Rmu_pbq _srmk_rga iglcrga kmbcj eclcp_rgml dmp fcrcpmeclcmsq a_r_jwqgq md pclcu_`jc dccbqrmaiq

G_l A_gjjg_s

Qsncptgqmpq8 Npmd, bp, gp, Hmpgq Rfw`_sr* Npmd, bp, gp, Ictgl T_l Ecck Amslqcjjmpq8 @pgegrrc Bctmafr* Djmpclac Tcpkcgpc

K_qrcp%q bgqqcpr_rgml qs`kgrrcb gl mpbcp rm m`r_gl rfc _a_bckga bcepcc md K_qrcp md Qagclac gl Afckga_j Cleglccpgle

Bcn_prkclr Md K_rcpg_jq* Rcvrgjcq ?lb Afckga_j Cleglccpgle Af_gp8 Npmd, bp, N_sj Igciclq D_asjrw md Cleglccpgle _lb ?pafgrcarspc ?a_bckga wc_p 0./5+0./6

Rmu_pbq _srmk_rga iglcrga kmbcj eclcp_rgml dmp fcrcpmeclcmsq a_r_jwqgq md pclcu_`jc dccbqrmaiq

G_l A_gjjg_s

Qsncptgqmpq8 Npmd, bp, gp, Hmpgq Rfw`_sr* Npmd, bp, gp, Ictgl T_l Ecck Amslqcjjmpq8 @pgegrrc Bctmafr* Djmpclac Tcpkcgpc

K_qrcp%q bgqqcpr_rgml qs`kgrrcb gl mpbcp rm m`r_gl rfc _a_bckga bcepcc md K_qrcp md Qagclac gl Afckga_j Cleglccpgle

Bcn_prkclr Md K_rcpg_jq* Rcvrgjcq ?lb Afckga_j Cleglccpgle Af_gp8 Npmd, bp, N_sj Igciclq D_asjrw md Cleglccpgle _lb ?pafgrcarspc ?a_bckga wc_p 0./5+0./6

Acknowledgments

Habemus thesis! We have a thesis. As with any scientific work, this work is the product of more than one person and I’d like to acknowledge the people who helped make this possible.

First of all, I would like to thank Prof. dr. ir. Guy B. Marin for providing me with the opportunity to complete my thesis and education at LCT. I’d also like to thank both of my promotors: prof. dr. ir. Joris Thybaut & Prof. dr. ir. Kevin M. Van Geem. Both have provided me with a perspective and way of approaching problems which I will carry with me for the rest of my professional life.

I have also been blessed to have two counselors, ir. Brigitte Devocht and ir. Florence Vermeire, who have gone above and beyond to make this work the best work it could have been. I’d like to thank you both for always providing me with help and guidance when I needed it. For being patient with the many silly questions and the somewhat muddled way in which I often express myself. When you approach a new subject, it is easy to take the forest for the trees and you have consistently helped me see the bigger picture. Whenever I got lost on one of my (many) tangents, you helped point my gaze towards (more) productive avenues. I have benefited immensely from your input and feedback.

I’d also like to thank the people at LCT. I’d like to thank the Genesys group for helping me during the hackathons. I’d like to thank ir. Cato Pappijn in particular for her time and effort in the last few months.

Finally, I’d like to thank my family and friends for supporting me throughout my academic career. It’s been a thrilling ride.

FACULTY OF ENGINEERING AND ARCHITECTURE

Laboratory for Chemical Technology Director: Prof. Dr. Ir. Guy B. Marin

Laboratory for Chemical Technology

Declaration concerning the accessibility of the master thesis

Undersigned,

Ian Cailliau

Graduated from Ghent University, academic year 2017-2018 and is author of the master thesis with title:

Towards automatic kinetic model generation for heterogeneous catalysis of renewable feedstocks

The author(s) gives (give) permission to make this master dissertation available for consultation and to copy parts of this master dissertation for personal use. In the case of any other use, the copyright terms have to be respected, in particular with regard to the obligation to state expressly the source when quoting results from this master dissertation.

Laboratory for Chemical Technology • Technologiepark 914, B-9052 Gent • www.lct.ugent.be Secretariat : T +32 (0)9 33 11 756 • F +32 (0)9 33 11 759 • [email protected]

Towards automatic kinetic model generation for heterogeneous catalysis of renewable feedstocks

Ian Cailliau

Master's dissertation submitted in order to obtain the academic degree of Master of Science in Chemical Engineering

Academic year 2017-2018

Promotors: Prof. dr. ir. Joris Thybaut & Prof. dr. ir. Kevin Van Geem Coaches: ir. Brigitte Devocht & ir. Florence Vermeire

GHENT UNIVERSITY Faculty of Engineering and Architecture

Department of Chemical Engineering and Technical Chemistry Laboratory for Chemical Technology Chairman: Prof. dr. ir. Guy B. Marin

Abstract

The automatic network generation tool Genesys has been extended towards catalytic reactions. As detailed kinetic models can typically contain hundreds or thousands of reactions, the manual construction of these models becomes infeasible. Genesys, originally developed for gas-phase species and reactions, is expanded to allow for the correct representation of metal surface species. Additionally, the UBI-QEP method has been implemented to allow for the on-the-fly calculation of heats of adsorption and activation energies. As a proof of concept, a case study concerning the hydrodeoxygenation of propionic acid over a nickel catalyst is discussed.

Keywords: Automatic Network Generation, Metal Catalysis, UBI-QEP, Genesys Towards automatic kinetic model generation for heterogeneous catalysis of renewable feedstocks

Ian Cailliau

Promotor(s): Prof. dr. ir. Joris Thybaut and Prof. dr. ir. Kevin Van Geem Coaches: ir. Brigitte Devocht and ir. Florence Vermeire

Abstract: The automatic network generation tool Genesys has 3, 6, 7]. For catalytic systems RMG-Cat already has a basic been extended towards catalytic reactions. As detailed kinetic implementation which makes use of a Group Additivity scheme models can typically contain hundreds or thousands of reactions, for thermodynamic properties and Brönsted-Evans-Polanyi the manual construction of these models becomes infeasible. relationships for the determination of activation energies. In Genesys, originally developed for gas-phase species and reactions, this work, the Unity Bond Index Quadratic Exponential is expanded to allow for the correct representation of metal Potential (UBI-QEP) method, first reported by Shustorovich et surface species. Additionally, the UBI-QEP method has been implemented to allow for the on-the-fly calculation of heats of al. [8] is implemented. adsorption and activation energies. As a proof of concept, a case study concerning the hydrodeoxygenation of propionic acid over II. SPECIES REPRESENTATION a nickel catalyst is discussed. Keywords: Automatic Network Generation, Metal Catalysis, A. Graph representation of molecules UBI-QEP, Genesys Genesys makes use of a cheminformatics library, the Chemistry Development Kit (CDK) [3]. In this software I. INTRODUCTION package, a graph structure is used for the unambiguous With over 90% of all chemical processes worldwide making representation and manipulation of chemical species. The use of catalysts, there is need for a more thorough graph (i.e. a molecule) is composed of various edges (i.e. understanding of the catalytic processes [1]. Microkinetic atoms) and vertices (i.e. bonds). The edges and vertices are models are suited for this purpose as they help identify which weighted, i.e. they are given more information about lone pairs, surface species and surface reaction paths control the observed single electrons, element type, bond type, etc. product spectrum [1]. Detailed kinetic models can contain hundreds or thousands of reactions making manual Besides the graph-based structure, for input and output construction of these models tedious and error-prone [2]. In the purposes, molecule identifiers such as InChI and SMILES, are past this problem has been tackled for combustion and used. InChI’s are unique for a given species, making them an pyrolysis by use of automatic network generators. As of excellent choice for database searches. writing, most software packages deal only with gas-phase reactions and are unfit for heterogenous catalysis. B. Introducing catalytically active sites

Within this work, the goal is to implement metal catalyst Metal surface species are implemented in Genesys in a functionalities in an in-house developed automatic network similar way to gas-phase species, to allow for a smooth conversion of molecule identifier to graph structures and vice generator called Genesys [2, 3]. For this purpose, the focus will versa. Additionally, manipulations of the graph structure be with two of the main aspects of automatic network generation. The first is the correct representation of metal should result in the correct products being generated. surface species. The second is the calculation of In a first instance, use is made of a dummy atom to represent thermodynamic properties for surface species and kinetic the catalytically active site. In this method, the functionality of parameters for surface reactions. The representation of surface species has been done in literature by the use of a dummy atom an unused atom (e.g. helium) is changed to act as the catalyst to represent the active sites. ReNGeP has already correctly surface. An example of how this representation looks like for formic acid is given in represented surface species by use of this dummy atom [4, 5]. Figure 1. RMG-Cat [6] has been extended in a similar fashion and also makes use of a dummy atom. In a second stage, the decision is made to move beyond the conventional dummy atom towards a dummy catalyst block Thermodynamic properties and kinetic parameters have to be consisting of multiple, connected, dummy atoms, this determined for all species and reactions. Because of the nature of automatic network generation this has to be performed on- representation is also shown in the-fly, while the network is being generated. If available from Figure 1. extensive databases, the thermodynamic or kinetic data, A catalyst block was chosen for several reasons. On a obtained via experiment, regression or theoretical calculations, can be used. If not, fast on-the-fly calculations methods are technical level it simplifies the process of generating the correct used which are less computationally expensive, yet provide graph structure for a given species in the software. It eases the implementation of multidentate adsorption, dissociate reasonably accurate values for many species and reactions [2, adsorption, etc. (pseudo-)atom) and the gas-phase bond dissociation energies (DAB, where both A and B are (pseudo-)atoms) for a small number of covalent bonds.

The result is an incredible reduction in the number of parameters which have to be determined, whilst still providing the user with reasonably accurate predictions (15-20 kJ/mole) [8, 14, 15]. UBI-QEP has successfully been applied to several case-studies [16-18]. Because UBI-QEP can make reliable predictions under conditions of data scarcity, it is the method preferred in this work and implemented in Genesys. It is however confined to heterogeneous metal catalysis.

B. Methodology The UBI-QEP analytical equations are based on a set of mathematical assumptions [8]. The first relies on the

introduction of a new quantity, the bond index. The bond index is defined by its relationship to the distance between two atoms Figure 1 Molecule identifiers to a graph structure representation with as shown in equation (1). Here x is the bond index, r is the dummy atom and dummy block. distance between 2 atoms, r0 is the equilibrium distance and a is a scaling factor [8]. Another important reason for the usage of a dummy bock is (1) the implementation of UBI-QEP in Genesys. UBI-QEP is a fast calculation method for thermodynamics and kinetics. The UBI- It is assumed that the bond index is conserved for a given QEP method as well as its implementation will be the topic of system. Thus, every bond within a system considered has a section III. corresponding bond index (xi) and the sum of those bond indices will be equal to one in case of equilibrium (equation Along with representing the species, extra information on the (2)). Equations (1) and (2) form the Unity Bond Index (UBI) catalyst is stored. This currently includes the choice of the part of UBI-QEP [8]. dummy atom, the length of the dummy block and some ∑ 1 (2) parameters required to calculate thermodynamic and kinetic properties. In the future the information on the catalyst can be For the second assumption, the potential energy contribution expended in a flexible way. The decision was made to store this (Q) of a given bond is given as a function of the corresponding additional information independently of the representation. bond index (equation (3)). This function is the Quadratic Contrary to atoms and bonds which have their information (i.e. Exponential Potential part in UBI-QEP [8]. atomic number, symbol, atomic weight, abundance, …) all stored in the graph structure. 2 (3)

III. UBI-QEP Within the UBI-QEP framework, multiple notations are used for the heat of adsorption with subtle but important differences. A. The need for fast calculation of thermodynamic properties Within the UBI-QEP framework the coordination number, n, is and kinetic parameters the number of ligands a single atom has with the surface. QA During automatic kinetic model generation, often hundreds of denotes the heat of adsorption as observed experimentally and species and thousands of reactions are formed. All of those makes no claim as to the coordination number. Q0A is used to need to be assigned thermodynamic or kinetic data in a fast and denote the heat of adsorption for A given 1 single ligand on-the-fly manner. Some methods for surface species and (coordination number 1). QnA is used to denote the atomic heat reactions have been described in literature. The Benson Group of adsorption for A given a coordination number n. Both Q0A Additivity scheme has been extended to surface species [9, 10]. and QnA are therefore explicitly parameters as determined Additionally, Brönsted-Evans-Polanyi (BEP) relationships within the UBI-QEP scheme. This as opposed to QA which is have been successful for the determination of activation simply the observed heat of adsorption. energies in heterogeneous catalysis [11, 12]. Both are implemented in RMG-Cat [6] and for gas-phase species and C. Derivation of the analytical equations reactions in Genesys, but no extension to surface species has UBI-QEP uses equations (1)-(3) to derive analytical yet been made in the latter. These methods are able to give equations for different cases. The total energy of adsorption is accurate predictions, but the downside is the lack of databases calculated by considering the total energy of a system, E(n), for the required groups or parameters. which is obtained by summing up the energetic contributions for the relevant bonds as shown in Figure 2. If the active site is The Unity Bond Index Quadratic Exponential Potential (UBI- involved in the bond considered, the QEP contribution uses the QEP) [8, 13] method is able to determine molecular heats of atomic heat of adsorption. If it is a bond within the adsorbed adsorption, as well as activation energies for surface reactions, species, the QEP contribution uses the bond dissociation energy by using simple analytical equations and only few parameters. (BDE) of the gas-phase molecule. It should be noted that it is The independent variables that must be determined by the user are the atomic heats of adsorption (QA, with A the contact assumed that there is no interaction between atom B and the The first step (step 1 in Figure 3) is the identification of the surface. reaction, thereby classifying it into one of the implemented reactions (step 1.1): dissociative adsorption, non-dissociative adsorption, surface disproportionation and surface dissociation.

Depending on the type of reaction a different UBI-QEP equation will be used for the determination of the heat of adsorption and the activation energy. The species involved will then be mapped on to the corresponding equation (step 1.2). This mapping is required to assure that the proper thermodynamic properties (i.e. heats of adsorption for surface species and heats of formation for gas-phase analogues) are determined. Additionally, these values must be plugged in Figure 2 The total energy as the sum of the QEP contributions of the correctly into the final equation. individual bonds. As can be seen in the figure, n=3 for this specific case. In step 2.1 the heats of adsorption, denoted QA … QAB for surface species A…AB are calculated. These are also The total energy in Figure 2 is subsequently minimized under determined using the UBI-QEP methodology, the exact process the UBI constraint (equation (2)). The result is an analytical is described in the next subsection. For the gas-phase analogues equation shown in equation (4). Here Q is the heat of AB,n of A…AB the heat of formation, denoted H … H also have adsorption for a molecule AB and a given coordination number A AB to be determined. This uses the gas-phase thermodynamics n. In Figure 2, n is equal to three. D is the bond dissociation AB methods already available within Genesys. Finally, in a third energy for the AB bond. step, the activation energy is calculated.

, (4) 2) Heats of Adsorption

The heats of adsorption required, QA … QAB in Figure 3, are The structure of the molecule as well as the way the molecule calculated using the UBI-QEP equations for thermodynamic is adsorbed on the surface will determine which QEP properties. The algorithm is represented schematically in contributions can be neglected and which cannot. In turn, this Figure 4. The species in question is first identified. Genesys decision will determine the derived equation. first determines the case (step 1.1), i.e. the equation that is to be In a similar manner, equations can be derived to determine the used. activation energies for surface reactions. For now, only few reaction types are included in the UBI-QEP method. These include dissociation reactions and disproportionation reactions. Additionally, the method can also be used for both dissociative as well as non-dissociative adsorption.

D. Implementation in Genesys 1) Activation energies

In order to calculate the activation energy of a surface reaction, Genesys must go through the steps as shown in Figure 3. Figure 4: Steps for the calculation of adsorption heats for surface species via UBI-QEP. In blue, the general steps are shown. In green substeps are shown for the example of formic acid and the bond dissociation energy of the BX bond.

A decision tree as portrayed in Figure 5 is used within Genesys to determine the case. The selection of the proper case occurs by checking for several criteria. First the number of atoms is checked (step 1 in Figure 5). For single atoms, case I, Q0A is simply used as the heat of adsorption. If the species in question is indeed a molecule, the second criterion is the number of ligands (step 2). This will determine whether the equation used is one for mono adsorption (cases II-IV) or one

of the equations for di-adsorption. The main difference is that with mono-adsorption the interaction between B and the Figure 3: Steps in calculating the heat of adsorption or activation surface is neglected. The selection between cases II to IV is energy for a surface reaction using the UBI-QEP method. The general then made based on the strength of the bond with the surface steps are given in blue, in green substeps are given for the example of (i.e. how much does it behave like a radical). For multidentate a dissociation reaction. ligands the coordination type is first checked (step 3). This determines whether the final molecule is symmetric (case V), asymmetric (case VI) or a chelate (case VII-VIII). For chelates B. Input files the case used still depends on the nature of the ligands. If A and In addition to the input files also required for gas-phase B are radicals case VII is used, whereas case VIII is used systems, i.e. the species identifiers and the reaction families, otherwise. several new input files are introduced. The input file with the species identifiers remains the same, except that the catalyst dummy block needs to be provided in SMILES or InChI format. For the reaction families files, nothing changes except for the reaction families themselves, which are now specific for catalytic reactions. The first new file is the catalyst descriptor input which supplies the atomic heats of adsorption, the coordination number (i.e. the number of ligands a single surface-bound atom has with the surface) and the atomic number of the dummy atom (i.e. 2 for helium).

There are two more additional input files which are optional. The first relates to additional thermodynamic data that cannot be calculated by Genesys. This data needs to be provided in CHEMKIN format. The second is used to supply Genesys with information on the non-dissociated surface species for Figure 5 Decision tree for the determination of the type of equation. dissociation reactions. This is because the UBI-QEP equation Depending on the number of atoms, ligands, bond strength and if for dissociative adsorption requires the heat of adsorption for multidentate coordination type a UBI-QEP equation will be selected this species as well (e.g. QH2 for dissociative adsorption of for the calculation of heats of adsorption. hydrogen.) Both of these optional input files are added as temporary fixes, in the long term the software should be

modified so as to no longer need these. Once the correct equation has been determined, Genesys maps (step 1.2) the atoms onto set templates (symbols A, X, B in Figure 4). Which atoms are mapped depends on which bonds C. Output files have to be broken, which in turn depends on which bonds were After the generation of a kinetic model, this model can be accounted for during the derivation of the equation (i.e.. only used to simulate catalytic processes with reactor simulations. A the AB bond in Figure 2). well-known software often used for gas-phase systems is CHEMKIN. The correct writing of Genesys to CHEMKIN In step 2.1 on Figure 4, the bond dissociation energies are input files has already been implemented for gas-phase kinetic determined. This is done by fragmenting along the bond for models and is now extended to catalytic kinetic models. For the which the BDE has to be determined. A thermodynamic cycle correct operation of CHEMKIN, more information regarding is then used, like the one shown in step 2.2, to determine the the catalyst is required. This includes the BET-surface area, BDE. surface density, elemental composition, etc. In a later stadium, when reactor simulations can be done on-the-fly for kinetic Once all required parameters have been determined, the heat model reduction, these additional parameters could be provided of adsorption of the surface species, QAB , is then calculated through the catalytic descriptors input file. (step 3 in Figure 3). D. Heats of adsorption of propionic acid IV. CASE STUDY Because propionic acid uses multidentate ligands to adsorb on the surface, it is an excellent case study to test both the A. Introduction representation of the species, as well as the determination of heats of adsorption via UBI-QEP. In this work, the heat of Lignocellulosic biomass is the main substantial source of adsorption was calculated assuming a pure nickel catalyst. For renewable carbon. As a result valorization processes for this the atomic heats of adsorption the values are used as reported type of feedstock have garnered more interest in recent decades by Shustorovich et al. [8]. For the determination of the bond [19-22]. The pyrolysis of lignocellulosic biomass is one of the dissociation energies, the required heats of formation for the most promising avenues because of its high yield and quality fragments are taken either from the Genesys database or from compared to alternative bio-oils. Currently however the end- the RMG database [7, 29]. product still has a sub-par energy density and stability whilst being too corrosive in comparison to conventional fuels. These The resulting adsorption heat as reported by Genesys is 132.6 problems are related to lower H/C and higher O/C ratios [23- kJ/mole. The calculations in Genesys follow the scheme given 26]. Higher H/C ratios are associated with higher energy in Figure 4. Genesys correctly determined the species to follow densities whilst eliminating the oxygen hetero-atom tackles the chelate case and formed the correct fragments. The value issues with stability and corrosiveness [26, 27]. Within this deviates only marginally from manual calculations (137.7 context, a case study has been performed on the kJ/mole). The difference is completely attributable to the hydrodeoxygenation of propionic acid on nickel. Propionic dataset used. For manual calculations, all data was taken from acid is a model compound which can be used to study the the RMG database, for the Genesys calculations only the data chemical pathways involved in the removal of oxygen from for those species not available were taken from the RMG lignocellulosic biomass [28]. database.

Otyuskaya et al. [28] estimated the value to be 93.8 kJ/mole 12. Bligaard, T., et al., The Brønsted–Evans–Polanyi relation and the using regression to experimental data. Other values for volcano curve in heterogeneous catalysis. Journal of Catalysis, 2004. 224(1): p. 206-217. propionic acid on nickel were not found, but for formic acid on 13. Zeigarnik, A.V. and E. Shustorovich, The UBI-QEP method: Ni(111) the heat of adsorption was determined to be 117.1 Mechanistic and kinetic studies of heterogeneous catalytic kJ/mole [30]. Manual calculation of different species showed reactions. Russian Journal of Physical Chemistry B, 2007. 1(4): p. that UBI-QEP significantly overestimates the values for all 330-356. 14. Shustorovich, E. and A. Zeigarnik, The UBI-QEP method: Basic species for within the network of this case study. The problem formalism and applications to chemisorption phenomena on presumably lies with the assumption that the catalyst is transition metal surfaces. Chemisorption energetics. Russian properly described as a pure nickel catalyst whilst in fact it is a Journal of Physical Chemistry A, Focus on Chemistry, 2006. 80(1): Ni-Cu based catalyst. When using the heats of adsorption for p. 4-30. 15. Vannice, M.A. and W.H. Joyce, Kinetics of catalytic reactions. Vol. Cu, also present within this catalyst a value of 114.6 kJ/mole is 134. 2005: Springer. calculated. This highlights the importance of getting good 16. Moqadam, M., et al., A UBI-QEP microkinetic study for Fischer- values for the atomic heats of adsorption. If the composition of Tropsch synthesis on iron catalysts. Procedia Engineering, 2012. the catalyst is not fully known, the atomic heats of adsorption 42: p. 34-44. 17. Mirzanejad, A., Thermal chemistry of 2-halo-1-propanols on Ni (1 can be determined by regression to experimental data. 1 1) and Cu (1 1 1) surfaces: A UBI-QEP energetic modeling. Applied Surface Science, 2015. 359: p. 576-588. V. CONCLUSIONS 18. Abramova, L.A., et al., A kinetic Monte Carlo/UBI-QEP model of O2 adsorption on fcc (111) metal surfaces. Surface Science, 2004. Genesys is extended to allow for the automated construction 565(1): p. 45-56. of kinetic models for metal catalytic systems. This is achieved 19. Ragauskas, A.J., et al., The path forward for biofuels and by introducing a correct and systematic representation of the biomaterials. science, 2006. 311(5760): p. 484-489. 20. Bozell, J.J., Feedstocks for the future–biorefinery production of species within the software. First dummy atoms are chemicals from renewable carbon. CLEAN–Soil, Air, Water, 2008. implemented and later extended towards the use of a catalyst 36(8): p. 641-647. dummy block. For the on-the-fly fast calculation of 21. Zhou, C.-H., et al., Catalytic conversion of lignocellulosic biomass thermodynamic and kinetic parameters, the UBI-QEP method to fine chemicals and fuels. Chemical Society Reviews, 2011. 40(11): p. 5588-5617. is implemented. With this method, heats of adsorption as well 22. Corma, A., S. Iborra, and A. Velty, Chemical routes for the as activation energies for surface reactions can be calculated. transformation of biomass into chemicals. Chemical reviews, 2007. Finally, a case study for the hydrodeoxygenation of propionic 107(6): p. 2411-2502. acid over a nickel catalyst is presented in which the newly 23. Hengst, K., et al., CHAPTER 6 Hydrodeoxygenation of Lignocellulose-Derived Platform Molecules, in Catalytic implemented functionalities are tested. Hydrogenation for Biomass Valorization. 2015, The Royal Society of Chemistry. p. 125-150. REFERENCES 24. Prasomsri, T., T. Nimmanwudipong, and Y. Roman-Leshkov, Effective hydrodeoxygenation of biomass-derived oxygenates into unsaturated hydrocarbons by MoO3 using low H2 pressures. 1. Reyniers, M.-F. and G.B. Marin, Experimental and theoretical Energy & Environmental Science, 2013. 6(6): p. 1732-1738. methods in kinetic studies of heterogeneously catalyzed reactions. 25. Resasco, D.E., What should we demand from the catalysts Annual review of chemical and biomolecular engineering, 2014. 5: responsible for upgrading biomass pyrolysis oil? 2011, ACS p. 563-594. Publications. 2. Van de Vijver, R., et al., Automatic Mechanism and Kinetic Model 26. Serrano-Ruiz, J.C. and J.A. Dumesic, Catalytic routes for the Generation for Gas‐and Solution‐Phase Processes: A Perspective conversion of biomass into liquid hydrocarbon transportation fuels. on Best Practices, Recent Advances, and Future Challenges. Energy & Environmental Science, 2011. 4(1): p. 83-99. International Journal of Chemical Kinetics, 2015. 47(4): p. 199-231. 27. Phan, B.M.Q., et al., Evaluation of the production potential of bio- 3. Vandewiele, N.M., et al., Genesys: Kinetic model construction oil from Vietnamese biomass resources by fast pyrolysis. Biomass using chemo-informatics. Chemical engineering journal, 2012. 207: and Bioenergy, 2014. 62: p. 74-81. p. 526-538. 28. Otyuskaya, D., et al., Fast pyrolysis oil stabilization kinetics over a 4. Thybaut, J. and G. Marin, Single-Event MicroKinetics: Catalyst Ni-Cu catalyst using propionic acid as a model compound. Applied design for complex reaction networks. Journal of catalysis, 2013. Catalysis B: Environmental, 2018. 308: p. 352-362. 29. Weitkamp, J., HYDROCRACKING, CRACKING AND 5. Navidi, N., G.B. Marin, and J.W. Thybaut, A Single-Event ISOMERIZATION OF HYDROCARBONS. Erdol & Kohle Erdgas Microkinetic model for ethylene hydroformylation to propanal on Petrochemie, 1978. 31(1): p. 13-22. Rh and Co based catalysts. Applied Catalysis A: General, 2016. 30. Zhao, W., et al., Energetics of adsorbed formate and formic acid on 524: p. 32-44. Ni (111) by calorimetry. Journal of Catalysis, 2017. 352: p. 300- 6. Goldsmith, C.F. and R.H. West, Automatic Generation of 304. Microkinetic Mechanisms for Heterogeneous Catalysis. The Journal of Physical Chemistry C, 2017. 121(18): p. 9970-9981. 7. Gao, C.W., et al., Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms. Computer Physics Communications, 2016. 203: p. 212-225. 8. Shustorovich, E. and H. Sellers, The UBI-QEP method: a practical theoretical approach to understanding chemistry on transition metal surfaces. Surface Science Reports, 1998. 31(1-3): p. 1-119. 9. Kua, J., F. Faglioni, and W.A. Goddard, Thermochemistry for Hydrocarbon Intermediates Chemisorbed on Metal Surfaces: CH n-m (CH3) m with n= 1, 2, 3 and m≤ n on Pt, Ir, Os, Pd, Rh, and Ru. Journal of the American Chemical Society, 2000. 122(10): p. 2309-2321. 10. Vorotnikov, V., S. Wang, and D.G. Vlachos, Group additivity for estimating thermochemical properties of furanic compounds on Pd (111). Industrial & Engineering Chemistry Research, 2014. 53(30): p. 11929-11938. 11. Wang, S., et al., Brønsted–Evans–Polanyi and transition state scaling relations of furan derivatives on Pd (111) and their relation to those of small molecules. ACS Catalysis, 2014. 4(2): p. 604-612. Table of contents i

Table of contents

List of symbols ...... v

List of abbreviations ...... viii

Introduction ...... 1

The Automatic Network Generator Genesys ...... 2

Goal ...... 3

Outline ...... 3

References ...... 5

Representation of Surface Species ...... 7

State of the art ...... 8

Thermal versus catalytic network generators ...... 8

Representation of species for thermal network generators ...... 9

Representation of surface species for catalytic network generators ...... 11

Molecule representation and identification in Genesys ...... 12

Basic Datatypes ...... 12

Identifier to graph ...... 13

Implementation of Surface species ...... 16

Representation of active sites and metal surface species ...... 16

Matching atomic properties for surface species ...... 20

Future Work ...... 22

Conclusions ...... 24

References ...... 25

Thermo & kinetics ...... 27

State of the art ...... 27

Introduction ...... 27

ii Table of contents

Thermodynamic properties for catalytic systems ...... 30

Kinetic parameters for surface reactions...... 40

Thermochemistry and kinetics in Genesys ...... 49

Thermodynamic properties...... 49

Kinetic parameters...... 52

Future Work ...... 54

Conclusions ...... 55

References ...... 56

UBI-QEP ...... 59

Literature Survey ...... 59

Introduction ...... 59

Methodology ...... 61

Derivation of UBI-QEP equations ...... 64

Application and Exceptions ...... 75

Example: methanol adsorption on Pt(111) ...... 77

Accuracy ...... 78

Link with the representation of surface species ...... 80

Implementation in Genesys ...... 81

Underlying Assumptions ...... 81

Thermodynamics ...... 83

Kinetics ...... 87

Future Work ...... 91

Conclusions ...... 92

References ...... 93

Output files ...... 95

Introduction ...... 95 Table of contents iii

CHEMKIN ...... 96

CHEMKIN units for catalytic reactions ...... 97

Kinetic model input files ...... 99

MicroKinetic Engine ...... 101

Conclusions and future work ...... 102

References ...... 102

A Case Study: hydrodeoxygenation of Propionic acid as a model compound ...... 103

Introduction ...... 103

User input ...... 105

Species Input ...... 105

Reaction families input ...... 106

Catalyst descriptors input ...... 109

Adsorption map ...... 110

Additional thermodynamic data input ...... 111

Output files ...... 111

UBI-QEP thermodynamics and kinetics ...... 112

Enthalpy of adsorption of propionic acid ...... 112

Dissociative adsorption: the case of hydrogen...... 114

Surface reactions ...... 116

Future work - Remaining bugs ...... 118

Conclusions ...... 119

References ...... 119

Conclusion & Future Work ...... 121

Appendix A: Reaction families ...... 125

Appendix B: References to lab journal ...... 127

Appendix C: Case Study references ...... 128

List of symbols v

List of symbols

Where units are not available this is denoted N.A., where units can be chosen freely this is denoted a.u. (arbitrary units). In the case of QA the number of ligands is not known. Chapter 3

X Thermochemical property N.A.

Gj Group within a molcule -

° -1 ΔfH Enthalpy of formation kJ mol

-1 Δ Reaction enthalpy kJ mol S° Standard molar enthalpy J mol-1 K-1

-1 -1 Cp° Specific heat capacity J mol K

Aj Pre-exponential factor N.A.

Brönsted-Evans-Polanyi coefficient -

Brönsted-Evans-Polanyi intersect kJ mol-1

Sticking coefficient - R Universal gas constant kJ K-1 mol-1

Chapter 4

x Bond index - n Coordination number -

r0 Equilibrium distance a.u. r Distance between 2 atoms a.u a UBI-QEP fitting parameter a.u.

X Bond order sum -

-1 Q, Epot Potential energy kJ mol

-1 Q0A Atomic adsorption enthalpy of atom A with a single ligand kJ mol

-1 QnA Atomic adsorption enthalpy of atom A with n ligands kJ mol vi List of symbols

-1 QA Adsorption enthalpy for A as observed experimentally kJ mol

-1 HA Enthalpy of formation for A in the gas-phase kJ mol α Lagrange multiplier - E Energy barrier kJ mol-1

-1 Ea Activation energie kJ mol

-1 D Bond dissociation energy for the bond AB kJ mol Total bond dissociation energy for all bonds concerning A D kJ mol-1 except for the AB bond Φ Empirical UBI-QEP scaling factor - occupancy - Site ligand -

Chapter 5

-1 FA molar flow rate for A mol s

Wcat Catalyst weight kg * Surface site -

θ Surface coverage -

p Partial pressure of A bar -1. -1 k Rate constant bar s

-1. -1 kc Rate constant according to chemkin bar s

-1 -1. -1 kapp Apparent rate constant mol kg bar s

-1 -1 R Production rate mol s kg γ Surface density per unit area mol-1 m-2

O Occupancy of A - concentration of active sites mol kg-1 Total bond dissociation energy for all bonds concerning A A mol m-3 except for the AB bond Internal surface area of the catalyst per unit length of the A m2 m-1 reactor List of symbols vii

Chapter 6

-1 Enthalpy of adsorption for the AB molcule kJ mol -1 0 Atomic heat of adsorption for A with 1 ligand kJ mol -1 Bond Dissociation energy for the AX bond kJ mol

X Bond order sum -

-1 Q0A Atomic adsorption enthalpy of atom A with a single ligand kJ mol

-1 QnA Atomic adsorption enthalpy of atom A with n ligand kJ mol

-1 QA Adsorption enthalpy for A as observed experimentally kJ mol

-1 Activation energy for the forward rate kJ mol -1 Activation energy for the backwards rate kJ mol ∗ Surface site -

List of abbreviations viii

List of abbreviations

AE Absolute Error

BDE Bond Dissociation Energy

BEP Brönsted-Evans-Polanyi

BET Brunauer–Emmett–Teller

BI Bond Index

BOC Bond Order Conservation

CDK Chemistry Development Kit

DFT Density Functional Theory

ELSR Extended Linear Scaling Relationship

GA Group Additivity

GAV Group Additivity Value

HDO Hydrodeoxygenation

InChI International Chemical Identifier

JnI-InChI Java Native Interface - International Chemical Identifier

MAE Mean Absolute Error

MEP Minimum Energy Path

NNI Non-Nearest Neighbor

RES Resonance Corrections

RSC Ring Strain Corrections

SEMK Single Event Micro Kinetics

SMARTS SMILES ARbitrary Target Specification

SMILES Simplified Molecular-Input Line-Entry System

UBI-QEP Unity Bond Index – Quadratic Exponential Potential

μKE Microkinetic Engine

Introduction 1

INTRODUCTION

In recent decades the world has seen a steady growth in both world population and living standards. As the world population is projected to reach 9.5 billion in 2050, demand for non- renewable natural resources (i.e. gas, petroleum and coal) is only expected to increase [1, 2]. Alongside these trends, an ever-increasing understanding of anthropogenic climate change has made the questions of sustainability and renewability only more urgent. In a response major advances have been made in the last decade towards the synthesis of commodity chemicals from non-edible ligno-cellulosic biomass, as this is the only substantial source of renewable carbon [3- 6].

In order to shift from petroleum-based to biomass based feedstocks the chemical industry will need to extensively revise its production scheme [7]. As over 90% of all chemical processes worldwide use catalysts, there is a need for a more thorough understanding of catalytic processes. This includes those which allows for a sustainable production of fuels and bulk chemicals [8]. In this respect microkinetic models are uniquely suited to identify which surface species and surface reactions paths control the observed product spectrum [8]. By providing the user with a quantitative understanding of the reactions that take place, microkinetic models help define optimal process conditions. Detailed kinetic models however can typically contain hundreds or thousands of reactions making manual construction of these models infeasible [9]. In the past this challenge has been met by use of automatic network generators, but as of writing, most software packages deal only with gas-phase reactions and are unfit for heterogenous catalysis.

2 Introduction

The Automatic Network Generator Genesys

Automatic network generators are software packages which automate and systematize the way in which a reaction network is constructed. They are used to construct reliable and sizable kinetic models for chemical processes involving a large number of species and reactions. This is done by generating a reaction network, starting from a set of input species, via the use of predefined reaction families. In addition, many packages, i.e. the so-called kinetic model builders, contain methods to generate the thermodynamic properties of species and kinetic parameters of reactions on-the-fly. For this purpose, fast-calculation or calculation methods are used.

Within this work, the automatic network generator Genesys developed at the Laboratory for Chemical Technology (LCT) is used [10]. Genesys is currently capable of constructing kinetic models for thermal systems. Genesys is written in Java and makes use of the chemo-informatics libraries as supplied by Chemistry Development Kit (CDK) to represent species. It is capable of following both a rule-based network generation methodology, which relies on the chemical knowledge of the user to avoid creating unimportant species, and a rate-based methodology, which includes species only if their formation exceeds a given threshold. The representation of species is established in two ways. For the unambiguous manipulation of species, as is required for the purpose of reactions, a graph structure representation is used as provided by CDK. By using CDK instead of conventional matrix representations, different stereo-isomers can be unambiguously identified. This can be relevant as different conformers may have different reactivity [9].

For the input as well as output the more readable molecule identifiers InChI’s and SMILES are used, both are well established line notations for chemical species [11, 12]. InChI’s are also used for the purpose of database searching as they are unique for a given species. Both the thermodynamic properties of species, as well as the kinetic parameters for reactions can either be found in a database or generated on-the-fly using fast calculation methods like Group Additivity (GA) and Brönsted-Evans-Polanyi (BEP) relationships. Reactions are implemented via the use of reaction families and are defined in the user input in the format of a recipe. Introduction 3

Goal

Within this work the goal is to implement both the representation of surface species as well as the on-the-fly determination of surface kinetic parameters in the kinetic model builder Genesys. For this purpose, a dummy atom will first be introduced as this has already been shown to work in other similar network generation tools [13, 14]. Once species have been properly represented, the compatibility with reaction families, used to generate new reactions, will be checked. A literature survey will be conducted with the purpose of determining the appropriate method for on-the-fly calculation of species thermodynamic properties and kinetic parameters in a catalytic context. The goal is to then implement at least one method so as to get an estimate for the Arrhenius parameters of reactions. If successful, the newly implemented functionalities will be tested in a case study.

Outline

As the representation methods and fast-calculation techniques used in Genesys have not been extended for catalysis, the following chapters will deal with the challenges faced when implementing surface species and reactions. The possible ways in which these challenges can be met are discussed as well as the solutions which were finally implemented.

Chapter 2 concerns the representation of surface species. It lays out both the current methods of representation as well as the new implementation of surface species. The biggest challenge is to achieve the intended graph structure representation (i.e. as specified by the molecule identifier or reaction family). This graph structure has to be created successfully for a given input molecule identifier. When the graph structure is manipulated according to a reaction, the resulting product species must correspond to those specified in the reaction family.

Chapter 3 takes a bird’s eye view of thermochemistry and kinetics in network generation with a focus on how existing methods, like group additivity (GA) or Brönsted-Evans-Polanyi (BEP), can be expanded for the purpose of surface species and reactions. The implementation in the kinetic

4 Introduction

model builder RMG-Cat is also discussed as an example. Whilst UBI-QEP deals with these same issues, it is covered in much greater depth in a separate chapter.

Chapter 4 pertains to the UBI-QEP method. Within the UBI-QEP scheme both thermodynamics and kinetics are tackled as one single unit as the activation energies are directly tied to enthalpies of adsorption via an analytical equation. After a brief discussion of the theoretical underpinnings the most important equations are derived. Both the accuracy and possible exceptions in the use of the methodology are discussed. After this initial literature survey, the implementation of the method is considered. Here the main focus lies with the assumptions which were made to translate the method into algorithms and the connection between the newly introduced Java classes and methods.

Chapter 5 deals with the output which is produced in order for the kinetic model to be imported into CHEMKIN. CHEMKIN is a chemical software solution which allows for the simulation of reactors. The possibility of producing output for the microKinetic Engine, i.e. an in-house developed software package for kinetic modeling and simulation, is also briefly touched upon.

Chapter 6 explores the application of the newly implemented features through the use of a case study. Here the hydrodeoxygenation of propionic acid on a nickel-based catalyst is used as a model for biomass pyrolysis. Special attention is given to the additional input which is required and, in addition of the CHEMKIN output, the new output files which are generated.

Chapter 7 concludes this work and gives a broad outline of potential avenues for future work.

Introduction 5

References

1. EIA, U., International energy outlook 2017. International Engergy Outlook," US Engergy Information Adminstration Report September, 2017. 2017. 2. Nations, U., World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. 2017. 3. Ragauskas, A.J., et al., The path forward for biofuels and biomaterials. science, 2006. 311(5760): p. 484-489. 4. Bozell, J.J., Feedstocks for the future–biorefinery production of chemicals from renewable carbon. CLEAN–Soil, Air, Water, 2008. 36(8): p. 641-647. 5. Zhou, C.-H., et al., Catalytic conversion of lignocellulosic biomass to fine chemicals and fuels. Chemical Society Reviews, 2011. 40(11): p. 5588-5617. 6. Corma, A., S. Iborra, and A. Velty, Chemical routes for the transformation of biomass into chemicals. Chemical reviews, 2007. 107(6): p. 2411-2502. 7. Réocreux, R. and C. Michel, Rational Design of Heterogeneous Catalysts for Biomass Conversion– inputs from computational chemistry. Current Opinion in Green and Sustainable Chemistry, 2018. 8. Reyniers, M.-F. and G.B. Marin, Experimental and theoretical methods in kinetic studies of heterogeneously catalyzed reactions. Annual review of chemical and biomolecular engineering, 2014. 5: p. 563-594. 9. Van de Vijver, R., et al., Automatic Mechanism and Kinetic Model Generation for Gas‐and Solution‐Phase Processes: A Perspective on Best Practices, Recent Advances, and Future Challenges. International Journal of Chemical Kinetics, 2015. 47(4): p. 199-231. 10. Vandewiele, N.M., et al., Genesys: Kinetic model construction using chemo‐informatics. Chemical Engineering Journal, 2012. 207: p. 526-538. 11. O’Boyle, N.M., Towards a Universal SMILES representation‐A standard method to generate canonical SMILES based on the InChI. Journal of cheminformatics, 2012. 4(1): p. 22. 12. Weininger, D., SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. Journal of chemical information and computer sciences, 1988. 28(1): p. 31-36. 13. Goldsmith, C.F. and R.H. West, Automatic Generation of Microkinetic Mechanisms for Heterogeneous Catalysis. The Journal of Physical Chemistry C, 2017. 121(18): p. 9970-9981. 14. Navidi, N., G.B. Marin, and J.W. Thybaut, A Single‐Event Microkinetic model for ethylene hydroformylation to propanal on Rh and Co based catalysts. Applied Catalysis A: General, 2016. 524: p. 32-44.

Representation 7

REPRESENTATION OF SURFACE SPECIES

Within Genesys, two types of molecule representations are used. The first form is line-based. Here use is made of molecule identifiers, InChI or SMILES, which provide Genesys with a user- friendly way to represent a molecule for in and output purposes. In the case of the InChI’s the molecule identifier can even be used to search a database as InChI’s are unambiguous. The second form of representation is graph-based and this form of representation allows for the unambiguous manipulation of molecules, a feature required to perform chemical reactions. Both the ability to verify if species are identical (i.e. database lookup), as well as the ability to manipulate species (i.e. reactions) can be provided by chemo-informatics libraries. For this purpose, Genesys makes use of CDK (Chemistry Development Kit)[1, 2].

Within this chapter the goal is to first briefly explain how ordinary gas-phase species are represented. This knowledge is then used to explain how CDK comes to the correct graph structure representation for a molecule given a molecule identifier as input. With this prior information the modifications made in this thesis which allow for the representation of metal surface species are discussed. A dummy atom is first implemented to perform the role of an active site. A modified version of helium, which allows He to make multiple bonds, is selected as the dummy atom. The presence of this atom within a molecule then serves as a marker that the species is in fact a surface species. For reasons discussed in detail in subsection 2.3.1, the decision is subsequently made to move away from a single dummy atom towards a block of multiple (usually 4) helium atoms connected to each other. Finally, the process by which CDK determines what charge, number of electrons, hybridization, … an atom should have, is then altered. Consequently, CDK can now correctly create a graph structure for the surface species tested within this thesis. These include adsorbed hydrogen, alkanes, carboxylic acids and aldehydes.

8 Representation

State of the art

Thermal versus catalytic network generators

In addition to Genesys, dozens of automatic network generation tools have already been developed for gas-phase chemistry. To give but a few examples out of the many kinetic model generation codes which have sprung up in recent decades: NETGEN [13-19] was created at the University of Delaware. NETGEN helped serve as a basis for the development of RMG [20-25], which is developed by the William H. Green research group at MIT and the Richard H. West research group at Northeastern University. EXGAS [26-31] has successfully been used to describe gas-phase oxidation networks. In addition, the university of Minnesota has produced RING [32- 37] which allows for both qualitative and quantitative analysis of complex reaction networks. Finally, at the LCT, three in-house network generation tools are available: PRIM(-O) [38, 39], which focuses on reducing the size of complex reaction networks, ReNGeP [38, 40-44], and Genesys [10, 45]. ReNGeP makes use of the Single-Event Micro Kinetics or SEMK methodology, which can be used for quantifying the contributions of competing reaction pathways to the overall feed conversion [44].

Whilst the list for automatic kinetic model generators is vast for the gas-phase reactions, this is not at all the case for catalytic reactions. Only a fraction of the software tools listed above have some form of catalyst implementation. Some, like ReNGeP have already shown a proof of concept through the use of a dummy atom, an atom with modified functionality which represents catalyst sites [44, 46, 47]. RMG has been expanded for catalytic systems (RMG-Cat [48]) and even has a, simple, but promising, implementation for the determination of surface thermodynamic properties and kinetic parameters. Additionally, RDL++ [49-53], an extension of the network generator RDL, is able to describe solid-acid catalyzed reactions of hydrocarbons.

The LCT has two appropriate in-house network generation tools which could have been modified so as to deal with catalytic systems: ReNGeP and Genesys. Even though ReNGeP already has some form of representation via the use of a dummy Atom, Genesys was chosen. This decision was Representation 9 made based on its modular design and greater flexibility. Additionally, Genesys has already been applied successfully in several case studies as well [54-56].

Representation of species for thermal network generators

Automatic network generation for gas-phase reactions has been studied thoroughly in the past. Various codes have been reported, such as EXGAS, NETGEN, RMG and RNG, as discussed in in the previous section [3-6]. Species are represented either by molecule identifiers (InChI, SMILES, …) or by use of graph structures. Graph structures are the superior choice for the unambiguous manipulation of molecules (i.e. reactions) whereas molecule identifiers are used either for i/o purposes (InChI, SMILES) or database lookup (InChI, as these are unambiguous).

The graph structure representation for molecules can be implemented in different ways (Figure 2-1). With all of these implementations, the common goal is to represent a molecule in a systematized (mathematical) and preferably unambiguous way. The basic underpinnings remain the same. The graph represents the molecule as a whole whilst the edges and vertices are atoms and bonds respectively. Additionally, edges and vertices are weighted, i.e. they are given more information about lone pairs, single electrons, element type, bond type, etc. In this work three different representations are briefly discussed: adjacency matrices (used in ReNGeP), adjacency lists (used in RMG) and the representation method in the CDK Library (used in Genesys).

Figure 2-1: Different mathematical graph representations for formic acid.

10 Representation

In the case of the adjacency matrix a matrix is used where each atom is assigned a given number. As shown in Figure 2-1, bonds are represented within the matrix as elements. Thus, the element in column 1 and row 3 will correspond to the double bond between carbon (here given number 1) and oxygen (here given number 2). An atom cannot bond with itself so elements along the diagonal represent the atomic number instead. Here, for the 1st element of 1st column the number is 6 as this represents a carbon atom. Adjacency matrices are known to be less efficient for smaller molecules and harder to read by a human. Nonetheless, they are still used often because representing graphs as matrices allow for many of the theorems in the mathematical branch of matrix theory to be used [7].

A different approach is taken in RMG [8] which uses an adjacency list, also shown in Figure 2-1. Here once again each atom is assigned a number. The element symbol (e.g. C for Carbon) is stored, along with the number of unpaired electrons, the charge, and the bonds present. Thus in Figure 2-1 u0 denotes that carbon has zero unpaired electrons and c0 denotes that it has 0 charge. Carbon has three bonding partners, one of which is an oxygen with which it shares a double bond, denoted {3,D} as oxygen is assigned number 3 and D denotes the double bond. Graph representations are useful for the manipulation of species, but for database searches they are less useful. This is because in mathematical terms, checking if two species are identical is checking if two graphs are isomorphic, something for which there is as of writing no usable algorithm [7].

Finally Genesys makes use of chemo-informatics libraries, as provided by CDK (Chemistry Development Kit [1, 2]). As Genesys is the automatic network generator used throughout this work, it is discussed in a separate section (chapter 2.2).

Representation 11

Representation of surface species for catalytic network generators

Whether it is because of simplicity or as a stepping stone towards a more comprehensive implementation, several automatic network generators have converged on the use of a dummy atom to represent the active site [9-12]. As a result catalytic and thermal network generators share the use of both the line-based and graph-based representations of species. In this approach an unused element, usually a noble gas, is modified to function as a marker for species which are bound to the surface. A correct representation allows for the unambiguous formulation of reactions within the software. ReNGeP [11, 13] uses the concept of adjacency matrix representation. An example is shown in Figure 2-2, for the case of the adsorption of an aldehyde. The active sites on a metal surface are represented by a dummy atom called M. Indeed, the use of a dummy atom within ReNGeP is complementary to the use of the graph structure-based internal representation.

Figure 2-2: Adsorption of an aldehyde as represented in ReNGeP using adjacency matrices. Here M denotes the active site of a metal catalyst.

RMG-Cat [9], which uses adjacency lists, has also implemented the dummy atom. As RMG-Cat is based on an object-oriented structure similar to that of Genesys, RMG(-Cat) will serve as a reference throughout this chapter.

12 Representation

Molecule representation and identification in Genesys

Basic Datatypes

The representation of a molecule in Genesys is two-fold, i.e. the one defined by the user as input and the one internally used in the software. The first category includes molecule identifiers that are line-based. Molecule identifiers typically used are InChI’s and SMILES, predominantly for input and output purposes. InChI’s play an essential role for database searches because they have an unambiguous nature, i.e. 1 molecule has 1 corresponding InChI and vice versa, which SMILES don’t. The main downside of InChI’s is that they are not easy readable to humans. SMILES, in comparison, have a very simple structure in which atoms, bonds and connectivity can be easily recognized. However, each molecule has multiple, equally correct, corresponding SMILES so they aren’t unique for a given molecule. Some software tools work around this by creating canonical SMILES, but there are no general rules. An example of the InChI and SMILES for formic acid is given in Figure 2-3.

Additionally, an extension of the SMILES is used in the SMARTS language which allows for a precise representation of molecule fragments. In SMARTS more detailed information can be given of the valency, number of bonds, number of hydrogen atoms etc. of an atom. For example, in the case of propionic acid the carbon has 3 bonding partners (denoted X3) and a valency of 4 (denoted v4), it can therefore be represented as [C;X3v4]. This allows for very precise substructure searches.

Figure 2-3: Molecule identifiers are used for input and output purposes.

Representation 13

The second category is the graph structure which is supplied within the Chemistry Development Kit (CDK) Java libraries. The graph structure allows for unambiguous manipulation and identification of molecules within the Java code. Used in conjunction with SMARTS it also allows for substructure searches within a molecule.

The graph representation within Genesys is shown in Figure 2-4. A graph (or molecule) is composed of edges (or atoms) and vertices (or bonds). The edges and vertices are weighted, i.e. they are given more information about lone pairs, single electrons, element type, bond type, etc. Within CDK, this is all stored within Java classes. Atoms are stored in the “Atom” class, bonds in the “Bond” class and molecules in the “AtomContainer” class. Atoms store information on the element and charge. “Bond”s store information on which “Atoms” are bonded together as well as on the bond order. “AtomContainer” s consist of a list of both “Atom”s and “Bond”s and also store information on the presence of single electrons.

Figure 2-4: A weighted graph structure is used within the CDK package of Genesys.

The conversion from molecule identifiers to graph structure representation is the subject of the next section. This process is somewhat different for SMILES and InChI’s, for brevity’s sake the process is only discussed for InChI’s.

Identifier to graph

Genesys makes use of the graph structure representation for internal use (“AtomContainer” class of CDK). Genesys therefor needs a functionality to convert the input line-based molecule identifier to this graph structure. In order to do this IUPAC has written code which converts an InChI into a graph. As this code is written C+ and not in the Java language, it is run within a Java wrapper “jni-inchi-0.7.jar” which makes the connection between the code of IUPAC and CDK. For

14 Representation

the interested reader an instance is created eventually of a class called JnI-InChI. The main consequence of the use of the IUPAC code for the extension to surface species is that it is inaccessible and cannot be modified. It merely functions as a black box.

Once the graph structure has been created, it is converted to the class which will be used throughout Genesys for storage of graph structures or molecules called “AtomContainer”. The code then iterates over every atom in the graph structure and will try to compare it to a standard type, defined in the CDK database “cdk-atom-types.owl” (Figure 2-5). This is done to ensure that every “Atom” has been generated and given properties in a chemically relevant way. As an example, if a carbon atom has four bonding partners and no charge or single electrons, CDK will conclude that it is sp3 hybridized. Any additional information which was not present is then added to either the “Atom” or the “AtomContainer” (in the case of single electrons).

Figure 2-5: Every atom within a molecule is matched against standard atom types supplied by CDK. The atom is then modified accordingly.

The types themselves, which contain information on charge, maximum bond order, hybridization, valence electrons and so on are stored in a class called “AtomType”. It should be noted that this is important for the further implementation of surface species, as additional possibilities for chemically relevant atoms will need to be provided.

The process of matching every atom with a set template is performed by a single class: “CDKAtomTypeMatcher”. When a new molecule is generated this provides the user with some flexibility and recourse when the preliminary graph structure, as produced by the IUPAC code, does not correspond with the structure provided by the input identifier. The atom matching is also performed every time the graph structure is manipulated, i.e. when a reaction occurs and new products are formed. For example, when a bond is broken within Genesys a charge may appear. This charge is not added instantly but as a result of matching the atoms involved to Representation 15

different atom types (as they now have a different number of bonding partners). It is worth noting that for surface species, the predefined atom types, might not be complete.

A summary of the whole process of converting a molecule identifier into a graph representation of a molecule is shown in Figure 2-6. In short, CDK generates “AtomContainer”s which store the graph structure representation of molecules. This process can be split into two parts. In case of the primary conversion of the identifier to the graph structure, the user is not able to modify anything as the code is contained within a wrapper used to run code of a different programming language. In the second part, the software iterates over all the atoms within the molecule and checks if they correspond to a predefined template. This process is more accessible to manipulate. It should be noted that when reaction products contain erroneous charges or single electrons, especially in view of additions made for the catalyst implementation, the problem is almost always found in the process of matching with the correct “AtomType”.

Figure 2-6 : Summarizing scheme of how CDK converts a molecule identifier into a graph representation.

16 Representation

Implementation of Surface species

Representation of active sites and metal surface species

2.3.1.1 Dummy atoms and dummy blocks.

The simplest way to solve a programming issue is to use the resources that are already there. By modifying how Genesys treats a specific unused element (e.g. a noble gas like helium), a dummy atom can be introduced and used to represent the catalytic active sites. This procedure requires few modifications to the code and was, therefore, also the path taken in other catalytic kinetic model generators like RMG-Cat and ReNGeP [9, 11].

In order to use a noble gas as a dummy atom to represent the catalyst in Genesys, some modifications need to be made to the properties of this atom. As explained in section 2.2.2, the types of atoms that are allowed to be used in a molecule in CDK are predefined in an internal database. To use a dummy atom as an active site, modifications are made to the number of bonds the dummy atom can partake. In the original CDK database the number of bonds was zero for any noble gas. After modifications, helium, i.e. the selected the dummy atom, can have up to 4 single bonds. This means that the bond-order of the bonds the dummy partakes in is as of yet not used for the determination of the type of site. In the future, this could be changed.

The assumption is made to only use single bonds. This assumption is valid as only as the presence of only a single type of site is assumed. Within the new implementation another assumption is that the dummy atom, i.e. the active site, should never have any charge or single electrons. This assumption is made to allow easy debugging. In fact, when the dummy does have this charge, something went wrong with the recognition of the surrounding atoms. As an example, when a bond is broken this might initially have led to a positive charge on the dummy atom and a negative one on the contact atom. Because the decision was made the dummy atom should never have a charge, we can use the presence of a charge on the dummy atom, helium, to correctly restore the charge on the contact atom to the right value. Nevertheless, the extension to more bonds, higher bond orders and the addition of charges should be straightforward. Representation 17

In a next stage within this work the decision was made to use not just one dummy atom to represent one metal active site, but a block of dummy atoms, referred to as a dummy block. For the purpose of this thesis each active site is represented by a dummy block of four helium atoms bonded to each other via single bonds. Thus, when adding the surface to the list of input species the SMILES identifier is simply [He] [He] [He] [He].

The transition to the dummy block is made for several reasons. The use of single dummy atoms, whilst simple, gives no information on how a species is adsorbed on the surface and what the relative position of the bonds is. The fact that, within a block, each dummy atom can only partake in one bond with an adsorbed atom makes certain algorithms, like those applied for the UBI-QEP method, see Chapter 4, easier to code for. Finally, dissociative adsorption reactions are easier to implement as this no longer requires a dummy reaction. This is because currently reactions are implemented for up to 2 reactants. For dissociative adsorption this is not enough as both the gas- phase species and 2 empty surface sites are involved.

The catalyst representation is implemented in such a way as to maintain a certain flexibility. Should the user wish to use any element other than helium, this is possible. For example, in the case of nickel, algorithms will then simply look for the presence of the four consecutive nickel atoms. Should the user wish to use longer or shorter blocks this is also possible with little modifications needed. This could be useful as a feature if in the future multiple catalyst sites are to be implemented. For now, adjustments still need to be made in the source code of Genesys. It would however be useful to provide this information through a user-provided input file.

It should be noted however that the number of atoms used in a block is preferably even. This is due to assumptions made in the implementation of dissociative reactions (as will be shown in Chapter 6, section 6.2). Additionally, at least for the short term the author advises against the use of multiple metal atoms. Whilst possible this requires the user to recreate the molecule identifier of all implicated species. When multiple metals atoms are involved, some issues also arise with the InChI identifier. Additionally, programming errors are only fixed as they relate to helium. This makes a direct use of metal atoms, at least in the short term, more labor intensive than simply replacing helium where need be in post-processing.

18 Representation

One of the advantages of representing the metal surface site by a dummy atom or dummy block is that information on the surface can be stored separately in a different class. Thus, the SMILES molecule identifier of the input species does not need to be modified every time the network has to be generated for a new type of catalyst.

Whereas the “Atom” class contains information on the element symbol, number, atomic mass, … information on the catalyst is stored separately. In other words, whilst dummy atoms and catalyst blocks give information on the presence of the active site and the bonds with the adsorbed species, they do not store information on the nature of the surface (or catalyst site) they represent. Within Genesys, this is done in a separate class: “Catdescriptor.java”. The information already stored in this class is the topic of the next subsection (2.3.1.2).

2.3.1.2 Additional catalyst information

For the simple generation of a network of catalytic species, the usage of a dummy block can be sufficient. However, building a kinetic model requires much more information that can be useful to select the most accurate kinetic and thermodynamic data. Moreover, the automatic construction of output files that are compatible with post-processing software, might need more information about the catalysts besides the corresponding kinetic model. This will be discussed more extensively in chapter 5.

Additional information regarding the catalyst is stored in a separate class called “CatDescriptor.java” and can be provided by the user via one input file. This procedure is chosen because additional information that might not be important at this point, can be easily added in a later stadium of development. “CatDescriptor.java” is a simple container class where all information regarding the catalyst is stored. This class contains the most important variables such as the atom number of the dummy atom (i.e. 2 for helium), the symbol of the catalyst (e.g. Ni for nickel) and parameters related to the UBI-QEP method which will be discussed in Chapter 4. These parameters include the coordination number, which is the number of surface-atom ligands involved in binding a single atom to the surface, as well as the enthalpy of adsorption for the elements carbon, oxygen and hydrogen. Because only one type of metal catalyst with one type Representation 19

of active site is allowed for each reaction network, only one, unique “CatDescriptor” object or instance is created and shared. An example of the input file can be seen in Figure 2-7.

Element: 2 Coordination Number: 3

Q_0C: 429.4836 Q_0H: 158.231 Q_0O: 288.696

Figure 2-7: The input file containing the catalyst descriptors is a text file which stores the values required for UBI-QEP.

Information that has not yet been added but can be of interest in a later state is the information needed by CHEMKIN (Chapter 5) to do on-the-fly reactor simulations, i.e. precise elemental composition, surface density, BET-surface, concentration of active sites etc. More information on the catalyst blocks could also be added, such as the desired length of the blocks or the possibility to use multiply active sites in separate blocks.

In summary, as shown in Figure 2-8, Genesys now uses a catalyst block to represent catalytic active sites and adsorbed species in conjunction with a separate class to store information on the catalyst (i.e. catalyst descriptors).

Figure 2-8: The final representation in Genesys uses both a catalyst block and a separate class to store surface information.

20 Representation

Matching atomic properties for surface species

When implementing the catalyst and surface species in Genesys, the matching process between atoms and atom templates, as explained in section 2.2.2, proved to be very important. Every issue related to the correct interpretation of a molecule identifier or the manipulation of a molecule (i.e. reactions) could be reduced to problems with the matching process. Because of its relevance, the matching process and its functioning with surface species is briefly explained.

For all atoms, bound to the surface or not, the matching process takes place in two steps as shown in Figure 2-9. First Genesys will determine what element it is trying to match (i.e. carbon). It will then use this information to start the matching process using a Java method specific to that atom (i.e. perceivecarbons). Then the properties (i.e. charge, valence electrons, bond partners, …) are used to find the appropriate match.

Figure 2-9: Schematic representation of the process of matching atoms with their correct recipe (called AtomTypes).

As a solution a separate library of predefined templates was implemented. These templates are specific to contact atoms. This means that an atom will only use this library if it shares a bond with a surface atom. For all other atoms, the standard CDK dictionary is applied as these behave exactly as they would for gas-phase species. The user can find this dictionary at cdk/dict/data/cdk-atom-types.owl.

For atoms which do share a bond with a dummy atom, a new class “CDKCatTypeMatcher” is created. This was done to introduce three new functionalities. First, a new dictionary is added which contains the new templates that are allowed for adsorbed atoms (located at cdk/dict/data/surface-atom-types.owl). Except for helium, the recipes are the same as in the CDK dictionary, except for one important difference. This difference is the addition of one extra single bond which represents the ligand with the surface. This dictionary can easily be extended for other catalytic systems. Representation 21

The second functionality is the correct matching of each atom with the desired template. The methods that deal with oxygen, carbon and hydrogen are modified. Other species should be modified as the user sees fit, but for the purpose of this work these atoms were modified first. The most important changes deal with manipulations that happen before the actual matching takes place.

Specifically, for oxygen, the number of implicit hydrogens, the charge, the presence of double bonds with the dummy atom and the number of single electrons are checked for first. If there are erroneous charges present, these will be due to the bond of the adsorbed atom with the dummy atom (as this is different from a normal gas-phase molecule). The dummy atom will therefore also have a charge and this information can be used to set the charge of oxygen correctly before any matching takes place. The same is done for implicit hydrogens and single electrons. It is assumed that by rectifying these errors before an atom type is selected the selection algorithm itself will need little modification. This process is illustrated in Figure 2-10. The species on the left hand side, propionic acid, is what should have been generated based on the molecule identifier. However, when the graph structure is generated based on the IUPAC code, errors are sometimes made. By changing the matching process these bugs were fixed. Similar modifications were made for carbon and hydrogen.

Figure 2-10: Example of the use of the new matching functionality to correctly correct for mistakes.

The last functionality is the introduction of catalyst specific checks. This is required as sometimes algorithms need to check for something they did not need to for gas-phase molecules. A concrete example is the occurrence of a wrong number of implicit hydrogen atoms for oxygen atoms in a carboxylic acid group. Genesys has to set the correct number of implicit hydrogens without also,

22 Representation

erroneously, adding them to oxygen atoms which already had the correct number. Thus, a method was introduced which checks if that specific oxygen is indeed part of a carboxylic acid group. If this condition is met, then for these species the implicit hydrogen number is modified so as to be correct.

Future Work

Potential future work can be divided into two aspects: the active site representation and additional information.

For the surface representation the most straightforward way forward is definitely the addition of multiple sites. At this moment, this has been implemented for a single site with a single coordination number. It would be very useful if reactions with multiple sites were possible. Additionally, catalyst properties could be made dependent on other variables. As one moves along a reactor the properties of the catalyst surface may change. Right now, an implicit assumption is made of complete homogeneity. All molecules adsorb on the same type of site and this has a fixed set of properties.

Secondly there is also the challenge of physisorption. Every bond within CDK contains 2 electrons. Now, a bond with the surface is implemented by using a single bond. There is in fact already a class present in CDK which is very similar to the “Bond” class used to describe chemical bonds: “Association”. This class was implemented to store information on bonds which do not share electrons but which may be relevant (i.e. hydrogen bonds). The author however strongly advises against the use of this class for the following reason. Both Genesys and CDK use algorithms which specifically search for or rely on an instance of the “Bond” class. When using the “Association” class, every such algorithm would have to be modified. As a result, it is by far simpler to introduce bonds which share zero electrons using the “Bond” class itself. A note of caution is required however. CDK provides the fundaments on which Genesys is built. Modifications made this far down the code could have undesirable effects which might not become apparent immediately. Representation 23

A final challenge for representation is the use of molecule identifiers. Originally the idea was to move beyond helium as the explicit reference to a noble gas was deemed to be unaesthetic. However as was shown CDK makes use of the IUPAC code to translate molecule identifiers into a graph structure representation. As this part of the code cannot be modified (or would have to be rewritten from scratch!) this makes the cost of moving away from helium a lot steeper. When using helium, errors can easily be fixed by matching with the correct atomic recipe. This would not be the case for newly introduced dummies as the code of IUPAC would not even recognize the element in question.

Secondly there is the issue of additional information where progress can be made. First, the process of manually creating a molecule identifier for every adsorbed species is very labor intensive. This should ideally be put into a database which maps gas-phase species onto their surface equivalents. The process could also be automated. For example, if it is found that carboxylic acids always adsorb via the same contact atoms, creating the surface representation for each species individually would prove to be redundant. Genesys could instead generate the correct surface molecule identifier for any species which ends in a carboxylic acid group adsorbed on the surface.

Furthermore, there is also the issue of catalyst descriptors and information. Catalyst properties like surface densities, used for CHEMKIN, could be stored in a database. Other examples are the BET surface and elemental composition. If a specific catalyst is used extensively within the Laboratory for Chemical Technology it may be worthwhile to regress the atomic heats of adsorption and store these values within a database so literature values do not have to be used. These values can then be used by the UBI-QEP method as will be explained in chapter 4. Features like these would make the process of catalytic automatic network generation significantly less labor intensive. The most urgent catalytic properties are surface densities and the catalyst properties which can be used to determine the surface density.

24 Representation

Conclusions

For kinetic model builders, the representation of chemical species can usually be split into two categories. First are the molecule identifiers, i.e. InChI and SMILES, which are used for input and output purposes. Because InChI’s are unique for a given molecule, they are also useful for database searching. For the unambiguous internal manipulation of chemical species, a graph structure representation is used. Within Genesys, this is done by the cheminformatics libraries of CDK which uses Java classes. Graph structures can also be represented by use of an adjacency list or adjacency matrix. The functionalities in Genesys which translate the molecule identifier, used as input, into a graph structure were discussed. This process can be divided into two steps. The first step is the creation of a graph structure using the code supplied by IUPAC. As this part of the code is written in C+, modification by the user is not possible. In the second step CDK iterates over all atoms within the graph structure and modifies them according to pre-specified recipes. The process of matching each atom with the right recipe is extremely important. Whenever an erroneous graph structure is observed, be it after manipulation or generation via the molecule identifier, fixes are probably best made in this section of the code.

With this background information in mind, active sites are, within the available literature, usually represented using a dummy atom which symbolizes the catalyst surface. Within this work however, the decision was made to move beyond the conventional dummy atom towards a catalyst block consisting of multiple, connected, dummy atoms. This was done for several reasons. First and foremost, it significantly simplifies the process of matching for surface species. Second, it simplifies both the implementation of UBI-QEP algorithms and non-dissociative adsorption. Third, because multiple, chemically bonded metal atoms do not occur in gas-phase species, it allows the user to use the correct metal atom as well should it be preferred. This makes the current choice less committal. Note that within this work, the assumption was made that the catalyst contains a type of active site. This should be changed in the future. Finally, additional information regarding the catalyst is stored separately. For now, this is limited to the atomic number of the dummy atom used and surface descriptors related to the UBI-QEP method which will be implemented in chapter 4. Representation 25

References

1. Willighagen, E.L., et al., The Chemistry Development Kit (CDK) v2. 0: atom typing, depiction, molecular formulas, and substructure searching. Journal of cheminformatics, 2017. 9(1): p. 33. 2. Steinbeck, C., et al., The Chemistry Development Kit (CDK): An open-source Java library for chemo- and bioinformatics. Journal of chemical information and computer sciences, 2003. 43(2): p. 493- 500. 3. Németh, A., et al., MECHGEN: computer aided generation and reduction of reaction mechanisms. Journal of chemical information and computer sciences, 2002. 42(2): p. 208-214. 4. Susnow, R.G., et al., Rate-based construction of kinetic models for complex systems. The Journal of Physical Chemistry A, 1997. 101(20): p. 3731-3740. 5. Karaba, A., et al., Generalized model of hydrocarbons pyrolysis using automated reactions network generation. Industrial & Engineering Chemistry Research, 2013. 52(44): p. 15407-15416. 6. Heyberger, B., et al., Comprehensive mechanism for the gas-phase oxidation of propene. Combustion and Flame, 2001. 126(4): p. 1780-1802. 7. Mehta, D.P. and S. Sahni, Handbook of data structures and applications. 2004: CRC Press. 8. Gao, C.W., et al., Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms. Computer Physics Communications, 2016. 203: p. 212-225. 9. Goldsmith, C.F. and R.H. West, Automatic Generation of Microkinetic Mechanisms for Heterogeneous Catalysis. The Journal of Physical Chemistry C, 2017. 121(18): p. 9970-9981. 10. Rangarajan, S., A. Bhan, and P. Daoutidis, Identification and analysis of synthesis routes in complex catalytic reaction networks for biomass upgrading. Applied Catalysis B: Environmental, 2014. 145: p. 149-160. 11. Navidi, N., G.B. Marin, and J.W. Thybaut, A Single-Event Microkinetic model for ethylene hydroformylation to propanal on Rh and Co based catalysts. Applied Catalysis A: General, 2016. 524: p. 32-44. 12. Hsu, S.-H., et al., A systematic approach for automated reaction network generation, in Computer Aided Chemical Engineering. 2006, Elsevier. p. 973-978. 13. Thybaut, J.W. and G.B. Marin, Single-Event MicroKinetics: Catalyst design for complex reaction networks. Journal of Catalysis, 2013. 308(0): p. 352-362.

Thermo & kinetics 27

THERMO & KINETICS

State of the art

Introduction

The goal of a kinetic model is to gain insight into the reactive behavior of a chemical process and to make predictions under circumstances not seen before. The modeling of chemical processes establishes the bridge between theory and practice [1]. During the development of a model, a trade-off between accuracy and computational expense has to be made. A complex microkinetic model may have a higher accuracy and allow predictions within a larger set of operating conditions, but this will come at a higher computational cost. This means that it can be more expensive to create as well as use such extensive models. For monitoring reactor behavior around a stable operating point one may opt for a phenomenological power law model whilst reactor or catalyst design may require more complex models [1, 2]. One example of a trade-off between accuracy and computational expense is the determination of thermodynamic and kinetic parameters via fast calculation methods. The assignment of these parameters can be fast with a higher uncertainty. Another option is to determine these parameters with experimental techniques or theoretical calculations, however at a much higher computational cost.

One of the major aspects of automatic kinetic model generation is to allow for fast assignment of kinetic and thermodynamic data. When data is available, properties can be assigned via a database search of kinetic and thermodynamic data. Often experimental or theoretical data are not available and a method of fast calculation of these parameters is required. Automatic kinetic 28 Thermo & kinetics

model builders can rely on (semi-)empirical techniques which are tied together by a common philosophy: molecular properties can be systematically organized on the basis of structural similarities [3]. Thus, by relating physical or structural properties to a small number of parameters, the required thermodynamic and kinetic data can be estimated well for network generation, though some caution is required.

In this work three important methods will be discussed to calculate thermodynamic and kinetic properties: Benson group additivity (GA), Brönsted-Evans-Polanyi (BEP) relationships and the unity bond index, quadratic exponential potential (UBI-QEP) method [4-7]. Benson GA is a method for calculating thermodynamic properties of gas-phase species which can be expanded towards adsorbed species [4, 8-10]. BEP-relationships can be used to estimate activation energies based on enthalpies of formation. They are thus often used complimentary to GA methods [11]. Scaling relationships, such as the BEP-relationships, can also be used in conjunction with both UBI-QEP and GA methods to compensate for a lack of data on a particular catalyst [4]. Whilst for gas-phase kinetics, group additivity has also been applied to the determination of Arrhenius parameters [12, 13], the author is not aware of any similar effort for surface reactions.

The different calculation techniques and the link to applications in catalysis are explained in more detail in this chapter. In Genesys, Benson GA and BEP-relationships are already implemented for gas phase modelling. The extension towards catalysis has not yet been done but should be straightforward. The main problem with these techniques with respect to catalytic species and reactions is the lack of databases.

UBI-QEP is a method which uses simple analytical equations to relate the enthalpy of adsorption for surface species to parameters which are easier to determine. These parameters include the atomic heats of adsorption and gas-phase bond dissociation energies. Additionally, the method can also provide the user with values for activation energies for surface reactions. This method will be discussed in Chapter 4 as it is implemented in Genesys in order to allow fast calculation of adsorption enthalpies as well as activation energies.

For both surface thermodynamics and kinetics, the implementation in the network generation software RMG-Cat (Reaction Mechanism Generator [11]), developed by William H. Green and Thermo & kinetics 29

Richard H. West, is discussed. Like Genesys, RMG constructs kinetic models composed of elementary reaction steps as discussed in Chapter 2. It is written in an object-based language and uses adjacency lists to represent molecules as discussed before. Additionally, RMG applies GA to calculate gas-phase thermodynamic properties and kinetic parameters. All of these similarities make RMG a good analogue to study.

30 Thermo & kinetics

Thermodynamic properties for catalytic systems

3.1.2.1 Group additivity

3.1.2.1.1 Benson group additivity for gas-phase thermodynamics

Group additivity (GA) [14] is a semi-empirical method, developed by Sidney Benson, which can be used to estimate thermochemical properties of compounds for which no other, more accurate, data is available. Conceptually, GA divides a chemical compound into a set of smaller structural units (groups) so that certain properties can be calculated as the sum of the constants associated with these structural units. A group is defined here as a central polyvalent atom with all of its neighbors [3]. A group is labeled as X-(A)i(B)j(C)k(D)l with X the central atom having i neighbors of atom A, j of atom B and so on. A subscript can be used to differentiate between, for

example, single (X), double (Xd) or triple (Xt) bonds. Two examples are given in Figure 3-1 for propylene and acetaldehyde. Propylene is used as an example because it contains both sp3

carbon, denoted C, as well as sp2 carbon, denoted Cd. For the two groups around Cd the double bond is implied. For double bonds between carbon and oxygen this is solved differently. Here C=O is taken to be one group, this allows the user to differentiate between a C=C bond and a C=O bond [15].

Figure 3-1: Depiction of Benson groups for propylene (a) and acetaldehyde (b). [15] (a) Benson groups in propylene. (b) Benson groups in acetaldehyde. The carbonyl group is seen as one polyvalent center to differentiate between a C = C bond and a C = O bond.

Thermo & kinetics 31

Initially designed only for gas-phase reactions the GA relationship can thus be written as equation (3-1).

(3-1) = (G) Where is the (invariant) contribution associated with a given group Gj. Here X is the G ° thermochemical property, e.g. the enthalpy of formation (ΔfH ), entropy (S°) or heat capacity

(Cp°), associated with a given molecule. Group contributions can be derived both from experiments as well as ab initio calculations. As will be the case for BEP-relationships and UBI- QEP, the source of the data used matters. For GA, when molecule properties are estimated by groups that originate from different sources, upper limits for the introduced error can no longer be guaranteed based on the reported maximum deviations of the individual groups [16].

In practice a more complicated form of equation (3-1) is used and several corrections are also applied: non-next-neighbor interactions (NNI), resonance corrections (RES) and ring strain interactions are all commonly applied thus resulting in equation (3-2) [15, 17, 18].

(3-2) = + + + Interestingly for gas-phase molecules the method has also been expanded for the purpose of calculating activation energies and pre-exponential factors [12, 13]. This is done by focusing on the differences in the reactive centers relative to a reference reaction [12]. The author is not aware of any similar effort for metal catalysis. Currently BEP-relationships are mainly used to calculate activation energies [11].

32 Thermo & kinetics

3.1.2.1.2 Group additivity for simple surface species

A first attempt to expand GA beyond gas-phase thermodynamics was published in 2000 by Kua et al. [9]. In this work, all groups that do not make contact with the surface retain their gas-phase values which are defined by Benson et al. [14] . All expansions for surface species rely on the use of additional groups to deal with the direct environment around the ligand with the surface. As will be discussed later, this is not completely correct as when species adsorb on the surface some functional groups or atoms within the molecule may become more densely packed or the distance between molecules might become significantly smaller. These effects are compensated for by introducing additional corrections.

In the work of Kua et al. [9], surface groups were determined for methyl/alkyl fragments with up to four carbons on three different sites (on-top, bridging, and cap sites) and for the six platinoids Pt, Ir, Os, Pd, Rh, and Ru. Here the metal surface was modeled as the closest packed planar cluster as illustrated in Figure 3-2. As also shown in Figure 3-2, a cluster of 8 atoms, denoted the M8 cluster, is used to represent the surface.

Figure 3-2: (left) M8 cluster model for closed-packed surfaces of platinoid metals. (right) Top view of best binding structures for CHx on Pt8 [9]. After the theoretical calculation of the heats of formation for the different species and metals, group values were assigned in a scheme analogous to Benson GA. For the calculation of heats of

formation, a reference is required. Here the M8 metal cluster (ΔfH°(298K) =0), gas-phase CH4

(ΔfH°(298K)=-74.9 kJ/mole) and gas-phase C2H6 (ΔfH°(298K) =-83.7 kJ/mole) are used.

Thermo & kinetics 33

Figure 3-3: GA as applied to n-propyl and isopropyl radicals adsorbed on Pt.

Thus for the adsorption of a n-propyl radical vs an isopropyl radical on Pt, for example, the heats of formation can be calculated as follows [9] (see Figure 3-3):

°(298, /) = ()()() + ()() + ()() = 26.4 20.6 42.7 = 89.7

(298, /) = ()()() +2 ()() = 16.8+2∗ (42.7) = 68.5 For carbon atoms not bound to the metal surface regular Benson GA values are used, whilst new groups are created to deal specifically with the atoms bound to the metal. As with the gas-phase GA schemes some corrections may then be applied, for example to count for strain energy in the

case of a M2C2-unit [9].

3.1.2.1.3 Group additivity for simple oxygenates

More recently there has been a renewed interest in using GA schemes for catalytic reaction networks [4, 10, 19, 20]. The methodology as presented by Kua et al. has largely been preserved, i.e. the implementation of new catalyst-adsorbate bonds using new groups as well as the usage of already available gas-phase GA values defined by Benson et al [14]. The GA scheme has successfully been applied to polyols, ketones, aldehydes, acids, ethers and esters as well as furans by a combination of broadening the catalyst-adsorbate group as well as the introduction of new corrections. An attempt was also made to extrapolate from one catalyst to another by combining the GA scheme with Extended Linear Scaling Relations (ELSR) [4]. 34 Thermo & kinetics

For oxygenates for example the formation of groups centering around a single atom, as was the case for Benson GA, is not possible. This is because it does not allow for the prediction of the interactions between lone pairs of electrons on the oxygen atom with the metal surface and the adjacent carbon atom. By defining larger groups, this problem is solved at the expense of having to determine more groups and thus group constants with experimental or theoretical techniques [19].

Additionally, several new, higher-order corrections must be applied, some specific to catalysts or even the type of adsorbate. For simple oxygenates (alcohols, polyols) there are three new corrections: ring-strain corrections specific to catalysts, weak interactions between unbound groups and the metal surface and hydrogen bonding. A further expansion of groups and correction terms is required so as to include more complex oxygenates (esters, ethers, …) and furans.

This methodology can be applied for example to a dehydrogenated glycerol species adsorbed on a Pt slab as seen in Figure 3-4. The resulting calculations, which use corrected values, are given in Table 1 [19]. The paper however was only capable of comparing uncorrected group values, i.e. without the group values without additional corrections, with uncorrected DFT calculations. Here a difference of less than 12.5 kJ/mole was found [19]. If the difference between the value calculated via Table 1, and corrected DFT values is assumed to be similar, this is a very good result.

Figure 3-4: Two-dimensional representation of 2,3-hydroxypropanal on Pt. Figure taken from Salciccioli [19]. Thermo & kinetics 35

Table 1: Calculations for the enthalpy of formation of 2,3 hydroxypropanal on Pt using the Zero Point Energy and temperature corrected group values as provided by Salciccioli [19].

Example: Calculating enthalpies of formation. Value (kJ/mole) [C(C,H,M)-O(H)] -229.3 Groups – Catalyst [C(C,H,M)-O(M)] -150.2

[C-(C)2(H)(O)] -30.1 Groups - Benson [O-(C)(H)] -158.6

Weak stabilization CO center and surface -5 Corrections Hydrogen bonding between 2nd and 3rd hydroxyl group -20.9 Sum -594.1

3.1.2.1.4 Group additivity for complex oxygenates

When interactions are more complex, a common approach as shown in the previous sections, is to increase the size of the groups. In this way the difficulties are essentially side-stepped at the cost of having to determine more group additive values (GAVs) (as the group is specific to a smaller number of adsorbates). In the previous section C-O centers were classified as a single group in order to increase the accuracy of the GA method by accounting for the weak O-Pt interactions prevalent in saturated alcohol functions. The above method only works as long as the oxygen atoms are associated with one carbon atom and the alcohol functions are terminal [21]. This is the case for alcohols or polyols but not the case for acid, ether and ester functions.

Once there is overlap between CO groups, as is the case for adsorbed HCOO or CH2OCH2 species, shown in Figure 3-5, issues arise.

Figure 3-5: Schematics showing the diatomic centered groups of (A) HCOO and (B) CH2OCH2. The red ellipsoids demonstrate that groups centered around C=O may show difficulties for molecules where there is overlap [21]. 36 Thermo & kinetics

In theory one could side-step this issue by once again defining two- or three-centered groups. As the number of groups introduced increases, each group would apply to fewer and fewer species. A trade-off is thus made between having more groups which apply to fewer species or fewer groups which apply to more species but require more corrections. The issue of overlapping (C=O) groups is solved by the addition of weakly binding oxygen-metal groups. An illustration of the latter is the treatment of adsorbed formate-functions as shown in Figure 3-6. Here the formate function is treated as a combination of an OM bond and a weak carbonyl interaction with the surface. Though not completely correct physically it yields accurate enough results [21].

Figure 3-6: subdivision of formate functions according to the GA method for catalysts [21].

Thermo & kinetics 37

3.1.2.1.5 Group additivity for furanic compounds

In recent years the GA method has also been successfully used for the calculation of thermochemical properties of adsorbed furanic compounds [4, 20]. The previously described method is left unchanged but there are new group corrections introduced specific to these compounds. Some corrections, like the adsorbed furan ring correction, could probably be generalized further if more data is available [20].

Table 2: Different second-order corrections for the application of GA for adsorbed furanic compounds. As per Vorotnikov et al. [20].

Type Description Weak H-M Gas-phase derived group contributions do not account for weak but additive H-M interactions.

Furan ring Accounts for level of hydrogenation. During dehydrogenation, carbon

deformation lacks sp3 hybridization due to steric effects resulting in some strain.

Adsorbed Furan Correction for the increased stability of adsorbed furan rings. This ring may be inherent to aromatic surface components but has not yet

been generalized.

C(M)2-C(M)- Upon adsorption, furan rings have multiple hetero-atoms as C(M) neighbors. This results in further ring strain which has a destabilizing

effect.

38 Thermo & kinetics

3.1.2.2 RMG-Cat: Surface thermodynamic properties

RMG-Cat [11] is an extension of the automatic kinetic model builder RMG for the purpose of catalysis. Like Genesys, RMG makes use of a group additivity scheme for thermodynamic properties in addition to direct species lookup. As of writing, the number of surface species which have been implemented is 21 in total (Table 3). Values are available only for Ni(111), as this was used for the presented case study of dry reforming on methane [22].

Table 3: Species currently available in the RMG-Cat database. As per Goldsmith [11].

adsorbate metal H* H2* C* CH* CH2* O* Ni(111) CH3* OH* CH4* H2O* CO* COH* HCO* CH2O* CHOH* OO* CH3O* CH2OH* CO2* COOH* CH3OH*

Additionally, some surface groups values are already available. These bridge the gap between the gas-phase section of the molecule, for which the Benson scheme is used, and the actual surface. Like RMG, RMG-Cat stores its data using a hierarchical tree as this makes it easier for algorithms to search for a specific element within the tree. All groups for which values are currently available are shown, within the tree structure in Figure 3-7. Multi-dentate adsorption has not been implemented as of writing but is promised to be incorporated soon [11].

Figure 3-7: For RMG-Cat groups for adsorbate thermochemistry are structured in a hierarchical tree. R represents any atom, M is the metal surface site. Higher order bonds are depicted using double and triple lines. Physisorption is depicted using dashed vertical lines. The lines leaving the final row indicate that further refinement is still possible. As per Goldsmith [11]. Thermo & kinetics 39

As the algorithm moves down the tree the groups become more and more specific. Because of the restrictions imposed by data scarcity, information is not always available for the desired group. RMG-Cat tries to solve this problem by using the most specific group for which data is available in the tree. This method however leads to very high uncertainties for thermodynamic properties of some species. Whenever RMG-Cat encounters a new, non-duplicate species for which it cannot find an exact match in the database it goes through the following algorithm:

1. Trying to find the closest match. For any of the species in the database, replace any H atom with a wild card and determine which species is closest for the adsorbate in question. 2. Look up associated adsorption correction. Every species already in the database has an associated adsorption correction. 3. Find gas-phase precursor. By breaking the metal-adsorbate bond the gas-phase precursor associated with the adsorbed species is determined. 4. Look up or estimate gas-phase properties. Gas-phase properties are either plucked from a database or estimated through GAVs if no match is found. 5. Add both contributions. Sum both the adsorption correction and the gas-phase thermochemistry.

For any thermodynamic quantity, denoted X, the value for the adsorbate can therefor be calculated as the sum of the quantity for the gas-phase species and a contribution for adsorption as shown in equation (3-5). For the enthalpy of formation of a surface species at 298K this results in equation (3-4).

(3-3) = + Δ (3-4) Δ°(298) = Δ°(298) + Δ (Δ°(298))

40 Thermo & kinetics

Kinetic parameters for surface reactions

3.1.3.1 Brönsted-Evans-Polanyi (BEP) relationships

Determining kinetic parameters for a model, especially a complex one, is usually much harder than determining thermodynamics of single adsorbed species. Not only is the transition state usually unknown, but even for moderately sized sets of chemical compounds a very large number of possible reactions exists. Along with all these reactions comes an even greater number of parameters that have to be estimated [6]. Especially within automatic chemical network generation this becomes a challenge and some methods are required to decrease the computational cost. Brönsted-Evans-Polanyi (BEP) relationships allow for quick assignment of kinetic parameters by correlating the kinetics of a chemical reaction with the thermodynamics. BEP-relationships give an estimation for the activation energy of a reaction (equation (3-5)).

(3-5) = Δ + Where is the activation energy as estimated by the BEP-relationship, the heat of the Δ reaction and parameters which are determined by regression and are assumed to be , constant within a reaction family. The BEP slope, , is a measure of the position of the transition state geometry relative to reactants and products and ranges from 0 to 1. Very small or large values imply that the transition state geometry is very similar to either the reactants or the products. The BEP slopes for the same set of reactions written in the opposite directions sum up to unity [23], hence the results do not depend on the direction of the reaction.

Thermo & kinetics 41

3.1.3.1.1 Grouping of reactions

BEP-relationships rely on the fact that for similar reactions, the structure of the transition-states remains similar [6]. Thus, whilst it is possible to use one universal BEP-relationship for a given catalyst surface, the accuracy of BEP-relationships is heavily dependent on which reactions are lumped together and which are not. A first subdivision can be made based on the nature of the reaction. As an example the scission of certain bonds in a furan ring on Pd(111) is given in Figure 3-8 [6]. Here depending on both the nature of the scission (the bonding partners) as well as the consequence of the scission (i.e. whether it results in a ring-opening) different BEP-relationships may be derived. In Figure 3-8,the universal BEP-relationship is applied. Other criteria can be the nature of the catalyst (i.e. the elemental composition) or the size of the molecule involved. Subdivisions can also be made for a given range of reaction enthalpies as the optimal slope of the BEP-relationship is not constant with Δ [15].

Figure 3-8: Universal BEP-relationships for scission reactions of Furan surface species with Pd(111) as a catalyst.

42 Thermo & kinetics

3.1.3.1.2 Accuracy as a function of grouping

For gas-phase reactions, universal BEP-relationships tend to be inaccurate if used over too broad range of reaction heats (Δ ). This can be solved either by using a non-linear equation, as is the approach taken in Blowers-Masel relationships, or by using multiple BEP-relationships, each confined to a range of ‘s. In Figure 3-9 an example is given for gas-phase reactions [15]. Δ Examples for surface reactions are given below. In Figure 3-9 the red points represent calculated pairs of activation energies and enthalpies of reaction for hydrogen abstraction reactions by a hydrogen radical. These were calculated at the CBS-QB3 ab initio level of theory. Along with a universal BEP-relationship (blue), the non-linear Blowers-Masel model is plotted in green. Finally, three BEP-relationships were regressed for different ranges. Here the reactions with the highest exothermicity are hydrogen abstractions leading to the formation of resonance stabilized radicals. On the other end of the spectrum one finds (endothermic) reactions which lead to the formation of phenylic radicals. In between these two regions, hydrogen abstraction from an sp3- hybridized carbon atom are found.

Figure 3-9: Comparison between a universal BEP-relationship, a Blowers-Masel approximation and the use of three separate BEP-relationships, each confined to their own region. Values given are for gas-phase reactions.

The downside is that for every new relationship new parameters have to be determined. Therefor a trade-off has to be made between the benefits provided by grouping fewer reactions, and the cost of having to determine these parameters. As an illustration of this trade-off, the aforementioned example of Furans on Pd(111) by Wang et al. [6] is given. In this paper several BEP-relationships, indicated as ‘own’, ‘small’, ‘combined’ and ‘universal’ in Figure 3-10, were Thermo & kinetics 43

constructed for reactions involving a group of 109 species containing furans with different

functional groups as well as C2 molecules (CHxCHYOHz, with x=0-3, y=0-2 and z=0-1) [6]. The effect of grouping one way or another is shown in Figure 3-10.

Figure 3-10: Mean and maximum absolute error (AE) for different types of reactions given a variety of BEP-relationships.

The Universal BEP-relationship, involving all species, had a Mean Absolute Error (MAE) of ~30 kJ/mole, whilst this was only ~15 and ~10 kJ/mole for C-H abstraction and scission respectively if they are treated separately. The ~30 kJ/mole MAE even somewhat underestimates the benefits of using more groups as it underestimates the errors for some reactions. For ring-opening reactions, to name one example, deviations will consistently be higher. Even a BEP-equation derived just for specific ring-opening reactions (e.g. C-C ring-opening or C-O ring-opening) will have a MAE of ~40 kJ/mole. This example shows that benefits of clustering clearly depend on which reactions are being clustered. For some reactions, the performance will always be poor. For others, BEP are very accurate given the computational expense. The latter is mainly the case in groups that have highly correlated transition states. The lower the homogeneity of the transition state, the worse the performance.

It should be noted that ring-opening reactions in furan rings are a worst-case scenario. For other species, BEP-relationships can sometimes be very useful even when extrapolating to more complex species [24]. This is the case with simple and complex (poly-)alcohols on a ruthenium catalyst. For simple alcohols the MAE can be reduced to a mere 10 kJ/mole by splitting the data 44 Thermo & kinetics

for bond dissociation reactions depending on the position of the reactive center relative to the alcohol functionality group as shown in Figure 3-11.

Figure 3-11: Error distribution for BEP-relationships in the case of monoalcohol dehydrogenation considering all the points together and using the pre-established model to predict CHα , CHβ and OH respectively.[24] This is in comparison to the 40 kJ/mole for the universal BEP-relationship without splitting. Zaffran et al. [25] also showed that the BEP-relationship for simple alcohols can be used to predict more complex alcohols like glycerol with a systematic error of less than 10 kJ/mole. The reason for this, rather small, systematic deviation is attributed to the presence of hydrogen bonds [24]. In comparison with the errors usually made when using fast estimation methods 10 kJ/mole is a very small value indeed. When it comes to catalysts the effect is probably much smaller than when splitting according to reactions [25]. At least for alcohols the MAE can still be below 10 kJ/mole when using a universal BEP-relationship agnostic with regards to the catalyst used. This may still present a problem however if the purpose is to compare different types of catalysts as the MAE does not give any information on systematic deviations for a given catalyst [25].

Thermo & kinetics 45

3.1.3.2 Determination of pre-exponential factors

Neither BEP-relations nor UBI-QEP, i.e. the method implemented and applied in this work (Chapter 4), provide the user with estimations of pre-exponential factors. Additionally, whilst GAVs have been used to determine pre-exponential factors in gas-phase reactions [12, 13, 26- 28], the author is not aware of any current effort to do the same for catalytic reactions.

Similar to grouping in BEP-relationships, pre-exponential factors of surface reactions are often held constant for a group of reactions which share a given criteria. This criterium can be either the mobility of the surface species or depend on the reaction families (i.e. H-Abstraction, bond- scission or abstraction of an R-group). This is also the approach taken in RMG-Cat [11, 21, 29]. The latter will be discussed in more depth in the next subsection.

For the calculation of their pre-exponential factors Chorkendorff and Niemantsverdriet [30] rely on the mobility and rotational properties of the adsorbed state, the desorbed state and the transition state. Their approach is illustrated in Figure 3-12 [30]. Based on the same approach, typical values of pre-exponential factors, reported by Murzin et al. [29], are given in Table 4.

Figure 3-12: Values of pre-exponential factors depending on the adsorption-desorption and transition state. By Chorkendorff and Niemantsverdriet [30].

46 Thermo & kinetics

Table 4: Reactions and pre-exponential factors as per Murzin et al. [29]

Type of reaction Aj

Molecular Adsorption Mobile transition state 103 Pa-1s-1 Immobile Transition state 101 Pa-1s-1

Dissociative Adsorption Mobile transition state 103 Pa-1s-1 Immobile Transition state 101 Pa-1s-1

Langmuir-Hinshelwood reaction Mobile surface species with rotation 108 s-1 Mobile surface species without rotation 1011 s-1 Immobile surface species without rotation 1013 s-1

Eley-Rideal reaction Mobile transition state 103 Pa-1s-1 Immobile Transition state 101 Pa-1s-1

Molecular Desorption Same freedom for adsorbed and transition state 1013 s-1 More rotational and translational freedom for transition state 1016 s-1

Associative Desorption Mobile adsorbed and transition states with full rotational freedom 108 s-1 Mobile adsorbed and transition states without rotation 1011 s-1 Immobile adsorbed and transition states 1013 s-1 Immobile species, more rotational & translational freedom for transition state 1016 s-1

Thermo & kinetics 47

3.1.3.1 RMG-Cat: Kinetics

In addition to the 40 distinct reaction families available for gas-phase reactions in RMG, RMG-Cat [11] contains three additional reaction families which are specific to surface chemistry. These are adsorption/desorption reactions, bond scission and hydrogen abstraction reactions. The latter has also been expanded towards abstraction reactions for R-groups (with R being any functional group that is not hydrogen). Each of these reaction families have a slightly different implementation. In all cases, the kinetic database is first checked for a match. If no match is found, different relationships are used.

For scission reactions a BEP-relationship is used, with the BEP parameters being automatically derived based on the implemented database of known rate coefficients [31-33]. Pre-exponential factors for scission reactions were taken from Delgato et al. [22] which was also used for a preliminary case study on the dry reforming of methane using a nickel-based catalyst. It should be noted that pre-exponential factors are expressed in .

= 1.0 × 10 ( + ΔHrxn) = 0 (3-6) () = exp = 185.3

= 0.84 If no match is found for an abstraction reaction then a set equation is used. Depending on whether the group that was abstracted is a hydrogen or not, different parameters are used. All values are based on the work of Delgato et al. [22] on dry reforming of methane (equation (3-7)- (3-8).

= 1.0 × 10 (3-7) () = exp = 0 = 40.0

48 Thermo & kinetics

= 1.0 × 10 (3-8) () = exp = 0 = 80.0

Adsorption and desorption reactions make use of sticking coefficients (S0) instead of Arrhenius parameters. A value of 0.01 [-] is used for all reactions. Once again, a constant activation energy is used with different values for dissociative adsorption and non-dissociative adsorption as shown in equations (3-9) and (3-10).

= 1.0 × 10 . = 0 (3-9) 0 () = exp = 41.8

= 1.0 × 10 . = 0 (3-10) 0 () = exp = 10.0

Additionally, all of the above equations do not take the number of single events into account. Within RMG-Cat, the number of single events is not given along with the Arrhenius parameters but separate from it. As an example, for the hydrogen abstraction reaction C* + CH3CH2* → CH*+ CH3CH*, the value as calculated by equation (3-7) will still be doubled as the reaction can occur for both H-atoms. Thermo & kinetics 49

Thermochemistry and kinetics in Genesys

Within Genesys, methods have already been implemented to calculate thermodynamic properties and kinetic parameters. Before the implementation of UBI-QEP, all methods implemented dealt solely with gas phase species and reactions. In order to implement new methods however, a solid understanding is required of what is already available. For this purpose the way thermodynamic data is stored and generated is discussed in section 3.2.1. Similarly, the main methods implemented for calculating gas-phase reaction parameters are discussed in section 3.2.3. Each time, a section has been added about how this can be modified in the future for the purpose of catalytic systems.

Thermodynamic properties

3.2.1.1 NASA-polynomials

Thermodynamic data of species can be determined over a wide temperature range on only a few properties being the enthalpy of formation at 298K, the total entropy at 298K and the heat capacities at different temperatures. Often, these properties are regressed and provided in a format called NASA-polynomials. The thermodynamic data in the output of Genesys is written in terms of NASA-polynomials, as this is the format required by CHEMKIN. NASA-polynomials are shown in equations (3-11) till (3-13).

(3-11) () = + + + + () (3-12) = + + + + + 2 3 4 5 (3-13) () = ln T + + + + + 2 3 4 In Genesys the parameters are stored as an array for two temperature ranges: one going from Tlow to Tcommon and one going from Tcommon to Thigh. These three temperatures are also provided within the NASA-polynomials.

50 Thermo & kinetics

3.2.1.2 Determination of thermodynamic properties

To determine thermodynamic parameters of gas phase species, Genesys goes through two steps. First, Genesys tries a direct species lookup in the extensive databases. For this purpose, several databases exist which contain entries for species determined via ab initio calculations. If a match is found, the NASA-polynomial is returned. If no data is found for that species the implemented GA scheme will be used to calculate the desired values. The GAVs required are provided in several databases. Once again, if all the required information (i.e. all groups necessary to cover the complete molecule) is available, the NASA-polynomial is returned. Otherwise Genesys will generate an error indicating that it was unable to determine the correct thermodynamic data for that species. This process is summarized in Figure 3-13.

Figure 3-13: Schematic representation of how a NASA-polynomial is obtained given an input species.

Thermo & kinetics 51

3.2.1.3 Storage and lookup of surface thermodynamic data

First of all, it should be noted that detailed thermodynamic properties like for gas-phase species, are usually not available for surface species. For surface species there is currently no thermodynamic data available within the databases of Genesys. In this work the UBI-QEP approach, as will be discussed in Chapter 4, only calculates heats of adsorption (as opposed to heats of formation). With only this information, no NASA-polynomials can be regressed.

Thermodynamic properties in gas phase kinetic models are typically used to ensure thermodynamic consistency. This means that the reverse rate coefficient of a reaction is calculated from the thermodynamic properties of all species in the reaction. For this reason, the entropic contribution for adsorption is also desirable [11]. Goldsmith et al. [11] has formulated a method which makes use of transition states to determine the changes in enthalpy, entropy and heat capacity upon adsorption. In practice, at least for now, each reaction family is assigned a constant pre-exponential factor [11].

Towards the future the thermodynamic properties algorithm for surface species could be expanded with an end-result similar to that of gas-phase species: First a direct species lookup which is followed up by UBI-QEP if unsuccessful. The main issue for now is data scarcity.

Another challenge, next to the lack of data, lies with how the data is stored as it depends not just on the gas-phase species but on the catalyst as well. One way of tackling this issue is to implement a map of InChI’s of the surface species to the thermodynamic data for each catalyst active site, starting with single element catalysts. It is important to use the identifiers for the surface species as a single gas-phase species can have multiple ways of adsorbing on the surface. Additionally, if new sites are implemented, a modified catalyst block could be used. Thereby allowing the InChI to contain information on the site in question.

To demonstrate this principle the case of hydrogen gas is used as shown in Figure 3-14. Here the InChI for the hydrogen in the gas-phase is InChI=1S/H2/h1H. If the map to surface InChI’s as proposed in the future work section of Chapter 2 is implemented then Gensesys will automatically search for either InChI=1S/H2He4/c1-3-4(2)6-5-3, i.e. the non-dissociatiated 52 Thermo & kinetics

species or InChI=1S/HHe4/c1-3-4-2/h3H for the dissociated species. A couple of both the surface InChI and the element number of the catalyst could then be mapped onto thermodynamic data to get the correct one.

Figure 3-14: Schematic depiction of how the correct thermodynamic data could be chosen.

The above system would only hold for the current implementation with UBI-QEP (discussed in Chapter 4). This is because when using UBI-QEP, the literature usually assumed there is only one active site present on the surface. Should multiple sites be implemented, the above scheme would have to be further refined. One way would be to implement active sites using the bond order of the bond with the dummy atom as this would also show up in the InChI.

Kinetic parameters

For gas-phase species kinetic parameters can be added to the kinetic model in several ways, in the form of simple or modified Arrhenius parameters to cover a wide temperature range. In all cases the choice is provided by the user in the input file for every reaction family. This means all reactions that share a reaction family will use the same method. Similar to the assignment of thermodynamic properties, a large database with ab initio calculated Arrhenius parameters exists. This database will be searched for prior to the use of the user-defined method.

Single event Arrhenius parameters can be specified by the user with the key-word “ARRHENIUS” for the specific reaction family in the corresponding input file. If the user does not want the pre- exponential factor to be multiplied by the number of single events of a certain reaction, the key- word “RATE_RULE” can be used. Other forms of calculation methods are implemented. Of those methods several have already been discussed in the literature survey: Brönsted-Evans-Polanyi Thermo & kinetics 53

(“EVANS_POLANYI”), which is a linear scaling method relying on species thermodynamic properties and group additivity (“GROUP_ADDITIVITY”). Additionally, the Blowers-Masel (“BLOWERS_MASEL”) method, which extends BEP by using a non-linear equation instead of the traditional linear one, is available. For these methods, parameters need to be provided by the user, or a link must be made to e.g. databases with group additive values.

54 Thermo & kinetics

Future Work

For the future, progress can be made on two fronts. First there is the use of data which is already available within the Laboratory for Chemical Technology. A database should be implemented for both surface species and reactions. If experimental or ab initio values are available, they should be used over the values as calculated by methods such as UBI-QEP.

Second, significant improvements can still be made for both the determination of pre- exponential factors and activation energies. In this work, pre-exponential factors were discussed in the literature survey but they have not had a solid implementation yet for surface reactions. Instead a dummy value has been simply hardcoded. A distinction should probably be made between adsorption and desorption reactions on the one hand and surface reactions on the other. For adsorption/desorption the literature consistently seemed to prefer sticking coefficients. This was the case for both RMG-Cat, all papers involving simple calculation methods for automatic network generation as well as examples of kinetic models given by Deutschmann [34]. Sticking coefficients should therefor probably be implemented and used for adsorption and desorption reactions. For other surface reactions it may prove worthwhile to use different pre- exponential factors depending on the type of reaction as is the case with RMG-Cat. Here the values given in Table 4 on page 46 can be used as a reference.

In addition to UBI-QEP, BEP-relationships should be implemented as these offer a second method to determine activation energies whilst requiring little additional effort. Here the main challenge would be acquiring the data for both the BEP-relationship itself (the parameters) as well as the surface species. In principle one could always use the UBI-QEP values to determine the value for the adsorbed species in question, but then the new estimate using BEP would still be dependent on the UBI-QEP method. BEP-relationships have already been implemented in RMG-Cat by Goldsmith et al. [11].

Finally, as will be further discussed in Chapter 4, it may be worthwhile to introduce means of regression. Constructing a good kinetic model is an iterative process where the values as provided by on-the-fly methods are very valuable as an initial guess. Postprocessing could then be used to determine which pathways and species are most important. This information could Thermo & kinetics 55

then be used to, selectively, use more computationally expensive methods. Additionally, these parameters could be tuned (i.e. varied within a certain range) to optimally fit experimental data.

Conclusions

In order to determine the thermodynamic and kinetic parameters for surface reactions some computationally inexpensive calculation method is required to deal with catalyst complexity and data scarcity. One potentially favorable route would be to expand the already existing group additivity scheme to allow for catalysis. Not only has this been proven to work, it also has precedent by being implemented in RMG-Cat [11]. Brönsted-Evans-Polanyi relationships could then be used in conjunction with GA to allow for estimation of kinetic parameters. Whilst a perfectly defensible reasoning, within this work the UBI-QEP method (discussed in the subsequent chapter) was chosen instead. The main reasoning behind this decision was the enormous amount of information needed to accurately make the GA scheme work. In the absence of such data, the decision would have to be made to either not return any result or return a result which is known to be incorrect within the method’s own terms. In the long term Genesys would still benefit from an expanded GA implementation, but for the midterm UBI-QEP is preferred.

56 Thermo & kinetics

References

1. Thybaut, J. and G. Marin, Single-Event MicroKinetics: Catalyst design for complex reaction networks. Journal of catalysis, 2013. 308: p. 352-362. 2. Froment, G.F., K.B. Bischoff, and J. De Wilde, Chemical Reactor-Analysis and Design. 2011. 3. Cohen, N. and S. Benson, Estimation of heats of formation of organic compounds by additivity methods. Chemical Reviews, 1993. 93(7): p. 2419-2438. 4. Vorotnikov, V. and D.G. Vlachos, Group additivity and modified linear scaling relations for estimating surface thermochemistry on transition metal surfaces: application to furanics. The Journal of Physical Chemistry C, 2015. 119(19): p. 10417-10426. 5. Shustorovich, E. and H. Sellers, The UBI-QEP method: a practical theoretical approach to understanding chemistry on transition metal surfaces. Surface Science Reports, 1998. 31(1-3): p. 1-119. 6. Wang, S., et al., Brønsted–Evans–Polanyi and transition state scaling relations of furan derivatives on Pd (111) and their relation to those of small molecules. ACS Catalysis, 2014. 4(2): p. 604-612. 7. Abild-Pedersen, F., Computational catalyst screening: Scaling, bond-order and catalysis. Catalysis Today, 2016. 272: p. 6-13. 8. Kua, J. and W.A. Goddard, Chemisorption of Organics on Platinum. 2. Chemisorption of C2H x and CH x on Pt (111). The Journal of Physical Chemistry B, 1998. 102(47): p. 9492-9500. 9. Kua, J., F. Faglioni, and W.A. Goddard, Thermochemistry for Hydrocarbon Intermediates Chemisorbed on Metal Surfaces: CH n-m (CH3) m with n= 1, 2, 3 and m≤ n on Pt, Ir, Os, Pd, Rh, and Ru. Journal of the American Chemical Society, 2000. 122(10): p. 2309-2321. 10. Salciccioli, M., S. Edie, and D. Vlachos, Adsorption of acid, ester, and ether functional groups on Pt: fast prediction of thermochemical properties of adsorbed oxygenates via DFT-based group additivity methods. The Journal of Physical Chemistry C, 2012. 116(2): p. 1873-1886. 11. Goldsmith, C.F. and R.H. West, Automatic Generation of Microkinetic Mechanisms for Heterogeneous Catalysis. The Journal of Physical Chemistry C, 2017. 121(18): p. 9970-9981. 12. Sabbe, M.K., et al., Carbon-Centered Radical Addition and β-Scission Reactions: Modeling of Activation Energies and Pre-exponential Factors. ChemPhysChem, 2008. 9(1): p. 124-140. 13. Saeys, M., et al., Ab initio group contribution method for activation energies of hydrogen abstraction reactions. ChemPhysChem, 2006. 7(1): p. 188-199. 14. Benson, S.W. and J.H. Buss, Additivity rules for the estimation of molecular properties. Thermodynamic properties. The Journal of Chemical Physics, 1958. 29(3): p. 546-572. 15. Van de Vijver, R., et al., Automatic Mechanism and Kinetic Model Generation for Gas-and Solution-Phase Processes: A Perspective on Best Practices, Recent Advances, and Future Challenges. International Journal of Chemical Kinetics, 2015. 47(4): p. 199-231. 16. Vandewiele, N., Kinetic model construction using chemoinformatics. 2014, Ghent University. 17. Sabbe, M.K., et al., Group additive values for the gas phase standard enthalpy of formation of hydrocarbons and hydrocarbon radicals. The Journal of Physical Chemistry A, 2005. 109(33): p. 7466-7480. 18. Vandeputte, A.G., et al., Modeling the Gas-Phase Thermochemistry of Organosulfur Compounds. Chemistry-A European Journal, 2011. 17(27): p. 7656-7673. 19. Salciccioli, M., Y. Chen, and D.G. Vlachos, Density functional theory-derived group additivity and linear scaling methods for prediction of oxygenate stability on metal catalysts: adsorption of open-ring alcohol and polyol dehydrogenation intermediates on Pt-based metals. The Journal of Physical Chemistry C, 2010. 114(47): p. 20155-20166. Thermo & kinetics 57

20. Vorotnikov, V., S. Wang, and D.G. Vlachos, Group additivity for estimating thermochemical properties of furanic compounds on Pd (111). Industrial & Engineering Chemistry Research, 2014. 53(30): p. 11929-11938. 21. Salciccioli, M., et al., A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior. Chemical Engineering Science, 2011. 66(19): p. 4319-4355. 22. Delgado, K.H., et al., Surface reaction kinetics of steam-and CO2-reforming as well as oxidation of methane over nickel-based catalysts. Catalysts, 2015. 5(2): p. 871-904. 23. Sutton, J.E. and D.G. Vlachos, Effect of errors in linear scaling relations and Brønsted–Evans– Polanyi relations on activity and selectivity maps. Journal of Catalysis, 2016. 338: p. 273-283. 24. Zaffran, J.r.m., et al., Linear energy relations as predictive tools for polyalcohol catalytic reactivity. ACS Catalysis, 2014. 4(2): p. 464-468. 25. Zaffran, J.r.m., et al., Trade-off between accuracy and universality in linear energy relations for alcohol dehydrogenation on transition metals. The Journal of Physical Chemistry C, 2015. 119(23): p. 12988-12998. 26. Sumathi, R., H.-H. Carstensen, and W.H. Green, Reaction rate predictions via group additivity. Part 3: Effect of substituents with CH2 as the mediator. The Journal of Physical Chemistry A, 2002. 106(22): p. 5474-5489. 27. Sumathi, R., H.-H. Carstensen, and W.H. Green, Reaction rate prediction via group additivity, part 2: H-abstraction from alkenes, alkynes, alcohols, aldehydes, and acids by H atoms. The Journal of Physical Chemistry A, 2001. 105(39): p. 8969-8984. 28. Sumathi, R., H.-H. Carstensen, and W.H. Green, Reaction rate prediction via group additivity part 1: H abstraction from alkanes by H and CH3. The Journal of Physical Chemistry A, 2001. 105(28): p. 6910-6925. 29. Murzin, D.Y. and T. Salmi, Catalytic kinetics. 2005: Elsevier. 30. Chorkendorff, I. and J.W. Niemantsverdriet, Concepts of modern catalysis and kinetics. 2017: John Wiley & Sons. 31. Bligaard, T., et al., The Brønsted–Evans–Polanyi relation and the volcano curve in heterogeneous catalysis. Journal of Catalysis, 2004. 224(1): p. 206-217. 32. Wang, S., et al., Universal brønsted-evans-polanyi relations for c–c, c–o, c–n, n–o, n–n, and o–o dissociation reactions. Catalysis letters, 2011. 141(3): p. 370-373. 33. Medford, A.J., et al., CatMAP: a software package for descriptor-based microkinetic mapping of catalytic trends. Catalysis Letters, 2015. 145(3): p. 794-807. 34. Deutschmann Chemistry. Available from: http://www.detchem.com/html/mechanisms.html.

UBI-QEP 59

UBI-QEP

Literature Survey

Introduction

The central challenge of this literature survey is how to get around the problem of having to determine all kinetic and thermodynamic parameters for all species and reactions on-the-fly in a fast way. In Chapter 3 the expansion of group additivity (GA) to surface species was discussed. Second a literature review has been performed on Brönsted-Evans Polanyi (BEP) relationships used to calculate activation energies. The problem of assigning parameters is resolved by mapping the kinetic parameters which have to be acquired onto the, much smaller, set of available heats of formation. These are then once again mapped onto a smaller set of group values and corrections. Thus, at the expense of accuracy, a much smaller amount of information has to be available, as shown in Figure 4-1.

Figure 4-1: The double mapping which both the GA combined with BEP and UBI-QEP methodologies try to perform. The Qs denote heats of adsorption for elements and species respectively [1].

60 UBI-QEP

UBI-QEP (Unity Bond Index – Quadratic Exponential Potential) is an alternative approach which can be applied for metal surface species and reactions. It is capable of determining both adsorption heats and activation energies using simple analytical equations.

For UBI-QEP, instead of determining group values, one determines adsorption enthalpies for a finite number of elements, i.e. the heat of adsorption for a carbon atom on Ni(111). This is used together with values for the bond dissociation energy, i.e. the energy required to break the bond between the atom which is adsorbed (the contact atom) and the rest of the molecule, to calculate both adsorption heats and activation energies. The adsorption heats that have to be determined are specific to the metal surface. As shown in Figure 4-2 some information has to be known about the catalyst to determine which site will be assumed, i.e. on-top, bridge or three-fold. There is a general agreement that the UBI-QEP method is powerful and reasonably accurate (up to 8-15 kJ/mole) when comparing calculated heats of formation to those determined experimentally [1-3]. The accuracy of the method is discussed further in section 4.1.6.

Figure 4-2: on-top, bridge and three-fold sites available for an atom sticking on the (111) surface. Bridge mode adsorption of a typical AB molecule on the surface can be (a) diatomic (b) multi-atomic or (c) chelate structure.

UBI-QEP 61

Methodology

4.1.2.1 Unity Bond Index (UBI)

UBI-QEP, short for unity bond index - quadratic exponential potential, is a phenomenological approach to chemisorption. The method is based on the concept of bond order conservation (BOC) which states that the total binding capacity for a single atom with the surrounding atoms is constant [4]. The bond order was later renamed to the bond index (BI), hence unity bond index or UBI in UBI-QEP. Intuitively UBI states that when more atoms bind to a central atom the binding power is redistributed and thus decreases for each singular bond. Conversely when a chemical bond is broken, the energy of the other remaining bonds increases to maintain a constant total bond index which is taken to be unity. Mathematically speaking, the bond index is conserved and this bond index, defined later, has some relationship to the bond energies involved. What makes this useful is that the bond index is well-defined mathematically and has a one to one relationship with the distance. The bond index x is defined using the following equation:

(4-1) Here r is the distance between the contact atom A and the respective metal atom M, which can be expressed in arbitrary units as this will only change the value of a, r0 is the equilibrium distance between M and A for a single metal-contact (M-A) interaction and a is a fitting parameter. Each M-A interaction is treated separately and has one corresponding distance and thus bond order as shown in Figure 4-3. The bond index relates to both the distance and the bond energies. Different distances result in different bond orders but in equilibrium the distances are such that the sum of the bond indices is unity.

Figure 4-3: An atom A adsorbs on a metallic surface. The white circles represent the surface metal atoms. Each bond has a corresponding bond order which is a function of r1 to r3 respectively.

62 UBI-QEP

Shustorovich and Sellers [5] postulated the existence of a bond index, its conservation (equation (4-2)) and its mathematical relationship to the distance between two atoms (equation (4-1)). An exponential function was postulated as the atomic and diatomic wavefunctions are also known to have exponential radial parts. Other functions might have been suggested to work from a purely mathematical point of view if they display certain desirable features:

a) There is a one-to-one correspondence between x(r) and r b) x(r) is non-negative c) x(r) decreases monotonically to zero as r increases without bound

d) at equilibrium x(r0)=1

UBI thus states that for all groups, i, the sum of their bond orders must sum up to unity, as expressed in equation (4-2). A group can be an atom but can also be a pseudo-atom which is just a moiety which is treated as if it is an atom. It should be noted that while UBI is a mathematical assumption required for the method, it has also been verified by ab initio calculations [5].

1 (4-2) In Figure 4-4, the concept of Bond Index (BI) conservation, the BI in UBI, is demonstrated. Here the AB molecule is shown in a three-atom binding site of a bimetallic surface. A is the contact atom. In principle each interaction (black lines), has its own bond order resulting in three M-A (xA), one M-B (xB) and one A-B bond-order (xAB) which must sum up to unity. As will be shown in section 4.1.3.1 the three M-A bond orders are treated as one. When applying equation (4-2) to some molecule AB, with A being the contact atom, i.e. the atom which adsorbs on the surface, and B being the rest of the molecule the conservation of the bond index simplifies. The result is shown in equation (4-3).

Figure 4-4: AB molecule in a three-atom binding site of a bimetallic surface with A as contact atom. UBI-QEP 63

(4-3) + + 1 The common theme in deriving the analytical equations for a specific case, like the one shown in Figure 4-2, is that equations can be simplified by making an intelligent guess about what terms can be neglected. This principle will become clearer in section 4.1.3 when the final analytical equations are derived.

4.1.2.2 Quadratic Exponential Function (QEP)

The second part of UBI-QEP is Quadratic Exponential Potential (QEP). QEP postulates a 2nd relationship, this time between the bond index x and the potential energy Q or Epot for a system of 2 atoms. Instead of describing the two-body interaction potential as a function of distance it is described relative to the bond index. More specifically a quadratic exponential potential in the form of a Morse potential is used [6].

(4-4) − −2 −

Where Q0A is the bond energy between the metal catalyst (M) and the atomic adsorbate (A) in equilibrium and x is the bond index defined in equation (4-1). As function of the distance, there is only one minimum, i.e. the equilibrium position when the bond index x = 1 and r = r0.

Q0A is the value for a single catalyst-adsorbate (M-A) interaction. QA is used to denote heats of adsorption which have been determined experimentally. It makes no claim as to the nature of the site on which A is adsorbed. Within the UBI-QEP framework, the heat of adsorption of

A on a specific surface is denoted QnA, which is the heat of adsorption for A given n M-A interactions. The number n is also called the coordination number. Physically the coordination number is the number of metal atoms involved. By using QnA instead of QA attention is drawn to the fact that the coordination number is either known or assumed. Whenever using tabulated values from the literature, it is important to note that the author may choose to tabulate either Q0A or QA.

A final assumption is that the total interaction energy of the system for the M-A system can be calculated as the sum of all two-center M-A interactions.

64 UBI-QEP

Derivation of UBI-QEP equations

The purpose of the UBI and QEP assumptions is to eventually derive simple equations that can estimate adsorption heats as well as activation energies for surface reactions for specific cases. The following section is intended to derive some examples of these equations. The end results for more cases that are available in literature can be seen in Table 4-3 and Table 4-4 . An example for determining the heat of formation is given in section 4.1.4 and for the determination of activation energies in the case study in Chapter 6. The derivations below are published by Shustorovich et al. [5].

4.1.3.1 Atomic chemisorption

The simplest case is one of atomic chemisorption on uni-metallic surfaces as published by Shustorovich et al. [5]. This kind of atomic chemisorption can be described using an energy function, E, which is a summation of n pairwise interactions between the contact atom (A) and the n metal atoms (M) of the surface that form the binding site (e.g. Figure 4-4). What is derived in this section is the relationship between the adsorption heat for one contact atom A with just one metal atom interaction and with more than one metal atom (e.g. 2,3,…,n).

(4-5) − 2 To obtain the atomic binding energy E, equation (4-5) is optimized under the UBI constraint specified in equation (4-3). This is done using the method of Lagrangian multipliers. The Lagrangian function to optimize is:

(4-6) − − 1 Here α is the Lagrangian multiplier of the UBI constraint and the summation runs from 1 to n. The derivation lies beyond the scope of this thesis but can be found in Appendix A of Schustorovich 1998 review paper [5]. The following expression for the binding energy E (also indicated as QA and QnA) is eventually derived as a function of the number of interactions n:

1 (4-7) − 2 − UBI-QEP 65

Here QnA is the experimental heat of adsorption for a given coordination number n of the atom on the surface. Q0A is the maximum 2-center (M-A) bond energy and is usually determined using this equation (equation (4-7)). This is because QA is used to denote what is measured experimentally, agnostic to the coordination number. In practice once one makes a guess as to the value of n, QnA is used instead of QA. This means that equation (4-7) is often applied from left to right. The resulting value, Q0A, is next often tabulated. This is done such that the user can then directly use these values and plug them into the equations derived later, or use equation (4-7) to get the value for any other coordination number. It should be noted that the literature is not fully consistent and some authors report QA values instead (Table 4-1).

E(n) increases with n and reaches a maximum in the n-fold hollow site. This implies atoms that will always prefer this site [5, 6]. This has been empirically verified but in practice steric constraints may also play a role. Real surfaces also reconstruct and this changes the effective coordination number. The uncertainty introduced by assuming a certain coordination number, n, can be a source of error though the magnitude usually is relatively small (order of 5%) [5].

66 UBI-QEP

Table 4-1: Experimental heats of adsorption (QA) for a specific adsorbed atom (contact atom) are tabulated for given metal surfaces. Values are in kJ/mole. Based on Vannice et al. [6]. Values between brackets were not measured but assumed by the original source. Values in kJ/mole.

Atom Metal Surface H O N C W(110) 285 523 649 (837) Fe(110) 268 (494) 586 (837) Ru(001) 280 418 - - Rh(111) 255 427 531 - Ir(111) 243 389 565 - Ni(111) 264 481 544 715 Pd(111) 259 364 485 (669) Pt(111) 255 356 - (628) Cu(111) 234 431 - (502) Ag(111) 218 335 - 460 Au(110) 192 314 - 452

Table 4-2: Influence of the number of metal atoms involved in the adsorption, n, on the predicted adsorption heat for atomic A. Based on Vannice et al. [6].

a n site surface QnA/Q0A 1 On-top 1.00

2 Bridge C2v 1.50

3 Hollow C3v hcp(001) 1.67 fcc(111) bcc(110)

4 Hollow C4v fcc(100) 1.75

5 Hollow C4v bcc(100) 1.80

6-9b Stepped, kink 1.83-1.89

12 1.92 a) QnA/Q0 = 2 – 1/n b) Possible high coordination on rough surfaces UBI-QEP 67

4.1.3.2 Mono-coordinated chemisorption

The derivation in the previous subchapter concerned only one atom. It was shown how the adsorption heats, QnA, are related to each other. As a consequence one only needs the value for one coordination number, usually Q0A. The values for other coordination numbers can be then be derived (equation (4-7)). In the subsequent section the formulas for the determination of the adsorption heats are determined for molecules. Figure 4-4 serves as an example. Molecules are represented as A-B, where both A and B can either be an atom or a group. A is assumed to be the contact atom, B is assumed to be turned away from the surface. As a result, eventually the metal – B interaction (M-B) will be neglected. In the literature a distinction is made between strong and weak adsorption of molecules. The main consequence is that if you classify something as adsorbing weakly, the Q0A values specified in section 4.1.3.1 are used in the equations for molecular heats of adsorption. For strong chemisorption (e.g. in case of radicals) either QnA or QA values can be used. QnA is used if the user makes an assumption about the coordination number and calculates the value based on tabulated Q0A values. If the value used is determined experimentally and does not come with a coordination number, which can be found for a given surface in Table 4-2, the notation QA is used.

The derivations below are for weak adsorption. The derivation for strong binding groups is similar and can be found in the work of Shustorovich et al. [5]. Users should always check if the values in the literature are Q0A or QA values, the latter being experimental results. The literature is not consistent in this [6, 7]. For the chemisorption of a molecule AB, BI conservation implies:

(4-8) + + 1 As each bond order is related to a bond energy via the QEP equation (equation (4-4)) this thus, implicitly, relates the heat of adsorption for the molecule, QAB, to the heats of adsorption for its constituent parts (here atoms A and B) as well as the gas-phase dissociation energy DAB. Considering equation (4-8) still has too many variables to allow for the desired analytical solution, effective group terms are sometimes used (equation (4-9)). Thus, instead of

68 UBI-QEP describing each M-A interaction individually, you treat it as one interaction. The difference is reflected in the adsorption heat used.

(4-9) 1 The difference is made more explicit when comparing equations (4-10a) and (4-10b).

(4-10a) + ⋯ + + + 1 (4-10b) + + 1 Instead of writing out (4-10a) in full, (4-10b) an be used granted one uses QnA instead of Q0A.

This is made possible by the derivation in subsection 4.1.3.1 (equation (4-7)). Q0A describes the situation for a singular M-A interaction whilst QnA is the value which takes into account all the M-A interactions (section 4.1.3.1).

In practice the equation is usually also further simplified on a case-by-case basis. When considering the coordination of an AB molecule with A the contact atom it is often assumed that B is directed away from the surface which means M-B interactions can be neglected (xB is then no longer part of the equation). For the n-fold coordination, the UBI-QEP energy is then just the sum of the pairwise potentials:

(4-11) , − 2, + − 2 Lagrange-multipliers are then once again used to minimize the equation above under the UBI constraint. This results in an analytical solution for the binding energy in case of mono- coordination via atom A:

, (4-12) + Here the bond dissociation energy (BDE) of AB in the gas-phase, DAB, is used. BDEs are usually calculated using thermodynamic cycles or even GAV for gas-phase chemistry. As with the case of a simple atom discussed in the previous section, when applying equation (4-12) the atom with the highest Q0A value is assumed (and predicted) to be the contact atom as this gives the highest adsorption heat. Sometimes steric hindrance may play a role and this assumption may be incorrect. UBI-QEP 69

4.1.3.3 Di-coordinated chemisorption

In the previous section it was assumed that if B is oriented away from the surface the interaction with the surface can be neglected. This significantly simplified the equation and its derivation but the assumption is often not correct. Sometimes the AB molecule is di- coordinated on a bridge site shown in

Figure 4-5. A and B can be either atoms or groups treated as quasi-atoms. Quasi-atoms are groups which are treated as if they are atoms with a single heat of adsorption assigned to them.

Figure 4-5: Molecule in di-coordinated mode on a bridge site. A and B can either be atoms or groups treated as quasi-atoms. M is the metal surface.

In this case clearly the M-B interaction cannot be neglected and a different equation must be derived. More interactions translate to more terms in describing the total interaction potential. The total interaction potential as estimated by UBI-QEP is given by:

(4-13) − 2 + − 2 + − 2 With the associated UBI constraint:

(4-14) + + 1 After energy minimization this results in the following analytical equation:

+ + − (4-15) + + Where

+ 2 (4-16) + And

+ 2 (4-17) +

70 UBI-QEP

Thus, the fact that the interaction between B and the surface cannot be neglected results in more complex equations. However, the principle remains the same: for each case the corresponding UBI-QEP energy, the estimate for the total interaction potential, E(n), can be minimized and an analytical equation can be derived. This analytical equation can then be used to quickly estimate the heat of adsorption of the desired molecule based on tabulated values for the individual atoms or subgroups as well as BDE which can be calculated using thermodynamic cycles or GAVs.

4.1.3.4 Activation energy

For the determination of activation energies Schustorovich [5] derived UBI-QEP for three types of elementary reactions [5, 8]: dissociative adsorption, dissociation on the surface and disproportionation. In this section, the derivation of the activation energy will only be done for dissociative adsorption. The interested reader is referred to the original paper [5]. The tools applied are similar and the main differences lie with which bonds are stretched.

DAB

Figure 4-6: Chemisorption and dissociation of AB.

In Figure 4-6 the Lennard-Jones potential is drawn for the dissociation and recombination of AB. For the 1-dimensional case (blue + red) the profile is constructed via the intersection of the two opposite cases: the Minimum Energy Path (MEP) for the adsorption of AB (red) as well as the summed adsorption energies for the separate A and B surface species (blue). In reality during the reaction the path taken is described by the combination of the blue, green and red curves. The UBI-QEP scheme first derives an estimate for the intersection point and then tries to compensate for the discrepancy. UBI-QEP 71

Under the UBI-QEP assumptions the total energy of the system can be written as a function of the different bond indices. For a diatomic molecule AB interacting with the solid surface, i.e. the red curve, this gives the following equation [9]:

(4-18) , , − 2 + − 2 + − 2 For which the usual UBI constraint still holds.

(4-19) + + 1 Minimization under this constraint with respect to xA and xB leads to the following expression:

(4-20) + + 2 − + − − With

(4-21) + The other curve of the Lennard-Jones potential (Figure 4-6), i.e. the blue one, is just the summed up UBI-QEP energy for the individual surface compounds A and B. As A and B move away from each other, their distance goes to infinity and the bond order for this interaction starts to approach zero (equation (4-1) for large r). As an approximation equation (4-18) is used with xAB=0. This simplifies things greatly but will lead to an overestimation as will be shown later. Because there is no bond between A and B, UBI does not apply and there is no UBI constraint.

(4-22) , − 2 + − 2 The estimation of the intersection (Figure 4-6) thus requires xAB=0 in equation (4-20) and gives the following Lennard-Jones maximum for the energy of the system, thereby providing us with the barrier which needs to be bridged for a reaction to occur:

(4-23) − − +

The previous value, EAB, is correct but it has been derived with respect to the vacuum. The activation energy has to be defined with respect to the minimum energy for the AB surface species. This is what is done in the subsequent section and the end result is given in equation (4-26).

72 UBI-QEP

As EAB refers to the same energy in both equation (4-23) and (4-24), Δ can then be derived in reference to the minimum for adsorbed AB. This can then be further simplified as the term

[QAB + DAB – QA - QB] is just the enthalpy difference, ΔH between the adsorbed A + B surface species and the AB surface species. The Lennard-Jones estimation for the activation energy is then given by equation (4-26).

(4-24) + Δ − + + Δ

(4-25) Δ + + − − + +

(4-26) Δ ΔH + +

This estimate is an overestimation since xAB will never be 0 in reality, an assumption which was made. This is usually corrected for by adding an empirical factor φ which lies between 0 and 1, see equation (4-27). Here φ=1 is the case described so far with the transition state corresponding to the dissociated state (xAB=0), i.e. a very late transition state, whereas φ=0 implies a very early transition state. As there is usually not enough empirical data available φ=0.5 is usually assumed [10].

(4-27) Δ Φ ΔH + +

UBI-QEP 73

4.1.3.5 Tabulated equations

Not all equations have been derived. For reference purposes all equations used within this thesis are tabulated below.

Table 4-3: Equations used for calculating heats of adsorption [11].

Description Equation Eq

1 n Atomic heat of adsorption in an n-fold site (η μ ) 1 (4-28) 2 −

1 n AB adsorbed weakly through A (η μ ) , (4-29) + AB adsorbed strongly through A (η1μn) (4-30) + AB adsorbed in a medium strength through A (η1μn) 1 (4-31) , + 2

AB bridged through two sites (η2μ2) + + − (4-32) + + 2 2 A2 bridged through two sites (η μ ) 9 (4-33) 6 + 16

AXB molecule adsorbed through A and B atoms to form (4-34) 2 2 + − a chelate structure (η μ ) +

AXB chelate structure, with A & B radicals (η2μn) (4-35) + − +

(4-36) + 2 (4-37) + + (4-38) + 2 (4-39) + + (4-40) (4-41) + + a Q0A is the atomic heat of adsorption in an on-top site

b DAB, DAX, DBX is the gas-phase bond dissociation energy of A-B, A-X and B-X respectively

Note: for di-coordinated adsorbed diatomic molecules DAi and DBi are zero.

74 UBI-QEP

Table 4-4: Equations used to calculate activation energies using UBI-QEP. Multiple sources used [5, 11-13].

Description Equation notes Eq

Non-dissociative (4-42) 0 a adsorption −

+ ∗ → ∗

Dissociative adsorption Δ − + Δ − − (4-43) ∗ ∗ + 2 ∗ → + − − 1 − Δ D + − +

Dissociation Δ + Δ − − (4-44) ∗ ∗ ∗ D + − → + + 1 − Δ +

Δ Δ + + − Surface disproportionation Δ + (4-45) − ∗ ∗ ∗ ∗ + → + − 1 − Δ D + − − a Technically not a UBI-QEP result but an assumption made when the model has to conform to forward + backward reactions b is usually taken to be ½ and is related to the position of the transition state

UBI-QEP 75

Application and Exceptions

4.1.4.1 Single Fragments

Whilst all methods rely on the work by Schustorovich [5] it should be noted that the literature is not always consistent when it comes to the choice of equations. For example, when determining adsorption heats for carbon dioxide, the unmodified UBI-QEP method does not seem to provide reliable estimates. Zeigarnik et al. [14] work around this problem by proposing to use a fragment, as if it were adsorbed oxygen instead CO combined with ⋯ 2, a bond energy value, , specific to CO2, while other authors use different methods [7]. ⋯ Though the latter methods give similar results the point remains that some operator experience may be required to get good results.

4.1.4.2 Activation energies

Similarly, for the determination of activation energies most use a similar set of equations, but some variety exists [5, 6, 11-13]. All derived equations are for the forward activation energies. To get the reverse activation energy the enthalpy difference is simply subtracted as shown in Table 4-3 and Table 4-4. Because technically there is no UBI-QEP formula for simple non- dissociative adsorption, usually the forward activation energy is taken to be zero. The backwards activation energy is then just negative the heat of formation. A similar procedure is followed when the calculated activation energy drops below zero: it is simply set to zero [13, 15]. This is also the approach used in this work.

For the calculation of activation energies there is also a parameter φ, see section 4.1.3, which is usually set equal to 0.5. This parameter relates to the position of the transition state and is sometimes varied. For example, a value of 0.85 or 0.15 may be used instead of 0.5 to represent the fact that the user knows the transition state should be respectively late or early. This can be done based on DFT correlations, the BEP slope (as this also contains information on the TS) or simply based on regression to experimental data [12]. Note that in the latter case the correct term is tuning as regression is not done to a global minimum of least squares but based both on the data which have to be approximated (hence the regression part) as well as some knowledge of the transition state.

76 UBI-QEP

The pre-exponential factor is then modified along with φ [12]. This is usually within the context of a broader scheme where UBI-QEP is used as an initial guess and parameters are then tweaked, within well-specified bounds, to fit the experimental data.

It is also important to note what UBI-QEP cannot do. A separate method is required to determine pre-exponential factors. Moreover, whilst there are corrections available which can compensate to some extent, UBI-QEP does not take coverage into account, nor does it allow the user to discriminate between isomers [3]. Thus, in the trade-off between information, which must always be acquired, and accuracy UBI-QEP is probably closer to BEP- relationships than the expanded GAV scheme. The latter requires more information but is also more flexible.

UBI-QEP 77

Example: methanol adsorption on Pt(111)

In this section, the adsorption heat for methanol (CH3OH) on Pt(111) is calculated with the UBI-QEP method as an example [16]. Methanol is known to adsorb with the oxygen atom as the contact atom [6]. The value for the adsorption of an oxygen atom on Pt(111) can be found in Table 4-1 and is 356 kJ/mole. The values in Table 4-1 are experimental values which already take the coordination number into account. Thus Q0A has to be calculated from the available

QA values. This is done by multiplying QA by 0.6 (equation (4-7)) as a hollow site (with three interactions) is assumed for Pt(111), see Table 4-2.The bond dissociation energy, denoted as

DH-O-CH3, can be calculated to be 812 kJ/mole based on values given by Vannice et al. [16]. This is the energy for breaking both bonds involving oxygen. The information required is thus limited to a tabulated value, a bond dissociation energy which can be calculated (via gas- phase GA for example) and an assumption with respect to the coordination number. Next the right-hand side of equation (4-5) is calculated. Because of the weak adsorption with a single contact atom equal this yields the following value for the adsorption heat for methanol:

355 ∗ 0.6 , 51.4 355 ∗ 0.6 + + 812 3 This result is within 9 kJ/mole from the value found in the literature of 60.6 kJ/mole [17]. The value of 60.6 kJ/mole is determined experimentally via microcalorimetry and is valid for low coverage.

78 UBI-QEP

Accuracy

There is general agreement that the UBI-QEP method is powerful and reasonably accurate (up to 8-15 kJ/mole for heats of formation) when compared to experimental data [1-3]. For activation energies the data is scarcer. Shustorovich and Zeigarnik report deviations from experimental data between 5-20 kJ/mole [18]. Some caution is advised however. Some ab initio calculations only are within 10-20 kJ/mole with respect to experimental determined values [19, 20]. It is therefore likely that the 5-20 kJ/mole estimate underestimates the average deviation seen in actual use. As some important deviations can occur it can be useful to know when and why these occur as well as to what extent inaccuracies can be mitigated.

Deviations in the UBI-QEP method however can be somewhat systematized:

a) UBI-QEP eventually relies on a set of adsorption enthalpies which still have to be determined. Often this is done via DFT which may show big deviations from method to method, thus mixing values of different DFT methods may yield inaccurate results [3]. b) φ is usually taken to be 0.5. c) Coverage dependence is not taken into account. d) Errors may increase for smaller bond lengths, see Figure 4-7. e) Assumptions made about weak vs strong binding. f) Some molecules, like carbon monoxide, may not strictly adhere to the UBI-QEP scheme.

Figure 4-7: Absolute error on the binding energies (in eV) as a function of the bond length. UBI-QEP 79

Each of these bullets can be seen as a specific case and in many of these cases measures can be taken. One can make sure to either consistently use the same (Density Functional Theory) DFT method or use experimental data (case a). The latter case can also be useful in case of idiosyncratic issues (case f). When uncertain whether to assume weak or strong binding one can introduce a parameter (between 0 and 1) and a weighted average of both scenarios to fit experimental data [21]. To compensate for coverage dependence the used adsorption enthalpies for the contact atoms do not need be fixed. Some functions, usually a simple linear one, can be used to make the UBI-QEP estimates scale somewhat with coverage. This is of course at the cost of both requiring more information and being more computationally expensive [12]. Sometimes the same is done for temperature dependence (once again a linear function). Finally, again at the cost of computational expense, DFT can be used to estimate the φ, the empirical factor which relates to the location of the transition state, thus making the UBI-QEP estimations more in line with DFT calculations whilst still being considerably less computationally expensive than full DFT calculations [10]. It should also be noted that UBI- QEP is, first and foremost, a method to acquire an initial value for metal species properties and surface reaction parameters. Depending on the specific needs and the resources available the user can decide to optimize UBI-QEP estimates within their margins of error to fit the experimental data, change parameters based on educated guesses (as was the case with φ), or even perform a sensitivity analysis and decide to calculate the most important parameters using more expensive computational methods [22-24].

80 UBI-QEP

Link with the representation of surface species

The implementation of UBI-QEP in Genesys is largely about recognition: recognizing which equations to use, which bonds to break and which enthalpies to determine. Therefore, the choice to opt for UBI-QEP and the method of representation are linked. To illustrate this link, the surface representation of propionic acid is shown in Figure 4-8. The representation with a dummy atom block, as described in Chapter 2, has important advantages specific for UBI- QEP compared the more classical representation with a single dummy atom, as shown right in Figure 4-8.

One advantage is linked to the need to break all bonds with the surface for UBI-QEP to work properly, i.e. two bonds for propionic acid in Figure 8. Genesys makes use of CDK to build the graph representation starting from molecule identifiers. Because of this fixed structure, problems with the representation which may arise that are difficult to solve. Breaking multiple bonds with the same dummy atom will lead to errors and this will, for example, lead to incorrect charges for the involved atoms. Making use of a catalyst block is a more elegant solution and makes breaking bonds and some other algorithms, which deal with assigning the right equation or case to a surface species, more straightforward.

Figure 4-8: Representation of propionic acid with both a dummy atom block, as implemented in Genesys, and a single dummy atom, as encountered in literature.

UBI-QEP 81

Implementation in Genesys

Underlying Assumptions

The preceding literature survey served as a basis to implement the UBI-QEP method in Genesys. As is the case with UBI-QEP itself, several additional assumptions were made which are discussed first.

The catalyst surface is assumed to be homogenous and invariant throughout the reactor, regardless of operating conditions or space time. The coordination number is picked based on the catalyst surface chosen (i.e. Ni(111)) and is not varied depending on the species. In practice this means that for a single species the heat of adsorption is fixed: independent of temperature or space time. Additionally, these species are assumed to conform to one of the cases supplied by UBI-QEP. Something which is rather doubtful if aromatic rings are involved. The accuracy for these species is sacrificed in favor of a simpler scheme.

The UBI-QEP method does have several extensions, most of which relate to modifying the atomic heats of adsorption used or making it a function of surface coverage or temperature. These extensions have not yet been implemented but underline a recurring pattern when using the UBI-QEP method. Within UBI-QEP, calculated values agree with experiments to the extent you are able to compensate for surface phenomena via the atomic heats of adsorption.

Next there are the assumptions made in the translation of the method to the algorithm. Bonds with the surface have to be categorized as either weak, intermediate or strong. This is achieved on the basis of three criteria: the number of bonds involved, the presence of single electrons (radical or not) and the presence of resonance bonds. Molecules which are not bound to the surface via radicals are taken to be bonded weakly. Molecules tied to the surface by radicals which are not resonance stabilized and have a maximum of two bonding partners are taken to be strongly bonded. Molecules which fit neither of these two categories, i.e. the vast majority, are taken to correspond to the intermediate case. A more rudimentary check based purely onto the calculated heat of adsorption is also possible. Though if the values supplied by Shustorovich were used this seemed to classify most molecules as being weakly bound whilst in the literature most were found to be classified as intermediate.

82 UBI-QEP

To check if a molecule, denoted A-B here, is symmetric it is fragmented along the A-B bond. The fragments are then matched against each other. If they are the same, the molecule is assumed to be symmetric. To check if a molecule is a chelate, the neighbors of A and B are checked against each other. In order to be a chelate A and B cannot be neighbors and they have to have exactly one neighbor in common as is discussed in subsection 4.3.2.

Finally, the same subdivision has to be made for reactions. Within the UBI-QEP implementation there are two big categories which each contain two subcategories. The first category is that of adsorption and desorption reactions. These can be either dissociative or non-dissociative. This is checked for by first looking for the presence of a gas-phase molecule in either the products or reactants (depending on the direction of the reaction). The distinction between dissociative and non-dissociative is made on the basis of the presence of an empty catalyst block (no adsorbed species). If one is present, the adsorption reaction is taken to be non-dissociative. Non-dissociative adsorption is of the form AB + * → AB* (with * representing a surface site). The second category is the category of surface reactions. Here the presence of an empty catalyst site is used as a criterion to identify dissociation reactions. This is because dissociation reactions are of the form AB* + * → A* + B* If all graph structures represent adsorbed species then the reaction is taken to be a disproportionation reaction of the form A* + B* → C* + D*. Note that reactions are classified not just by type but also by direction so Genesys checks whether if the reaction involved occurs in the forwards or the backwards direction.

As UBI-QEP does not provide the user with pre-exponential factors, these are assumed to be constant for all reaction families for now. This assumption is made out of necessity as data is scarce concerning pre-exponential factors. In the literature survey it was found that these were either taken to be constant as well, as is the case with RMG-Cat [25] , or tuned in post- processing.

UBI-QEP 83

Thermodynamics

The process by which Genesys calculates heats of adsorptions using the UBI-QEP method can be split up into three consecutive steps. First is the identification of the species. This consists of choosing the equation which will be used, often denoted as the “case” as well as mapping the species onto a UBI-QEP template (i.e. A, B, X in Figure 4-9). These are respectively step 1.1 and 1.2 in in Figure 4-9. Once an equation has been chosen several values have to be determined to plug into the final equations. These are BDEs, which are determined via fragmentation (Step 2.1 and 2.2), and atomic heats of adsorption, which are provided as user input. In a third and final step, the heat of adsorption for the surface species is then calculated.

Figure 4-9: Schematic overview of the implementation of UBI-QEP for the automatic calculation of heats of adsorption. 4.3.2.1 Determination of the case

The first step is determining the equation which will be used eventually. This is performed by the “UBIType” class for which the selection procedure is shown schematically in Figure 4-10. There are eight potential cases each corresponding to an equation as shown in Table 4-3. Species are assigned to one of the cases based on the number of atoms (step 1), the number of ligands (Step 2), the bond strength (Step 3 or 4) and the coordination type (Step 3). The latter relates to whether a multidentate molecule is symmetric, asymmetric or a chelate.

84 UBI-QEP

Figure 4-10: Decision tree on the basis of which Genesys decides which case (equation) to use. Decision criteria are shown in blue. Cases are shown in green. The equations in question can be found in Table 4-3.

The order in which decisions are made within the decision tree is also shown in Figure 4-10. Once the corresponding case number (I-VII) is assigned Genesys can move on to the next step.

4.3.2.2 Mapping of reactive centers

The second step is the creation of a UBI-QEP map, shown for a chelate in Figure 4-11. This map serves to identify the reactive centers and is subsequently used to fragment molecules alongside the bonds of which a BDE has to be determined. In this context, reactive centers are the atoms which are involved in either adsorption to the surface or the process of fragmentation for the determination of BDEs (i.e. A,B, … ). The map relates the reactive centers to the index number of the corresponding atoms within the graph structure representation. Every type of Map has a corresponding class in Genesys, i.e. “Monomap” for monoadsorbing species, “AAMap” for symmetric multidentate species, “ABMap” for assymetric multidentate species and “ChelateMap” for chelates. The behavior of each of those classes is for technical reasons specified by the interface “ICatMap”. UBI-QEP 85

Figure 4-11: Schematic representation of the generation of a specific map.

In the map classes the index numbers of the recognized atoms are stored. The choice to store index numbers instead of atoms is made for several reasons. The first reason is rather practical: index numbers do not change when creating a clone of the graph structure. Secondly, this way is already present in Genesys, i.e. during network generation also index numbers are used instead of the atoms themselves. Thirdly, in the long run should the team ever decide to move away from CDK the code can be mostly retained with fewer modifications necessary.

The algorithm determines the desired index numbers by first searching for bonds that include exactly one dummy atom. Then it takes the index number of the atom bonded to the dummy atom and stores it as either A or B (if A already has a value). The neighbors of A and B are also stored in a list of index numbers, though surface atoms as well as A and B themselves are excluded. This is done for two reasons. First for the determination of the BDEs the corresponding bonds will have to be broken. X, the common bonding partner for multidentate chelates, is determined by finding the index number that is common to both lists. That is, if both the neighbors of A and B are a set of integers, the intersection must be unique and correspond to X.

86 UBI-QEP

4.3.2.3 Determination of BDEs via fragmentation

The previously mentioned BDEs are determined in the 3rd step: the process of fragmentation. Here a clone of the molecule is fragmented along the bond(s) for which a BDE has to be determined. For this purpose, a thermodynamic cycle is used which can be described in its most general form by equation (4-46).

(4-46) Δ − Δ As this is rather abstract, the example of a chelate is given in Figure 4-12. Here one of the BDEs which has to be considered is that of the BX bond. Genesys will calculate this BDE by fragmenting along the BX bond and looking up the heat of formation of each of the resulting species. This enthalpy is then subtracted from the enthalpy for the gas-phase.

Figure 4-12: BDE’s are calculated via fragmentation. Here an example is given for the determination of DBX for a chelate. Within Genesys, the associated species required to make use of equation (4-46) are generated and stored for each BDE. This is done by making use of a fragment class. The naming of these fragment classes follows the same pattern as with the map classes: “MonoFrag” for mono-adsorbing species, “AAFrag” and “ABFrag” for symmetric and asymmetric multidentate species, and “ChelateFrag” for chelates. Once again, for technical reasons these classes all extend the “IFrag” interface.

It is important to note that fragmentation classes contain only the graph structure for all species involved in the use of equation (4-46). Each BDE thus has an associated set of species, but the heats of formation required, as shown in the example of Figure 4-12, still have to be determined.

The actual determination of the BDE using these fragments is done by the thermo module already implemented in Genesys for gas-phase species. First all fragments are sent to the UBI-QEP 87 thermo module for the determination of their thermodynamic properties (i.e. their NASA polynomial) and subsequently equation (4-46) is used.

It should be noted that when fragmenting, species generated may often contain atoms hat contain more than one single electron. If this is the case, no thermodynamic properties are available in the GAV databases of Genesys. For UBI-QEP however, this may pose a problem. As a (temporary) workaround two new functionalities are added. First Genesys writes out all species for which it cannot find thermodynamic data in a separate output file. Thermodynamic data can then be retrieved from another source, e.g. RMG, in CHEMKIN input format [26]. A new CHEMKIN reader class was also written for this purpose which is more robust than the previous one. This class allows the user to import the desired thermodynamic data.

4.3.2.4 Calculation of the adsorption heat

The last step is the calculation of the heat of adsorption for the desired surface species. This is achieved by first gathering all the generated data together into one big class (“UBICalc.java”). This is done to ease the accessibility as well as the coding (only one input is required). The actual calculation is performed by the “UBICalc” class.

Kinetics

Analogously to the Thermodynamics section, the calculation of activation energies can be split up into three consecutive steps. First is the identification of the reaction. This consists of choosing the equation which will be used, often denoted as the “case” as well as mapping the different surface species onto the UBI-QEP equation (i.e. A, B, AB in Figure 4-13). These are respectively step 1.1 and 1.2 in in Figure 4-13. Once an equation has been chosen the heats of adsorption have to be determined for the surface species involved. Additionally, for gas-phase species as well as the gas-phase analogues of surface species the heats of

formation have to be determined (Step 2.1, QA … QAB and HA … HAB in Figure 4-13). Obtaining these values is done by the “UBIHub” class, discussed in section 4.3.3.5. This class also deals with all functionalities relating to data-management for UBI-QEP. Finally, in the third step the values are plugged in in the desired equation.

88 UBI-QEP

Figure 4-13: Steps in calculating the heat of adsorption or activation energy for a surface reaction using the UBI-QEP method.

The user can make use of UBI-QEP by simply using its associated kinetics type (“UBI_QEP”). Within Genesys, all methods which are used to deal with kinetics (i.e. group additivity or Brönsted-Evans-Polanyi) extend the “AbstractArrheniusParameter” class. To integrate the new UBI-QEP method fully within Genesys, the same was done. The class in question is called “UBIKinCalculator”.

4.3.3.1 Determining the case

The first step (Step 1.1) to calculate the correct kinetics is the classification of a reaction into one of the four types of reactions implemented: dissociative adsorption, non-dissociative adsorption, surface dissociation and surface disproportionation. This function is performed by the “UBIClassifier” class and the process by which this is done has already been discussed in section 4.3.1. In short, both the presence of a gas-phase molecule and the presence of an empty site is used to determine which reaction type the reaction belongs to.

4.3.3.2 Mapping of species

In step 1.2, species are mapped onto their equation. First the molecules are mapped onto the symbols within the general formula. As an example, for surface dissociation the equation takes the form AB* → A* + B*. Thus, the graph structure representations for the molecules involved will be mapped to AB*, as well as A* and B*, as shown in Figure 4-13. This is done by several classes, depending on the case, all extending the “ISurfaceCalc” interface.

UBI-QEP 89

4.3.3.3 Retrieval of species properties

Once this is done the desired heats of adsorption and gas-phase enthalpies of formation will be retrieved via the “UBIHub” class, which will be discussed in subsection 4.3.3.5. Of note here is that along with the heat of adsorption required for surface species, enthalpies of formation are required for their gas-phase analogues as these are required for the calculation of the enthalpy difference over the reaction (ΔHr).

4.3.3.4 Calculation

Once all required variables are available (i.e. heats of adsorption and gas-phase enthalpies of formation) they are then plugged into the appropriate equation (equations are given in Table 4-4 on page 74). Both the forward and the reverse rate are stored and can be accessed via the “UBIHub” class.

4.3.3.5 Data management

All data management functions which relate to the UBI-QEP method, including the retrieval of the correct adsorption heatss and gas-phase heats of formation, are centralized in the “UBIHub” class. The aforementioned thermodynamic data is mapped to the molecule. For reactions, the activation energies are mapped onto the reaction itself. Finally, “UBIHub” will also store the molecule identifiers (i.e. InChI or SMILES) for each of the species and reactions required. Data management was centralized in a single class for two reasons. First, it makes it easier to keep things which naturally belong together. As an example, this means that when calculating the heat of adsorption for a single species, the output information is generated in the same location where the calculation is actually performed. The second advantage is ease of access. Because all calculations, with all their intermediates, are always available, it makes debugging significantly easier as the user has greater control over which information needs to be written to output.

90 UBI-QEP

Future Work

The basic scheme of UBI-QEP has successfully been implemented, though several additions suggested by Shustorovich have yet to be implemented [5]. In the literature, only two functionalities are consistently added to UBI-QEP. The first is a linear dependence on temperature for the atomic heats of adsorption. The second is making atomic heats of adsorption coverage dependent. The author knows of no recent paper which implemented additional equations or which relied on the interpolation between 2 metals as suggested by Shustorovich. One should be wary of using a method for purposes it was not intended for. Additional extensions may make the method no more accurate whilst increasing the chance of overfitting and diverting resources elsewhere. It is for this reason that in the near future only the aforementioned temperature and coverage dependences should be added.

The UBI-QEP can occupy two important niches. First it is especially useful both for studying general trends within a system, as one changes temperature or catalyst composition. Second UBI-QEP provides the user with a solid initial guess, which can then be used in the construction of a kinetic model within the context of a broader process of iterative refinement. For this reason, one could also look to the addition of regression. Trying to understand to which Arrhenius parameters the network is most sensitive could be a worthwhile. This is case especially for UBI-QEP [27] . Here UBI-QEP could be used to get a first estimate for the kinetic model which can then be tuned based on which parameters are deemed to be important. The user could then select a number of parameters and determine these with a more computationally expensive method. Another option would be to use regression to finetune the parameters, within predetermined bounds, to experimental data. Unphysical Arrhenius parameters are much less likely to occur if values are kept within range of UBI-QEP estimates.

A third potential addition is the link to a database containing literature values for atomic heats of adsorption. It should be noted that as of writing most of the current literature uses the values as provided by Shustorovich.

UBI-QEP 91

Conclusions

The observation that UBI-QEP is still used decades after the original concept of bond order conservation was introduced is a testament to its usefulness. UBI-QEP is simple and drastically reduces the amount of information that has to be acquired. Because of this it is one of few methods which is uniquely suited for kinetic model generation where data scarcity is still a major challenge. Additionally, it is useful as a framing device as it helps guide the discussion of what aspects of catalysis are worthwhile to implement and which could be neglected. The author is of the opinion that UBI-QEP should rightly be respected for what is: a computationally inexpensive method of determining heats of adsorption and gaging trends in the dependence on catalyst composition.

Care should always be taken when applying UBI-QEP, to ensure that it is not used for something the method was not intended for. The method is by no means a replacement for more computationally expensive methods like DFT calculations. Calculated values are only first estimates and may deviate more than expected. This is especially true when aromatic structures are involved. It should be noted that these are challenges all similar methods face and are not unique to catalytic reactions either. Catalysis simply adds an extra layer of complexity on top.

Within this chapter UBI-QEP was also successfully implemented in Genesys and the processes of determining the correct equations, BDEs and heats of adsorptions were automated. With this a first step has been made in bridging the gap between gas-phase and catalytic automated network generation.

92 UBI-QEP

References

1. Salciccioli, M., et al., A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior. Chemical Engineering Science, 2011. 66(19): p. 4319-4355. 2. Shustorovich, E. and A. Zeigarnik, The UBI-QEP method: Basic formalism and applications to chemisorption phenomena on transition metal surfaces. Chemisorption energetics. Russian Journal of Physical Chemistry A, Focus on Chemistry, 2006. 80(1): p. 4-30. 3. Baraldi, A., Model studies of hydrogen reactivity and production on metal surfaces. 4. van Santen, R.A., Modern Heterogeneous Catalysis: An Introduction. 2017: John Wiley & Sons. 5. Shustorovich, E. and H. Sellers, The UBI-QEP method: a practical theoretical approach to understanding chemistry on transition metal surfaces. Surface Science Reports, 1998. 31(1-3): p. 1-119. 6. Vannice, M.A. and W.H. Joyce, Kinetics of catalytic reactions. Vol. 134. 2005: Springer. 7. Shustorovich, E. and A.V. Zeigarnik, The UBI–QEP treatment of polyatomic molecules without bond-energy partitioning. Surface Science, 2003. 527(1-3): p. 137-148. 8. Leszczynski, J., Computational Materials Science. 2004: Elsevier. 9. Pignataro, B., New Strategies in Chemical Synthesis and Catalysis. 2012: John Wiley & Sons. 10. Maestri, M. and K. Reuter, Semiempirical rate constants for complex chemical kinetics: first- principles assessment and rational refinement. Angew Chem Int Ed Engl, 2011. 50(5): p. 1194- 7. 11. Mirzanejad, A., Thermal chemistry of 2-halo-1-propanols on Ni (1 1 1) and Cu (1 1 1) surfaces: A UBI-QEP energetic modeling. Applied Surface Science, 2015. 359: p. 576-588. 12. Maestri, M., et al., Steam and dry reforming of methane on Rh: Microkinetic analysis and hierarchy of kinetic models. Journal of Catalysis, 2008. 259(2): p. 211-222. 13. GOISIS, S. and A. OSIO, Computational fluid dynamics of gas solid catalytic reactors based on microkinetic description of surface chemistry. 2011. 14. Zeigarnik, A., et al., Prediction of comparative catalytic activity in the series of single crystalline surfaces in a water-gas shift reaction. Kinetics and catalysis, 2005. 46(4): p. 509-515. 15. Moqadam, M., et al., A UBI-QEP microkinetic study for Fischer-Tropsch synthesis on iron catalysts. Procedia Engineering, 2012. 42: p. 34-44. 16. Vincent, R.S., Detailed modelling of catalytic chemistry in short contact time reactors. 2008. 17. Karp, E.M., et al., Energetics of adsorbed methanol and methoxy on Pt (111) by microcalorimetry. Journal of the American Chemical Society, 2012. 134(50): p. 20388-20395. 18. Zeigarnik, A.V. and E. Shustorovich, The UBI-QEP method: Mechanistic and kinetic studies of heterogeneous catalytic reactions. Russian Journal of Physical Chemistry B, 2007. 1(4): p. 330- 356. 19. Piccini, G., M. Alessio, and J. Sauer, Ab initio calculation of rate constants for molecule–surface reactions with chemical accuracy. Angewandte Chemie International Edition, 2016. 55(17): p. 5235-5237. 20. Fishtik, I. and R. Datta, A UBI–QEP microkinetic model for the water–gas shift reaction on Cu (1 1 1). Surface Science, 2002. 512(3): p. 229-254. 21. Van Belleghem, J., et al., A Single-Event MicroKinetic model for the cobalt catalyzed Fischer- Tropsch Synthesis. Applied Catalysis A: General, 2016. 524: p. 149-162. 22. Maestri, M., Escaping the trap of complication and complexity in multiscale microkinetic modelling of heterogeneous catalytic processes. Chemical Communications, 2017. 53(74): p. 10244-10254. 23. Sutton, J.E. and D.G. Vlachos, Effect of errors in linear scaling relations and Brønsted–Evans– Polanyi relations on activity and selectivity maps. Journal of Catalysis, 2016. 338: p. 273-283. UBI-QEP 93

24. Sawatmongkhon, B., et al., Combination of Langmuir-Hinshelwood-Hougen-Watson and microkinetic approaches for simulation of biogas dry reforming over a platinum-rhodium alumina catalyst. International Journal of Hydrogen Energy, 2017. 42(39): p. 24697-24712. 25. Goldsmith, C.F. and R.H. West, Automatic Generation of Microkinetic Mechanisms for Heterogeneous Catalysis. The Journal of Physical Chemistry C, 2017. 121(18): p. 9970-9981. 26. RMG Website. 27. Stegelmann, C., A. Andreasen, and C.T. Campbell, Degree of rate control: How much the energies of intermediates and transition states control rates. Journal of the American Chemical Society, 2009. 131(23): p. 8077-8082.

Output files 95

OUTPUT FILES

Introduction

The main goal of Genesys is to automate the process of generating a kinetic model. Its output is thus a network containing both a list of species and a list of reactions. Thermodynamic properties of species are calculated automatically and for reactions Arrhenius parameters are provided in the output as well. To model a chemical process, reactor simulations need to be performed using this kinetic model as input. Given the complex nature of the kinetic models generated by Genesys, the link is made to existing kinetic modeling and simulation software packages. Two examples of such software packages are CHEMKIN and the microKinetic Engine [1, 2]. The former package is used worldwide for gas-phase and catalytic systems but is not open-source. The microKinetic Engine is an in-house developed software tool for catalytic systems that enables coupling with regression.

The software tools for reactor simulations need a predetermined format of the kinetic model to ensure correct implementation. In this chapter, both software packages CHEMKIN and the microkinetic engine are discussed, as well as the format used. For gas-phase reactions, the format required for CHEMKIN is already implemented in Genesys, but the extension needs to be made to catalytic input files.

96 Output files

CHEMKIN

CHEMKIN is a software tool for solving complex chemical kinetics problems. Within the context of Genesys, CHEMKIN can be used in two different ways:

1. During (rate-based) network generation CHEMKIN can be used to simulate the actual rates in a reactor and use those as a threshold to either reject or add a new species. In this case CHEMKIN is called by Genesys during the generation (on-the-fly) of the kinetic model. 2. After network generation the resulting network can be used to simulate the reactor and observe changes depending on operating conditions.

For this thesis the focus lies with the second case.

The input required by CHEMKIN for this purpose can be divided into two parts. First there is the information required on catalyst properties, reactor conditions, and experimentally observed reactor model responses. Secondly there is the input which is generated by Genesys. This deals with input species, reactions and the corresponding thermodynamics and kinetics and will be discussed more in depth in subsection 5.2.2. Genesys is modified in order to write the correct output that can be used as an input for catalytic reactions in CHEMKIN. It should be noted that as the focus within this thesis lies with the generation of the kinetic model, the discussion is restricted to plug flow reactors. First, the typical units used by CHEMKIN are discussed, as these are not the typical units often used in literature.

Output files 97

CHEMKIN units for catalytic reactions

For the rate equation of catalytic reactions, different approaches are possible concerning the pre- exponential factor. This is because the observed pre-exponential factor depends on the number of active sites present. The user can therefor decide whether to assume this number to be constant and if so whether to include it in the pre-exponential factor or not. As a result, pre- exponential factors may be given in terms of either catalyst surface area or catalyst mass. CHEMKIN expresses every surface concentration in terms of catalyst surface area, whilst in the literature one might find values expressed in terms of catalyst mass. For this purpose, CHEMKIN makes use of both the surface density per unit area as well as the internal surface area of catalyst per unit length of the reactor. The symbol, location as well as the name within CHEMKIN is given in Table 5-1.

Table 5-1: Within CHEMKIN both the surface density as well as the internal surface area must be provided by the user.

name surface density per unit area Internal surface area of catalyst per unit length of reactor

symbol γ AL

- -1 units mole mcat ² mcat² mreactor name of input file kinetic model input file reactor input file CHEMKIN name SDEN Internal surface area per unit length

In the end this means that if both of these values are chosen or calculated correctly their effect on the pre-exponential factor cancels out. For the user however, it means additional checks are required to ensure that no conversion errors are made when calculating the pre-exponential factor. If one considers the case of the adsorption of A on two sites as shown in equation (5-1), the rate of production on a catalyst mass basis will be given by equation (5-2), where R is the rate of production, r is the reaction rate, k is the rate coefficient, pA is the partial pressure of A, C is the total concentration of active sites on a mass basis (expressed in ) and ∗∗ the surface θ fraction of two adjacent free sites.

∗∗ (5-1) A ∗∗→ A ∗∗ (5-2) R r k ∙ p ∙ C ∙ θ 98 Output files

Within CHEMKIN a different formulation is used based on surface area instead, the result is

equation (5-3) with kc the rate coefficient according to CHEMKIN and OA the occupancy of A (i.e.

2 in this example) and ∗ the surface fraction of free sites . θ

γ ∗ (5-3) R r k. p. θ O Additionally, the equations are solved with respect to the reactor length as opposed to the mass

of the catalyst which means RC , the rate of production as defined within CHEMKIN, is multiplied

by the internal surface area of the catalyst per unit length of the reactor, AL. Usually in mass- based formulation the specific surface area of the catalyst is given by the user as input (units:

.

It can be derived that if the rate constant on mass basis, k, and the total concentration of active sites per kg, C, are given, and k (which are required input) can be calculated as follows: γ C

(5-4) k ∗

C (5-5) γ A

Finally, it is also possible that an apparent rate constant kapp is given which already incorporates

the total active site concentration. In this case kc can simply be calculated as follows:

(5-6) k ∗ ∗ γ

Output files 99

Kinetic model input files

For the reactor simulation to work, Genesys has to provide CHEMKIN with the species, the reactions and the thermodynamic data and kinetic parameters in the correct format. All this information is contained within 2 files: a surface input file and a gas-phase input file. The gas- phase chemistry input file of CHEMKIN is divided in 4 sections: an element section, a species section, a thermodynamics section and a reaction section.

5.2.2.1 Gas-phase input file

The first input file describes the gas-phase species (and if applicable reactions), an example of the top section of such an input file is given in Figure 5-1. The first line is an “ELEMENTS” line which contains all the symbols of the elements. As this is a catalytic reaction this includes nickel. Note that the element used here is that representative of the real catalyst, and not the element of the dummy atom used within Genesys. Second the CHEMKIN names are given under the “SPECIES” section. These names are usually based on the structure formula and will be used in the reaction section as well (Figure 5-2). As NASA polynomials are only valid within a given temperature range, these ranges are given as well below “THERMO ALL”. The first 7 constants

(a1->a7) are valid from 298K to 600K. The second 7 are valid from 600 to 1000K. These polynomials are defined for all gas-phase species. It should be noted that this format is very strict, i.e. all values have to start and stop at the column number specified by CHEMKIN.

Figure 5-1: Gas-Phase input file for CHEMKIN.

100 Output files

5.2.2.2 Surface input file

Surface reactions and species are given by the user in a separate input file which is largely similar in structure. Nonetheless, the surface input file in CHEMKIN differs in a few important aspects (as shown in Figure 5-2). As the gas-phase input file for catalytic systems often does not contain any gas-phase reactions, The format used in the input files for reactions is discussed here instead.

Figure 5-2: Surface input file for CHEMKIN.

The surface file lacks the “ELEMENTS” section as these have already been provided by the gas- phase input. Instead the surface density (SDEN) is given in the first section. The CHEMKIN names are defined somewhat differently than they are in the gas-phase. The CHEMKIN name of the surface can be chosen freely but, within the literature, some reference to the surface is usually used (S, _S_ or (S) have all been observed by the author). Species which are adsorbed on the surface are given their gas-phase CHEMKIN name with an additional suffix added, _S in Figure 5-2. This can be chosen freely as long as it is consistent. Output files 101

The “THERMO ALL” section is completely analogous to the gas-phase section with the three temperatures defining the range over which the supplied NASA polynomials hold. For surface species, it should be noted, no thermodynamic data is used within CHEMKIN, so the NASA parameters are all set to zero. As a consequence, the rate-coefficients of the reverse reactions cannot be calculated assuming thermodynamic consistency but need to be defined by the user in the reaction section, if applicable. Note that in Figure 5-2 only the first of the thermodynamic data was shown to save space, a block is required for every species defined in the surface species section.

Finally, the reactions section is given below the “REACTIONS” keyword. Here the units for the activation energy are clearly in kJ/mol. This can be changed if desired to cal/mole (“CAL/MOLE”), kcal/mole (“KCAL/MOLE”) or J/mole (“JOULES/MOLE”). The first parameter in line is the pre- exponential factor. Here care should be taken with the units as they need to conform to the scheme described in section 5.2.1.

Both files are automatically generated by the Genesys software. For now, the element of the catalyst (nickel) and the surface density are still hardcoded for technical reasons. This is because the thermo module within Genesys is used for the generation of part of the CHEMKIN input files.

MicroKinetic Engine

The microKinetic Engine (µKE) [1] is a software package for the simulation and regression of chemical kinetics developed at the Laboratory for Chemical Technology, Ghent University. Because of its ability to perform regression, it could be worthwhile to create a link with the network generation as performed by Genesys. As was discussed in Chapter 4, methods used to calculate kinetic parameters like UBI-QEP (Unity Bond Index, Quadratic exponential potential) could then be used as a first guess, after which values are tuned within a specified interval (i.e +- 15 kJ/mole for activation energies, an order of magnitude for pre-exponential factors). A first step towards this goal would be the creation of an input file which could be read by the µKE.

Conclusions and future work 102 Output files

Because of CHEMKIN’s capabilities in simulating reactors, Genesys already contained the functionality to create gas-phase input files for this software package. This functionality was expanded upon to allow for the generation of the gas-phase and surface input files, both of which are required for simulating catalytic reactions. When using pre-exponential factors from the literature, special attention should be paid to the units. Finally, a small section was devoted to the microkinetic engine as the regression capabilities of this software package could potentially be beneficial to Genesys. Regression allows for the tuning of the model parameters to better fit experimental data as well as the determination of sensitivities. In the latter case insight is gained in what parameters matter most. As the construction of a kinetic model is an iterative process this could significantly improve the strength of the model.

References

1. Thybaut, J.W. and G.B. Marin, Single-Event MicroKinetics: Catalyst design for complex reaction networks. Journal of Catalysis, 2013. 308(0): p. 352-362. 2. Design, R., CHEMKIN 10131. Reaction Design, San Diego, CA, 2013.

Case study 103

A CASE STUDY: HYDRODEOXYGENATION OF PROPIONIC ACID AS A MODEL COMPOUND

Introduction

To test the new functionalities in Genesys, a case study is presented related to the processing of ligno-cellulosic biomass by pyrolysis. The process has gathered interest because of its high yield and quality compared to alternative bio-oils. Currently however the end-product still has sub-par energy density and stability whilst being too corrosive in comparison to conventional fuels. These problems are related to H/C and O/C ratio’s [1-4]. Higher H/C ratios are associated with higher energy densities whilst eliminating the oxygen hetero-atom tackles issues with stability and corrosiveness [4, 5].

Within, this thesis propionic acid will be used as a model compound for these oxygen-bearing species. For this purpose, experimental data on the hydrodeoxygenation of propionic acid as a model compound, using a nickel-copper based catalyst is used. The experimental data was kindly provided by Daria Otyuskaya [6]. More specifically a simplified kinetic model of the hydrodeoxygenation of propionic acid on a nickel-based catalyst will be considered. The reaction network will be modeled based on an already established network as presented in Figure 6-1.

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k6 hydrogenation decarbonylation methanation

k2 , K2

Figure 6-1: The reaction network for the hydrodeoxygenation of propionic acid on a nickel-based catalyst.

Reaction families were chosen so as to only generate the reactions shown in Figure 6-1. The actual reactions are defined in the original paper by Daria Otyuskaya [6]. It should be noted that this is not the general goal of automatic network generation, but the purpose of this section is to test if the new implementations work in a correct way and to debug the problems in Genesys that arise for catalytic systems. The only deviation from the original network was the addition of a reaction for hydrogen gas adsorption to demonstrate that dissociative adsorption works properly.

Using the newly implemented functionalities in Genesys, the network was correctly replicated within this work. All reactions and species were recognized correctly and the correct UBI-QEP equation was chosen. There are some issues still unresolved relating to the availability of thermodynamic data, the use of the UBI-QEP equations and use of pre-exponential factors.

In this chapter, the reader is given a clear view of what input is required and which aspects differ from gas-phase reaction networks. Next, the different output files created by Genesys are discussed. References for all input and output files for this case study are added to the appendix (Appendix C). Finally, examples are given of UBI-QEP calculations for some selected reactions of this case study together with the output generated by Genesys.

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User input

In addition to the species input file and the reaction families input file which are required for gas- phase network generation, one additional file must be provided: the catalyst descriptor file. This file contains the required UBI-QEP (Unity Bond Index, Quadratic Exponential Potential) parameters as well as the atomic number of the atom used for dummy purposes. As shown in Figure 6-2, two additional input files can be used: an adsorption map and additional thermodynamic data. These only come into play in specific cases. Ideally, in future work, these files no longer need to be provided.

Figure 6-2: Different input files required for catalytic network generation.

In case of dissociative adsorption, Genesys still needs information on what the surface product would look like in the non-dissociated state. This is because the enthalpy of adsorption of the non-dissociated species must be determined to calculate the activation barrier. This information needs to be provided by the user in the adsorption map input file. A more in-depth discussion of this input file is given in section 6.4.2. . The last input file, i.e. additional thermo, deals with thermodynamic data which cannot be calculated by Genesys and is imported from an another source (usually the RMG database [7, 8]). In this case, a CHEMKIN input format is used with thermodynamic data in NASA polynomials format.

For all input files discussed, references to complete versions used for the case study can be found in appendix C.

Species Input

Input species are given by their molecule identifiers which can be either InChI or SMILES, as discussed in Chapter 2. The input species used for this case study in SMILES are given in

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Figure 6-3: Species input file for the hydro-deoxygenation of propionic acid on a nickel based catalyst. Here species are defined using SMILES.. Reaction families input

In Genesys reactions are generated through the use of recipes and reaction families. Recipes are a finite set of actions that represent the reactions. Examples of actions are the formation or breaking of a bond, the addition or removal of a charge or radical or the changing of the bond order. These actions are then used to manipulate the graph structure of a set of matching reactants. SMARTS identifiers define the moiety (subgraph) where the reaction takes place. The atoms involved in the actual manipulations are called reactive centers and are given a symbol (A,B, …), which is the same as the ones defined in the recipe. An example of a reaction family for the adsorption of propionic acid with the different parts highlighted is given in figure Figure 6-4. In addition the reaction families used can be found in Appendix A.

Figure 6-4: Implementation of reaction families and recipes in Genesys for the adsorption of propionic acid. Case study 107

Reaction families also allow for the specification of constraints which exclude molecules that cannot partake in the reaction. For catalytic purposes this is very important as it allows the user to make sure species only adsorb onto empty catalyst blocks. In this work, the constraints are specified to allow only the predefined reactions, as specified by the original case study [6], to occur.

Finally, in the reaction family, also the source for kinetic data needs to be specified (see Chapter 3). For surface reactions, users should use UBI-QEP (“UBI_QEP”) as the kinetics type. For now, the pre-exponential factor used for the surface reactions with an activation energy calculated by UBI-QEP, is hard-coded in Genesys and set equal to 107. In future work, a value could be provided by the user in the kinetics section of the reaction family input file.

Two specific reaction families are explained into more detail to show how the functionality works.

6.2.2.1 Hydrogen gas dissociative adsorption

The adsorption of hydrogen on the surface (Figure 6-5) follows the process of non-dissociative adsorption and will therefor be used as an example. The recipe consists of 4 actions. First the bond between the two hydrogens is broken (reactive centers A and B). Second the catalyst block is split into two by breaking the bond between reactive centers C and D. Third and fourth, a bond is formed between A and C and B and D respectively.

Figure 6-5: Schematic representation of the reaction family for the dissociative adsorption of hydrogen.

For dissociation some additional modifications were necessary as Genesys can currently only perform reactions of up to 2 reactants. This means that the desired reaction of hydrogen and two surface sites, for example, cannot be performed directly. As a result, either catalyst blocks of both size 2 and 4 would be present within the network or an additional dummy reaction

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would have to be added which is cumbersome and unaesthetic. Some issues may also arise with reaction-families as the user now has to keep track of the size of the catalyst block. Within this work, an implementation for Dissociative Adsorption which fixes this problem was therefor introduced.

For the input of the reaction families, the catalyst block is split in two and the hydrogen adsorbs onto two smaller catalyst blocks half the size as shown above in Figure 6-5. It should be noted that this implies the catalyst block is an even number. If a reaction uses UBI-QEP kinetics, it will instantly be identified. In this case it will be identified as dissociative adsorption. The reaction, as an object within the reaction network in Genesys, will then be manipulated. An additional surface site is added to the reactant side and the length of the catalyst block of both products will be increased to its normal size (Figure 6-6).

Figure 6-6: Dissociative adsorption reactions are modified after the fact so as to have a consistent catalyst block number.

Additionally, for the equations used for dissociative adsorption, the enthalpy of adsorption is

required for the non-dissociated surface species (i.e. (H2)surface for H2). Genesys thus needs the graph representation of this species even though it is not given as input via the reaction family. In order to solve this an additional input file is given which contains this information. If the recommendations regarding representation in Chapter 2 are implemented, this process can be automated via a database and an additional input file should no longer be required for most if not all species.

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6.2.2.2 Hydrogen addition to CO

For surface reactions, little has changed with respect to gas-phase reaction families. The major difference being the use of UBI-QEP as the kinetics type. It should be noted that the definition of reaction families becomes significantly simpler if only the required catalyst reactive centers are used for the SMARTS string (i.e. using only the attached dummy as opposed to the whole catalyst block). As an example of a surface reaction, the addition of an adsorbed hydrogen atom to adsorbed carbon monoxide is shown in Figure 6-7. Here, using a CO attached to a single dummy atom as your reactant 1 works just as well as including the whole catalyst block in the SMARTS representation of the reactive center. For the recipe it is simply a matter of decreasing the CO bond order (between A and B), breaking the bond between the adsorbed hydrogen atom and the surface (C and D) and forming a bond between the hydrogen atom in question and the oxygen (A and C).

Figure 6-7: Schematic representation of the hydrogen addition reaction to CO. Catalyst descriptors input

In the catalyst descriptors file (Figure 6-8), the atomic number of the element used for the catalyst block is given, here 2 for helium. Also the values required for the UBI-QEP are provided as input. These are the coordination number as well as the atomic heats of adsorption for carbon, hydrogen and oxygen. The required units are kJ/mole. As of now the block size is currently dependent on the molecule identifier used to represent the catalyst. This means that passing on [He] [He] [He] [He] in the species input file will result in a catalyst block size of 4. In the future it is desirable to just add [He] and modify the block size using the CatDescriptor input.

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Figure 6-8: Example for an inputfile concerning the catalyst descriptors for the hydro-deoxygenation of propionic acid on a nickel based catalyst. Adsorption map

When calculating UBI-QEP enthalpies of adsorption for dissociative adsorption, the corresponding equation requires the enthalpy of adsorption in case of non-dissociative adsorption as explained in Chapter 4. This is because for the derivation of the equation, UBI-QEP splits dissociative adsorption into adsorption on the surface followed by dissociation. As an

example, the dissociative adsorption of H2 can be considered. Here the graph structure of both

** * the adsorbed H2 as well the H surface species are required. The reaction family only supplies

* ** information on the H surface species. Thus, the H2 species must be supplied by the user, which is done according to an additional input file as shown in Figure 6-9. This additional input file acts as a temporary fix and allows Genesys to obtain the InChI identifier of this surface species separately from the adsorption map input file. As shown in Figure 6-9 the InChI of the gas-phase species is given first. The InChI of the surface species must be present on the same line. Both must be separated by an arrow as illustrated in Figure 6-9.

Figure 6-9: Input file specific to non-dissociative adsorption for the hydro-deoxygenation of propionic acid on a nickel based catalyst.

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Additional thermodynamic data input

Finally, when calculating bond dissociation energies (BDEs), species must be fragmented along those bonds. Here, the author found the resulting fragments often involve species with more than one single electron attached. In the case of an adsorbed carbon, multiple bonds are sometimes broken resulting in carbene centers. Because Genesys currently does not provide information on this in the databases, the ability to read thermodynamic data from another source in CHEMKIN format, i.e. NASA polynomials, was added. The choice was made to use the CHEMKIN format as this format can simply be copied directly from the RMG website [7, 8].

Output files

Several output files are generated by Genesys., The files that differ from gas-phase reaction networks are shown in Figure 6-10. The different output consists of the gas-phase and surface CHEMKIN input files. The format of these files is already discussed in detail in Chapter 5. References to the files that were created by Genesys for this case study are added to appendix C.

For UBI-QEP calculations, both the equations chosen by Genesys as well as the required parameters are printed in an output file. Thermodynamic properties and kinetic parameters are reported separately. Every detail of the calculation made for UBI-QEP is stored in the “UBI-Hub” class. With little effort, more information can therefore be printed out.

Finally, if for any species generated during fragmentation, thermodynamic data could not be found or calculated, the species identifier is given in InChI format in a separate text file.

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Figure 6-10: Output files for catalytic network generation. UBI-QEP thermodynamics and kinetics

For the calculation of the enthalpy of adsorption as well as the activation energy, an example within the context of the case study is given of the procedure followed by Genesys. Also the output information printed by Genesys is discussed.

Enthalpy of adsorption of propionic acid

As an example, the enthalpy of adsorption for propionic acid is given in Figure 6-11. Propionic acid is conform with the chelate structure as shown in Chapter 4. Bond dissociation energies which have to be determined are as provided in Figure 6-11.

Figure 6-11: For the calculation of the enthalpy of adsorption for propionic acid, the chelate equation is used. The calculation of D_BX is shown as an example. Case study 113

The equations which are therefor used are given in equations (1-3). The full table can be found in section 4.1.3.5 of chapter 4.

00 (6-1) = 0 + 0 − 0 + 0 2 (6-2) 0 0 = (0 + ) 2 (6-3) 0 0 = (0 + )

In the output file, the following information will be produced:

Surface species: InChI=1S/C3H6He4O2/c1-2-3-8-6(4)7(5)9-3/h8H,2H2,1H3

C(C(C=1O([He]([He]([He])O1)[He])[H])([H])[H])([H])([H])[H] GasPhase species: InChI=1S/H [H] A == O Q_0A: 288.7 Q_A: 481.2 B == O Q_0B: 288.7 Q_B: 481.2

D_Ai: 0.0 D_Bi: 465.5

D_AX: 1092.5 D_BX: 459.5

Q_AB: 132.6

All values in the output file are given in kJ/mole. After first assigning species identifiers to both the surface and gas-phase species, the contact atoms are identified and their atomic heats of adsorption are given. Finally, for all bond dissociation energies, denoted as in Figure 6-11, the calculated value is given. Especially for species which give BDE values which are significantly off these should be checked as they will give information on where to find the fragments with erroneous thermodynamic data. The final result is the enthalpy of adsorption, denoted Q_AB which is 132.6 kJ/mole. The value is similar to a manually calculated value of 137.7 kJ/mole with the difference being attributable to differences in values between the Genesys database and the

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RMG database. The value calculated by Genesys only uses RMG when no values were available within Genesys, the manual calculation used RMG values throughout. The values can simply be plugged into the above equations to get the resulting enthalpy of adsorption. The case study on which this is based [6] regressed a value of 93.8 kJ/mole with a margin of error of 0.6 kJ/mole which is significantly lower. However the same error was found for all species within the case study and is systematic. It is most probably attributable to the assumption that the atomic enthalpies of adsorption for a pure Nickel catalyst are a good catalyst descriptor. In the case study a Ni-Cu based catalyst was used. When using the enthalpies of adsorption for Cu, also present within this catalyst a value of 114.6 kJ/mole is calculated. This underscores the importance of getting good values for the atomic enthalpies of adsorption.

Dissociative adsorption: the case of hydrogen.

To study dissociative adsorption and the calculation of the corresponding activation energy, the example of the adsorption of hydrogen is given. As shown in Figure 6-12, dissociative adsorption differs from non-dissociative adsorption. In the case of non-dissociative adsorption, the enthalpy of adsorption is used for the backwards direction (with 0 being used for the forwards direction) making it essentially a purely thermodynamical calculation. For dissociative adsorption, the dissociation of the species on the surface is also involved, thus a UBI-QEP equation pertaining to kinetics is also used.

Figure 6-12: UBI-QEP approach to dissociative adsorption for the case of hydrogen. Case study 115

The general form is shown in equation 6-4, where * denotes a catalyst site. The activation barriers is calculated using equation 6-5 and 6-6. The bond dissociation energy is calculated according to equation 6-8, whilst the enthalpy of the reaction is given by equation 6-7.

∗ ∗ (6-4) + 2 ∗ → + (6-5) = Δ − + + (6-6) = − − (1 − )Δ +

(6-7) Δ = − − (6-8) D = + −

Genesys will then generate the following output for this adsorption reaction:

H2+Ni(S)+Ni(S)=>H(S)+H(S) H(S)+H(S)=>H2+Ni(S)+Ni(S)

Dissociative adsorption: A: InChI=1S/HHe4/c1-3-4-2/h3H B: InChI=1S/HHe4/c1-3-4-2/h3H AB: InChI=1S/H2/h1H

Q_A: 263.7 Q_B: 263.7 Q_AB: 28.3

H_A: 222.8 H_B: 222.8 H_AB: 6.5

dH: -88.4 D_AB: 439.1

Ea_fw: 7.6 Ea_bw: 96.0

First the forwards and backwards reactions are given according to the CHEMKIN format. The type of reaction used is subsequently shown (dissociative adsorption) along with which molecules were assigned to which symbol in equation 4. The value calculated for the heat of adsorption of

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hydrogen, QAB, is in agreement with values calculated using the same method as in the literature [9]. It is also in agreement with experimentally determined values for nickel as regressed in the case study[6] (27.3 kJ/mole with a margin of error of 1.2 kJ/mole). The forwards and backwards activation energies however are wildly off with experimental values of 35.8 kJ/mole and 40.7 kJ/mole being reported for the forward barrier instead [10]. The same source [10] also used UBI- QEP and got much closer by using equations different from Shustorovich (using the equation for dissociation instead of dissociative adsorption instead). This is a broader phenomenon where the literature is not consistent in what equations it uses. The optimal use of equations is something which should be studied further in the future.

Surface reactions

As an example of a surface reaction, the dissociation of propanoyl to carbon monoxide and an ethyl radical is given as shown in Figure 6-11.

Figure 6-13: Dissociation of propanoyl on the surface with an ethyl radical and C=O as the resulting products.

The general form for surface dissociation, as described within the UBI-QEP framework, is shown in equation 9, where * denotes a catalyst site. The activation barriers can be calculated using equation 10 and 11. The bond dissociation energy is calculated according to equation 12, whilst the enthalpy of the reaction is given by equation 13.

∗ ∗ (6-9) ∗ + ∗ → + (6-10) = + + Case study 117

(6-11) = − (1 − ) + (6-12) Δ = − − (6-13) D = + − Genesys will then generate the following output for this dissociation reaction:

C3H5O(S)+Ni(S)=>C2H5(S)+CO(S) C2H5(S)+CO(S)=>C3H5O(S)+Ni(S) Dissociation on the surface: A: InChI=1S/C2H5He4/c1-2-6(4)5-3/h2H2,1H3 B: InChI=1S/CHe4O/c2-4-5(3)1-6 AB: InChI=1S/C3H5He4O/c1-2-3-6(4)7(5)8-3/h2H2,1H3

Q_A: 203.7 Q_B: 218.1

H_A: 135.3 H_B: -103.6 H_AB: -8.9

dH: -381.3 D_AB: 40.6

Ea_fw: 0.0 Ea_bw: 381.3

Here once again, the reaction is first given in CHEMKIN format followed by the case Genesys has selected. The species have once again been assigned a symbol as is appropriate for equation 9. The end result however, is clearly wrong. The forwards activation energy is taken to be 0, this implies that the activation energy was set to be zero as the calculated value had a negative sign. Conversely a backwards energy barrier is found of 381 kJ/mole. As the reaction did not have a backwards component in the original case study, the latter cannot be checked. For the forward direction an activation energy of 97.6 kJ/mole was found[6], with a margin of error of 1.7 kJ/mole. This is much more than the negative value Genesys calculated. On deeper inspection it is found that for the calculation of this reaction a CO-fragment is found for which the C is a biradical. For this fragment, the gas-phase CO thermodynamic was erroneously used. This results in bond dissociation energies which are incorrect. The incorrect assignment of thermodynamic data for biradicals has to be solved in future work.

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Future work - Remaining bugs

Although the generation of the kinetic model for the case study is successful, some programming errors still remain in the software and the output files that should be solved in the near future. The problems deal either with the writing of output or the matching of molecules.

When the CHEMKIN surface output file is written, the reactions of some reaction families are reported multiple times. Hence, the recognition of duplicate reactions still has some bugs that need to be solved. Reaction families that deal with adsorption do not have this problem, only some hydrogen addition families. As an example, for the addition of adsorbed hydrogen to the

adsorbed carbon radical (C->CH->…->CH4) the reaction family was printed once for each iteration. Thus the reaction family was printed a first time for the C->CH reaction. A second time for both

the C->CH and CH->CH2 reaction and so on. This is an output problem however as all the reactions which need to be present, are in fact present. The resulting output file is attached in the appendix.

A second problem involves the matching of molecules, usually for the purpose of a database look- up. This problem was found in two cases. First, during fragmentation, a carbon with two single electrons and a double bond with oxygen was created. This biradical is matched with the carbon monoxide instead and values are completely off. A similar problem occurs when reading in additional thermodynamic data as supplied by RMG-Cat. In the case study thermodynamic data was used of the RMG database for a total of 3 species. The thermodynamic data is read correctly for all species but used for only two of the three involved. In the latter case, the use of SMILES over InChI’s may solve the issue as the matching happens between the graph structure representations and not the identifiers themselves. SMILES are used over InChI’s because within this context, a single SMILES can be used for every species. Additionally, in the experience of the author, SMILES have tended to produce fewer erroneous graph structures better when radicals were involved. In the former case however, the bug may prove more difficult to fix. It is not entirely clear if the issue is related to the representation of biradicals in Genesys or specific to the UBI-QEP method as implemented within Genesys.

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Conclusions

In conclusion the main functionalities implemented in this thesis work. For all species within this case study the proper graph structure representation was generated. The software is capable of consistently using the correct UBI-QEP cases and will, with some exceptions, correctly calculate enthalpies of adsorption if thermodynamic data is available. When values are not in agreement with the literature this can have one of three reasons. First, sometimes the correct thermodynamic data is not available or the wrong thermodynamic data is used (as was the case with the biradical CO). Second, the atomic heats of adsorption used are those of a pure catalyst, these values may not be representative and future work is needed to address the issue of determining correct atomic enthalpies of adsorption. Third the final equations used may not be the optimal UBI-QEP equations used. This is a broader issue and further work is needed to determine which set of equations used in the literature should be used within Genesys.

References

1. Hengst, K., et al., CHAPTER 6 Hydrodeoxygenation of Lignocellulose-Derived Platform Molecules, in Catalytic Hydrogenation for Biomass Valorization. 2015, The Royal Society of Chemistry. p. 125-150. 2. Prasomsri, T., T. Nimmanwudipong, and Y. Roman-Leshkov, Effective hydrodeoxygenation of biomass-derived oxygenates into unsaturated hydrocarbons by MoO3 using low H2 pressures. Energy & Environmental Science, 2013. 6(6): p. 1732-1738. 3. Resasco, D.E., What should we demand from the catalysts responsible for upgrading biomass pyrolysis oil? 2011, ACS Publications. 4. Serrano-Ruiz, J.C. and J.A. Dumesic, Catalytic routes for the conversion of biomass into liquid hydrocarbon transportation fuels. Energy & Environmental Science, 2011. 4(1): p. 83-99. 5. Phan, B.M.Q., et al., Evaluation of the production potential of bio-oil from Vietnamese biomass resources by fast pyrolysis. Biomass and Bioenergy, 2014. 62: p. 74-81. 6. Otyuskaya, D., et al., Fast pyrolysis oil stabilization kinetics over a Ni-Cu catalyst using propionic acid as a model compound. Applied Catalysis B: Environmental, 2018. 7. Gao, C.W., et al., Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms. Computer Physics Communications, 2016. 203: p. 212-225. 8. RMG Website. 9. Shustorovich, E. and H. Sellers, The UBI-QEP method: a practical theoretical approach to understanding chemistry on transition metal surfaces. Surface Science Reports, 1998. 31(1-3): p. 1-119. 10. Yen, P.-S., N.D. Deveau, and R. Datta, Dissociative Adsorption, Dissolution, and Diffusion of Hydrogen in Liquid Metal Membranes. A Phenomenological Model. Industrial & Engineering Chemistry Research, 2018. 57(5): p. 1607-1620.

Conclusion & future work 121

CONCLUSION & FUTURE WORK

Whilst the plethora of existing reaction network generators are a testament to their effectiveness, almost all of these are limited to gas-phase reactions. As the vast majority of all chemical compounds produced make use of catalysts in their production process, the extension of these software packages to catalytic systems could prove invaluable. For this reason, metal surface species and reactions were implemented in the existing reaction network generator of Genesys. First, a dummy atom was implemented to represent active sites. This was subsequently expanded to several dummy atoms grouped together in a catalyst “block”. The use of catalyst blocks has several advantages. The first among these is that it simplifies the generation of a correct internal graph structure representation. Additionally, it allows the user to provide slightly more information on the relative position of bonds with the surface.

The species tested within this thesis are those involved in the hydrodeoxygenation (HDO) case study. The hydrodeoxygenation of propionic acid as a model compound was studied on a Ni-Cu based catalyst. Species tested include carboxylic acids, aldehydes, and simple alkanes. The graph structures were correct regardless of whether the species were given as an input reactants (via the molecule line identifier) or products formed through one of the reaction families. Further testing is still needed to ensure that graph structures are also generated correctly for other compounds.

122 Conclusion & future work

The UBI-QEP method has successfully been implemented as a first step towards the determination of surface thermodynamic properties and kinetic parameters. Additionally, for the reactions studied throughout this thesis, the correct UBI-QEP equation is always automatically selected and used both for the determination of enthalpies of adsorption as well as for activation energies. This work can therefor serve as a starting point towards a more complete catalyst implementation.

In future work both the representation and thermochemistry still have a lot of potential for future expansion. For the catalyst representation significant progress can be made by making the procedure more user-friendly. Within this work every species which was adsorbed on the surface needed a corresponding line identifier which was created manually. This process should be automated. If, for example, a carboxylic acid will usually adsorb on the surface via the two oxygens in its moiety, then there is no reason to manually create the InChI or SMILES for every carboxylic acid.

Second, within this work the assumption was made that there is only one type of active site. Multiple types of active sites could, however, have a significant effect on the kinetics. If the goal is to remain within the UBI-QEP framework, then in principle the only distinctions made are related to the coordination number, n (i.e. on-top for n=1, bridging for n=2 and hollow for n=3). In this scenario one could in principle use bond orders to distinguish between different sites. The UBI-QEP method, as implement, could then directly use the correct coordination number for every species (it just has to check for the bond order). If a more universal approach is desired, then one could change the catalyst block to represent different sites, using different elements.

Third, databases can also be expanded and systematized. Information available on surface reactions and species, both those obtained via regression and by ab initio calculations, should be systematized into databases. In theory UBI-QEP should occupy a niche similar to that of Group Additivity for gas-phase reactions: it should only be used when a direct look-up is not possible. In tandem with this BEP-relationships should be implemented based on the reactions for which Conclusion & future work 123

information is already available. Ideally, the BEP-relationships would be updated automatically for each reaction family based on the information available in the databases. In some cases, this would allow the user to use values from the database in conjunction with BEP and compare them to UBI-QEP values, thus giving two independent values.

Fourth there is still progress to be made concerning the determination of Arrhenius parameters. Pre-exponential factors currently are set equal to a threshold value and though a first exploration was made in the literature survey, a proper implementation is required. For activation energies the literature is not consistent when it comes to the use of the corresponding UBI-QEP equations. Whilst many are using equations which are highly similar, the equations are such that small changes amount to both large philosophical differences (i.e. does the enthalpy of the non- dissociated state contribute?) and big differences in the resulting activation energy. Along with this it would be wise to implement at least some of the expansions Shustorovich has recommended. Most notable are those regarding to surface coverage and temperature dependence.

Fifth, there is the coupling with the microKinetic Engine and other post-processing software. Creating a kinetic model via automatic network generation should be seen as an iterative process. UBI-QEP provides the user with a starting point for all thermodynamic values and activation energies. The goal should then be to perform a sensitivity analysis and look at which species and pathways affect the product spectrum the most.

Appendices 125

A Reaction families for the hydrodeoxygenation of propionic acid on nickel

Reaction Family Reactants Reactants: SMARTS Molecular Kinetics constraints 3 carbon Propionic acid C(=[O])([OH]) 0 helium Adsorption 0 carbon “UBI_QEP” propionic acid Empty site [#2][#2][#2][#2] 0 hydrogen

0 oxygen

Hydrogen [H][H] 0 helium

Adsorption 0 carbon “UBI_QEP” Hydrogen Empty site [#2][#2][#2][#2] 0 hydrogen

0 oxygen

Propionic acid C=1O([#2]([#2](O=1)[#2])[#2])[H] Dehydration “UBI_QEP” Hydrogen [He]([He])([H])[He][He] 0 carbon

Propanoyl [CX3]=1[#2]([#2](O=1)[#2])[#2] 1 oxygen Hydrogenation 1 “UBI_QEP” Hydrogen [He]([He])([H])[He][He] 0 carbon

126 Appendices

Molecular Reaction Family Reactants Reactants: SMARTS Kinetics constraints Propanal [CX4]=1[#2]([#2](O=1)[#2])[#2] Hydrogenation 2 “UBI_QEP” Hydrogen [He]([He])([H])[He][He] 0 carbon

-OH [O;X2]([H])([He]) 1 oxygen Hydrogenation mono “UBI_QEP” oxygen Hydrogen [He]([He])([H])[He][He] 0 carbon

-OH [O;X3]([H])([C;X4])([He]) 1 oxygen Dehydration Alcohol “UBI_QEP” Hydrogen [He]([He])([H])[He][He] 0 carbon 1 oxygen Propanoyl CC[C;H0]([He]1[He])=O[He]1[He]

0 carbon Split to CO “UBI_QEP” Empty [#2][#2][#2][#2] 0 hydrogen site 0 oxygen 1 carbon -COH [He][C][O][H]

0 carbon Split COH “UBI_QEP” Empty [#2][#2][#2][#2] 0 hydrogen site 0 oxygen

-CO [He](C=O) 1 oxygen CO +H “UBI_QEP” Hydrogen [He]([He])([H])[He][He] 0 carbon

-C [C;!X5][He] 0 oxygen Hydrogenation C “UBI_QEP” Hydrogen [He]([He])([H])[He][He] 0 carbon

Appendices 127

B References to lab journal

UBI-QEP: method and package

Topic Pages Description Directory UBI-QEP: 47-51 Input and output files /RunCat Thermodynamics for calculation of /RunCat/cat.out enthalpies of adsorption for input species

UBI-QEP templates 41 Equations in template /HDOcase_CHEMKIN.xlsx form which allow the user to easily calculate UBI-QEP values manually

128 Appendices

C Case Study references

Case Study: Input files

Topic Description Directory

Reaction families Input-file containing all the /PropionicAcidCAT/ReactionFam.xml reaction families for the HDO case study

Species input Molecule identifiers for input /PropionicAcidCAT/Speciesinput.inp species

Catalyst Descriptor Catalyst descriptor input file /PropionicAcidCAT/catDescriptor.inp

Dissociative Adsorption map Map of molecule identifiers /PropionicAcidCAT/DAspecies.inp used to generate non- dissociated species for UBI- QEP calculations

SpeciesThermo Extra species /PropionicAcidCAT/SpeciesThermo.inp thermodynamic data, provided in CHEMKIN format, for species not available in the standard Genesys Database

Appendices 129

Case Study: Output files

Topic Description Directory

CHEMKIN gasphase Gasphase input date for /PropionicAcidCAT/Output/Gasphase.inp CHEMKIN

CHEMKIN surface Surface input date for /PropionicAcidCAT/Output/Surface.inp CHEMKIN

Missing Thermo data Fragments for which there /PropionicAcidCAT/MissingUBISpecies.txt was no data available have their InChI’s written out.

Surface species UBI-QEP Input, output and /PropionicAcidCAT/UBIad.out calculations intermediate values used for the calculation of enthalpies of adsorption via the UBI-QEP equations are stored here.

Surface reactions UBI-QEP Input, output and /PropionicAcidCAT/UBIrxn.out calculations intermediate values used for the calculation of activation energies via the UBI-QEP equations are stored here.

Case Study: Manual calculations

Topic Description Directory

Case study Excel file Contains all manual /PropionicAcidCAT/HDOcase_CHEMKIN.xlsx calculations performed for the Daria case study.