Solvent promotion on the metal-support interaction and activity of Pd@ZrO2 Catalyst: Formation of metal hydrides as the new catalytic active phase at the Solid-Liquid interface
Graphical abstract
Introduction
For aqueous reactions catalyzed by the widely-used supported metal catalysts, the solvent waters play a critical role on the rate, yield and selectivity of reactions.[1] That dramatic influence sometimes comes from the following factors: different solubility and mass transport limitations in aqueous phase[2], the unexpected contributions of the soluble species[3], [4] and the structural transformations of the catalyst itself, such as collapse[5], leaching[6], agglomeration[7], [8], fouling[9] and coking[10], [11]. Meanwhile, the more essential factor should relate to the influence of waters on the active sites and those reactive intermediates. For example, the solvent waters could compete for the adsorption with reactants[12], [13], and the solvation could change the stability of the key intermediates and transition states[14], [15], [16], [17]. What is more, except a few techniques (such as melt infiltration and atomic layer deposition), the commonly used preparing methods of supported metal catalysts (such as precipitation, impregnation, and the colloidal synthesis) most undergo in aqueous solutions[18]. Even the “dry impregnation” (or called “incipient wetness impregnation”) needs limited aqueous solution to fill the pore volume of support[19]. That means even for the supported metal catalysts used in gas or vapor phase, solvent waters might still influence their properties at the stage of preparation in aqueous phase.
However, the mechanistic details of how solvent waters influence the metal-support interface, and in turn their catalytic performances, are still unclear. Such limited understanding could partially be attributed to the hard experimental characterization in aqueous phase, such as the background noise in some traditional spectroscopies.[20] Besides the fast-developing in situ and time-resolved characterization tools, the theoretical calculations based on density functional theory (DFT) could be helpful to provide mechanistic insights into the solvent effect, especially when explicit waters are used to model the aqueous phase.[21] Although its computational cost is high, with the fast development of high-performance computing systems, there are more and more DFT calculations using the explicit solvent model during the past two decades.
Such realistic but time-consuming calculations were first applied to simulate the metal-water interfaces[22], [23], [24] and the surface hydration of metallic oxide in water[25], [26], [27]. Then, the explicit solvation model was further applied to catalytic reactions. In electrocatalytic reactions, such as oxygen reduction reaction (ORR)[28], [29] and carbon dioxide reduction reactions (CO2RRs)[30], [31], the solvent waters on metal electrode could not only change the stability of intermediates and transition states, but also, in certain cases, react with intermediates to create new pathways. During the acetic acid ketonization catalyzed by monoclinic ZrO2, Cai et al.[32] showed that unlike the Langmuir-Hinshelwood mechanism in vapor phase, the reaction with explicit solvent waters followed the Eley-Rideal mechanism by forming the β-keto acid intermediate. Wang and co-worker[33] studied the photocatalytic oxygen evolution reaction (OER) on rutile TiO2 with explicit solvent waters, where the experimental unsatisfactory efficiency was suggested to be limited by the TiO2 photoholes rather than the inherent reaction barriers. Recently, Zhao et al.[34] conducted the DFT calculation with explicit solvent waters on the aldol condensation of formaldehyde and acetone catalyzed by anatase TiO2, and the dehydration was suggested to be promoted by solvent waters.
The metal nano-catalysts are usually supported on oxide supports in experiments. However, due to the higher computational cost of involving both the metal and support into the model, in most previous theoretical calculations, especially when explicit solvent waters were involved, pure metal surfaces were applied as a compromise to represent the supported metal catalysts. For instances, by DFT calculations, Zope et al.[35] showed in aqueous phase, both solution-mediated and metal-catalyzed elementary steps were involved in alcohol oxidation at the Au-water interface, although in their experiment the Au particles were supported on TiO2 or C surfaces. According to the ab initio molecular dynamics (AIMD) simulations by Yoon and co-workers[36], the explicit solvent waters on Pt(1 1 1) and Ni(1 1 1) surfaces could strongly influence the energetic preference of keto/enol tautomerization during phenol hydrogenation, resulting in different product selectivity, although the supported Pt and Ni catalysts were used in experiments[37], [38], [39]. Such ignorance of support was suggested to have no significant impact when these metal particles were large in scale of several nanometers. However, there were also lots of reports that the supports, especially those metallic oxide supports, could dramatically change the catalytic activity and selectivity by distinct metal-support interactions[40], [41]. For these reactions, obviously the theoretical study on pure metal surfaces could deviate from the actual situation in experiment, where the support should also be involved in modelling.
The above-mentioned theoretical works largely explored the solid–liquid interface for catalysts in aqueous phase, but all of them focused on either the metal-water interface or the oxide–water interface. There are rare DFT calculations concerning how waters influence the metal-oxide interface. According to the AIMD simulation on Au11@TiO2 by Camellone and Marx[42], the surrounding explicit waters would influence the charges of metal atoms by solvation. Such charge variations on metal atoms were also observed in AIMD simulation of Pt6@CeO2 in explicit waters by Camellone and Fabris[43], where the solvent water dissociation took place. In the recent theoretical study on Au@TiO2 by Chandler et al.[44], with some explicit waters added on both sides of Au nanorods, it was shown the H2 activation could be suppressed by waters. These DFT calculations indeed provide important insights of how solvent waters influence the charges of metal clusters and their catalytic performances. However, in their models[42], [43], [44], the metal clusters were directly supported on the anhydrous support. As stated above, the supported metal catalysts are in most cases prepared in aqueous solution. For many oxide supports, such as the commonly-used Al2O3, TiO2, ZrO2, CeO2, some of their anhydrous surfaces could soon be hydrated by water dissociation,[25], [32] although on specific surfaces such hydration was shown harder.[45] That surface hydration can be determined by many factors, including the chemical composition of oxide, the index of exposed oxide crystal surface and the electric double layer formed at the solid-aqueous interface.[46], [47], [48] The degree of surface hydration can be crucial to the characteristics of supported metal cluster. For example, the feasible shapes of Pt and Pd cluster on the anhydrous and hydroxylated γ-Al2O3 surfaces were dramatically different due to the distinct metal-support interactions.[49] Of course the oxide surfaces could undergo the dehydration during the subsequent drying or calcination in experiment. Nevertheless, it is still of importance to investigate the evolution of supported metal catalysts on the hydrated oxide surface in water, which can provide insights into the properties of metal-support interface in aqueous phase.
In experiment, the supported Pd nanocluster catalysts are widely applied on both oxidation reaction and reduction reaction, such as CO oxidation[15], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], methanol oxidation[60], [61], [62], phenol hydrogenation[37], [63], [64], [65], [66], [67], [68], and furfural hydrogenation[69], where in many cases the ZrO2 is chosen as the support[51], [52], [53], [56], [61], [62], [67], [68]. Most of these hydrogenation reactions take place in aqueous phase[37], [63], [64], [65], [66], [69]. For those CO and methanol oxidation, the present of water has also been proven benefit for the supported Pd catalysts[15], [60], [70]. In addition, the recent study by Cargnello et. al.[71] shows the steam pretreatment of Pd/Al2O3 catalysts could promote methane oxidation by the grain boundary density through crystal twinning and the associated defective strain, suggesting the waters could not only work during catalytic reactions, but also activate the supported metal catalysts themselves. However, there were seldom theoretical simulations on how solvent waters influence the metal-support interface and in turn its catalytic activity. In this work, the dynamic evolution of Pd@ZrO2 in aqueous phase was systematically investigated by DFT calculations and AIMD simulations. With the Pd cluster, ZrO2 support and abundant explicit solvent waters together involved in the model, it was found the metal-support interaction could be enhanced in aqueous phase by spontaneous water dissociation at the Pd-ZrO2 interface. During dissociation, the generated Hδ+ would undergo the proton coupled electron transfer (PCET) from Pd to form the hydride (Hδ-). It is the spatial separation of the generated H on Pd and the remaining OH on ZrO2 polarizes the metal-support interface, which then flattens the cluster shape. What is more, the generated hydride could promote the activity of CO oxidation as the new catalytically active phase. This work reveals the possibility that in aqueous solution, the metal-support interaction of the supported metal catalyst could be promoted by interfacial water dissociation, resulting in the metal-hydride as the active phase, which might be crucial but lacks of investigation in previous studies.
Section snippets
Computational methods
The stable configurations and transition states are optimized by spin polarized DFT calculation within the framework of periodic boundary condition by using the VASP code[72]. Projected augmented wave (PAW) method[29] is used to describe the ion–core electron interaction, where the exchange and correlation interactions are described by PBE functional[73]. Grimme’s D3 corrections[74] are involved to describe the dispersion interactions. The () surface is the stable surface of monoclinic ZrO2
Simulation of Pd13@ZrO2 catalyst in explicit solvent waters.
In previous theoretical studies of supported metal catalysts, the support surfaces are often modelled by either anhydrous surfaces[42], [43], [49] or surface filled artificially with hydroxyl groups[49]. To get the representative hydrated ZrO2 surface in aqueous phase, as shown in Fig. S1, the explicit waters with the density around 1 g/mL were first put above the anhydrous ZrO2 surface, followed by the 12 ps AIMD simulation at the typical supporting temperature of 353 K[80]. Similar to the
Conclusion
With the Pd@ZrO2 catalyst modelled in abundant explicit solvent waters, by DFT calculations and AIMD simulations, it was found the metal-support interaction could be enhanced in aqueous phase by spontaneous water dissociation at the metal-support interface. Such interfacial water dissociation is new and more favorable than the reported dissociations of adsorption waters on metal surfaces and solvent waters adjacent to metal cluster. What is more, this type of dissociation was not limited to one
Funding Information
This work was financially supported by NSFC (No. 22022504; No. 22003022) of China, Natural Science Foundation of Guangdong, China (No. 2021A1515010213), Guangdong “Pearl River” Talent Plan (No. 2019QN01L353), Higher Education Innovation Strong School Project of Guangdong Province of China (2020KTSCX122) and Guangdong Provincial Key Laboratory of Catalysis (No. 2020B121201002).
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was financially supported by NSFC (No. 22022504; No. 22003022) of China, Natural Science Foundation of Guangdong, China (No. 2021A1515010213), Guangdong “Pearl River” Talent Plan (No. 2019QN01L353), Higher Education Innovation Strong School Project of Guangdong Province of China (2020KTSCX122) and Guangdong Provincial Key Laboratory of Catalysis (No. 2020B121201002). The computational resource is supported from the Center for Computational Science and Engineering at SUSTech. The
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