Guidelines to Achieving High Selectivity for the Hydrogenation of α,ß-Unsaturated Aldehydes with Bimetallic and Dilute Alloy Catalysts: A Review.

Selective hydrogenation of α,ß-unsaturated aldehydes to unsaturated alcohols is a challenging class of reactions, yielding valuable intermediates for the production of pharmaceuticals, perfumes, and flavorings. On monometallic heterogeneous catalysts, the formation of the unsaturated alcohols is thermodynamically disfavored over the saturated aldehydes. Hence, new catalysts are required to achieve the desired selectivity. Herein, the literature of three major research areas in catalysis is integrated as a step toward establishing the guidelines for enhancing the selectivity: reactor studies of complex catalyst materials at operating temperature and pressure, surface science studies of crystalline surfaces in ultrahigh vacuum, and first-principles modeling using density functional theory calculations. Aggregate analysis shows that bimetallic and dilute alloy catalysts significantly enhance the selectivity to the unsaturated alcohols compared to monometallic catalysts. This comprehensive review focuses primarily on the role of different metal surfaces as well as the factors that promote the adsorption of the unsaturated aldehyde via its C═O bond, most notably by electronic modification of the surface and formation of the electrophilic sites. Furthermore, challenges, gaps, and opportunities are identified to advance the rational design of efficient catalysts for this class of reactions, including the need for systematic studies of catalytic processes, theoretical modeling of complex materials, and model studies under ambient pressure and temperature.

Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy.

X-ray absorption spectroscopy is a common method for probing the local structure of nanocatalysts. One portion of the X-ray absorption spectrum, the X-ray absorption near edge structure (XANES) is a useful alternative to the commonly used extended X-ray absorption fine structure (EXAFS) for probing three-dimensional geometry around each type of atomic species, especially in those cases when the EXAFS data quality is limited by harsh reaction conditions and low metal loading. A methodology for quantitative determination of bimetallic architectures from their XANES spectra is currently lacking. We have developed a method, based on the artificial neural network, trained on ab initio site-specific XANES calculations, that enables accurate and rapid reconstruction of the structural descriptors (partial coordination numbers) from the experimental XANES data. We demonstrate the utility of this method on the example of a series of PdAu bimetallic nanoalloys. By validating the neural network-yielded metal-metal coordination numbers based on the XANES analysis by previous EXAFS characterization, we obtained new results for in situ restructuring of dilute (2.6 at% Pd in Au) PdAu nanoparticles, driven by their gas and temperature treatments.

Predicting a Sharp Decline in Selectivity for Catalytic Esterification of Alcohols from van der Waals Interactions.

Controlling the selectivity of catalytic reactions is a critical aspect of improving energy efficiency in the chemical industry; thus, predictive models are of key importance. Herein the performance of a heterogeneous, nanoporous Au catalyst is predicted for the complex catalytic self-coupling of the series of C2 -C4 alkyl alcohols, based solely on the known kinetics of the elementary steps of the catalytic cycle for methanol coupling, using scaling methods augmented by density functional theory. Notably, a sharp decrease in selectivity for ester formation with increasing molecular weight to favor the aldehyde due to van der Waals interactions of reaction intermediates with the surface was predicted and subsequently verified quantitatively by experiment. Further, the agreement between theory and experiment clearly demonstrates the efficacy of this approach for building a predictive model of catalytic behavior for a homologous set of reactants using a small set of experimental information.

Enhancing catalytic performance of dilute metal alloy nanomaterials.

Dilute alloys are promising materials for sustainable chemical production; however, their composition and structure affect their performance. Herein, a comprehensive study of the effects of pretreatment conditions on the materials properties of Pd0.04Au0.96 nanoparticles partially embedded in porous silica is related to the activity for catalytic hydrogenation of 1-hexyne to 1-hexene. A combination of in situ characterization and theoretical calculations provide evidence that changes in palladium surface content are induced by treatment in oxygen, hydrogen and carbon monoxide at various temperatures. In turn, there are changes in hydrogenation activity because surface palladium is necessary for H2 dissociation. These Pd0.04Au0.96 nanoparticles in the porous silica remain structurally intact under many cycles of activation and deactivation and are remarkably resistant to sintering, demonstrating that dilute alloy catalysts are highly dynamic systems that can be tuned and maintained in a active state.

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.

Toward digitally controlled catalyst architectures: Hierarchical nanoporous gold via 3D printing.

Monolithic nanoporous metals, derived from dealloying, have a unique bicontinuous solid/void structure that provides both large surface area and high electrical conductivity, making them ideal candidates for various energy applications. However, many of these applications would greatly benefit from the integration of an engineered hierarchical macroporous network structure that increases and directs mass transport. We report on 3D (three-dimensional)-printed hierarchical nanoporous gold (3DP-hnp-Au) with engineered nonrandom macroarchitectures by combining 3D printing and dealloying. The material exhibits three distinct structural length scales ranging from the digitally controlled macroporous network structure (10 to 1000 µm) to the nanoscale pore/ligament morphology (30 to 500 nm) controlled by dealloying. Supercapacitance, pressure drop, and catalysis measurements reveal that the 3D hierarchical nature of our printed nanoporous metals markedly improves mass transport and reaction rates for both liquids and gases. Our approach can be applied to a variety of alloy systems and has the potential to revolutionize the design of (electro-)chemical plants by changing the scaling relations between volume and catalyst surface area.

New Architectures for Designed Catalysts: Selective Oxidation using AgAu Nanoparticles on Colloid-Templated Silica.

A highly modular synthesis of designed catalysts with controlled bimetallic nanoparticle size and composition and a well-defined structural hierarchy is demonstrated. Exemplary catalysts-bimetallic dilute Ag-in-Au nanoparticles partially embedded in a porous SiO2 matrix (SiO2 -Agx Auy )-were synthesized by the decoration of polymeric colloids with the bimetallic nanoparticles followed by assembly into a colloidal crystal backfilled with the matrix precursor and subsequent removal of the polymeric template. This work reports that these new catalyst architectures are significantly better than nanoporous dilute AgAu alloy catalysts (nanoporous Ag3 Au97 ) while retaining a clear predictive relationship between their surface reactivity with that of single-crystal Au surfaces. This paves the way for broadening the range of new catalyst architectures required for translating the designed principles developed under controlled conditions to designed catalysts under operating conditions for highly selective coupling of alcohols to form esters. Excellent catalytic performance of the porous SiO2 -Agx Auy structure for selective oxidation of both methanol and ethanol to produce esters with high conversion efficiency, selectivity, and stability was demonstrated, illustrating the ability to translate design principles developed for support-free materials to the colloid-templated structures. The synthetic methodology reported is customizable for the design of a wide range of robust catalytic systems inspired by design principles derived from model studies. Fine control over the composition, morphology, size, distribution, and availability of the supported nanoparticles was demonstrated.