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Exploring the dynamic interaction of non-thermal plasma (NTP) with catalytic processes is critical to unravelling elusive catalyst structure–function relationships under NTP conditions, specifically dielectric barrier discharges (DBD).
Fischer–Tropsch synthesis (FTS) in a 3D-printed stainless steel (SS) microchannel microreactor was investigated using Fe@SiO2 catalysts. The catalysts were prepared by two different techniques: one pot (OP) and autoclave (AC). The mesoporous structure of the two catalysts, Fe@SiO2 (OP) and Fe@SiO2 (AC), ensured a large contact area between the reactants and the catalyst. They were characterized by N2 physisorption, H2 temperature-programmed reduction (H2-TPR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron microscopy (XPS), and thermogravimetric analysis–differential scanning calorimetry (TGA-DSC) techniques. The AC catalyst had a clear core–shell structure and showed a much greater surface area than that prepared by the OP method. The activities of the catalysts in terms of FTS were studied in the 200–350 °C temperature range at 20-bar pressure with a H2/CO molar ratio of 2:1. The Fe@SiO2 (AC) catalyst showed higher selectivity and higher CO conversion to olefins than Fe@SiO2 (OP). Stability studies of both catalysts were carried out for 30 h at 320 °C at 20 bar with a feed gas molar ratio of 2:1. The Fe@SiO2 (AC) catalyst showed higher stability and yielded consistent CO conversion compared to the Fe@SiO2 (OP) catalyst.
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. Furthermore, 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.
Abstract Cu is the most promising metal catalyst for CO 2 electroreduction (CO 2 RR) to multi-carbon products, yet the structure sensitivity of the reaction and the stability versus restructuring of the catalyst surface under reaction conditions remain controversial. Here, atomic scale simulations of surface energies and reaction pathway kinetics supported by experimental evidence unveil that CO 2 RR does not take place on perfect planar Cu(111) and Cu(100) surfaces but rather on steps or kinks. These planar surfaces tend to restructure in reaction conditions to the active stepped surfaces, with the strong binding of CO on defective sites acting as a thermodynamic driving force. Notably, we identify that the square motifs adjacent to defects, not the defects themselves, as the active sites for CO 2 RR via synergistic effect. We evaluate these mechanisms against experiments of CO 2 RR on ultra-high vacuum-prepared ultraclean Cu surfaces, uncovering the crucial role of step-edge orientation in steering selectivity. Overall, our study refines the structural sensitivity of CO 2 RR on Cu at the atomic level, highlights the self-activation mechanism and elucidates the origin of in situ restructuring of Cu surfaces during the reaction.
Three formulations of a molybdenum phosphide (MoP) catalyst system were characterized for the higher alcohol synthesis (HAS) reaction using in situ X-ray absorption spectroscopy (XAS) and X-ray diffraction (XRD), monitoring the chemical phase evolution during activation and under reaction conditions. Here, the in situ study herein provides important insights into the effect of the support and of K that lead to high performance in HAS for K-promoted MoP supported on carbon, as evidenced by previous studies (ethanol selectivity: 29%; conversion: 5%). During the activation process, XAS shows that the carbon-supported samples reduce and reach a highly crystalline state at a lower temperature than the SBA-15-supported sample, indicating a substantial difference in catalyst activation. After activation, the samples are introduced to relevant reaction conditions resulting in spectra fairly similar to one another. XRD results corroborate the difference in the degree of crystallinity of these samples, in alignment with the XAS, and reveal the formation of crystalline potassium pyrophosphate (K 4 P 2 O 7 ) during the activation period of the K-promoted samples. This K 4 P 2 O 7 phase remains present under reaction conditions. Taken together, these results provide insights into the roles played by the carbon support and K promotion, connecting activity to electronic and crystal structure.
Electrocatalytic nitrate reduction to ammonia offers a sustainable approach for nitrogenous waste upcycling. This review outlines recent advances in the design of electrocatalysts through cross-scale structural engineering.
Abstract The novel electrophilic organo‐tantalum catalyst AlS/TaNp x ( 1 ) (Np=neopentyl) is prepared by chemisorption of the alkylidene Np 3 Ta=CH t Bu onto highly Brønsted acidic sulfated alumina (AlS). The proposed catalyst structure is supported by EXAFS, XANES, ICP, DRIFTS, elemental analysis, and SSNMR measurements and is in good agreement with DFT analysis. Catalyst 1 is highly effective for the hydrogenolysis of diverse linear and branched hydrocarbons, ranging from C2 to polyolefins. To the best of our knowledge, 1 exhibits one of the highest polyolefin hydrogenolysis activities (9,800 (CH 2 units) ⋅ mol(Ta) −1 ⋅ h −1 at 200 °C/17 atm H 2 ) reported to date in the peer‐reviewed literature. Unlike the AlS/ZrNp 2 analog, the Ta catalyst is more thermally stable and offers multiple potential C−C bond activation pathways. For hydrogenolysis, AlS/TaNp x is effective for a wide variety of pre‐ and post‐consumer polyolefin plastics and is not significantly deactivated by standard polyolefin additives at typical industrial concentrations.
The novel electrophilic organo-tantalum catalyst AlS/TaNp x (1) (Np=neopentyl) is prepared by chemisorption of the alkylidene Np 3 Ta=CH t Bu onto highly Brønsted acidic sulfated alumina (AlS). Here, the proposed catalyst structure is supported by EXAFS, XANES, ICP, DRIFTS, elemental analysis, and SSNMR measurements and is in good agreement with DFT analysis. Catalyst 1 is highly effective for the hydrogenolysis of diverse linear and branched hydrocarbons, ranging from C2 to polyolefins. To the best of our knowledge, 1 exhibits one of the highest polyolefin hydrogenolysis activities (9,800 (CH 2 units) ⋅ mol(Ta) −1 ⋅ h −1 at 200 °C/17 atm H 2 ) reported to date in the peer-reviewed literature. Unlike the AlS/ZrNp 2 analog, the Ta catalyst is more thermally stable and offers multiple potential C−C bond activation pathways. For hydrogenolysis, AlS/TaNp x is effective for a wide variety of pre- and post-consumer polyolefin plastics and is not significantly deactivated by standard polyolefin additives at typical industrial concentrations.
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The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.
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Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.
X-ray Absorption Near-Edge Structure (XANES) spectroscopy is a powerful tool to reveal key structural and electronic features of isolated catalytic sites, yet insights into molecular structure and more detailed orbital analysis through a combination of experimental and computed XANES analysis are necessary for accurate interpretation of the spectra, especially when significant heterogeneity exits among the catalytic sites. Herein, we present an integrated computational and experimental strategy to determine both primary and secondary bonding interactions within the XANES pre-edge region for organovanadium complexes, which was developed using a series of well-defined molecular vanadium complexes and then applied to the characterization of a supported organovanadium olefin hydrogenation catalyst. Time-dependent density functional theory is used to predict the energy of pre-edge XANES features for a series of vanadium complexes with a variety of oxidation states and local coordination environments. Further, a calibration scheme incorporating different density functionals and basis sets is established, resulting in an optimized scheme that accurately predicts pre-edge energies with a mean absolute error of 0.40 eV. Second-shell coordination (e.g., V---V) effects within XANES are identified through the analysis of the computed dominant orbital contributions for multi-vanadium complexes. Orbital analysis also provided confirmation that the vanadium-hydride formation combined with the heterogeneity of the catalytic active species in ole-fin hydrogenation caused the energy shift and broadening of the pre-edge peak after hydrogen treatment of the silica-supported organovanadium pre-catalyst. This work further elucidates computational XANES simulations and techniques potentially guiding characterization in surface organometallic chemistry.
The ultrafast excited state pathways and dynamics of NiII-bipyridine complexes influence the yield of photochemical processes involved in their catalytic cross-coupling reactions. Here we present ultrafast Ni K x-ray emission spectroscopy (XES) and x-ray solution scattering (XSS) of a NiII-bipyridine aryl halide complex, [Ni(t-Bubpy)(o-tol)Br], to quantify the excited state population dynamics and structural changes of the pre-catalyst. Due to the local spin-sensitivity of XES, the population dynamics of metal-to-ligand charge transfer (MLCT) and metal-centered (MC) excited states is established. A rapid ground state recovery pathway is newly identified, representing a significant deactivation pathway during photocatalysis. Furthermore, the pseudotetrahedral structure of the long-lived MC excited state is unambiguously identified and refined by XSS. The results advance our understanding of the ultrafast relaxation mechanisms that impact the photocatalytic mechanism and yield for NiII-bipyridine aryl halide cross-coupling catalysts.
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