Mechanocatalytic Hydrogenolysis of the Lignin Model Dimer Benzyl Phenyl Ether over Supported Palladium Catalysts

This work demonstrates the mechanocatalytic hydrogenolysis of the ether bond in the lignin model compound benzyl phenyl ether (BPE) and hardwood lignin isolated by hydrolysis with supercritical water. Pd catalysts with 4 wt % loading on Al2O3 and SiO2 supports achieve 100% conversion of BPE with a toluene production rate of (2.6-2.9) × 10-5 mol·min-1. The formation of palladium hydrides under H2 gas flow contributes to an increase in the turnover frequency by a factor of up to 300 compared to Ni on silica-alumina. While a near-quantitative toluene yield is obtained, some of the phenolic products remain adsorbed on the catalyst.

Restructuring dynamics of surface species in bimetallic nanoparticles probed by modulation excitation spectroscopy

Restructuring of metal components on bimetallic nanoparticle surfaces in response to the changes in reactive environment is a ubiquitous phenomenon whose potential for the design of tunable catalysts is underexplored. The main challenge is the lack of knowledge of the structure, composition, and evolution of species on the nanoparticle surfaces during reaction. We apply a modulation excitation approach to the X-ray absorption spectroscopy of the 30 atomic % Pd in Au supported nanocatalysts via the gas (H2 and O2) concentration modulation. For interpreting restructuring kinetics, we correlate the phase-sensitive detection with the time-domain analysis aided by a denoising algorithm. Here we show that the surface and near-surface species such as Pd oxides and atomically dispersed Pd restructured periodically, featuring different time delays. We propose a model that Pd oxide formation is preceded by the build-up of Pd regions caused by oxygen-driven segregation of Pd atoms towards the surface. During the H2 pulse, rapid reduction and dissolution of Pd follows an induction period which we attribute to H2 dissociation. Periodic perturbations of nanocatalysts by gases can, therefore, enable variations in the stoichiometry of the surface and near-surface oxides and dynamically tune the degree of oxidation/reduction of metals at/near the catalyst surface.

The Inner Shell Spectroscopy beamline at NSLS-II: a facility for in situ and operando X-ray absorption spectroscopy for materials research

The Inner Shell Spectroscopy (ISS) beamline on the 8-ID station at the National Synchrotron Light Source II (NSLS-II), Upton, NY, USA, is a high-throughput X-ray absorption spectroscopy beamline designed for in situ, operando, and time-resolved material characterization using high monochromatic flux and scanning speed. This contribution discusses the technical specifications of the beamline in terms of optics, heat load management, monochromator motion control, and data acquisition and processing. Results of the beamline tests demonstrating the quality of the data obtainable on the instrument, possible energy scanning speeds, as well as long-term beamline stability are shown. The ability to directly control the monochromator trajectory to define the acquisition time for each spectral region is highlighted. Examples of studies performed on the beamline are presented. The paper is concluded with a brief outlook for future developments.

Dilute Alloys Based on Au, Ag, or Cu for Efficient Catalysis: From Synthesis to Active Sites

The development of new catalyst materials for energy-efficient chemical synthesis is critical as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive processes specifically using heterogeneous catalysis. Catalytic performance is a complex interplay of phenomena involving temperature, pressure, gas composition, surface composition, and structure over multiple length and time scales. In response to this complexity, the integrated approach to heterogeneous dilute alloy catalysis reviewed here brings together materials synthesis, mechanistic surface chemistry, reaction kinetics, in situ and operando characterization, and theoretical calculations in a coordinated effort to develop design principles to predict and improve catalytic selectivity. Dilute alloy catalysts─in which isolated atoms or small ensembles of the minority metal on the host metal lead to enhanced reactivity while retaining selectivity─are particularly promising as selective catalysts. Several dilute alloy materials using Au, Ag, and Cu as the majority host element, including more recently introduced support-free nanoporous metals and oxide-supported nanoparticle "raspberry colloid templated (RCT)" materials, are reviewed for selective oxidation and hydrogenation reactions. Progress in understanding how such dilute alloy catalysts can be used to enhance selectivity of key synthetic reactions is reviewed, including quantitative scaling from model studies to catalytic conditions. The dynamic evolution of catalyst structure and composition studied in surface science and catalytic conditions and their relationship to catalytic function are also discussed, followed by advanced characterization and theoretical modeling that have been developed to determine the distribution of minority metal atoms at or near the surface. The integrated approach demonstrates the success of bridging the divide between fundamental knowledge and design of catalytic processes in complex catalytic systems, which can accelerate the development of new and efficient catalytic processes.

Latent Representation Learning for Structural Characterization of Catalysts

Supervised machine learning-enabled mapping of the X-ray absorption near edge structure (XANES) spectra to local structural descriptors offers new methods for understanding the structure and function of working nanocatalysts. We briefly summarize a status of XANES analysis approaches by supervised machine learning methods. We present an example of an autoencoder-based, unsupervised machine learning approach for latent representation learning of XANES spectra. This new approach produces a lower-dimensional latent representation, which retains a spectrum-structure relationship that can be eventually mapped to physicochemical properties. The latent space of the autoencoder also provides a pathway to interpret the information content "hidden" in the X-ray absorption coefficient. Our approach (that we named latent space analysis of spectra, or LSAS) is demonstrated for the supported Pd nanoparticle catalyst studied during the formation of Pd hydride. By employing the low-dimensional representation of Pd K-edge XANES, the LSAS method was able to isolate the key factors responsible for the observed spectral changes.

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

Trained neural networks are used to extract the first partial coordination numbers from XANES spectra. In bimetallic nanoparticles, the four local structure descriptors provide rich information on structural motifs.