1932

Abstract

Chemical reactor modelling based on insights and data on a molecular level has become reality over the last few years. Multiscale models describing elementary reaction steps and full microkinetic schemes, pore structures, multicomponent adsorption and diffusion inside pores, and entire reactors have been presented. Quantum mechanical (QM) approaches, molecular simulations (Monte Carlo and molecular dynamics), and continuum equations have been employed for this purpose. Some recent developments in these approaches are presented, in particular time-dependent QM methods, calculation of van der Waals forces, new approaches for force field generation, automatic setup of reaction schemes, and pore modelling. Multiscale simulations are discussed. Applications of these approaches to heterogeneous catalysis are demonstrated for examples that have found growing interest over the last few years, such as metal-support interactions, influence of pore geometry on reactions, noncovalent bonding, reaction dynamics, dynamic changes in catalyst nanoparticle structure, electrocatalysis, solvent effects in catalysis, and multiscale modelling.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-chembioeng-060817-084141
2018-06-07
2024-05-08
Loading full text...

Full text loading...

/deliver/fulltext/9/1/annurev-chembioeng-060817-084141.html?itemId=/content/journals/10.1146/annurev-chembioeng-060817-084141&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  Van Santen R 2017. Modern Heterogeneous Catalysis: An Introduction Weinheim, Ger: Wiley-VCH
  2. 2.  Nørskov JK, Studt F, Abild-Pedersen F, Bligaard T 2014. Fundamental Concepts in Heterogeneous Catalysis Hoboken, NJ: John Wiley & Sons
  3. 3.  Chorkendorff I, Niemantsverdriet JW 2017. Concepts of Modern Catalysis and Kinetics Hoboken, NJ: John Wiley & Sons, 3rd ed..
  4. 4.  Peters B 2017. Reaction Rate Theory and Rare Events Amsterdam, Neth: Elsevier
  5. 5.  Golibrzuch K, Bartels N, Auerbach DJ, Wodtke AM 2015. The dynamics of molecular interactions and chemical reactions at metal surfaces: testing the foundations of theory. Annu. Rev. Phys. Chem. 66:399–425
    [Google Scholar]
  6. 6.  Martin RM 2004. Electronic Structure: Basic Theory and Practical Methods Cambridge, UK: Cambridge Univ. Press
  7. 7.  Sholl D, Steckel JA 2009. Density Functional Theory: A Practical Introduction Hoboken, NJ: John Wiley & Sons
  8. 8.  Engel E, Dreizler RM 2011. Density Functional Theory: An Advanced Course Berlin: Springer-Verlag
  9. 9.  Becke AD 2014. Perspective: fifty years of density-functional theory in chemical physics. J. Phys. Chem. 140:18A301
    [Google Scholar]
  10. 10.  Jones RO 2015. Density functional theory: its origins, rise to prominence, and future. Rev. Mod. Phys. 87:897–923
    [Google Scholar]
  11. 11.  Tao J, Perdew JP, Staroverov VN, Scuseria GE 2003. Climbing the density functional ladder: nonempirical meta-generalized gradient approximation designed for molecules and solids. Phys. Rev. Lett. 91:146401
    [Google Scholar]
  12. 12.  Zhao Y, Truhlar DG 2008. The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals. Theor. Chem. Account. 120:215–41
    [Google Scholar]
  13. 13.  Medvedev MG, Bushmarinov IS, Sun J, Perdew JP, Lyssenko KA 2017. Density functional theory is straying from the path toward the exact functional. Science 355:49–52
    [Google Scholar]
  14. 14.  Kepp KP 2017. Comment on “Density functional theory is straying from the path toward the exact functional.”. Science 356:496
    [Google Scholar]
  15. 15.  Pribham-Jones A, Gross DA, Burke K 2015. DFT: A theory full of holes?. Annu. Rev. Phys. Chem. 66:283–304
    [Google Scholar]
  16. 16.  Cohen AJ, Mori-Sanchez P, Yang W 2012. Challenges for density functional theory. Chem. Rev. 112:289–320
    [Google Scholar]
  17. 17.  Ren X, Rinke P, Joas C, Scheffler M 2012. Random-phase approximation and its applications in computational chemistry and materials science. J. Mater. Sci. 47:7447–71
    [Google Scholar]
  18. 18.  Anisimov VI, Aryasetiawan F, Lichtenstein AI 1997. First-principles calculations of the electronic structure and spectra of strongly correlated systems: the LDA + U method. J. Phys. Condens. Matter 9:767–808
    [Google Scholar]
  19. 19.  Blöchl PE, Walther CFJ, Pruschke T 2011. Method to include explicit correlations into density-functional calculations based on density-matrix functional theory. Phys. Rev. B 84:205101
    [Google Scholar]
  20. 20.  Gori-Giogi P, Vignale G, Seidl M 2009. Electronic zero-point oscillations in the strong-interaction limit of density functional theory. J. Chem. Theory Comput. 5:743–53
    [Google Scholar]
  21. 21.  Helgaker T, Jørgensen P, Olsen J 2013. Molecular Electronic-Structure Theory Hoboken, NJ: John Wiley & Sons
  22. 22.  Hermann J, DiStasio RA, Tkatchenko A 2017. First-principles models for van der Waals interactions in molecules and materials: concepts, theory, and applications. Chem. Rev. 117:4714–58
    [Google Scholar]
  23. 23.  Berland K, Cooper VR, Lee K, Schröder E, Thonhauser T et al. 2015. Van der Waals forces in density functional theory: a review of the vdW-DF method. Rep. Prog. Phys. 78:066501
    [Google Scholar]
  24. 24.  Grimme S 2011. Density functional theory with London dispersion corrections. WIREs Comput. Mol. Sci. 1:211–28
    [Google Scholar]
  25. 25.  Tkatchenko A 2015. Current understanding of van der Waals effects in realistic materials. Adv. Funct. Mater. 25:2054–61
    [Google Scholar]
  26. 26.  Grimme S, Antony J, Ehrlich S, Krieg H 2010. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for 94 elements H-Pu. J. Chem. Phys. 132:154104
    [Google Scholar]
  27. 27.  Stöhr M, Michelitsch GS, Tully JC, Reuter K, Maurer RJ 2016. Communication: charge-population based dispersion interactions for molecules and materials. J. Chem. Phys. 144:151101
    [Google Scholar]
  28. 28.  Berland K, Hyldgaard P 2014. Exchange functional that tests the robustness of the plasmon description of the van der Waals density functional. Phys. Rev. B 89:035412
    [Google Scholar]
  29. 29.  Keil FJ 2012. Multiscale modelling in computational heterogeneous catalysis. Top. Curr. Chem. 307:69–107
    [Google Scholar]
  30. 30.  Bowler DR, Miyazaki T 2012. O(N) methods in electronic structure calculations. Rep. Prog. Phys. 76:036503
    [Google Scholar]
  31. 31.  Rupp M 2015. Machine learning for quantum mechanics in a nutshell. Int. J. Quantum Chem. 115:1058–73
    [Google Scholar]
  32. 32.  Botu V, Ramprasad R 2015. Adaptive machine learning framework to accelerate ab initio molecular dynamics. Int. J. Quantum Chem. 115:1074–83
    [Google Scholar]
  33. 33.  Behler J 2016. Perspective: machine learning potentials for atomistic simulations. J. Chem. Phys. 145:170901
    [Google Scholar]
  34. 34.  Behler J 2017. Hochdimensionale neuronale Netze für Potentialhyperflächen großer molekularer und kondensierter Systeme. Angew. Chem. 129:13006–20
    [Google Scholar]
  35. 35.  Hansen K, Biegler A, Ramakrishnan R, Pronobis W, von Lilienfeld OA et al. 2015. Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. Phys. Chem. Lett. 6:2326–31
    [Google Scholar]
  36. 36.  Marx D, Hutter J 2009. Ab Initio Molecular Dynamics: Basis Theory and Advanced Methods Cambridge, UK: Cambridge Univ. Press
  37. 37.  Casida ME, Huix-Rotllaut M 2012. Progress in time-dependent density-functional theory. Annu. Rev. Phys. Chem. 63:287–323
    [Google Scholar]
  38. 38.  Car R, Parinello M 1985. Unified approach for molecular dynamics and density-functional theory. Phys. Rev. Lett. 55:2471
    [Google Scholar]
  39. 39.  Vafai K 2015. Handbook of Porous Media Boca Raton, FL: CRC Press, 3rd ed..
  40. 40.  Jain SK, Pellenq RJM, Pikunic JP, Gubbins KE 2006. Molecular modeling of porous carbons using the hybrid reverse Monte Carlo method. Langmuir 22:9942–48
    [Google Scholar]
  41. 41.  Gao M, Teng Q, He X, Zo C, Li ZJ 2016. Pattern density function for reconstruction of three-dimensional porous media from a single two-dimensional image. Phys. Rev. E 93:012140
    [Google Scholar]
  42. 42.  Ye G, Sun Y, Zhou X, Zhu K, Zhou J, Coppens M-O 2017. Method for generating pore networks in porous particles of arbitrary shape, and its application to catalytic hydrogenation of benzene. Chem. Eng. J. 329:56–65
    [Google Scholar]
  43. 43.  Aghighi M, Hoek MA, Lehnert W, Merle G, Gostick J 2016. Simulation of a full fuel cell membrane electrode assembly using pore network modeling. J. Electrochem. Soc. 163:F384–F92
    [Google Scholar]
  44. 44.  Keil FJ 2012. Modeling reactions in porous media. Modeling and Simulation of Heterogeneous Catalytic Reactions: From the Molecular Process to the Technical System O Deutschmann 149–86 Weinheim, Ger: Wiley-VCH
    [Google Scholar]
  45. 45.  Trogadas P, Nigra MM, Coppens M-O 2016. Nature-inspired optimization of hierarchical porous media for catalytic and separation processes. New J. Chem. 40:4016–26
    [Google Scholar]
  46. 46.  Xiong Q, Baychev TG, Jučkov AP 2016. Review of pore network modelling of porous media: experimental characterisations, network constructions and applications to reactive transport. J. Contam. Hydrol. 192:101–17
    [Google Scholar]
  47. 47.  Gostick J, Aghighi M, Hinebaugh J, Tranter T, Hoek MA et al. 2016. OpenPNM: a pore network modeling package. Comput. Sci. Eng. 18:460–74
    [Google Scholar]
  48. 48.  Michels N-L, Mitchell S, Pérez-Ramírez J 2014. Effects of binders on the performance of shaped hierarchical MFI zeolites in methanol-to-hydrocarbons. ACS Catal 4:2409–17
    [Google Scholar]
  49. 49.  Kärger J 2015. Transport phenomena in nanoporous materials. ChemPhysChem 16:24–51
    [Google Scholar]
  50. 50.  Krishna R, van Baten JM 2013. Influence of adsorption thermodynamics on guest diffusivities in nanoporous crystalline materials. Phys. Chem. Chem. Phys. 15:7994–8016
    [Google Scholar]
  51. 51.  Frenkel D, Smit B 2002. Understanding Molecular Simulations: From Algorithms to Applications San Diego, CA: Academic, 2nd ed..
  52. 52.  Tuckerman M 2010. Statistical Mechanics: Theory and Molecular Simulation Oxford, UK: Oxford Univ. Press
  53. 53.  Dubbeldam D, Torres-Knoop A, Walton KS 2013. On the inner workings of Monte Carlo codes. Mol. Simul. 39:1253–92
    [Google Scholar]
  54. 54.  He Y, Seaton NA 2005. Monte Carlo simulation and pore size distribution analysis of the isosteric heat of adsorption of methane in activated carbon. Langmuir 21:8297–301
    [Google Scholar]
  55. 55.  Maurer RJ, Ruiz VG, Camarillo-Cisneros J, Liu W, Ferri N et al. 2016. Adsorption structures and energetics of molecules on metal surfaces: bridging experiment and theory. Prog. Surf Sci. 91:72–100
    [Google Scholar]
  56. 56.  Ruiz V, Liu W, Zojer E, Scheffler M, Tkatchenko A 2012. Density-functional theory with screened van der Waals interactions for the modeling of hybrid inorganic-organic systems. Phys. Rev. Lett. 108:146103
    [Google Scholar]
  57. 57.  Kroes G-J, Diaz C 2016. Quantum and classical dynamics of reactive scattering of H2 from metal surfaces. Chem. Soc. Rev. 45:3658–700
    [Google Scholar]
  58. 58.  Meyer J, Reuter K 2014. Modeling heat dissipation at the nanoscale: an embedding approach for chemical reaction dynamics on metal surfaces. Angew. Chem. Int. Ed. 53:4721–24
    [Google Scholar]
  59. 59.  Bukas VJ, Reuter K 2017. Photonic dissipation during “hot” adatom motion: a QM/Me study of O2 dissociation at Pd surfaces. J. Chem. Phys. 146:014702
    [Google Scholar]
  60. 60.  Olsen RA, Busnengo HF, Salin A, Somers MF, Kroes GJ, Baerends EJ 2002. Constructing accurate potential energy surfaces for a diatomic molecule interacting with a solid surface: H2 + Pt(111) and H2 + Cu(100). J. Chem. Phys. 116:7841–55
    [Google Scholar]
  61. 61.  Schröder M, Meyer HD 2017. Transforming high-dimensional potential energy surfaces into sum-of-products form using Monte Carlo methods. J. Chem. Phys. 147:064105
    [Google Scholar]
  62. 62.  Chuang YY, Radhakrishnan ML, Fast PL, Cramer CJ, Truhlar DG 1999. Direct dynamics for free radical kinetics in solution: solvent effect on the rate constant for the reaction of methanol with atomic hydrogen. J. Phys. Chem. A 163:4893–909
    [Google Scholar]
  63. 63.  Diaz C, Pijper E, Olsen RA, Busnengo HF, Auerbach DJ, Kroes G-J 2009. Chemically accurate simulation of a prototypical surface reaction: H2 dissociation on Cu(111). Science 326:832–34
    [Google Scholar]
  64. 64.  Fan Z, Chen W, Vierimaa V, Harju A 2017. Efficient molecular dynamics simulations with many-body potentials on graphics processing units. Comput. Phys. Commun. 218:10–16
    [Google Scholar]
  65. 65.  Valsson O, Tiwary P, Parrinello M 2016. Enhancing important fluctuations: rare events and metadynamics from a conceptual viewpoint. Annu. Rev. Phys. Chem. 67:159–84
    [Google Scholar]
  66. 66.  Zamora RJ, Uberuage BP, Perez D, Voter AF 2016. A modern temperature-accelerated dynamics approach. Annu. Rev. Chem. Biomol. Eng. 7:87–110
    [Google Scholar]
  67. 67.  Myers AL, Prausnitz JM 1965. Thermodynamics of mixed gas adsorption. AIChE J 11:121–27
    [Google Scholar]
  68. 68.  Krishna R, van Baten JM 2005. Diffusion of alkane mixtures in zeolites: validating the Maxwell-Stefan formulation using MD simulations. J. Phys. Chem. B 109:6386–96
    [Google Scholar]
  69. 69.  McDaniel JG, Schmidt JR 2016. Next-generation force fields from symmetry-adapted perturbation theory. Annu. Rev. Phys. Chem. 67:467–88
    [Google Scholar]
  70. 70.  Giese TJ, Huang M, Chen H, York DM 2014. Recent advances toward a general purpose linear-scaling quantum force field. Accounts Chem. Res. 47:2812–20
    [Google Scholar]
  71. 71.  Jafari M, Zimmerman PM 2017. Reliable and efficient reaction path and transition state finding for surface reactions with the growing string method. J. Comput. Chem. 38:645–58
    [Google Scholar]
  72. 72.  Ulissi ZW, Medford AJ, Bligaard T, Nørskow JK 2017. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8:14621
    [Google Scholar]
  73. 73.  Proppe J, Husch T, Simm GN, Reiher M 2016. Uncertainty quantification for quantum chemical models of complex reaction networks. Faraday Discuss 195:497–520
    [Google Scholar]
  74. 74.  Chiavazzo E, Gear CW, Dsilva CJ, Rabin N, Kevrekidis IG 2014. Reduced models in chemical kinetics via nonlinear data mining. Processes 2:112–40
    [Google Scholar]
  75. 75.  Gao CW, Allen JW, Green WH, West RH 2016. Reaction mechanism generator: automatic construction of chemical kinetic mechanisms. Comput. Phys. Commun. 203:212–25
    [Google Scholar]
  76. 76.  Stamatakis M 2015. Kinetic modelling of heterogeneous catalytic systems. J. Phys. Condens. Matter 27:013001
    [Google Scholar]
  77. 77.  Liu D-J, Garcia A, Wang J, Ackermann DM, Wang C-J, Evans JW 2015. Kinetic Monte Carlo simulation of statistical mechanical models and coarse-grained mesoscale descriptions of catalytic reaction-diffusion processes: 1D nanoporous and 2D surface systems. Chem. Rev. 115:5979–6050
    [Google Scholar]
  78. 78.  Berendsen HJC 2007. Simulating the Physical World: Hierarchical Modeling from Quantum Mechanics to Fluid Dynamics Cambridge, UK: Cambridge Univ. Press
  79. 79.  Deutschmann O, , ed.. 2011. Modelling Heterogeneous Catalytic Reactions: From the Molecular Process to the Technical System Weinheim, Ger: Wiley-VCH
  80. 80.  Goldsmith BR, Sanderson ED, Bean D, Peters B 2013. Isolated catalyst sites on amorphous supports: a systematic algorithm for understanding heterogeneities in structure and reactivity. J. Chem. Phys. 138:204105
    [Google Scholar]
  81. 81.  Peters B, Scott SL 2015. Single atom catalysts on amorphous supports: a quenched disorder perspective. J. Chem. Phys. 142:104708
    [Google Scholar]
  82. 82.  Carrasco J, Barrio L, Liu P, Rodriguez JA, Ganduglia-Pirovano MV 2013. Theoretical studies of the adsorption of CO and C on Ni(111) and Ni/CeO2(111). J. Phys. Chem. C 117:8241–50
    [Google Scholar]
  83. 83.  Bruix A, Rodriguez JA, Ramirez PJ, Senanayake SD, Evans J et al. 2012. A new type of strong metal-support interaction and the production of H2 through the transformation of water on Pt/CeO2(111) and Pt/CeOx/TiO2(110) catalysts. J. Am. Chem. Soc. 134:8968–74
    [Google Scholar]
  84. 84.  Mao M, Lu H, Li Y, Yang Y, Zeng M et al. 2016. Metal support interaction in Pt nanoparticles partially confined in the mesopores of microsized mesoporous CeO2 for highly efficient purification of volatile organic compounds. ACS Catal 6:418–27
    [Google Scholar]
  85. 85.  Saito M, Roberts CA, Ling C 2015. DFT + U study of the adsorption and oxidation of an iron oxide cluster on CeO2 support. J. Phys. Chem. C 119:17202–8
    [Google Scholar]
  86. 86.  Prates LM, Ferreira GB, Carneiro JWM, de Almeida WB, de M, Cruz MT 2017. Effect of the metal-support interaction on the adsorption of NO on Pd4/γ-Al2O3: a density functional theory and natural bond orbital study. J. Phys. Chem. C 121:14147–55
    [Google Scholar]
  87. 87.  Jenness GR, Schmidt JR 2013. Unraveling the role of metal-support interactions in heterogeneous catalysis: oxygenate selectivity in Fischer-Tropsch synthesis. ACS Catal 3:2881–90
    [Google Scholar]
  88. 88.  Ewing CS, Veser G, McCarthy JJ, Johnson JK 2015. Effect of support preparation and nanoparticle size on catalyst-support interactions between Pt and amorphous silica. J. Phys. Chem. C 119:19934–40
    [Google Scholar]
  89. 89.  Van der Mynsbrugge J, Janda A, Sharada SM, Lin L-C, Van Speybroeck V et al. 2017. Theoretical analysis of the influence of pore geometry on monomolecular cracking and dehydrogenation of n-butane in Brønsted acidic zeolites. ACS Catal 7:2685–97
    [Google Scholar]
  90. 90.  Sastre G 2016. Confinement effects in methanol to olefins catalysed by zeolites: a computational review. Front. Chem. Sci. Eng. 10:76–89
    [Google Scholar]
  91. 91.  Keil FJ 1999. Methanol-to-hydrocarbons: process technology. Micropor. Mesopor. Mater. 29:49–66
    [Google Scholar]
  92. 92.  Stöcker M 1999. Methanol-to-hydrocarbons: catalytic materials and their behavior. Micropor. Mesopor. Mater. 29:3–48
    [Google Scholar]
  93. 93.  Sauer J 2016. Brønsted activity of two-dimensional zeolites compared to bulk materials. Faraday Discuss 188:227–34
    [Google Scholar]
  94. 94.  Wang C-M, Brogaard RY, Xie Z-K, Studt F 2015. Transition-state scaling relations in zeolite catalysis: influence of framework topology and acid-site reactivity. Catal. Sci. Technol. 5:2814–20
    [Google Scholar]
  95. 95.  Rodriguez-Reyes JCF, Siler CGF, Liu W, Tkatchenko A, Friend CM, Madix RJ 2014. Van der Waals interactions determine selectivity in catalysis by metallic gold. J. Am. Chem. Soc. 136:13333–40
    [Google Scholar]
  96. 96.  Karakalos S, Xu Y, Kabeer FC, Chen W, Rodriguez-Reyes JCF et al. 2016. Noncovalent bonding controls selectivity in heterogeneous catalysis: coupling reactions on gold. J. Am. Chem. Soc. 138:15243–50
    [Google Scholar]
  97. 97.  Pastorczak E, Corminboeuf C 2017. Perspective: found in translation: quantum chemical tools for grasping non-covalent interactions. J. Chem. Phys. 146:120901
    [Google Scholar]
  98. 98.  van Santen RA, Tranca I 2016. How molecular is the chemisorptive bond?. Phys. Chem. Chem. Phys. 18:20868–94
    [Google Scholar]
  99. 99.  Martinez-Suárez L, Frenzel J, Marx D 2014. Cu/ZnO nanocatalysts in response to environmental conditions: surface morphology, electronic structure, redox state and CO2 activation. Phys. Chem. Chem. Phys. 16:26119–36
    [Google Scholar]
  100. 100.  Martinez-Suárez L, Frenzel J, Marx D, Meyer B 2013. Tuning the reactivity of a Cu/ZnO catalyst via gas phase pressure. Phys. Rev. Lett. 110:086108
    [Google Scholar]
  101. 101.  Martinez-Suárez L, Siemer N, Frenzel J, Marx D 2015. Reaction network of methanol synthesis over Cu/ZnO nanocatalysts. ACS Catal 5:4201–18
    [Google Scholar]
  102. 102.  Kiss J, Frenzel J, Nair NN, Meyer B, Marx D 2011. Methanol synthesis on ZnO (0001). III. Free energy landscapes, reaction pathways, and mechanistic insights. J. Chem. Phys. 134:064710
    [Google Scholar]
  103. 103.  Frenzel J, Kiss J, Mair NN, Meyer B, Marx D 2013. Methanol synthesis on ZnO from molecular dynamics. Phys. Status Solidi B 250:1174–90
    [Google Scholar]
  104. 104.  De Wispelaere K, Ensing B, Ghyselo A, Meyer EJ, Van Speybroeck V 2015. Complex reaction environments and competing reaction mechanisms in zeolite catalysis: insights from advanced molecular dynamics. Chem. Eur. J. 21:9385–96
    [Google Scholar]
  105. 105.  Jackson B, Nattino F, Kroes G-J 2014. Dissociative chemisorption of methane on metal surfaces: tests of dynamical assumptions using quantum models and ab initio molecular dynamics. J. Chem. Phys. 141:054102
    [Google Scholar]
  106. 106.  Groß A 2003. Theoretical Surface Science Berlin: Springer Verlag
  107. 107.  Latimer AA, Aljama H, Kakekhani A, Yoo JS, Kulkarni A et al. 2017. Mechanistic insights into heterogeneous methane activation. Phys. Chem. Chem. Phys. 19:3575–81
    [Google Scholar]
  108. 108.  Skúlason E, Jónsson H 2017. Atomic scale simulations of heterogeneous electrocatalysis: recent advances. Adv. Phys. 2:481–95
    [Google Scholar]
  109. 109.  Zheng Y, Jiao Y, Jaroniec M, Qiao SZ 2015. Advancing the electrochemistry of the hydrogen-evolution reaction through combining experiment and theory. Angew. Chem. Int. Ed. 54:52–65
    [Google Scholar]
  110. 110.  Bandarenka AS, Koper MTM 2013. Structural and electronic effects in heterogeneous electrocatalysis: toward a rational design of electrocatalysts. J. Catal. 308:11–24
    [Google Scholar]
  111. 111.  Singh MR, Clark EL, Bell AT 2015. Thermodynamic and achievable efficiencies for solar-driven electrochemical reduction of carbon dioxide to transportation fuels. PNAS 112:E6111–E18
    [Google Scholar]
  112. 112.  Singh MR, Clark EL, Bell AT 2015. Effects of electrolyte, catalyst, and membrane composition and operating conditions on the performance of solar-driven electrochemical reduction of carbon dioxide. Phys. Chem. Chem. Phys. 17:18924–36
    [Google Scholar]
  113. 113.  Artuso P, Zuccari F, Dell'Era A, Orecchini F 2010. PV-electrolyzer plant: models and optimization procedure. J. Sol. Energy Eng. 132:031016
    [Google Scholar]
  114. 114.  Rothschild A, Dotan H 2017. Beating the efficiency of photovoltaics: powered electrolysis with tandem cell photoelectrolysis. ACS Energy Lett 2:45–51
    [Google Scholar]
  115. 115.  Neuman J, Thomas-Alyea KE 2004. Electrochemical Systems Hoboken, NJ: Wiley-Intersci, 3rd ed..
  116. 116.  Singh MR, Kwon Y, Lum Y, Ager JW, Bell AT 2016. Hydrolysis of electrolyte cations enhances the electrochemical reduction of CO2 over Ag and Cu. J. Am. Chem. Soc. 138:13006–12
    [Google Scholar]
  117. 117.  Goodpaster JD, Bell AT, Head-Gordon M 2016. Identification of possible pathways for C-C bond formation during electrochemical reduction of CO: new theoretical insights from an improved electrochemical model. J. Phys. Chem. Lett. 7:1471–77
    [Google Scholar]
  118. 118.  Tapia O, Bertrán J, eds. 2003. Solvent Effects and Chemical Reactivity Dordrecht, Neth: Kluver Acad.
  119. 119.  Dyson PJ, Jessop PG 2016. Solvent effects in catalysis: rational improvements of catalysts via manipulation of solvent interactions. Catal. Sci. Technol. 6:3302–16
    [Google Scholar]
  120. 120.  Plauck A, Stangland EE, Dumesic JA, Mavrikakis M 2016. Active sites and mechanisms for H2O2 decomposition over Pd catalysts. PNAS 113:E1973–E82
    [Google Scholar]
  121. 121.  Behtash S, Lu J, Mamun O, Williams CT, Monnier JR, Heyden A 2016. Solvation effects in hydrodeoxygenation of propanoic acid over a model Pd(211) catalyst. J. Phys. Chem. C 120:2724–36
    [Google Scholar]
  122. 122.  Faheem M, Suthirakun S, Heyden A 2012. New implicit solvation scheme for solid surfaces. J. Phys. Chem. C. 116:22458–62
    [Google Scholar]
  123. 123.  Behtash S, Lu J, Walker E, Mamun O, Heyden A 2016. Solvent effects in the liquid phase hydrodeoxygenation of methyl propionate over a Pd(111) catalyst model. J. Catal. 333:171–83
    [Google Scholar]
  124. 124.  Faheem M, Saleheen M, Lu J, Heyden A 2016. Ethylene glycol reforming on Pt(111): first-principles microkinetic modeling in vapor and aqueous phases. Catal. Sci. Technol. 6:8242–56
    [Google Scholar]
  125. 125.  Salciccioli M, Stamatakis M, Caratzoulas S, Vlachos DG 2011. A review of multiscale modeling of metal-catalyzed reactions: mechanism development for complexity and emergent behavior. Chem. Eng. Sci. 66:4319–55
    [Google Scholar]
  126. 126.  Vlachos DG 2012. Multiscale modeling for emergent behavior, complexity and combinatorial explosion. AIChE J 58:1314–25
    [Google Scholar]
  127. 127.  Grew KN, Chiu WKS 2012. A review of modeling and simulation techniques across the length scales for the solid oxide fuel cell. Power Sources 199:1–13
    [Google Scholar]
  128. 128.  Franco AA 2013. Multiscale modelling and numerical simulation of rechargeable lithium batteries: concepts, methods and challenges. RSC Adv 3:13027–58
    [Google Scholar]
  129. 129.  Mushrif SH, Vasudevan V, Krisnamurthy CB, Venkatesh B 2015. Multiscale molecular modeling can be an effective tool to aid the development of biomass conversion technology: a perspective. Chem. Eng. Sci. 121:217–35
    [Google Scholar]
  130. 130.  Bhatt JS, Chutia A, Catlow RA, Coppens M-O 2016. Quantum and statistical mechanics simulations for porous catalyst modelling. Modern Developments in Catalysis G Hutchings, MG Davidson, R Catlow, C Hardacre, NJ Turner, P Collier, pp. 253–88 London: World Sci. Publ. Eur.
    [Google Scholar]
  131. 131.  Matera S, Maestri M, Cuoci A, Reuter K 2014. Predictive-quality surface reaction chemistry in real reactor models: integrating first-principles kinetic Monte Carlo simulations into computational fluid dynamics. ACS Catal 4:4081–92
    [Google Scholar]
  132. 132.  Maestri M, Cuoci A 2013. Coupling CFD with detailed microkinetic modeling in heterogeneous catalysis. Chem. Eng. Sci. 96:106–17
    [Google Scholar]
  133. 133.  Hansen N, Keil FJ 2012. Multiscale modeling of reaction and diffusion in zeolites: from the molecular level to the reactor. Soft Mater 10:179–201
    [Google Scholar]
  134. 134.  Hansen N, Krishna R, van Baten JM, Bell AT, Keil FJ 2009. Analysis of diffusion limitation in the alkylation of benzene over H-ZMS-5 by combining quantum chemical calculations, molecular simulations, and a continuum approach. J. Phys. Chem. C 113:235–46
    [Google Scholar]
  135. 135.  Hansen N, Krishna R, van Baten JM, Bell AT, Keil FJ 2010. Reactor simulation of benzene ethylation and ethane dehydrogenation catalyzed by ZSM-5: a multiscale approach. Chem. Eng. Sci. 65:2472–80
    [Google Scholar]
  136. 136.  Walton KS, Sholl DS 2015. Predicting multicomponent adsorption: 50 years of the ideal adsorption solution theory. AIChE J 61:2757–62
    [Google Scholar]
  137. 137.  Ulissi ZW, Medford AJ, Bligard T, Nørskov JK 2017. To address surface reaction network complexity using scaling relations machine learning and DFT calculations. Nat. Commun. 8:14621
    [Google Scholar]
/content/journals/10.1146/annurev-chembioeng-060817-084141
Loading
/content/journals/10.1146/annurev-chembioeng-060817-084141
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error