Aldehyde Hydrogenation by Pt/TiO₂ Catalyst in Aqueous Phase: Synergistic Effect of Oxygen Vacancy and Solvent Water

Abstract

The aldehyde hydrogenation for stabilizing and upgrading biomass is typically performed in aqueous phase with supported metal catalysts. By combining density functional theory calculations and ab initio molecular dynamics simulations, the model reaction of formaldehyde hydrogenation with a Pt/TiO 2 catalyst is investigated with explicit solvent water molecules. In aqueous phase, both the O vacancy (Ov) on support and solvent molecules could donate charges to a Pt cluster, where the Ov could dominantly reduce the Pt cluster from positive to negative. During the formaldehyde hydrogenation, the water molecules could spontaneously protonate the O in the aldehyde group by acid/base exchange, generating the OH* at the metal−support interface by long-range proton transfer. By comparing the stoichiometric and reduced TiO 2 support, it is found that the further hydrogenation of OH* is hard on the positively charged Pt cluster over stoichiometric TiO 2 . However, with the presence of Ov on reduced support, the OH* hydrogenation could become not only exergonic but also kinetically more facile, which prohibits the catalyst from poisoning. This mechanism suggests that both the proton transfer from solvent water molecules and the easier OH* hydrogenation from Ov could synergistically promote aldehyde hydrogenation. That means, even for such simple hydrogenation in water, the catalytic mechanism could explicitly relate to all of the metal cluster, oxide support, and solvent waters. Considering the ubiquitous Ov defects in reducible oxide supports and the common aqueous environment, this synergistic effect may not be exclusive to Pt/TiO 2 , which can be crucial for supported metal catalysts in biomass conversion.

Modulating Oxygen Vacancy in Pt/TiO2 for Thermo-Photo Reforming Lignin and Its Derivatives to H2 and Value-Added Product

“…This is due to the additional charge deflection near the oxygen vacancies due to the flow from TiO 2 –O V to Pt due to the strong built-in electric field . The ELF results showed that the introduction of oxygen vacancies obviously induce the delocalization of charges around O atoms (Figure S7), and area charges were redistributed from O atom to Ti and Pt atom (Figure g,h). , The delocalization of oxygen vacancies is conducive to the activation of reactive molecules, thereby optimizing the reaction energy barrier and promoting the reaction process . Therefore, the above electronic analysis demonstrates Ti, O, and Pt have correlative interactions; in addition, the redistribution of electrons in the orbitals may affect the binding energy of the substrate.…”

Ti-Doping in Silica-Supported PtZn Propane Dehydrogenation Catalysts: From Improved Stability to the Nature of the Pt–Ti Interaction

“…A similar transfer from Ti III to M was experimentally described in the case of TiO x -supported Pd and Pt NPs. 57 , 58 Notably, the spin density is fully transferred onto Pt which indicates that the originally unpaired d-electron of Ti III is now fully localized on Pt ( Figure 5 A); it is thus best to describe the Pt–Ti pair as Pt -I –Ti IV rather than Pt 0 –Ti III . Such a change in the electronic structure is expected to affect the adsorption properties of Pt as well as the spectroscopic signature of Ti (see Section 2.3.3 ).…”

XPK: Toward Accurate and Efficient Microkinetic Modeling in Heterogeneous Catalysis

“…It is worth noting that electronic structure theory is the base for a multiscale modeling of catalytic systems. Hence, developing more accurate, efficient, and applicable electronic structure computation methods for more complex systems is a very important research direction in computational catalysis. − Moreover, the compositions and structures of the active phase of the catalysts may also change as the reaction network evolves and as the reaction conditions change. − Given the complexity of real catalytic systems, ab initio molecular dynamics empowered by enhanced sampling methods, ,,− as well as many machine learning based techniques − will certainly become indispensable tools in future catalysis research.…”