.\cite{Tao:2010}
114
115 This work is an attempt to understand the mechanism and timescale for
116 surface restructuring by using molecular simulations. Since the dynamics
117 of the process are of particular interest, we employ classical force
118 fields that represent a compromise between chemical accuracy and the
119 computational efficiency necessary to simulate the process of interest.
120 Since restructuring typically occurs as a result of specific interactions of the
121 catalyst with adsorbates, in this work, two metal systems exposed
122 to carbon monoxide were examined. The Pt(557) surface has already been shown
123 to undergo a large scale reconstruction under certain conditions.\cite{Tao:2010}
124 The Au(557) surface, because of a weaker interaction with CO, is seen as less
125 likely to undergo this kind of reconstruction. However, Peters et al.\cite{Peters:2000}
126 and Piccolo et al.\cite{Piccolo:2004} have both observed CO induced
127 reconstruction of a Au(111) surface. Peters et al. saw a relaxing of the
128 22 x $\sqrt{3}$ cell. They argued that a very small number of Au atoms
129 would become adatoms, limiting the stress of this reconstruction while
130 allowing the rest of the row to relax and approach the ideal (111)
131 configuration. They did not see the ``herringbone'' pattern being greatly
132 affected by this relaxation. Piccolo et al. on the other hand, did see a
133 disruption of the ``herringbone'' pattern as CO was adsorbed to the
134 surface. Both groups suggested that the preference CO shows for
135 low-coordinated Au particles was the primary driving force for these reconstructions.
136
137
138
139 %Platinum molecular dynamics
140 %gold molecular dynamics
141
142 \section{Simulation Methods}
143 The challenge in modeling any solid/gas interface is the
144 development of a sufficiently general yet computationally tractable
145 model of the chemical interactions between the surface atoms and
146 adsorbates. Since the interfaces involved are quite large (10$^3$ -
147 10$^6$ atoms) and respond slowly to perturbations, {\it ab initio}
148 molecular dynamics
149 (AIMD),\cite{KRESSE:1993ve,KRESSE:1993qf,KRESSE:1994ul} Car-Parrinello
150 methods,\cite{CAR:1985bh,Izvekov:2000fv,Guidelli:2000fy} and quantum
151 mechanical potential energy surfaces remain out of reach.
152 Additionally, the ``bonds'' between metal atoms at a surface are
153 typically not well represented in terms of classical pairwise
154 interactions in the same way that bonds in a molecular material are,
155 nor are they captured by simple non-directional interactions like the
156 Coulomb potential. For this work, we have used classical molecular
157 dynamics with potential energy surfaces that are specifically tuned
158 for transition metals. In particular, we used the EAM potential for
159 Au-Au and Pt-Pt interactions\cite{EAM}. The CO was modeled using a rigid
160 three-site model developed by Straub and Karplus for studying
161 photodissociation of CO from myoglobin.\cite{Straub} The Au-CO and
162 Pt-CO cross interactions were parameterized as part of this work.
163
164 \subsection{Metal-metal interactions}
165 Many of the potentials used for modeling transition metals are based
166 on a non-pairwise additive functional of the local electron
167 density. The embedded atom method (EAM) is perhaps the best known of
168 these
169 methods,\cite{Daw84,Foiles86,Johnson89,Daw89,Plimpton93,Voter95a,Lu97,Alemany98}
170 but other models like the Finnis-Sinclair\cite{Finnis84,Chen90} and
171 the quantum-corrected Sutton-Chen method\cite{QSC,Qi99} have simpler
172 parameter sets. The glue model of Ercolessi et al. is among the
173 fastest of these density functional approaches.\cite{Ercolessi88} In
174 all of these models, atoms are conceptualized as a positively charged
175 core with a radially-decaying valence electron distribution. To
176 calculate the energy for embedding the core at a particular location,
177 the electron density due to the valence electrons at all of the other
178 atomic sites is computed at atom $i$'s location,
179 \begin{equation*}
180 \bar{\rho}_i = \sum_{j\neq i} \rho_j(r_{ij})
181 \end{equation*}
182 Here, $\rho_j(r_{ij})$ is the function that describes the distance
183 dependence of the valence electron distribution of atom $j$. The
184 contribution to the potential that comes from placing atom $i$ at that
185 location is then
186 \begin{equation*}
187 V_i = F[ \bar{\rho}_i ] + \sum_{j \neq i} \phi_{ij}(r_{ij})
188 \end{equation*}
189 where $F[ \bar{\rho}_i ]$ is an energy embedding functional, and
190 $\phi_{ij}(r_{ij})$ is a pairwise term that is meant to represent the
191 repulsive overlap of the two positively charged cores.
192
193 % The {\it modified} embedded atom method (MEAM) adds angular terms to
194 % the electron density functions and an angular screening factor to the
195 % pairwise interaction between two
196 % atoms.\cite{BASKES:1994fk,Lee:2000vn,Thijsse:2002ly,Timonova:2011ve}
197 % MEAM has become widely used to simulate systems in which angular
198 % interactions are important (e.g. silicon,\cite{Timonova:2011ve} bcc
199 % metals,\cite{Lee:2001qf} and also interfaces.\cite{Beurden:2002ys})
200 % MEAM presents significant additional computational costs, however.
201
202 The EAM, Finnis-Sinclair, and the Quantum Sutton-Chen (QSC) potentials
203 have all been widely used by the materials simulation community for
204 simulations of bulk and nanoparticle
205 properties,\cite{Chui:2003fk,Wang:2005qy,Medasani:2007uq}
206 melting,\cite{Belonoshko00,sankaranarayanan:155441,Sankaranarayanan:2005lr}
207 fracture,\cite{Shastry:1996qg,Shastry:1998dx} crack
208 propagation,\cite{BECQUART:1993rg} and alloying
209 dynamics.\cite{Shibata:2002hh} One of EAM's strengths
210 is its sensitivity to small changes in structure. This arises
211 from the original parameterization, where the interactions
212 up to the third nearest-neighbor were taken into account.\cite{Voter95a}
213 Comparing that to the glue model of Ercolessi et al.\cite{Ercolessi88}
214 which only parameterized up to the nearest-neighbor
215 interactions, EAM is a suitable choice for systems where
216 the bulk properties are of secondary importance to low-index
217 surface structures. Additionally, the similarity of EAMs functional
218 treatment of the embedding energy to standard density functional
219 theory (DFT) approaches gives EAM, and conclusions derived, a firm theoretical footing.
220 \cite{Foiles86,PhysRevB.37.3924,Rifkin1992,mishin99:_inter,mishin01:cu,mishin02:b2nial,zope03:tial_ap,mishin05:phase_fe_ni}
221
222
223
224
225 \subsection{Carbon Monoxide model}
226 Previous explanations for the surface rearrangements center on
227 the large linear quadrupole moment of carbon monoxide.\cite{Tao:2010}
228 We used a model first proposed by Karplus and Straub to study
229 the photodissociation of CO from myoglobin because it reproduces
230 the quadrupole moment well.\cite{Straub} The Straub and
231 Karplus model, treats CO as a rigid three site molecule with a massless M
232 site at the molecular center of mass. The geometry and interaction
233 parameters are reproduced in Table~\ref{tab:CO}. The effective
234 dipole moment, calculated from the assigned charges, is still
235 small (0.35 D) while the linear quadrupole (-2.40 D~\AA) is close
236 to the experimental (-2.63 D~\AA)\cite{QuadrupoleCO} and quantum
237 mechanical predictions (-2.46 D~\AA)\cite{QuadrupoleCOCalc}.
238 %CO Table
239 \begin{table}[H]
240 \caption{Positions, Lennard-Jones parameters ($\sigma$ and
241 $\epsilon$), and charges for the CO-CO
242 interactions in Ref.\bibpunct{}{}{,}{n}{}{,} \protect\cite{Straub}. Distances are in \AA, energies are
243 in kcal/mol, and charges are in atomic units.}
244 \centering
245 \begin{tabular}{| c | c | ccc |}
246 \hline
247 & {\it z} & $\sigma$ & $\epsilon$ & q\\
248 \hline
249 \textbf{C} & -0.6457 & 3.83 & 0.0262 & -0.75 \\
250 \textbf{O} & 0.4843 & 3.12 & 0.1591 & -0.85 \\
251 \textbf{M} & 0.0 & - & - & 1.6 \\
252 \hline
253 \end{tabular}
254 \label{tab:CO}
255 \end{table}
256
257 \subsection{Cross-Interactions between the metals and carbon monoxide}
258
259 Since the adsorption of CO onto a Pt surface has been the focus
260 of much experimental \cite{Yeo, Hopster:1978, Ertl:1977, Kelemen:1979}
261 and theoretical work
262 \cite{Beurden:2002ys,Pons:1986,Deshlahra:2009,Feibelman:2001,Mason:2004}
263 there is a significant amount of data on adsorption energies for CO on
264 clean metal surfaces. An earlier model by Korzeniewski {\it et
265 al.}\cite{Pons:1986} served as a starting point for our fits. The parameters were
266 modified to ensure that the Pt-CO interaction favored the atop binding
267 position on Pt(111). These parameters are reproduced in Table~\ref{tab:co_parameters}.
268 The modified parameters yield binding energies that are slightly higher
269 than the experimentally-reported values as shown in Table~\ref{tab:co_energies}. Following Korzeniewski
270 et al.,\cite{Pons:1986} the Pt-C interaction was fit to a deep
271 Lennard-Jones interaction to mimic strong, but short-ranged partial
272 binding between the Pt $d$ orbitals and the $\pi^*$ orbital on CO. The
273 Pt-O interaction was modeled with a Morse potential with a large
274 equilibrium distance, ($r_o$). These choices ensure that the C is preferred
275 over O as the surface-binding atom. In most cases, the Pt-O parameterization contributes a weak
276 repulsion which favors the atop site. The resulting potential-energy
277 surface suitably recovers the calculated Pt-C separation length
278 (1.6~\AA)\cite{Beurden:2002ys} and affinity for the atop binding
279 position.\cite{Deshlahra:2012, Hopster:1978}
280
281 %where did you actually get the functionals for citation?
282 %scf calculations, so initial relaxation was of the four layers, but two layers weren't kept fixed, I don't think
283 %same cutoff for slab and slab + CO ? seems low, although feibelmen had values around there...
284 The Au-C and Au-O cross-interactions were also fit using Lennard-Jones and
285 Morse potentials, respectively, to reproduce Au-CO binding energies.
286 The limited experimental data for CO adsorption on Au required refining the fits against plane-wave DFT calculations.
287 Adsorption energies were obtained from gas-surface DFT calculations with a
288 periodic supercell plane-wave basis approach, as implemented in the
289 {\sc Quantum ESPRESSO} package.\cite{QE-2009} Electron cores were
290 described with the projector augmented-wave (PAW)
291 method,\cite{PhysRevB.50.17953,PhysRevB.59.1758} with plane waves
292 included to an energy cutoff of 20 Ry. Electronic energies are
293 computed with the PBE implementation of the generalized gradient
294 approximation (GGA) for gold, carbon, and oxygen that was constructed
295 by Rappe, Rabe, Kaxiras, and Joannopoulos.\cite{Perdew_GGA,RRKJ_PP}
296 In testing the Au-CO interaction, Au(111) supercells were constructed of four layers of 4
297 Au x 2 Au surface planes and separated from vertical images by six
298 layers of vacuum space. The surface atoms were all allowed to relax
299 before CO was added to the system. Electronic relaxations were
300 performed until the energy difference between subsequent steps
301 was less than $10^{-8}$ Ry. Nonspin-polarized supercell calculations
302 were performed with a 4~x~4~x~4 Monkhorst-Pack {\bf k}-point sampling of the first Brillouin
303 zone.\cite{Monkhorst:1976,PhysRevB.13.5188} The relaxed gold slab was
304 then used in numerous single point calculations with CO at various
305 heights (and angles relative to the surface) to allow fitting of the
306 empirical force field.
307
308 %Hint at future work
309 The parameters employed for the metal-CO cross-interactions in this work
310 are shown in Table~\ref{tab:co_parameters} and the binding energies on the
311 (111) surfaces are displayed in Table~\ref{tab:co_energies}. Charge transfer
312 and polarization are neglected in this model, although these effects are likely to
313 affect binding energies and binding site preferences, and will be addressed in
314 a future work.\cite{Deshlahra:2012,StreitzMintmire:1994}
315
316 %Table of Parameters
317 %Pt Parameter Set 9
318 %Au Parameter Set 35
319 \begin{table}[H]
320 \caption{Best fit parameters for metal-CO cross-interactions. Metal-C
321 interactions are modeled with Lennard-Jones potentials. While the
322 metal-O interactions were fit to Morse
323 potentials. Distances are given in \AA~and energies in kcal/mol. }
324 \centering
325 \begin{tabular}{| c | cc | c | ccc |}
326 \hline
327 & $\sigma$ & $\epsilon$ & & $r$ & $D$ & $\gamma$ (\AA$^{-1}$) \\
328 \hline
329 \textbf{Pt-C} & 1.3 & 15 & \textbf{Pt-O} & 3.8 & 3.0 & 1 \\
330 \textbf{Au-C} & 1.9 & 6.5 & \textbf{Au-O} & 3.8 & 0.37 & 0.9\\
331
332 \hline
333 \end{tabular}
334 \label{tab:co_parameters}
335 \end{table}
336
337 %Table of energies
338 \begin{table}[H]
339 \caption{Adsorption energies for a single CO at the atop site on M(111) at the atop site using the potentials
340 described in this work. All values are in eV.}
341 \centering
342 \begin{tabular}{| c | cc |}
343 \hline
344 & Calculated & Experimental \\
345 \hline
346 \multirow{2}{*}{\textbf{Pt-CO}} & \multirow{2}{*}{-1.9} & -1.4 \bibpunct{}{}{,}{n}{}{,}
347 (Ref. \protect\cite{Kelemen:1979}) \\
348 & & -1.9 \bibpunct{}{}{,}{n}{}{,} (Ref. \protect\cite{Yeo}) \\ \hline
349 \textbf{Au-CO} & -0.39 & -0.40 \bibpunct{}{}{,}{n}{}{,} (Ref. \protect\cite{TPD_Gold}) \\
350 \hline
351 \end{tabular}
352 \label{tab:co_energies}
353 \end{table}
354
355 \subsection{Pt(557) and Au(557) metal interfaces}
356 Our Pt system has dimensions of 18~x~24~x~9 in a box of size
357 54.482~x~50.046~x~120.88~\AA while our Au system has
358 dimensions of 18~x~24~x~8 in a box of size 57.4~x~51.9285~x~100~\AA.
359 The systems are arranged in a FCC crystal that have been cut
360 along the (557) plane so that they are periodic in the {\it x} and
361 {\it y} directions, and have been oriented to expose two aligned
362 (557) cuts along the extended {\it z}-axis. Simulations of the
363 bare metal interfaces at temperatures ranging from 300~K to
364 1200~K were performed to observe the relative
365 stability of the surfaces without a CO overlayer.
366
367 The different bulk melting temperatures (1337~K for Au
368 and 2045~K for Pt) suggest that any possible reconstruction should happen at
369 different temperatures for the two metals. The bare Au and Pt surfaces were
370 initially run in the canonical (NVT) ensemble at 800~K and 1000~K
371 respectively for 100 ps. The two surfaces were relatively stable at these
372 temperatures when no CO was present, but experienced increased surface
373 mobility on addition of CO. Each surface was then dosed with different concentrations of CO
374 that was initially placed in the vacuum region. Upon full adsorption,
375 these concentrations correspond to 0\%, 5\%, 25\%, 33\%, and 50\% surface
376 coverage. Higher coverages resulted in CO double layer formation, which introduces artifacts that are not relevant to (557) reconstruction.
377 Because of the difference in binding energies, nearly all of the CO was bound to the Pt surface, while
378 the Au surfaces often had a significant CO population in the gas
379 phase. These systems were allowed to reach thermal equilibrium (over
380 5 ns) before being run in the microcanonical (NVE) ensemble for
381 data collection. All of the systems examined had at least 40 ns in the
382 data collection stage, although simulation times for some of the
383 systems exceeded 200~ns. Simulations were run using the open
384 source molecular dynamics package, OpenMD.\cite{Ewald,OOPSE}
385
386 % Just results, leave discussion for discussion section
387 % structure
388 % Pt: step wandering, double layers, no triangular motifs
389 % Au: step wandering, no double layers
390 % dynamics
391 % diffusion
392 % time scale, formation, breakage
393 \section{Results}
394 \subsection{Structural remodeling}
395 Tao et al. have shown experimentally that the Pt(557) surface
396 undergoes two separate reconstructions upon CO
397 adsorption.\cite{Tao:2010} The first involves a doubling of
398 the step height and plateau length. Similar behavior has been
399 seen to occur on numerous surfaces at varying conditions: Ni(977), Si(111).
400 \cite{Williams:1994,Williams:1991,Pearl} Of the two systems
401 we examined, the Pt system showed a larger amount of
402 reconstruction when compared to the Au system. The amount
403 of reconstruction is correlated to the amount of CO
404 adsorbed upon the surface. This appears to be related to the
405 effect that adsorbate coverage has on edge breakup and on the surface
406 diffusion of metal adatoms. While both systems displayed step-edge
407 wandering, only the Pt surface underwent the doubling seen by
408 Tao et al. within the time scales studied here.
409 Only the 50\% coverage Pt system exhibited
410 a complete doubling in the time scales we
411 were able to monitor. Over longer periods (150~ns) two more double layers formed on this interface.
412 Although double layer formation did not occur in the other Pt systems, they show
413 more lateral movement of the step-edges
414 compared to their Au counterparts. The 50\% Pt system is highlighted
415 in Figure \ref{fig:reconstruct} at various times along the simulation
416 showing the evolution of a step-edge.
417
418 The second reconstruction on the Pt(557) surface observed by
419 Tao involved the formation of triangular clusters that stretched
420 across the plateau between two step-edges. Neither system, within
421 the 40~ns time scale, experienced this reconstruction.
422
423 \subsection{Dynamics}
424 Previous atomistic simulations of stepped surfaces were largely
425 concerned with the energetics and structures at different conditions
426 \cite{Williams:1991,Williams:1994}. Consequently, the most common
427 technique has been Monte Carlo. Monte Carlo gives an efficient
428 sampling of the equilibrium thermodynamic landscape at the expense
429 of ignoring the dynamics of the system. Previous work by Pearl and
430 Sibener\cite{Pearl}, using STM, has been able to show the coalescing
431 of steps on Ni(977). The time scale of the image acquisition,
432 $\sim$70 s/image provides an upper bound for the time required for
433 the doubling to occur. In this section we give data on dynamic and
434 transport properties, e.g. diffusion, layer formation time, etc.
435
436
437 \subsubsection{Transport of surface metal atoms}
438 %forcedSystems/stepSeparation
439 The movement or wandering of a step-edge is a cooperative effect
440 arising from the individual movements, primarily through surface
441 diffusion, of the atoms making up the steps An ideal metal surface
442 displaying a low index facet, (111) or (100) is unlikely to experience
443 much surface diffusion because of the large energetic barrier that must
444 be overcome to lift an atom out of the surface. The presence of step-edges
445 on higher-index surfaces provide a source for mobile metal atoms.
446 Breaking away from the step-edge on a clean surface still imposes an
447 energetic penalty around $\sim$~40 kcal/mol, but is much less than lifting
448 the same metal atom vertically out of the surface, \textgreater~60 kcal/mol.
449 The penalty lowers significantly when CO is present in sufficient quantities
450 on the surface. For certain distributions of CO, the penalty can be as low as
451 $\sim$~20 kcal/mol. Once an adatom exists on the surface, the barrier for
452 diffusion is negligible ( \textless~4 kcal/mol) and these adatoms are well
453 able to explore the terrace before rejoining either the original step-edge or becoming a part
454 of a different edge. Atoms traversing separate terraces is a more difficult
455 process, but can be overcome through a joining and lifting stage which is
456 examined in the discussion section. By tracking the mobility of individual
457 metal atoms on the Pt and Au surfaces we were able to determine the relative
458 diffusion constants, as well as how varying coverages of CO affect the diffusion. Close
459 observation of the mobile metal atoms showed that they were typically in
460 equilibrium with the step-edges, dynamically breaking apart and rejoining the edges.
461 At times, their motion was concerted and two or more adatoms would be
462 observed moving together across the surfaces. The primary challenge in
463 quantifying the overall surface mobility was in defining ``mobile" vs. ``static" atoms.
464
465 A particle was considered mobile once it had traveled more than 2~\AA~
466 between saved configurations of the system (typically 10-100 ps). An atom that was
467 truly mobile would typically travel much greater distances than this, but the 2~\AA~ cutoff
468 was to prevent swamping the diffusion data with the in-place vibrational
469 movement of buried atoms. Diffusion on a surface is strongly affected by
470 local structures and in this work, the presence of single and double layer
471 step-edges causes the diffusion parallel to the step-edges to be different
472 from the diffusion perpendicular to these edges. Parallel and perpendicular
473 diffusion constants are shown in Figure \ref{fig:diff}.
474
475 \subsubsection{Double layer formation dynamics}
476 The increased amounts of diffusion on Pt at the higher CO coverages plays a primary role in the formation of the double layers observed on Pt. However, this is not a complete explanation as seen by the 33\% Pt system which has higher diffusion constants but did not show any signs of undergoing the doubling. This difference will be explored more fully in the discussion. On the 50\% Pt system, three separate layers were formed over the extended run time of this system. Previous experimental work has given some insight into the upper bounds of the time required for step coalescing.\cite{Williams:1991,Pearl} In this system, 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, the complete layer has been smoothed. The double layer could be considered ``complete" by 37 ns but is a bit rough or wavy. From the appearance of the first node to the first observed double layer, ignoring roughening, the process took $\sim$20 ns. Another $\sim$40 ns was necessary for the layer to completely straighten. The other two layers in this simulation form over a period of 22 ns and 42 ns respectively. Comparing this to the upper bounds of the image scan, it is likely that aspects of this reconstruction occur very quickly. A possible explanation for this rapid reconstruction is the elevated temperatures our systems were run at. It is likely that the process would take longer at lower temperatures and is an area of exploration for future work.
477
478 %Evolution of surface
479 \begin{figure}[H]
480 \includegraphics[width=\linewidth]{ProgressionOfDoubleLayerFormation_yellowCircle.png}
481 \caption{The Pt(557) / 50\% CO system at a sequence of times after
482 initial exposure to the CO: (a) 258 ps, (b) 19 ns, (c) 31.2 ns, and
483 (d) 86.1 ns. Disruption of the (557) step-edges occurs quickly. The
484 doubling of the layers appears only after two adjacent step-edges
485 touch. The circled spot in (b) nucleated the growth of the double
486 step observed in the later configurations.}
487 \label{fig:reconstruct}
488 \end{figure}
489
490 \begin{figure}[H]
491 \includegraphics[width=\linewidth]{DiffusionComparison_errorXY_remade.pdf}
492 \caption{Diffusion constants for mobile surface atoms along directions
493 parallel ($\mathbf{D}_{\parallel}$) and perpendicular
494 ($\mathbf{D}_{\perp}$) to the (557) step-edges as a function of CO
495 surface coverage. Diffusion parallel to the step-edge is higher
496 than that perpendicular to the edge because of the lower energy
497 barrier associated with traversing along the edge as compared to
498 completely breaking away. Additionally, the observed
499 maximum and subsequent decrease for the Pt system suggests that the
500 CO self-interactions are playing a significant role with regards to
501 movement of the Pt atoms around and across the surface. }
502 \label{fig:diff}
503 \end{figure}
504
505
506
507
508 %Discussion
509 \section{Discussion}
510 In this paper we have shown that we were able to accurately model the initial reconstruction of the
511 Pt(557) surface upon CO adsorption as shown by Tao et al. \cite{Tao:2010}. More importantly, we
512 were able to observe the dynamic processes necessary for this reconstruction.
513
514 \subsection{Mechanism for restructuring}
515 Since the Au surface showed no large scale restructuring throughout
516 our simulation time our discussion will focus on the 50\% Pt-CO system
517 which did undergo the doubling featured in Figure \ref{fig:reconstruct}.
518 Comparing the results from this simulation to those reported previously by
519 Tao et al.\cite{Tao:2010} the similarities in the Pt-CO system are quite
520 strong. As shown in Figure \ref{fig:reconstruct}, the simulated Pt
521 system exposed to a large dosage of CO will restructure by doubling the terrace
522 widths and step heights. The restructuring occurs in a piecemeal fashion, one to two Pt atoms at a time and as such is a fairly stochastic event.
523 Looking at individual configurations of the system, the adatoms either
524 break away from the step-edge and stay on the lower terrace or they lift
525 up onto the higher terrace. Once ``free'', they will diffuse on the terrace
526 until reaching another step-edge or rejoining their original edge.
527 This combination of growth and decay of the step-edges is in a state of
528 dynamic equilibrium. However, once two previously separated edges
529 meet as shown in Figure 1.B, this meeting point tends to act as a focus
530 or growth point for the rest of the edge to meet up, akin to that of a zipper.
531 From the handful of cases where a double layer was formed during the
532 simulation, measuring from the initial appearance of a growth point, the
533 double layer tends to be fully formed within $\sim$35 ns.
534
535 A number of possible mechanisms exist to explain the role of adsorbed
536 CO in restructuring the Pt surface. Quadrupolar repulsion between adjacent
537 CO molecules adsorbed on the surface is one likely possibility. However,
538 the quadrupole-quadrupole interaction is short-ranged and is attractive for
539 some orientations. If the CO molecules are ``locked'' in a specific orientation
540 relative to each other, through atop adsorption for example, this explanation
541 gains some weight. The energetic repulsion between two CO located a
542 distance of 2.77~\AA~apart (nearest-neighbor distance of Pt) with both in
543 a vertical orientation is 8.62 kcal/mol. Moving the CO apart to the second
544 nearest-neighbor distance of 4.8~\AA~or 5.54~\AA~drops the repulsion to
545 nearly 0 kcal/mol. Allowing the CO's to leave a purely vertical orientation
546 also quickly drops the repulsion, a minimum of 6.2 kcal/mol is reached at $\sim$24 degrees between the 2 CO when the carbons are locked at a distance of 2.77 \AA apart.
547 As mentioned above, the energy barrier for surface diffusion
548 of a Pt adatom is only 4 kcal/mol. So this repulsion between CO can help
549 increase the surface diffusion. However, the residence time of CO on Pt was
550 examined and while the majority of the CO is on or near the surface throughout
551 the run, it is extremely mobile. This mobility suggests that the CO are more
552 likely to shift their positions without necessarily dragging the Pt along with them.
553
554 Another possible and more likely mechanism for the restructuring is in the
555 destabilization of strong Pt-Pt interactions by CO adsorbed on surface
556 Pt atoms. This would then have the effect of increasing surface mobility
557 of these atoms. To test this hypothesis, numerous configurations of
558 CO in varying quantities were arranged on the higher and lower plateaus
559 around a step on a otherwise clean Pt(557) surface. One representative
560 configuration is displayed in Figure \ref{fig:lambda}. Single or concerted movement
561 of Pt atoms was then examined to determine possible barriers. Because
562 the movement was forced along a pre-defined reaction coordinate that may differ
563 from the true minimum of this path, only the beginning and ending energies
564 are displayed in Table \ref{tab:energies}. These values suggest that the presence of CO at suitable
565 locations can lead to lowered barriers for Pt breaking apart from the step-edge.
566 Additionally, as highlighted in Figure \ref{fig:lambda}, the presence of CO makes the
567 burrowing and lifting of adatoms favorable, whereas without CO, the process is neutral
568 in terms of energetics.
569
570 %lambda progression of Pt -> shoving its way into the step
571 \begin{figure}[H]
572 \includegraphics[width=\linewidth]{lambdaProgression_atopCO.png}
573 \caption{A model system of the Pt(557) surface was used as the framework
574 for exploring energy barriers along a reaction coordinate. Various numbers,
575 placements, and rotations of CO were examined as they affect Pt movement.
576 The coordinate displayed in this Figure was a representative run. As shown
577 in Table \ref{tab:rxcoord}, relative to the energy of the system at 0\%, there
578 is a slight decrease upon insertion of the Pt atom into the step-edge along
579 with the resultant lifting of the other Pt atom when CO is present at certain positions.}
580 \label{fig:lambda}
581 \end{figure}
582
583
584
585 \subsection{Diffusion}
586 As shown in the results section, the diffusion parallel to the step-edge tends to be
587 much larger than that perpendicular to the step-edge, likely because of the dynamic
588 equilibrium that is established between the step-edge and adatom interface. The coverage
589 of CO also appears to play a slight role in relative rates of diffusion, as shown in Figure \ref{fig:diff}.
590 The
591 Thus, the bottleneck of the double layer formation appears to be the initial formation
592 of this growth point, which seems to be somewhat of a stochastic event. Once it
593 appears, parallel diffusion, along the now slightly angled step-edge, will allow for
594 a faster formation of the double layer than if the entire process were dependent on
595 only perpendicular diffusion across the plateaus. Thus, the larger $D_{\perp}$, the
596 more likely a growth point is to be formed.
597 \\
598
599
600 %breaking of the double layer upon removal of CO
601 \begin{figure}[H]
602 \includegraphics[width=\linewidth]{doubleLayerBreaking_greenBlue_whiteLetters.png}
603 %:
604 \caption{(A) 0 ps, (B) 100 ps, (C) 1 ns, after the removal of CO. The presence of the CO
605 helped maintain the stability of the double layer and upon removal the two layers break
606 and begin separating. The separation is not a simple pulling apart however, rather
607 there is a mixing of the lower and upper atoms at the edge.}
608 \label{fig:breaking}
609 \end{figure}
610
611
612
613
614 %Peaks!
615 \begin{figure}[H]
616 \includegraphics[width=\linewidth]{doublePeaks_noCO.png}
617 \caption{At the initial formation of this double layer ( $\sim$ 37 ns) there is a degree
618 of roughness inherent to the edge. The next $\sim$ 40 ns show the edge with
619 aspects of waviness and by 80 ns the double layer is completely formed and smooth. }
620 \label{fig:peaks}
621 \end{figure}
622
623
624 %Don't think I need this
625 %clean surface...
626 %\begin{figure}[H]
627 %\includegraphics[width=\linewidth]{557_300K_cleanPDF.pdf}
628 %\caption{}
629
630 %\end{figure}
631 %\label{fig:clean}
632
633
634 \section{Conclusion}
635 In this work we have shown the reconstruction of the Pt(557) crystalline surface upon adsorption of CO in less than a $\mu s$. 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.
636
637 %Things I am not ready to remove yet
638
639 %Table of Diffusion Constants
640 %Add gold?M
641 % \begin{table}[H]
642 % \caption{}
643 % \centering
644 % \begin{tabular}{| c | cc | cc | }
645 % \hline
646 % &\multicolumn{2}{c|}{\textbf{Platinum}}&\multicolumn{2}{c|}{\textbf{Gold}} \\
647 % \hline
648 % \textbf{Surface Coverage} & $\mathbf{D}_{\parallel}$ & $\mathbf{D}_{\perp}$ & $\mathbf{D}_{\parallel}$ & $\mathbf{D}_{\perp}$ \\
649 % \hline
650 % 50\% & 4.32(2) & 1.185(8) & 1.72(2) & 0.455(6) \\
651 % 33\% & 5.18(3) & 1.999(5) & 1.95(2) & 0.337(4) \\
652 % 25\% & 5.01(2) & 1.574(4) & 1.26(3) & 0.377(6) \\
653 % 5\% & 3.61(2) & 0.355(2) & 1.84(3) & 0.169(4) \\
654 % 0\% & 3.27(2) & 0.147(4) & 1.50(2) & 0.194(2) \\
655 % \hline
656 % \end{tabular}
657 % \end{table}
658
659 \section{Acknowledgments}
660 Support for this project was provided by the National Science
661 Foundation under grant CHE-0848243 and by the Center for Sustainable
662 Energy at Notre Dame (cSEND). Computational time was provided by the
663 Center for Research Computing (CRC) at the University of Notre Dame.
664
665 \newpage
666 \bibliography{firstTryBibliography}
667 \end{doublespace}
668 \end{document}