Revealing the structure of a catalytic combustion active-site ensemble combining uniform nanocrystal catalysts and theory insights.

Supported metal catalysts are extensively used in industrial and environmental applications. To improve their performance, it is crucial to identify the most active sites. This identification is, however, made challenging by the presence of a large number of potential surface structures that complicate such an assignment. Often, the active site is formed by an ensemble of atoms, thus introducing further complications in its identification. Being able to produce uniform structures and identify the ones that are responsible for the catalyst performance is a crucial goal. In this work, we utilize a combination of uniform Pd/Pt nanocrystal catalysts and theory to reveal the catalytic active-site ensemble in highly active propene combustion materials. Using colloidal chemistry to exquisitely control nanoparticle size, we find that intrinsic rates for propene combustion in the presence of water increase monotonically with particle size on Pt-rich catalysts, suggesting that the reaction is structure dependent. We also reveal that water has a near-zero or mildly positive reaction rate order over Pd/Pt catalysts. Theory insights allow us to determine that the interaction of water with extended terraces present in large particles leads to the formation of step sites on metallic surfaces. These specific step-edge sites are responsible for the efficient combustion of propene at low temperature. This work reveals an elusive geometric ensemble, thus clearly identifying the active site in alkene combustion catalysts. These insights demonstrate how the combination of uniform catalysts and theory can provide a much deeper understanding of active-site geometry for many applications.

Recycling of Solvent Allows for Multiple Rounds of Reproducible Nanoparticle Synthesis.

Metal nanoparticles have superior properties for a variety of applications. In many cases, the improved performance of metal nanoparticles is tightly correlated with their size and atomic composition. To date, colloidal synthesis is the most commonly used technique to produce metal nanoparticles. However, colloidal synthesis is currently a laboratory scale technique that has not been applied at larger scales. One of the greatest challenges facing large-scale colloidal synthesis of metal nanoparticles is the large volume of long-chain hydrocarbon solvents and surfactants needed for the synthesis, which can dominate the cost of nanoparticle production. In this work, we demonstrate a protocol, based on solvent distillation, which enables the reuse of colloidal nanoparticle synthesis surfactants and solvents for over 10 rounds of successive syntheses and demonstrates that pure solvents and surfactants are not necessarily needed to produce uniform nanocrystals. We show that this protocol can be applied to the production of a wide variety of mono- and bimetallic nanoparticles with reproducible sizes and compositions, which leads to reproducible performance as heterogeneous catalysts. A techno-economic assessment demonstrates the potential of this technique to greatly reduce the solvent-related costs of colloidal metal nanoparticle synthesis, which could contribute to its wider application at commercial scale.

Probing Atomic Distributions in Mono- and Bimetallic Nanoparticles by Supervised Machine Learning.

Properties of mono- and bimetallic metal nanoparticles (NPs) may depend strongly on their compositional, structural (or geometrical) attributes, and their atomic dynamics, all of which can be efficiently described by a partial radial distribution function (PRDF) of metal atoms. For NPs that are several nanometers in size, finite size effects may play a role in determining crystalline order, interatomic distances, and particle shape. Bimetallic NPs may also have different compositional distributions than bulk materials. These factors all render the determination of PRDFs challenging. Here extended X-ray absorption fine structure (EXAFS) spectroscopy, molecular dynamics simulations, and supervised machine learning (artificial neural-network) method are combined to extract PRDFs directly from experimental data. By applying this method to several systems of Pt and PdAu NPs, we demonstrate the finite size effects on the nearest neighbor distributions, bond dynamics, and alloying motifs in mono- and bimetallic particles and establish the generality of this approach.

Synthesis of Colloidal Pd/Au Dilute Alloy Nanocrystals and Their Potential for Selective Catalytic Oxidations.

Selective oxidations are crucial for the creation of valuable chemical building blocks but often require expensive and unstable stoichiometric oxidants such as hydroperoxides and peracids. To date, many catalysts that contain a single type of active site have not been able to attain the desired level of selectivity for partially oxidized products over total combustion. However, catalysts containing multiple types of active sites have proven to be successful for selective reactions. One category of such catalysts is bimetallic alloys, in which catalytic activity and selectivity can be tuned by modifying the surface composition. Traditional catalyst synthesis methods using impregnation struggle to create catalysts with sufficient control over surface chemistry to accurately tune the ensemble size of the desired active sites. Here we describe the synthesis of colloidal nanocrystals of dilute alloys of palladium and gold. We show that when supported on titania (TiO2), tuning the composition of the Pd/Au nanocrystal surface provides a synergistic effect in the selective oxidation of 2-propanol to acetone in the presence of H2 and O2. In particular, we show that certain Pd/Au surface ratios exhibit activity and selectivity far superior to Pd or Au individually. Through precise structural characterization we demonstrate that isolated atoms of Pd exist in the most active catalysts. The synergy between isolated Pd atoms and Au allows for the formation of reactive oxidizing species, likely hydroperoxide groups, responsible for selective oxidation while limiting oxygen dissociation and, thus, complete combustion. This work opens the way to more efficient utilization of scarce noble metals and new options for catalyzed selective oxidations.

Low-Temperature Restructuring of CeO2-Supported Ru Nanoparticles Determines Selectivity in CO2 Catalytic Reduction.

CO2 reduction to higher value products is a promising way to produce fuels and key chemical building blocks while reducing CO2 emissions. The reaction at atmospheric pressure mainly yields CH4 via methanation and CO via the reverse water-gas shift (RWGS) reaction. Describing catalyst features that control the selectivity of these two pathways is important to determine the formation of specific products. At the same time, identification of morphological changes occurring to catalysts under reaction conditions can be crucial to tune their catalytic performance. In this contribution we investigate the dependency of selectivity for CO2 reduction on the size of Ru nanoparticles (NPs) and on support. We find that even at rather low temperatures (210 °C), oxidative pretreatment induces redispersion of Ru NPs supported on CeO2 and leads to a complete switch in the performance of this material from a well-known selective methanation catalyst to an active and selective RWGS catalyst. By utilizing in situ X-ray absorption spectroscopy, we demonstrate that the low-temperature redispersion process occurs via decomposition of the metal oxide phase with size-dependent kinetics, producing stable single-site RuO x/CeO2 species strongly bound to the CeO2 support that are remarkably selective for CO production. These results show that reaction selectivity can be heavily dependent on catalyst structure and that structural changes of the catalyst can occur even at low temperatures and can go unseen in materials with less defined structures.

Langmuir-Blodgett Deposition of Graphene Oxide-Identifying Marangoni Flow as a Process that Fundamentally Limits Deposition Control.

Langmuir-Blodgett deposition is a popular route to produce thin films of graphene oxide for applications such as transparent conductors and biosensors. Unfortunately, film morphologies vary from sample to sample, often with undesirable characteristics such as folded sheets and patchwise depositions. In conventional Langmuir-Blodgett deposition of graphene oxide, alcohol (typically methanol) is used to spread the graphene oxide sheets onto an air-water interface before deposition onto substrates. Here we show that methanol gives rise to Marangoni flow, which fundamentally limits control over Langmuir-Blodgett depositions of graphene oxide. We directly identified the presence of Marangoni flow by using photography, and we evaluated depositions with atomic force microscopy and scanning electron microscopy. The disruptive effect of Marangoni flow was demonstrated by comparing conventional Langmuir-Blodgett depositions to depositions where Marangoni flow was suppressed by a surfactant. Because methanol is the standard spreading solvent for conventional Langmuir-Blodgett deposition of graphene oxide, Marangoni flow is a general problem and may partly explain the wide variety of undesirable film morphologies reported in the literature.

Supported Catalyst Deactivation by Decomposition into Single Atoms Is Suppressed by Increasing Metal Loading.

In the high-temperature environments needed to perform catalytic processes, supported precious metal catalysts severely lose their activity over time. Even brief exposure to high temperatures can lead to significant losses in activity, which forces manufacturers to use large amounts of noble metals to ensure effective catalyst function for a required lifetime. Generally, loss of catalytic activity is attributed to nanoparticle sintering, or processes by which larger particles grow at the expense of smaller ones. Here, by independently controlling particle size and particle loading using colloidal nanocrystals, we reveal the opposite process as a novel deactivation mechanism: nanoparticles rapidly lose activity by high-temperature nanoparticle decomposition into inactive single atoms. This deactivation route is remarkably fast, leading to severe loss of activity in as little as ten minutes. Importantly, this deactivation pathway is strongly dependent on particle density and concentration of support defect sites. A quantitative statistical model explains how for certain reactions, higher particle densities can lead to more stable catalysts.