Mechanism of Trichloroethene Hydrodehalogenation: A First

In longer simulations, the priority queue can become quite large and the probability to hit a disabled reaction increases. Therefore, we applied garba...
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The Mechanism of Trichloroethene Hydrodehalogenation: A First Principles Kinetic Monte Carlo Study Anna Maria Kausamo, Jenni Andersin, and Karoliina Honkala J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/jp503494g • Publication Date (Web): 07 Aug 2014 Downloaded from http://pubs.acs.org on August 12, 2014

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The Mechanism of Trichloroethene Hydrodehalogenation: A First-Principles Kinetic Monte Carlo Study A. Kausamo, J. Andersin, and K. Honkala



Department of Chemistry, Nanoscience Center, P. O. Box 35, University of Jyväskylä, 40014, Jyväskylä, Finland E-mail: karoliina.honkala@jyu.

Abstract

Keywords

A hydrodehalogenation (HDC) reaction of trichloroethene (TCE) has gained a lot of interest due to its possible application in water purication, but the reaction mechanism has been subject to much controversy. In this work, HDC of TCE on Pd(111) was examined by carrying out kinetic Monte Carlo simulations based on DFT-calculated thermodynamic and kinetic parameters. Obtained kMC results show that the HDC follows a so-called direct pathway, which means that after adsorption on a catalyst, TCE quickly dechlorinates producing CH-C and then, more slowly, hydrogenates to form hydrocarbon products. This is reected in the surface coverage snapshots, where intermediates corresponding the direct pathway are mostly seen. We also investigated the eect of lateral Cl-Cl repulsions to the distribution and coverage of intermediates, and to the reaction mechanism. In general, the adsorbed Cl atoms retard further dechlorinations leading to less effective HDC, which is in line with experimental observations.

heterogeneous catalysis;density functional theory;kinetic Monte Carlo,olens,hydrogenation



To whom correspondence should be addressed 1

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Introduction

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Reaction energetics on both pure and chlorinated Pd(111) surfaces have been computationally studied by some of our group 9,10 using density functional theory (DFT). These results suggest the direct mechanism is the most probable one. The sequential pathway is unlikely to occur, because the computed activation barriers corresponding to these elementary steps are high compared to the barriers of the direct pathway. A comparison of calculated activation barriers is a useful approach to search for the most probable reaction mechanism. However, for a reaction with many elementary steps and a large variety of reaction intermediates present on a catalyst, it is prudent to invoke a machinery, which takes details into account but still generates an overall picture. In this respect, further studies for TCE HDC were required to consider the proposed direct mechanism, and to shed light on the rate-determining step of the reaction, and the eect of chlorine atom poisoning. First principles kinetic Monte Carlo (kMC) methods oer an eective means to simulate the progress of chemical reactions in real time, eectively concentrating on relatively rare reaction events, and properly averaging over phenomena in shorter time and length scales. The parameters of rst-principles kMC simulations such as activation and adsorption energies are typically calculated with DFT. Therefore, they have a well-dened physical meaning and contain valuable information on the microscopic properties of a system. This information is embedded into the kMC setup, resulting in a comprehensive simulation with both explanatory and predictive power. Monte Carlo simulations largely enrich the description gained from DFT, which, as must be recalled, is a `0 K, vacuum' method. Both temperature and pressure eects are included in kMC description, and the interplay of all elementary processes has a well-dened statistical basis. Fur-

Trichloroethene (TCE) is widely used as an industrial degreasing agent. However, it is a dangerous compound, which can cause cancer and liver damage, and therefore its removal from exposure areas such as groundwater is essential. 1 A promising approach to achieve this is to convert TCE to hydrocarbons via catalytic hydrogehalogenations (HDCs). 2 Fast, complete, and industrially feasible hydrodehalogenations are, naturally, of utmost importance. Heterogeneous, especially palladium-based, catalysts have proved to be very eective in this respect. 25 In order to design an active, selective, and stable catalyst, it is crucial to get a thorough understanding of the reaction mechanism for a process at hand. The reaction pathway for TCE HDC has generally been proposed to be Langmuir-Hinshelwood type: First, the reactants, TCE and H2 , adsorb on a catalyst, TCE unimolecularly and dihydrogen dissociatively. Then the adsorbed TCE undergoes hydrogenation and dechlorination steps leading to hydrocarbon products, which desorb from the catalyst. However, what exactly happens after the reactants have adsorbed is not yet fully agreed upon. Two dierent, competing reaction mechanisms have been proposed namely the so-called direct 5,6 and sequential pathways 7,8 . The direct pathway consists of fast dechlorination steps followed by slower hydrogenations, and intermediates are radical-like species. In this description, TCE fully dechlorinates before hydrogenations. The sequential pathway involves alternating hydrogenations and dechlorinations, which lead, through partially dechlorinated and partially hydrogenated species, to hydrocarbons. Not only the type of mechanism but also the rate-determining step (RDS) of TCE HDC in any of the proposed mechanisms is still subject to some controversy. 2

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thermore, dierent adsorbate-adsorbate interaction models can be explicitly included which, in most cases, makes the kMC simulations more versatile than the rate equation approach to chemical kinetics. kMC has been successfully employed to analyse dierent catalytic systems of varying complexity. Perhaps the most studied reaction, partly due to its simplicity, is carbon monoxide oxidation on dierent surfaces. For example, Reuter et al. have addressed microscopic characteristics of oxidation of carbon monoxide on a RuO 2 (110) surface under dierent experimental conditions, ranging from ultra-high vacuum to the pressure of several atmospheres, thus managing to computationally investigate the process in technologically relevant environment. 11 Carbon monoxide electrocatalytic oxidation has been studied on platinum . 12 In the realm of organic chemistry, structure sensitivity of a Fischer-Tropsch reaction has been assessed by van Santen and coworkers 13 , and Hansen et al. have investigated the temperature-programmed decomposition of acetic acid 14 as well as ethylene hydrogenation 15 on Pd surfaces. On Rh(111) an ethanol decomposition mechanism has been successfully elucidated. 16 In many of these systems, the sucient model is that one adsorbate occupies one site on a surface, because the adsorbates are small or lack strong repulsive interactions. However, when large molecules, which can have local, strongly repulsive atomic groups, react, the one-site model might not be adequate. Moreover, if we have both large and small adsorbates, which can compete for adsorption sites, the one-site model can lead to a distorted view on the surface conguration. In these cases, one needs to enable single adsorbates to occupy a multitude of sites, in order to get a suciently accurate description of the surface composition. TCE HDC is an example of this kind of system, since it involves organic molecules consisting of many atoms including

chlorines, which repel the chlorine atoms of surrounding species. To explore the TCE HDC reaction mechanism we have implemented a general kinetic Monte Carlo code, which enables the versatile multisite and lateral interaction models. The process parameters are based on our recent periodic DFT calculations. 9 For generality and eciency reasons, the algorithm of choice is a revised form 17 of the First Reaction Method(FRM) 18 . In the following sections, the simulation details will be described and TCE HDC results are explored in detail to shed light on the questions outlined above.

Theoretical Background Kinetic Monte Carlo is a stochastic method, which simulates the Markovian Master equation 17

dP (c, t) X [P (c0 , t)kc0 c − P (c, t)kcc0 ] , (1) = dt c0 6=c where P (c, t) is the probability to nd the system at time t in a conguration c, and kcc0 is the probability for transition from a conguration c to a conguration c0 . The Master equation describes the time evolution of the system in conguration space. The rst reaction method is exact with respect to simulation of Equation (1), but a choice of model involves usually many assumptions, whose validity should be checked or, at least, recognized. By a model we mean decisions how the real system is coarse-grained, adsorbate-adsorbate interactions are described, and rate constants computed. The starting point of most kMC surface reaction models is a lattice-gas system, where a surface is modeled as a regular array of discrete lattice sites. To each site, one can assign dierent variables including for instance, site occupation and type, temporal information about the 3

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last modication of a site etc. Periodic boundary conditions are employed to imitate the real, continuous system. By the clever choice of the site labeling and boundary conditions, one can model surface defects, steps and even nanoparticles. 19,20 In the lattice-gas model, the surface is in contact with innite gas reservoir, which provides adsorbates to the lattice, where they react. In a typical kMC simulation, time evolution of the system is described as a discrete event  continuous time stochastic process, where each event corresponds to a process, that can occur at a specic lattice site. A process changes the system conguration from one to the other, where the conguration is dened as a combination of all lattice sites and their specic occupations. The processes in our program comprise of surface adsorptions, desorptions, and diusions as well as chemical reactions. The rate constants for kMC simulations were calculated using Transition State Theory (TST) 21,22 and DFT-based adsorption and activation energy data given in Table 1. For a chemical reaction

and, for desorption,

k=

The Algorithm

The FRM algorithm 17 consists of the following steps: 1. Start with a random conguration. Set time to some initial value t. Build a list of possible events for a chosen conguration. For each event, i, get a random number ξi ∈ [0, 1[ and compute the time of occurrence 1 ti = t − ln(1 − ξi ), ki

(2)

where ki is the rate constant of the event i. Store this list into a priority queue, which orders its elements according their occurrence times.

where T is the temperature, Q† is the equilibrium partition function for a transition state, QA and QB are equilibrium partition functions for reactants, and E0 corresponds the energy dierence between the transition state and the reactants. h and kB are Planck and the Boltzmann constants, respectively. For adsorption, TST gives

Asite p k=√ e−E0 /kb T 2πmkB T

(4)

Simulation Details

TST gives the rate constant

kB T Q† −E0 /kB T e , h QA QB

Asite kB T −E0 /kB T e . Auc h

In Equations (3) and (4), Asite is dened as the total area of a substrate divided by a number of sites in the substrate from which the adsorbate can desorb, and Auc is the area of the unit cell. It must be noted that, calculating rate constants this way, results in invoking all the assumptions of TST. 22 In addition, in the simulations described in the following section, the † assumption that, in Equation (2), QAQQB ≈ 1 is made.

A + B −→ C ,

k=

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2. Select, and remove from the priority queue, the event i, that fullls the condition ti ≤ tj for all j . If the event is enabled, go to step 3. If not, then go to step 4.

(3) 4

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3. Change the conguration corresponding to the event i. Update the clock according to the mapping t 7→ ti . Update the event list: for each event, that has become possible due to the new conguration, compute the time of occurrence

The main DFT results on a TCE HDC reaction over a bare Pd(111) surface are summarized in Table 1. This data suggest that the most plausible reaction pathway the direct mechanism, where complete dechlorination of TCE is followed by sequential hydrogenations leading to organic products. The presence of relative high Cl coverage was not observed to impact on the reaction mechanism. 9 However, high chlorine coverage hinders the adsorption of reactants, increases activation barriers of dechlorinations, and decreases activation barriers of desorption. We note that the accuracy of activation barriers generated by DFT is, generally, of the order of 0.20.3 eV. As rate constants depend exponentially on activation barriers, this can lead to errors that cannot always be considered insignicant 25 . It has been, however, observed that the errors can to compensate each other and thus reduce the impact on reaction kinetics even when the functional is changed. 26

1 ln(1 − ξi , ) ki where ξ ∈ [0, 1[ is a random number. ti = t −

Add these events to the priority queue. 4. If the stopping conditions are fullled, stop. If not, go to step 2. In longer simulations, the priority queue can become quite large and the probability to hit a disabled reaction increases. Therefore, we applied garbage collection for every 10000th cycle. The garbage collection removes disabled processes and, therefore, speeds up simulations. On the other hand, garbage collection involves going through the whole event list, so it should not be carried out too often. For a detailed discussion of the characteristics and performance of the FRM method, see Ref 17 .

The Lattice The fcc(111) surface is simulated with a square lattice, where each lattice point corresponds to a top, fcc or hcp site. These site types are permuted to generate the fcc(111) surface geometry. Periodic boundary conditions are applied to represent the real macroscopic surface. In the simulation program, a molecule is dened as a set of atoms where, to each atom in a molecule, a site is assigned. Which sites a polyatomic molecule occupies, depends on its DFToptimised adsorption geometry. 9 Each molecule is allowed to have any of its geometrically equivalent orientations in the lattice. In Figure 1, the optimal adsorption geometries of TCE and one of its dechlorination fragments, namely CH-CCl, are shown as an example of the data. 9 In our lattice model six and four lattice sites have been assigned for TCE

DFT calculations As noted in the Introduction, TCE HDC reaction has been subjected to a comprehensive DFT study. 9,10 The optimized geometries as well as activation energies were calculated for reagents and reaction intermediates on bare and chlorine-covered Pd(111) surfaces. The real catalytic systems consist of metal nanoparticles, and the employed surface model mimics to the most stable facet of these nanoparticles. Currently the role of low coordinated sites for the present reaction is not known and therefore they are omitted from this study. All DFT results were computed employing the GPAW software package 23 with the PBE 24 exchange and correlation functional. 5

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clude, for example, ethyne desorption for which the activation energy is about 2 eV. 27 Next, certain choices made to set up the applied model are discussed including the selection of elementary reaction steps, the impact of water, the role of the diusion, and the size of the lattice. In an experimental gas phase study 6 , ethane, instead of ethene, was produced in TCE hydrodehalogenation on alumina-supported palladium. The ethene conversion to ethane is excluded from the present study because recent DFT calculations highlight ethene formation before ethane and that the ethene hydrogenation is not limiting the hydrogenation process 28 . The C-C bond breaking is observed experimentally 6 but this is also excluded from the present model as DFT calculations give high activation barriers for the CC bond breakage for species derived for smallest olens. 29,30 Furthermore, the elementary reaction steps leading to ethylidyne formation and its subsequent hydrogenation are eliminated, since experimental 3135 as well as computational 36 studies show that ethylidyne is a spectator species and unreactive even under high hydrogen pressures. The role of water in TCE adsorption has been assessed computationally by introducing to the TCE-Pd(111) system a double layer of water 37 . The equilibrium distance between the water layer and Pd(111) was found to be 2Å and the water layer was not observed to aect the optimal adsorption geometry of TCE or other relevant surface species. The impact of water to TCE adsorption energy is also minor making it 0.2 eV more exothermic. The opening of dierent reaction pathways is unlikely. The reason for this is the relatively low polarity of TCE HDC intermediates compared to, for example, intermediates in Refs 38,39 , which contain O-H and C-O bonds with Pauling electronegativity values of 1.24 and 0.89, respectively. The diusion of adsorbates on metal surfaces

Figure 1: DFT-optimized adsorption geometries of TCE (a) and CH −CCl (b) on Pd(111). The color coding is as follows: light gray stands for Pd atoms while dark gray corresponds C atoms. Yellow and white are Cl and H atoms, respectively. and CH-CCl species, respectively to imitate the real molecular arrangement. The choice of the sites depends on the location of each atom in the molecule; for example, for TCE, two top sites, two fcc sites, and two hcp sites are assigned. This is, of course, only an approximation but still a much more accurate description than one would get by representing each adsorbate geometry by using only one site.

Elementary Processes Table 1 summarizes the elementary processes employed in the kMC simulation, together with their activation energies, which have been used to calculate occurrence times according to equations (2)-(4). Most of the following kMC analyses will employ activation barriers corresponding to zero Cl and H coverages (Table 1). In a few kMC runs, high Cl coverage parameters have been used. However, as will be discussed in section Results and Discussion, the chlorine coverage is not likely to reach such values, so these results represent a hypothetical upper limit scenario. Some processes, which would in principle be possible but which are, due to their high activation barriers 9 , very unlikely to take place have been omitted from consideration. These in6

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is typically very fast. To estimate diusion barriers on a metal surface a simple rule of thumb has been derived 40 and it says that the activation barrier is about 12% of the binding energy of an adsorbate with respect to its adsorption site. When the rule is applied to hydrogen, the resulting barrier is only about 0.07 eV for migration from one hollow site to the other, and the reverse process is equally fast. Therefore, the use of realistic diusion barriers leads to the large rate constants and most of the simulation time is wasted sampling hopping of adsorbates between sites. To increase the sampling eciency, we have in line with Ref. 19 xed the diusion rate constants to 1 · 104 s−1 . Unless otherwise stated, this value has been used throughout our kMC simulations. The value was chosen such that one achieves sucient mixing of the adsorbates, but avoids intolerably time-consuming simulations. A comparison of simulations with and without diusion produces essentially similar trends and mechanisms with only quantitative, not qualitative, dierences. As expected the calculations without diusion are signicantly faster. Therefore, the diusion-free treatment seems to be a suitable way to get a quick overview of the system, for example prior to longer calculations. In the present study, we have employed this approach to quickly check the system's response to variation of simulation time and energy data. Most simulations were carried out employing a periodic lattice consisting of 111 × 111 metal atoms. Increasing the lattice size did not change the observed trends, neither did decreasing the size to 60 × 60 metal atoms, which was actually large enough to generate equivalent results. Before the production runs the software were subjected to many tests calculations concerning both syntax and the algorithm. One important test was to check whether kMC produces same equilibrium adsorbate and coverage properties as Metropolis Monte Carlo sim-

Table 1: Elementary processes for TCE hydrodehalogenation, 9 and the corresponding activation energies with respect to the transition from an initial to a nal conguration. Process TCE adsorption TCE desorption H2 dissociative adsorption H2 associative desorption HCl dissociative adsorption HCl associative desorption VC desorption DCE desorption ethene desorption TCE−→CHCl-CCl+Cl CHCl-CCl+Cl−→TCE CHCl-CCl−→CH-CCl+Cl CH-CCl+Cl−→CHCl-CCl CH-CCl−→CH-C+Cl CH-C+Cl−→CH-CCl VC−→CH2 -CH+Cl CH2 -CH+Cl−→VC CH-CHCl−→CH-CH+Cl CH-CH+Cl−→CH-CHCl CHCl-CCl+H−→DCE DCE−→CHCl-CCl+H CH-C+H−→CH2 -C CH2 -C−→CH-C+H CH2 -C+H−→CH2 -CH CH2 -CH−→CH2 -C+H CH2 -CH+H−→ethene ethene−→CH2 -CH+H CH-CCl+H−→CH-CHCl CH-CHCl−→CH-CCl+H CH-CHCl+H−→VC VC−→CH-CHCl+H

Eact (eV ) 0.0 0.57 0.0 1.16 0.0 1.3 0.77 0.75 0.89 0.23 1.31 0.08 1.04 0.55 0.79 0.46 0.84 0.21 1.07 0.7 0.9 0.58 1.4 0.83 0.62 0.73 1.07 0.91 0.87 0.7 0.98

7

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ulations. 41 In this respect, we tested the hydrogen coverage in dierent (p, T ) conditions, and found good agreement with our Metropolis Monte Carlo equilibrium results. Unless otherwise stated, the system was simulated for 1 second. Substantially longer simulations, up to 1000 seconds, were performed with and without diusion included. In these longer runs,the surface was observed to become less active due to the competition of adsorption sites between TCE and hydrogen, and to the accumulation of Cl on the lattice. The competition is related to the model: in our lattice-gas setup, we consider direct, competitive adsorption of TCE and hydrogen gases, and the adsorption of dihydrogen is both sterically and energetically strongly favored over TCE adsorption. The chlorine accumulation reects the chlorine atom poisoning, which is identied experimentally 4244 and typical for TCE HDC. Owing to our main interest on the TCE HDC mechanism, we concentrated on the time scale, where the catalyst is most active. This approach has been successfully applied to other mechanistic kMC studies as well. 45

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that would reproduce a certain laboratory experiment. In kMC simulations TCE pressure is set to 2000 Pa since it is known to produce a sucient TCE coverage 6 . Unless otherwise stated temperature is 300K and hydrogen partial pressure equals to 100 Pa. The reason for relatively low hydrogen partial pressure was to increase the TCE concentration in the lattice. This choice is justied when one considers the eld catalyst, where the system does not consist of two, mixed, competitively adsorbing gases, and thus it is unlikely to have direct competition for adsorption sites between TCE and hydrogen. The fact that such a low hydrogen partial pressure suces to introduce enough reagent hydrogen atoms on the surface indicates that the availability of hydrogen is hardly a limiting factor in the TCE HDC reaction. It is also experimentally acknowledged that the dissociative adsorption takes place readily on palladium. 46 We performed simulations also at higher hydrogen pressures up to 1000 kPa. Increasing H2 pressure did not enhance hydrodehalogenation but introduced competition between adsorption sites and therefore suppressed TCE adsorption. Room temperature was chosen because the real system involves liquid water, which is available at narrow temperature range from 273 K to 373 K and therefore large temperature scans are unnecessary. However, additional simulations were performed at lower, 200K, and higher, 600K, temperature to validate the kinetic description. Increasing the temperature increased the rate of ethene production, as expected. Despite assumptions and simplications made for the lattice-gas kMC treatment, this kind of methodology has successfully been used to describe electrocatalytic reactions, that involve water phase in contact with a catalyst surface. 12 We carried out a set of kMC simulations to gain a deeper insight into the TCE HDC mechanism. One of the benets of kMC technique

Results and Discussion The real, environmental application of TCE HDC catalysis involves the catalyst in contact with water, where the contaminant exists in small concentrations. However, the present calculations simulate a reaction at the gas-solid interface, while the laboratory setup is again different since usually TCE is fed in as a toluene solution and hydrogen gas is driven into a reactor 7 . Therefore, both computational and laboratory investigations may probe dierent chemistry compared to the eld system. From the computational point of view, our aim was to choose conditions that would lead to best possible model of the real system rather than a setup 8

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is that it gives atomic-level information about intermediates and dominant elementary processes. These characteristics will be discussed next in the following subsections, where results will be presented for two cases: a) without strong adsorbate-adsorbate interactions other than the natural requirement that two adsorbates can not occupy the same site, and b) within a stronger repulsion model, which explicitly includes strong Cl-Cl repulsions. Under TCE HDC simulation conditions, the hydrogen coverage is generally high around 60 % The spatial requirement for dihydrogen dissociative adsorption is only to have two adjacent free adsorption sites, and thus this fast adsorption readily occurs. However, in our locally quite detailed surface model, TCE adsorption requires six properly aligned sites or even more, if Cl repulsions are included. Altogether we end up with the situation, where the high coverage of hydrogen eectively hinders further TCE adsorption. Even when reaction products desorb, both energetic and steric factors favor H 2 dissociative adsorption signicantly so that the new adsorbates are most likely hydrogen molecules rather than TCE. This is insensitive to small, up to 0.5eV, changes in adsorption energies, because dissociative adsorption energy of H 2 (1.16eV) is more exothermic compared to that of TCE (-0.57eV). All in all, this reects the fact that, even though kMC tends to be sensitive to small changes in activation barriers, the overall picture will not be distorted if the steric description is accurate and free energy dierences of elementary processes suciently large. If the partial pressure of hydrogen is set to a very low value (in the order of a tenth of Pascal), the resulting lower H 2 coverage leads to better TCE adsorption and, therefore, the enhanced hydrodehalogenation and ethene production owing to enhanced TCE adsorption. As mentioned earlier, this kind of competition is not likely to cause problems in the eld system.

Interestingly, the eect of steric adsorption requirements for the heterogeneous catalytic reaction has been recognized also for carbon monoxide oxidation on palladium. 47 Therefore, even the local, congurational level of description must be relatively accurate.

The zero interaction model

4041

6000

5464

7000

5464

5000

2532

4000 3000

2

2

1

0

195

1000

204

2000 104

Average reaction frequency

8000

l

Figure 2: Average reaction frequencies. Only events with nonzero average occurrence are plotted. Figure 2 depicts the occurrence frequencies of TCE HDC elementary steps showing that TCE readily adsorbs on Pd(111) and that rst two dechlorinations occur very fast and irreversibly. This reects the DFT data shown in Table 1: TCE adsorption is non-activated, and the barriers for rst two dechlorinations are only 0.23 eV and 0.08 eV, respectively. The last dechlorination leading to the formation of CH-C intermediate is slower and slightly more reversible than the previous two. This results from the higher activation energy for the CH-CCl −→ CH-C+Cl step. The barrier is about 0.55 eV being considerably larger compared to the rst two dechlorination barriers. Furthermore, the barrier for its reverse process is only 0.24 eV higher enhancing the rate for the reverse reaction. We tested the sensitivity of results to the height of the last 9

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Cl

ne

H

-CH

CH

->

l+H

-CC the

H

2-C

l

+C

-C+

>e

H-

H+

2-C

CH

CH H2

-C

CH

->

>C

H-

+H

2-C

CH

2-C

CH

-CH

H2

>C

H-

-C+

CH

l

CC H-

H >C

>C

Cl-

Cl-

-CH

CH

Cl

l+C

l

l+C

-CC CH

l->

CC

-C+ CH l->

-C+

CH

-CC

CH

CC

Cl-

CH

ion

rpt

ClCH

dso

E->

TC

Ea

TC

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tionally few ethenes and CH 2 -CH fragments. The formed CH-CCl remais on the surface, which results from the slowness and reversibility of the last dechlorination step but its overall coverage is low. Hydrogen coverage increases fast during the simulation and, ∼ 60 % of hollow sites are occupied by hydrogen, while chlorine atoms cover ∼ 30 % of hollow sites. The absence of partially dechlorinated and partially hydrogenated intermediates such as CH-CHCl supports the interpretation that the reaction mechanism follows the direct pathway. More precisely, the most likely pathway is

dechlorination barrier by decreasing it ∼ 0.3 eV and observed the complete dechlorination. No TCE desorption is observed, probably due to the fact that the rst dechlorination is so fast compared to desorption for which the activation barrier is 0.57 eV, and thus essentially all TCE, that adsorbs dechlorinates. The hydrogenation steps are slower than dechlorinations, the rst of them producing C-CH 2 occurs most readily while the last two have comparable rates. Altogether,the reaction pathway comprises of fast adsorption and dechlorination steps, and slower hydrogenation steps. Next, we analyze the nature and extent of surface species on Pd(111). Figure 3 displays the surface composition after the 1s simulation.

CHCl-CCl2 −→ CHCl-CCl + Cl CHCl-CCl −→ CH-CCl + Cl CH-CCl −→ CH-C + Cl CH-C + H −→ C-CH2 C-CH2 + H −→ CH-CH2 CH-CH2 + H −→ ethene . This is also depicted in Figure 4, where the relative energies of dierent intermediates and transition states are illustrated.

The strong interaction model Recent studies indicated that lateral interactions between adsorbed species can strongly impact on calculated rates 25,48 . Here, in particular, the repulsive interactions between chlorine atoms and chlorine and carbon atoms might even induce changes to the reaction scheme. To this end we forbade carbon and chlorine atoms to occupy adjacent sites on the surface, and an additional constrained based on previous DFT results was imposed that chlorines can not occupy sites that are closer than 3.7Å from each other. 9 Together these choices form a crude model for lateral interactions for TCE HDC. Fig. 5 summarizes the impact of the lateral interactions to the reaction frequencies of elementary steps. Again, TCE adsorption oc-

Figure 3: The number of dierent adsorbates at t=1.0 s. Only the organic fragments are included; H and Cl concentrations are high: ∼ 60 % (hydrogen) and ∼ 30 % (chlorine) of possible hollow sites are occupied. Roughly 70 % of the adsorbed TCE converts to totally dechlorinated and partially hydrogenated products, the rest 30 % dechlorinate partially. The forming fragments include CH 2 C to which roughly 50 % of adsorbed TCE transforms. The second abundant radicals are CH-C and CH-CCl, which are approximately formed in equal quantities. We observe addi10

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Figure 5: Average process frequencies. Only events with nonzero average occurrence are included. curs readily, but contrary to the zero interaction model this time we also observe desorption, which probably reects steric factors. In general, Cl-Cl repulsions hamper dechlorinations and decrease further reactivity with the result that some TCE stays intact on the surface having enough time to desorb. No desorption is seen within the zero interaction model where the rst dechlorination is fast. Regardless of the chosen interaction model, the majority of adsorbed TCE undergoes rst dechlorination completely and irreversibly. However, in the strong interaction model dechlorinations are controlled by a number and distribution of Cl atoms on the surface, which substantially reduce a number of available free sites. This is seen in Figure 5, where the rate of the second dechlorination step is only 60% of the rate of rst one, while in the zero repulsion model these steps are complete. In both models the last dechlorination step is the slowest one. The incorporation of the strong lateral interactions reduce complete dechlorinations such that only about 50 % of radicals, which have lost two chlorine atoms will lose the third one; a more moderate decrease is seen

Figure 4: The most probable reaction pathway for TCE HDC. The activation barriers correspond to those in Table 1; in addition, reaction energy data from Ref. 9 has been used. The zero of energy corresponds to TCE(g)+3H 2 (g).

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with zero interactions. Furthermore, we observe that lateral interactions impact on hydrogenations reducing their frequency, although one must keep in mind that the concentration of CH-C species, pivotal for hydrogenation, is already lower owing to incomplete TCE dechlorination. Kali edellinen edelleen vaikea We detect also some CH-C chlorinations and CHClCCl hydrogenations but to a substantially lesser extent than hydrogenation of totally dechlorinated species. Enhanced coverage of partially dechlorinated species originating from reduced dechlorination frequencies increases probability for dierent hydrogenation reactions. This is apparent from Figure 5 as some adsorbed TCE is discovered to form dichloroethene (DCE). Figure 6 gives, a snapshot of surface composition after a 1s simulation. As a rule the surface in mainly covered by hydrogen ( ∼ 70 % of hollow sites) while lateral interactions decrease chlorine coverage substantially ( ∼ 10%). The main organic surface intermediate is CHCl-CCl, which forms in the rst dechlorination step. The second and third abundant species is CHCCl and CH-C, respectively. Nearly 62% of the CH-C radicals hydrogenates to CH 2 -C,an intermediate leading to ethene, and few DCE are observed as well. The composition is in line with calculated frequencies shown in Figure 5. We notice that, compared to Figure 3, a number of partially dechlorinated fragments in particular CHCl-CCl increases.

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Figure 6: Number of dierent adsorbates at t = 1.0 s. Only the organic fragments are included; ∼ 70% ∼ 10% of possible hollow sites are occupied by hydrogen and chlorine, respectively. The adsorbate numbers correspond to the lattice with 111 × 111 surface metal atoms. son the surface over the time due to the spatial factors inherent in the model. There might, therefore, exist a risk that the reaction intermediates related to the indirect mechanism, that is partially dechlorinated, partially hydrogenated fragments, build up on the surface in longer simulations. To consider this possibility, we carried out diusion-free simulations for 100, and 1000 seconds with the both interaction models. No accumulation of CH x Cly species (y > 0) was observed, and thus the direct mechanism dictates whereas only a small amount of species such as DCE was seen. The rate-determining step of TCE HDC reaction has been a subject to much discussion in the literature. 5,6 However, in a multi-step reaction mechanism, it often happens that there is no single RDS. This seems to be case in TCE HDC, since hydrogenation rates are similar. This reects one of the strengths of kMC since in the deterministic approach to chemical kinetics assumptions are needed to express a rate equation. However, in the case of TCE

TCE HDC mechanism Our kMC simulations conrm the earlier DFTbased hypothesis that the TCE HDC reaction proceeds via the direct reaction mechanism. The conclusion does not depend on the treatment of lateral interactions as the pathway leading to full dechlorination dominates in the both interaction models examined. As mentioned in Introduction, hydrogen and chlorine atoms poi12

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HDC, most assumptions concerning, for example, a single rate-determining step, would immediately fail resulting in an oversimplied description of the overall dynamics. Chlorine-chlorine repulsions were observed to hinder the dechlorination steps and lead, in small quantities, to intermediates and products, which are related to the sequential pathway of TCE HDC. In experiments, indications for both mechanism have been observed. 58 As mentioned above, the laboratory system probably only imitate, rather than reproduce, the eld system, where TCE comes from the trace amounts in water, and thus its surface concentration is probably quite low and therefore reacting organic molecules are far apart. The situation is dierent in some of the experimental systems and in our computational model. The reality might be somewhere between the two hypotheses. However, the problems, that arise from chlorine-chlorine repulsions are very likely to be, so some extent, diminished in the eld system, where the TCE concentration is probably very low and where the water phase facilitates ecient removal of Cl atoms as HCl, so Cl coverage decreases.

soning of a Pd catalyst. The kMC results propose that the slowest steps in the TCE removal are the last two hydrogenation steps. In fact, all hydrogenation steps have comparable rates, which are considerably slower than dechlorination rates.

Acknowledgement We thank the Academy of Finland for funding of the underlying DFT project, and Nanoscience Center (University of Jyväskylä, Finland) and the Finnish IT Center for Science (CSC) (Espoo, Finland) for providing the computational resources.

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