Catalytic decomposition of formic acid on Cu(100): Optimization and dynamic Monte Carlo simulation

Journal Pre-proof Catalytic decomposition of formic acid on Cu(100): Optimization and dynamic Monte Carlo simulation

Marzieh Rafiee, Hadis Bashiri PII:

S1566-7367(20)30017-0

DOI:

https://doi.org/10.1016/j.catcom.2020.105942

Reference:

CATCOM 105942

To appear in:

Catalysis Communications

Received date:

31 August 2019

Revised date:

19 January 2020

Accepted date:

20 January 2020

Please cite this article as: M. Rafiee and H. Bashiri, Catalytic decomposition of formic acid on Cu(100): Optimization and dynamic Monte Carlo simulation, Catalysis Communications (2019), https://doi.org/10.1016/j.catcom.2020.105942

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© 2019 Published by Elsevier.

Journal Pre-proof

Catalytic decomposition of formic acid on Cu(100) : Optimization and dynamic Monte Carlo simulation

Marzieh Rafiee and Hadis Bashiri*

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Department of Physical Chemistry, Faculty of Chemistry, University of Kashan, Kashan, Iran

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To correspondence should be addressed. Fax: +98 31 55912397.

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* Email: [email protected] and [email protected]

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Journal Pre-proof Abstract Dynamic Monte Carlo simulation and response surface methodology have been used to study

optimum conditions for

the heterogeneous catalytic formic acid decomposition

reaction on Cu(100) for the first time. The mechanism, kinetic parameters, optimum conditions and the effects of temperature, pressure and reaction time on the hydrogen yield were studied. . Formate (HCOO) species is the active reaction intermediate and the adsorption of formic acid is considered as the the rate-controlling

a turnover frequency (TOF) of 0.044 s-1 for hydrogen

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optimum conditions provided

step. The proposed

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production.

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Keywords: Hydrogen production; Formic acid; Dynamic Monte Carlo simulation; Response

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surface methodology; Reaction mechanism.

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Journal Pre-proof 1. Introduction Hydrogen gas is a clean energy source and is widely used in industry for the production of many chemicals, steel, the hydrogenation of unsaturated hydrocarbons (food industry) and the desulfurization of gasoline in oil refineries [1]. Hydrogen gas has high energy density,  122 kJ/g, which is more than the energy of the combustion of hydrocarbon fuels, and it can therefore be considered as a good green fuel [2]. It is used in fuel cells for electricity

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generation. Hydrogen is largely produced by fossil fuels [3] and to a less extent by non-fossil sources, e.g., biomass and water electrolysis [4].

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Formic acid (HCOOH) is an inexpensive and toxic material produced from biomass [4].

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Since the hydrogen content of formic acid (FA) is 4.4 wt%, it is considered as a good source

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of hydrogen. The catalytic decomposition of FA has been studied over Ag, Pd, Ru, Ni and Cu metals [5]. Copper is an abundant transition metal and much more cost-effective as catalyst

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than other transition metals. It has a number of oxidation states (Cu+, Cu2+ and Cu3+) which

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makes it a potential good catalyst [6].

The analysis and understanding of a chemical reaction process is largely facilitated by

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performing kinetic experimental studies and investigating

reaction pathways via

computational approaches coupled with in situ spectroscopic experimental studies. Dynamic Monte Carlo (DMC) simulation is a computational method which can be used to investigate rival reaction mechanisms and obtain kinetic parameters [7]. The details of the reaction surface can be investigated using DMC simulation, which

is a suitable tool to study

adsorption, desorption and surface reactions in lattice-gas models [8]. Zhang et al. [9] developed a continuum (off lattice) kinetic Monte Carlo model to study the kinetics of oligomerization reactions, where the parameters required for the simulation were obtained from density functional theory (DFT) calculations.

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Journal Pre-proof To determine the optimum conditions and to increase the product yield of the process, optimization methods can be used. Optimization studies can be carried out using the response surface methodology (RSM) [10]. It is a collection of statistical and mathematical techniques for designing experiments, modeling and optimizing processes. In the present study, temperature, pressure and reaction time are investigated to maximize the yield of hydrogen production from the catalytic decomposition of FA. The time parameter, as an optimum condition, means the necessary time for FA adsorption and reaction to occur on the Cu(100)

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surface to obtain the maximum yield hydrogen production. Until now, to our knowledge, this

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method has not been applied to determine optimum conditions in simulation studies.

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For the first time, DMC and RSM will be used together for studying the optimum

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conditions of a heterogeneous catalytic reaction. The optimum conditions and the effects of temperature, pressure and reaction time on the yield of hydrogen production will be Another goal of this study is the investigation of hydrogen diffusion on

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investigated.

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Cu(100) surface and the mechanism and rate-determining step of hydrogen generation from FA. The mechanism, kinetic data and details of surface reaction steps using the lattice-gas

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model will be obtained by comparing the results of simulation with experimental data. The turnover frequency (mols of produced hydrogen per number of active sites per time), TOF, at the suggested optimum conditions will be determined, and the ability of the suggested method will be examined.

2. DMC simulation for the description of surface reaction In this method, the surface is modeled as a collection of sites. Each lattice site is considered to be occupied by an adsorbate or it can be empty. A set of elementary reaction

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Journal Pre-proof steps is considered on the lattice with periodic boundary conditions. Adsorption, desorption, diffusion and reaction are possible events. The time evolution of the reaction system can be described by the Master equation [11]:

𝑑𝑡

= ∑𝛽(𝜔𝛼𝛽 𝑃𝛽 − 𝜔𝛽𝛼 𝑃𝛼 )

where, P and P is

(1)

the probability of finding the system in configuration 𝛼 and 𝛽,

of

𝑑𝑃𝛼

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respectively.  is the transition probability per unit time, which specifies the rate of the

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process going from configuration 𝛼 to configuration 𝛽 for various elementary events, such as

solution for the Master equation [12].

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adsorption, desorption, diffusion, reaction, etc. The DMC method provides a numerical

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For our purposes, the CARLOS program developed by J. Lukkien and A.P.J. Jansen [13]

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is used. To simulate the chemical reactions that take place on the surface, an adlayer for the reaction steps is defined, and it is governed based on the Master equation [13]. Recently, we

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have used this program to simulate FA decomposition on Ni(100) surface [14]. Four adsorption sites per unit cell, namely: top, hollow and two independent bridge sites are considered on a square lattice of Cu(100) with periodic boundary condition. The lattice of 2800×2800 sites is large enough to minimize the produced data noise from the simulation. The amount of produced hydrogen per number of active sites per time is the turnover frequency (TOF) as already mentioned. One of the outputs of CARLOS is the number of desorbed H2 molecules per number of active sites per time in the time intervals. The average of these data is reported as TOF of the reaction.

3. Results and discussion

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Journal Pre-proof Dubois and his co-workers [15] have performed a Temperature Programmed Desorption (TPD) analysis of FA decomposition on Cu(100). The mechanism of FA decomposition on Cu(100) was investigated based on the best fitting of the simulated data with those obtained from the experimental TPD spectra. To obtain the best fitting, three pathways for FA decomposition were considered, and a schematic representation of these pathways is shown in Fig. 1S (Electronic Supporting Information, ESI). In path 1, FA is dehydrogenated to form an HCOO intermediate, then the latter is decomposed to H and CO2. In the second path,

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formic anhydride is produced, and it decomposes to formyl and formate species. These in

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turn are decomposed to H, CO and CO2. In the third path, COOH is produced and it is

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decomposed to CO2 and H [16].

formation of

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Interaction between two adsorbed H species at the nearest neighbor sites leads to the one H2 molecule, which directly desorbs. The study of the behavior of

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hydrogen diffusion on the surface can be useful in order to better understand hydrogen

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production rate. Hydrogen atoms diffuse from the hollow site to the next hollow site [17]. Lauderdale and Truhlar [18,19] reported an activation energy of hydrogen diffusion of 32.2

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kJ/mol. The pre-exponential factor and activation energy of hydrogen desorption using the heating rate variation of the TPD experiment have been estimated as 3.0×1011 1/s and 78.0 kJ/mol, respectively [18]. The activation energy of desorption of CO2 was estimated as 20.9 kJ/mol by the UBI-QEP method [19]. It was considered that FA, HCOO, COOH [20] and CO2 [21] adsorb on the bridge sites, while H atom adsorbs on the hollow site of Cu(100) surface [22]. Ying et al. [23] obtained an activation energy of HCOO formation of 125.5 kJ/mol. Iglesia and Boudart [24] reported a pre-exponential factor of

1.0×1014 1/s after using the heating rate variation of TPD

experiments. The activation energies for the forward and backward steps of HCOO decomposition were determined by Arrhenius plots of CO2 production rate and found to be

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Journal Pre-proof 133.3 kJ/mol [25] and 79.5 kJ/mol [26], respectively. Gokhale and his co-workers [27] reported that carboxyl is decomposed to CO2 and H on the Cu surface. We have examined several mechanisms for FA decomposition on Cu(100). In mechanism I, three pathways of hydrogen production were included, while in mechanisms II, III and IV, two pathways were considered. In mechanisms V, VI and VII, only one pathway was considered. All of these mechanisms were studied either with or without hydrogen diffusion

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3.1. TPD of FA decomposition experimental studies

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on the surface taking place.

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Dubois et al. [15] obtained experimental TPD spectra of FA decomposition on Cu(100).

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FA adsorbed at 120 K and 5.0×10-6 Torr on an empty and clean surface of Cu(100) and TPD was performed using a linear heating rate of 4 K/s. The simulated TPD spectra were fitted

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with the experimental one using activation energies, pre-exponential factors and assuming

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various mechanisms. The obtained mechanistic steps (based on best fitting of TPDs) are given in Table 1. The reported kinetic data from other investigations and the used data in the

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simulation, which have been estimated based on the reported data, are also shown in Table 1. Fig. 1 shows both experimental (dashed line) and simulated (solid line) TPD data of H2 and CO2 traces. The perfect agreement between experimental and simulated data confirms the suggested mechanism and kinetic parameters used. Desorption of FA was investigated in the 120-600 K range and the obtained results did not reveal FA desorption. Dubois et al. [15] also failed to observe the desorption of FA in their experimental studies. Considering hydrogen diffusion step, this leads to a good agreement between the simulated and experimental TPD curves. The results of simulations have shown also that the presence or absence of COOH species on the surface has no effect on the rate of reaction. When the mechanism of FA decomposition is simulated without the production of formic anhydride,

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Journal Pre-proof the simulated curve gives a better fitting of the experimental TPD spectra. The previous studies showed HCOO is more stable than COOH on the metal fcc(100) surfaces [16, 20, 27, 28]. DFT calculations on Cu(111) indicated that COOH formation from FA is more difficult than HCOO formation, and COOH decomposition is easier than HCOO decomposition [27, 28]. Dubois et al. [15] showed that formate is an intermediate of the reaction. The simulation results showed that formic anhydride is not produced from FA decomposition on Cu(100) surface. Ying and Madix [25] investigated FA decomposition on Cu(110) surface and they

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concluded the formation of HCOO on Cu(110). The present results showed that FA adsorbs

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on Cu(100), it then decomposes to H and HCOO intermediates, and the rate constant of FA

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adsorption is smaller than that of other steps. Thus, it is concluded that adsorption of FA is

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the rate-determining step of reaction, and hydrogen is thus produced through direct FA

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dehydrogenation.

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3.2. Influence of pressure on the rate of reaction To investigate the influence of pressure on the rate of reaction, variation in reaction rates by changing FA pressure was studied. Using the obtained mechanism and kinetic data, FA decomposition on Cu(100) was simulated at constant temperature, T= 492 K. The change of pressure with time was considered to be linear:

P = 5.0 × 10−6 + (3.75 × 10−5 t )

(2)

In Eq. (2), the coefficient 5.0 × 10−6 Torr is the initial pressure of FA, and t is the reaction time. The coefficient 3.75 × 10−5 is obtained with a trial-and-error method in order to

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Journal Pre-proof describe changes in the rate with pressure. The desorption rates of H2 and CO2 as a function of pressure are shown in Fig. 2. The rate of H2 desorption increases by increasing pressure, and its maximum value obtained is 0.5×10-1 (1/s) at 4.2×10-5 Torr. The desorption rate of CO2 passes through a maximum as pressure increases. The maximum CO2 desorption rate is 2.2×10-1 (1/s) and it occurs at 3.5×10-5 Torr. The maximum in pressure is different for H2 and CO2 formation rates. The activation energy of H2 desorption is larger than that of CO2 desorption (Table 1), and this is the reason that the maximum in pressure for the CO2

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a decrease in the maximum of H2 formation rate.

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formation rate is higher than that for the H2 formation rate. Furthermore, H diffusion leads to

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3.3. Response surface methodology

In this section, Design Expert software (Ver. 7.0.0) has been used to examine the effects

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of temperature, pressure and reaction time on hydrogen production. The number of the

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produced hydrogen molecules are chosen as the response variable. The generated quadratic model of response is presented by Eq (3):

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R = −7.95 × 109 − 1.13 × 107 𝑋1 − 7.20 × 109 𝑋2 + 1.12 × 107 𝑋3 − 5.18 × 106 𝑋1 𝑋2 − 41373.67𝑋1 𝑋3 + 4.83 × 106 𝑋2 𝑋3 + 1.63 × 105 𝑋12 − 1.63 × 109 𝑋22 − 1.79 × 105 𝑋32

(3)

where, X1 (temperature), X2 (pressure) and X3 (time) are chosen as three independent variables. The CCD design matrix and the corresponding experimental results are shown in Table 2. Statistical analysis of the model is evaluated by analysis of variance (ANOVA). The results of ANOVA for the generated quadratic model are summarized in Table 3. The Fvalue (43.76) with a very low probability value (P-value < 0.0001) confirms the generated quadratic model. The quadratic model presents a high determination coefficient (R2 =

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Journal Pre-proof 0.9752), explaining 97% of the variability. The value of the adjusted determination coefficient and the predicted R2 are high and indicate a high significance (adjusted R2=0.9530, predicted R2 =0.8026). The value of R2 is in a good agreement with the adjusted R2. The compression of the predicted value versus the simulated value of response is shown in Fig. 3. The values predicted by the model and the results obtained from the simulation are distributed evenly around a 45◦ line.

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3.3.1. Response surface and contour plots

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The response surface and contour plot of the produced hydrogen molecules as a function

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of temperature and pressure after 10.0 s of reaction time are shown in Fig. 4. The response

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increases with increasing temperature and the maximum response is located at 540 K. This plot also indicates that the effect of pressure is more important than temperature.

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The response surface as a function of temperature and time was also investigated at

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constant pressure (3.0×10-6 Torr). It is illustrated that temperature affects the response, while reaction time is more important. The highest response occurs at temperatures in the 520–540

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K range. The response surface as as a function of pressure and reaction time (T= 495 K) was also studied and it was found that both pressure and reaction time have important effects on the response surface.

3.3.2. Optimization and validation test The goal of the optimization is to obtain the conditions in which the yield of the reaction (R%) is maximum. It can be calculated using Eq. (4):

[H ]

2 des R% = [FA] × 100

(4)

ads

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Journal Pre-proof The optimum conditions were found to be T = 670 K, P = 1.9×10-6 Torr and reaction time, t = 16.9 s, which give a hydrogen yield of 86.83 %. For verification, the simulation was conducted under optimum conditions. The experimental value (80.32%) closely agrees with the predicted result of RSM. It confirms the ability of RSM in optimizing hydrogen production from FA decomposition. At the optimum conditions, Cu(100) can catalyze this reaction with TOF of 0.044 s-1. Other studies showed TOF values for the same reaction over Pd (0.01 s-1) [29], Au (0.002 s-1) [29], CoAuPd/C (0.02 s-1) [30] and Ag–Pd core–shell

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nanocatalyst of 0.034-0.17 s-1 [31]. This comparison dictates that the method presented in

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this work for the location of optimum conditions for largest TOF (FA decomposition) can be

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used effectively with less cost if compared to other experimental approaches reported in the

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4. Conclusions

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literature.

The mechanism of hydrogen production from FA decomposition on Cu(100) surface was

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obtained by fitting the simulated data with experimental

TPD spectra. Three reaction

pathways for FA decomposition were proposed in order to find the mechanism of hydrogen production. It was shown that HCOO and H are the only reaction intermediates present. Hydrogen atoms can diffuse on the Cu surface from the hollow to the next hollow site. The FA adsorption step was found to be the rate-determining step, and dehydrogenation of FA leads to direct production of hydrogen (fast recombination step of two H atoms). RSM and DMC were used to study the effects of operation variables and estimate the optimum conditions. The temperature is the most effective variable. At optimum conditions (700 K, 1.9×10-6 Torr and 16.9 s), the predicted value for the hydrogen yield of the reaction is 80.32%, and a value of TOF is 0.044 s-1. The high value of the predicted TOF shows that

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Journal Pre-proof using DMC and RSM together can be onsidered as an appropriate method to optimize heterogeneous catalytic reactions with lowest cost.

Acknowledgements The authors are grateful to University of Kashan for supporting this work by Grant No.

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(785108/4).

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3. 4. 5. 6. 7.

8. 9. 10. 11.

12.

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Jansen, A.P.J., Monte Carlo simulations of temperature-programmed desorption spectra. Physical Review B, 2004. 69(3): p. 035414. Rafiee, M. and H. Bashiri, Dynamic Monte Carlo simulations of the reaction mechanism of hydrogen production from formic acid on Ni(1 0 0). Applied Surface Science, 2019. 475: p. 720-728. Dubois, L.H., et al., New insights into the kinetics of formic acid decomposition on copper surfaces. Surface Science, 1986. 172(2): p. 385-397. Herron, J.A., et al., Trends in Formic Acid Decomposition on Model Transition Metal Surfaces: A Density Functional Theory study. ACS Catalysis, 2014. 4(12): p. 4434-4445. Ozawa, N., et al., Quantum states of a hydrogen atom adsorbed on $\mathrm{Cu}(100)$ and (110) surfaces. Physical Review B, 2007. 75(11): p. 115421. Genger, T., O. Hinrichsen, and M. Muhler, The temperature-programmed desorption of hydrogen from copper surfaces. Catalysis Letters, 1999. 59(2): p. 137-141. Shustorovich, E. and A.T. Bell, An analysis of methanol synthesis from CO and CO2 on Cu and Pd surfaces by the bond-order-conservation-Morse-potential approach. Surface Science, 1991. 253(1): p. 386-394. Li, S., J. Scaranto, and M. Mavrikakis, On the Structure Sensitivity of Formic Acid Decomposition on Cu Catalysts. Topics in Catalysis, 2016. 59(17): p. 1580-1588. Hu, Z.-M., K. Takahashi, and H. Nakatsuji, Mechanism of the hydrogenation of CO2 to methanol on a Cu(100) surface: dipped adcluster model study. Surface Science, 1999. 442(1): p. 90-106. Gómez, E.d.V., et al., DFT study of adsorption and diffusion of atomic hydrogen on metal surfaces. Applied Surface Science, 2017. 420: p. 1-8. Iglesia, E. and M. Boudart, Decomposition of formic acid on copper, nickel, and copper-nickel alloys: II. Catalytic and temperature-programmed decomposition of formic acid on CuSiO2, CuAl2O3, and Cu powder. Journal of Catalysis, 1983. 81(1): p. 214-223. Cheng, T., H. Xiao, and W.A. Goddard, Reaction Mechanisms for the Electrochemical Reduction of CO2 to CO and Formate on the Cu(100) Surface at 298 K from Quantum Mechanics Free Energy Calculations with Explicit Water. Journal of the American Chemical Society, 2016. 138(42): p. 13802-13805. Ying, D.H.S. and R.J. Madix, Ligand and cluster effects in the decomposition of formic acid on copper/nickel (110) single-crystal surfaces. Inorganic Chemistry, 1978. 17(5): p. 1103-1108. Rasmussen, P.B., et al., Methanol synthesis on Cu(100) from a binary gas mixture of CO2 and H2. Catalysis Letters, 1994. 26(3): p. 373-381. Gokhale, A.A., J.A. Dumesic, and M. Mavrikakis, On the Mechanism of Low-Temperature Water Gas Shift Reaction on Copper. Journal of the American Chemical Society, 2008. 130(4): p. 1402-1414. Chorkendorff, I., P.A. Taylor, and P.B. Rasmussen, Synthesis and hydrogenation of formate on Cu(100) at high pressures. Journal of Vacuum Science & Technology A, 1992. 10(4): p. 2277-2281. Bulushev, D.A., S. Beloshapkin, and J.R.H. Ross, Hydrogen from formic acid decomposition over Pd and Au catalysts. Catalysis Today, 2010. 154(1): p. 7-12. Wang, Z.-L., et al., An Efficient CoAuPd/C Catalyst for Hydrogen Generation from Formic Acid at Room Temperature. Angewandte Chemie International Edition, 2013. 52(16): p. 44064409. Tedsree, K., et al., Hydrogen production from formic acid decomposition at room temperature using a Ag–Pd core–shell nanocatalyst. Nature Nanotechnology, 2011. 6(5): p. 302-307. Ying, D.H.S. and J.M. Robert, Thermal desorption study of formic acid decomposition on a clean Cu(110) surface. Journal of Catalysis, 1980. 61(1): p. 48-56.

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Lauderdale, J.G. and D.G. Truhlar, Diffusion of hydrogen, deuterium, and tritium on the (100) plane of copper: Reaction-path formulation, variational transition state theory, and tunneling calculations. Surface Science, 1985. 164(2): p. 558-588. Yao, J., et al., Optimization of paeonol-loaded poly(butyl-2-cyanoacrylate) nanocapsules by central composite design with response surface methodology together with the antibacterial properties. European Journal of Pharmaceutical Sciences, 2017. 101: p. 189-199.

Used data in simulation

Elementary reactions v(s-1)

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1 HCOOH(g) + Site → HCOOH ∗ 2 HCOOH ∗ → HCOOH(g) + Site

1.0 × 1011

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3 HCOOH ∗ + Site → H ∗ + HCOO∗

Ea (kJ/mol)

-p

ID

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Table 1. Elementary reaction steps and associated kinetic rate data.

Reported data in other studies v(s-1)

Ea (kJ/mol)

586.9

8.0 × 1013

125.5

1.0 × 1014 [23]

125.5 [32]

1.0 × 1010

92.0

2.2 × 1010 [26]

82.0[27, 28]

2.0 × 1014

133.4

1.0 × 1013.9 [32]

133.3 [32]

6 H ∗ + CO2 ∗ → HCOO∗ + Site

1.0 × 1012

79.5

78.0 [26]

7 H ∗ + Site1 → Site2 + H ∗

1.0 × 106

32.2

32.2[33]

8 H ∗ + H ∗ → H2 (g) + 2 Site

3.0 × 1011

88.6

∗ 9 CO2 → CO2 (g) + Site

1.0 × 1011

20.9

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4 HCOO∗ + H ∗ → HCOOH ∗ + Site

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5 HCOO∗ + Site → H ∗ + CO2 ∗

14

3.0×1011[18, 34]

78.0 [6, 18, 33] 20.9[19]

Journal Pre-proof Table 2. Central composite design and corresponding results. results

Run

Temperature (K)

Pressure (Torr)

Response

1

521.76

1.66 × 10-6

4.05

0.60 × 106

2

495.00

7.50× 10-7

10.00

0.28 × 106

3

540.00

3.00 × 10-6

10.00

2.29 × 106

4

468.24

1.66 × 10-6

4.05

0.24 × 106

5

495.00

3.00 × 10-6

10.00

1.29 × 106

6

495.00

3.00 × 10-6

10.00

1.29 × 106

7

521.75

4.30 × 10-6

15.95

1.99 × 106

8

521.75

1.66 × 10-6

1.45 × 106

9

450.00

3.00 × 10-6

10.00

1.29 × 106

10

468.24

1.66 × 10-6

15.95

0.97 × 106

11

468.24

4.30 × 10-6

15.95

1.95 × 106

12

495.00

3.00 × 10-6

10.00

1.29 × 106

13

495.00

3.00 × 10-6

10.00

1.29 × 106

14

495.00

3.00 × 10-6

10.00

1.29 × 106

15

468.24

4.30 × 10-6

4.05

0.79 × 106

16

495.00

5.20 × 10-6

10.00

1.86 × 106

17

495.00

3.00 × 10-6

20.00

1.64 × 106

18

495.00

3.00 × 10-6

0.00

0.00 × 106

19

495.00

3.00 × 10-6

10.00

1.29 × 106

20

521.75

4.30 × 10-6

4.05

1.28 × 106

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Time (s)

-p

Independent variables

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na

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re

15.95

15

Journal Pre-proof Table 3. Analysis variance of quadratic model. SSa

DFb

Mean square

F -value

P -value

Model

6.77 × 1012

9

7.53 × 1011

44.34

< 0.0001

Residual

1.69 × 1011

10

1.69 × 1010

Lack of fit

1.69 × 1011

5

3.39 × 1010

Pure error

0.00

5

0.00

Cor. total

6.95 × 1012

19

of

Source

Pred R2 =0.80

Adequate precision = 23.83

𝐶. 𝑉. % = 10.66

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Adj R2 =0.95

𝑃𝑅𝐸𝑆𝑆 = 1.35 × 1012

Jo ur

na

lP

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-p

R2 =0.97

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8 CO2

6

H2 4 2

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Desorption rate (1/s)

10

0 200

300

400

500

600

700

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100

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Temperature (K)

Figure 1. The experimental (solid line) (Ref. [15]) and simulated (dashed line) data of TPD

lP na Jo ur

Cu(100) surface.

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traces of H2 (red curve) and CO2 (black curve) as the products of FA decomposition on

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Desorption rate (1/s)

0.20 0.15 H2_des

0.10

CO2_des 0.05 0.00 0

0.0001

0.0002

0.0003

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Pressure (Torr)

0.0004

Jo ur

na

lP

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as a function of FA pressure at T=492 K.

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Figure 2. The rates of H2 (red curve) and CO2 (black curve) desorption from Cu(100) surface

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Jo ur

na

lP

FA decomposition on Cu(100) surface.

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Figure 3. The predicted versus simulated values of the produced hydrogen molecules from

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Figure 4. Response surface and contour plot of the produced hydrogen molecules. The effects of temperature and pressure on the produced hydrogen molecules from FA

Jo ur

na

lP

decomposition on Cu(100) surface after 10 s of reaction time.

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Credit Authors Statement Hadis Bashiri: Supervision, Writing- Reviewing and Editing, Conceptualization, Methodology, Software. Marzieh Rafiee: Investigation, Writing- Original draft preparation, Software.

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Declaration of interests

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Highlights:

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1) The mechanism of hydrogen production from formic acid decomposition on Cu(100)

lP

surface was obtained.

2) Formate (HCOO) species is the active intermediate of HCOOH decomposition on

na

Cu(100) surface.

site.

Jo ur

3) Hydrogen atoms can diffuse on the Cu surface from the hollow to the next hollow

4) Dehydrogenation of formic acid leads to direct production of hydrogen. 5) RSM and DMC were used to estimate the optimum conditions of a heterogeneous catalytic reaction.

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Figure 1

Figure 2

Figure 3

Figure 4