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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}
24 \setlength{\belowcaptionskip}{30 pt}
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 investigation into the mechanism and timescale for
116 surface restructuring 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 relaxation to the
128 22 x $\sqrt{3}$ cell. They argued that only a few Au atoms
129 become adatoms, limiting the stress of this reconstruction while
130 allowing the rest to relax and approach the ideal (111)
131 configuration. They did not see the usual 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 atoms was the primary driving force for the reconstruction.
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 is 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) makes fitting DFT-derived cross potentials with adsorbates somewhat easier.
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 future work.
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 is an orthorhombic periodic box of dimensions
357 54.482~x~50.046~x~120.88~\AA~while our Au system has
358 dimensions of 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 confirm 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 the formation of a double layer of CO,
377 which introduces artifacts that are not relevant to (557) reconstruction.
378 Because of the difference in binding energies, nearly all of the CO was bound to the Pt surface, while
379 the Au surfaces often had a significant CO population in the gas
380 phase. These systems were allowed to reach thermal equilibrium (over
381 5~ns) before being run in the microcanonical (NVE) ensemble for
382 data collection. All of the systems examined had at least 40~ns in the
383 data collection stage, although simulation times for some Pt of the
384 systems exceeded 200~ns. Simulations were carried out using the open
385 source molecular dynamics package, OpenMD.\cite{Ewald,OOPSE}
386
387
388
389
390 % RESULTS
391 %
392 \section{Results}
393 \subsection{Structural remodeling}
394 The surfaces of both systems, upon dosage of CO, began
395 to undergo remodeling that was not observed in the bare
396 metal system. The surfaces which were not exposed to CO
397 did experience minor roughening of the step-edge because
398 of the elevated temperatures, but the
399 (557) lattice was well-maintained throughout the simulation
400 time. The Au systems were limited to greater amounts of
401 roughening, i.e. breakup of the step-edge, and some step
402 wandering. The lower coverage Pt systems experienced
403 similar restructuring but to a greater extent when
404 compared to the Au systems. The 50\% coverage
405 Pt system was unique among our simulations in that it
406 formed numerous double layers through step coalescence,
407 similar to results reported by Tao et al.\cite{Tao:2010}
408
409
410 \subsubsection{Step wandering}
411 The 0\% coverage surfaces for both metals showed minimal
412 movement at their respective run temperatures. As the CO
413 coverage increased however, the mobility of the surface,
414 adatoms and step-edges alike, also increased. Additionally,
415 at the higher coverages on both metals, there was more
416 step-wandering. Except for the 50\% Pt system, the step-edges
417 did not coalesce in any of the other simulations, instead preferring
418 to keep nearly the same distance between steps as in the
419 original (557) lattice. Previous work by Williams et al.\cite{Williams:1991, Williams:1994}
420 highlights the repulsion that exists between step-edges even
421 when no direct interactions are present in the system. This
422 repulsion exists because the entropy of the step-edges is constrained
423 since step-edge crossing is not allowed. This entropic repulsion
424 does not completely define the interactions between steps,
425 which is why some surfaces will undergo step coalescence,
426 where additional attractive interactions can overcome the
427 repulsion\cite{Williams:1991} and others will not. The presence
428 of adsorbates can affect these step interactions, potentially
429 leading to a new surface structure as the thermodynamic minimum.
430
431 \subsubsection{Double layers}
432 Tao et al. have shown experimentally that the Pt(557) surface
433 undergoes two separate reconstructions upon CO adsorption.\cite{Tao:2010}
434 The first involves a doubling of the step height and plateau length.
435 Similar behavior has been seen to occur on numerous surfaces
436 at varying conditions: Ni(977), Si(111).\cite{Williams:1994,Williams:1991,Pearl}
437 Of the two systems we examined, the Pt system showed a greater
438 propensity for reconstruction when compared to the Au system
439 because of the larger surface mobility and extent of step wandering.
440 The amount of reconstruction is correlated to the amount of CO
441 adsorbed upon the surface. This appears to be related to the
442 effect that adsorbate coverage has on edge breakup and on the
443 surface diffusion of metal adatoms. While both systems displayed
444 step-edge wandering, only the 50\% Pt surface underwent the
445 doubling seen by Tao et al. within the time scales studied here.
446 Over longer periods (150~ns) two more double layers formed
447 on this interface. Although double layer formation did not occur
448 in the other Pt systems, they show more step-wandering and
449 general roughening compared to their Au counterparts. The
450 50\% Pt system is highlighted in Figure \ref{fig:reconstruct} at
451 various times along the simulation showing the evolution of a step-edge.
452
453 The second reconstruction on the Pt(557) surface observed by
454 Tao involved the formation of triangular clusters that stretched
455 across the plateau between two step-edges. Neither system, within
456 the 40~ns time scale or the extended simulation time of 150~ns for
457 the 50\% Pt system, experienced this reconstruction.
458
459 \subsection{Dynamics}
460 Previous atomistic simulations of stepped surfaces dealt largely
461 with the energetics and structures at different conditions
462 \cite{Williams:1991,Williams:1994}. Consequently, the most common
463 technique utilized to date has been Monte Carlo sampling. Monte Carlo gives an efficient
464 sampling of the equilibrium thermodynamic landscape at the expense
465 of ignoring the dynamics of the system. Previous experimental work by Pearl and
466 Sibener\cite{Pearl}, using STM, has been able to capture the coalescing
467 of steps on Ni(977). The time scale of the image acquisition,
468 $\sim$70 s/image provides an upper bound for the time required for
469 the doubling to occur. In this section we give data on dynamic and
470 transport properties, e.g. diffusion, layer formation time, etc.
471
472
473 \subsubsection{Transport of surface metal atoms}
474 %forcedSystems/stepSeparation
475 The movement or wandering of a step-edge is a cooperative effect
476 arising from the individual movements of the atoms making up the steps. An ideal metal surface
477 displaying a low index facet, (111) or (100), is unlikely to experience
478 much surface diffusion because of the large energetic barrier that must
479 be overcome to lift an atom out of the surface. The presence of step-edges and other surface features
480 on higher-index facets provide a lower energy source for mobile metal atoms.
481 Breaking away from the step-edge on a clean surface still imposes an
482 energetic penalty around $\sim$~40 kcal/mol, but this is significantly easier than lifting
483 the same metal atom vertically out of the surface, \textgreater~60 kcal/mol.
484 The penalty lowers significantly when CO is present in sufficient quantities
485 on the surface. For certain distributions of CO, the penalty can fall as low as
486 $\sim$~20 kcal/mol. Once an adatom exists on the surface, the barrier for
487 diffusion is negligible ( \textless~4 kcal/mol for a Pt adatom). These adatoms are
488 able to explore the terrace before rejoining either the original step-edge or
489 becoming a part of a different edge. It is a more difficult process for an atom
490 to traverse to a separate terrace although the presence of CO can lower the
491 energy barrier required to lift or lower the adatom. By tracking the mobility of individual
492 metal atoms on the Pt and Au surfaces we were able to determine the relative
493 diffusion constants, as well as how varying coverages of CO affect the diffusion. Close
494 observation of the mobile metal atoms showed that they were typically in
495 equilibrium with the step-edges, dynamically breaking apart and rejoining the edges.
496 At times, their motion was concerted and two or more adatoms would be
497 observed moving together across the surfaces.
498
499 A particle was considered ``mobile'' once it had traveled more than 2~\AA~
500 between saved configurations of the system (typically 10-100 ps). An atom that was
501 truly mobile would typically travel much greater distances than this, but the 2~\AA~cutoff
502 was used to prevent swamping the diffusion data with the in-place vibrational
503 movement of buried atoms. Diffusion on a surface is strongly affected by
504 local structures and in this work, the presence of single and double layer
505 step-edges causes the diffusion parallel to the step-edges to be different
506 from the diffusion perpendicular to these edges. Parallel and perpendicular
507 diffusion constants are shown in Figure \ref{fig:diff}.
508
509 The lack of a definite trend in the Au diffusion data is likely due
510 to the weaker bonding between Au and CO. This leads to a lower
511 coverage ({\it x}-axis) when compared to dosage amount, which
512 then further limits the affects of the surface diffusion. The correlation
513 between coverage and Pt diffusion rates conversely shows a
514 definite trend marred by the highest coverage surface. Two
515 explanations arise for this drop. First, upon a visual inspection of
516 the system, after a double layer has been formed, it maintains its
517 stability strongly and is no longer a good source for adatoms. By
518 performing the same diffusion calculation but on a shorter run time
519 (20~ns), only including data before the formation of the double layer,
520 provides a $\mathbf{D}_{\perp}$ diffusion constant of $1.69~\pm~0.08$
521 and a $\mathbf{D}_{\parallel}$ diffusion constant of $6.30~\pm~0.08$.
522 This places the parallel diffusion constant more closely in line with the
523 expected trend, while the perpendicular diffusion constant does not
524 drop as far. A secondary explanation arising from our analysis of the
525 mechanism of double layer formation show the affect that CO on the
526 surface has with respect to overcoming surface diffusion of Pt. If the
527 coverage is too sparse, the Pt engages in minimal interactions and
528 thus minimal diffusion. As coverage increases, there are more favorable
529 arrangements of CO on the surface allowing the formation of a path,
530 a minimum energy trajectory, for the adatom to explore the surface.
531 As the CO is constantly moving on the surface, this path is constantly
532 changing. If the coverage becomes too great, the paths could
533 potentially be clogged leading to a decrease in diffusion despite
534 their being more adatoms and step-wandering.
535
536 \subsubsection{Dynamics of double layer formation}
537 The increased diffusion on Pt at the higher
538 CO coverages plays a primary role in double layer formation. However, this is not
539 a complete explanation -- the 33\%~Pt system
540 has higher diffusion constants but did not show
541 any signs of edge doubling in the observed run time. On the
542 50\%~Pt system, one layer formed within the first 40~ns of simulation time, while two more were formed as the system was run for an additional
543 110~ns (150~ns total). Previous experimental
544 work gives insight into the upper bounds of the
545 time required for step coalescence.\cite{Williams:1991,Pearl}
546 In this system, as seen in Figure \ref{fig:reconstruct}, the first
547 appearance of a double layer, appears at 19~ns
548 into the simulation. Within 12~ns of this nucleation event, nearly half of the step has
549 formed the double layer and by 86~ns, the complete layer
550 has been flattened out. The double layer could be considered
551 ``complete" by 37~ns but remains a bit rough. From the
552 appearance of the first nucleation event to the first observed double layer, the process took $\sim$20~ns. Another
553 $\sim$40~ns was necessary for the layer to completely straighten.
554 The other two layers in this simulation formed over periods of
555 22~ns and 42~ns respectively. Comparing this to the upper
556 bounds of the image scan, it is likely that most aspects of this
557 reconstruction occur very rapidly. A possible explanation
558 for this rapid reconstruction is the elevated temperatures
559 under which our systems were simulated. It is probable that the process would
560 take longer at lower temperatures.
561
562 %Evolution of surface
563 \begin{figure}[H]
564 \includegraphics[width=\linewidth]{ProgressionOfDoubleLayerFormation_yellowCircle.png}
565 \caption{The Pt(557) / 50\% CO system at a sequence of times after
566 initial exposure to the CO: (a) 258~ps, (b) 19~ns, (c) 31.2~ns, and
567 (d) 86.1~ns. Disruption of the (557) step-edges occurs quickly. The
568 doubling of the layers appears only after two adjacent step-edges
569 touch. The circled spot in (b) nucleated the growth of the double
570 step observed in the later configurations.}
571 \label{fig:reconstruct}
572 \end{figure}
573
574 \begin{figure}[H]
575 \includegraphics[width=\linewidth]{DiffusionComparison_errorXY_remade.pdf}
576 \caption{Diffusion constants for mobile surface atoms along directions
577 parallel ($\mathbf{D}_{\parallel}$) and perpendicular
578 ($\mathbf{D}_{\perp}$) to the (557) step-edges as a function of CO
579 surface coverage. Diffusion parallel to the step-edge is higher
580 than that perpendicular to the edge because of the lower energy
581 barrier associated with traversing along the edge as compared to
582 completely breaking away. Additionally, the observed
583 maximum and subsequent decrease for the Pt system suggests that the
584 CO self-interactions are playing a significant role with regards to
585 movement of the Pt atoms around and across the surface. }
586 \label{fig:diff}
587 \end{figure}
588
589
590
591
592 %Discussion
593 \section{Discussion}
594 We have shown that the classical potential models are able to model the initial reconstruction of the
595 Pt(557) surface upon CO adsorption as shown by Tao et al. \cite{Tao:2010}. More importantly, we
596 were able to observe features of the dynamic processes necessary for this reconstruction.
597
598 \subsection{Mechanism for restructuring}
599 Since the Au surface showed no large scale restructuring throughout
600 our simulation time our discussion will focus on the 50\% Pt-CO system
601 which did undergo the doubling featured in Figure \ref{fig:reconstruct}.
602 Similarities of our results to those reported previously by
603 Tao et al.\cite{Tao:2010} are quite
604 strong. The simulated Pt
605 system exposed to a large dosage of CO readily restructures by doubling the terrace
606 widths and step heights. The restructuring occurs in a piecemeal fashion, one to two Pt atoms at a time, but is rapid on experimental timescales.
607 The adatoms either
608 break away from the step-edge and stay on the lower terrace or they lift
609 up onto a higher terrace. Once ``free'', they diffuse on the terrace
610 until reaching another step-edge or rejoining their original edge.
611 This combination of growth and decay of the step-edges is in a state of
612 dynamic equilibrium. However, once two previously separated edges
613 meet as shown in Figure 1.B, this nucleates the rest of the edge to meet up, forming a double layer.
614 From simulations which exhibit a double layer, the time delay from the initial appearance of a nucleation point to a fully formed double layer is $\sim$35~ns.
615
616 A number of possible mechanisms exist to explain the role of adsorbed
617 CO in restructuring the Pt surface. Quadrupolar repulsion between adjacent
618 CO molecules adsorbed on the surface is one possibility. However,
619 the quadrupole-quadrupole interaction is short-ranged and is attractive for
620 some orientations. If the CO molecules are ``locked'' in a specific orientation
621 relative to each other, through atop adsorption for example, this explanation
622 gains some credence. The energetic repulsion between two CO located a
623 distance of 2.77~\AA~apart (nearest-neighbor distance of Pt) and both in
624 a vertical orientation, is 8.62 kcal/mol. Moving the CO apart to the second
625 nearest-neighbor distance of 4.8~\AA~or 5.54~\AA~drops the repulsion to
626 nearly 0 kcal/mol. Allowing the CO's to leave a purely vertical orientation
627 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.
628 As mentioned above, the energy barrier for surface diffusion
629 of a Pt adatom is only 4 kcal/mol. So this repulsion between neighboring CO molecules can
630 increase the surface diffusion. However, the residence time of CO on Pt was
631 examined and while the majority of the CO is on or near the surface throughout
632 the run, most molecules are mobile. This mobility suggests that the CO are more
633 likely to shift their positions without necessarily the Pt along with them.
634
635 Another possible and more likely mechanism for the restructuring is in the
636 destabilization of strong Pt-Pt interactions by CO adsorbed on surface
637 Pt atoms. This would then have the effect of increasing surface mobility
638 of these atoms. To test this hypothesis, numerous configurations of
639 CO in varying quantities were arranged on the higher and lower plateaus
640 around a step on a otherwise clean Pt(557) surface. One representative
641 configuration is displayed in Figure \ref{fig:lambda}. Single or concerted movement
642 of Pt atoms was then examined to determine possible barriers. Because
643 the movement was forced along a pre-defined reaction coordinate that may differ
644 from the true minimum of this path, only the beginning and ending energies
645 are displayed in Table \ref{tab:energies} with the corresponding beginning and ending reaction coordinates in Figure \ref{fig:lambdaTable}. These values suggest that the presence of CO at suitable
646 locations can lead to lowered barriers for Pt breaking apart from the step-edge.
647 Additionally, as highlighted in Figure \ref{fig:lambda}, the presence of CO makes the
648 burrowing and lifting of adatoms favorable, whereas without CO, the process is neutral
649 in terms of energetics.
650
651 %lambda progression of Pt -> shoving its way into the step
652 \begin{figure}[H]
653 \includegraphics[width=\linewidth]{lambdaProgression_atopCO_withLambda.png}
654 \caption{A model system of the Pt(557) surface was used as the framework
655 for exploring energy barriers along a reaction coordinate. Various numbers,
656 placements, and rotations of CO were examined as they affect Pt movement.
657 The coordinate displayed in this Figure was a representative run. As shown
658 in Table \ref{tab:rxcoord}, relative to the energy of the system at 0\%, there
659 is a slight decrease upon insertion of the Pt atom into the step-edge along
660 with the resultant lifting of the other Pt atom when CO is present at certain positions.}
661 \label{fig:lambda}
662 \end{figure}
663
664 \begin{figure}[H]
665 \includegraphics[totalheight=0.9\textheight]{lambdaTable.png}
666 \caption{}
667 \label{fig:lambdaTable}
668 \end{figure}
669
670
671 \subsection{Diffusion}
672 The diffusion parallel to the step-edge tends to be
673 much larger than that perpendicular to the step-edge. The dynamic
674 equilibrium that is established between the step-edge and adatom interface. The coverage
675 of CO also appears to play a slight role in relative rates of diffusion, as shown in Figure \ref{fig:diff}.
676 The
677 Thus, the bottleneck of the double layer formation appears to be the initial formation
678 of this growth point, which seems to be somewhat of a stochastic event. Once it
679 appears, parallel diffusion, along the now slightly angled step-edge, will allow for
680 a faster formation of the double layer than if the entire process were dependent on
681 only perpendicular diffusion across the plateaus. Thus, the larger $D_{\perp}$, the
682 more likely a growth point is to be formed.
683 \\
684
685
686 %breaking of the double layer upon removal of CO
687 \begin{figure}[H]
688 \includegraphics[width=\linewidth]{doubleLayerBreaking_greenBlue_whiteLetters.png}
689 \caption{(A) 0~ps, (B) 100~ps, (C) 1~ns, after the removal of CO. The presence of the CO
690 helped maintain the stability of the double layer and upon removal the two layers break
691 and begin separating. The separation is not a simple pulling apart however, rather
692 there is a mixing of the lower and upper atoms at the edge.}
693 \label{fig:breaking}
694 \end{figure}
695
696
697
698
699 %Peaks!
700 %\begin{figure}[H]
701 %\includegraphics[width=\linewidth]{doublePeaks_noCO.png}
702 %\caption{At the initial formation of this double layer ( $\sim$ 37 ns) there is a degree
703 %of roughness inherent to the edge. The next $\sim$ 40 ns show the edge with
704 %aspects of waviness and by 80 ns the double layer is completely formed and smooth. }
705 %\label{fig:peaks}
706 %\end{figure}
707
708
709 %Don't think I need this
710 %clean surface...
711 %\begin{figure}[H]
712 %\includegraphics[width=\linewidth]{557_300K_cleanPDF.pdf}
713 %\caption{}
714
715 %\end{figure}
716 %\label{fig:clean}
717
718
719 \section{Conclusion}
720 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.
721
722 %Things I am not ready to remove yet
723
724 %Table of Diffusion Constants
725 %Add gold?M
726 % \begin{table}[H]
727 % \caption{}
728 % \centering
729 % \begin{tabular}{| c | cc | cc | }
730 % \hline
731 % &\multicolumn{2}{c|}{\textbf{Platinum}}&\multicolumn{2}{c|}{\textbf{Gold}} \\
732 % \hline
733 % \textbf{Surface Coverage} & $\mathbf{D}_{\parallel}$ & $\mathbf{D}_{\perp}$ & $\mathbf{D}_{\parallel}$ & $\mathbf{D}_{\perp}$ \\
734 % \hline
735 % 50\% & 4.32(2) & 1.185(8) & 1.72(2) & 0.455(6) \\
736 % 33\% & 5.18(3) & 1.999(5) & 1.95(2) & 0.337(4) \\
737 % 25\% & 5.01(2) & 1.574(4) & 1.26(3) & 0.377(6) \\
738 % 5\% & 3.61(2) & 0.355(2) & 1.84(3) & 0.169(4) \\
739 % 0\% & 3.27(2) & 0.147(4) & 1.50(2) & 0.194(2) \\
740 % \hline
741 % \end{tabular}
742 % \end{table}
743
744 \section{Acknowledgments}
745 Support for this project was provided by the National Science
746 Foundation under grant CHE-0848243 and by the Center for Sustainable
747 Energy at Notre Dame (cSEND). Computational time was provided by the
748 Center for Research Computing (CRC) at the University of Notre Dame.
749
750 \newpage
751 \bibliography{firstTryBibliography}
752 \end{doublespace}
753 \end{document}