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Replication Data for: Latent Representation Learning for Structural Characterization of Catalysts
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.
Additive Manufactured Ultra-Fine Lattice Structures for Propulsion Catalysts
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Latent Representation Learning for Structural Characterization of Catalysts
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Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling
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.
Integrated Experimental and Computational K-Edge X-ray Absorption Near-Edge Structure Analysis of Vanadium Catalysts
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.
Ultrafast population and structural dynamics of a Ni-bipyridine photoredox catalyst reveal a significant deactivation pathway
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.
In situ structural study of manganese and iron oxide promoted rhodium catalysts for oxygenate synthesis
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Ru/MgO catalyst with dual Ru structure sites for efficient CO production from CO 2 hydrogenation
The development and comprehension of supported metal catalysts for CO 2 hydrogenation is of paramount importance in mitigating the net CO 2 emissions. Supported Ru catalysts have been widely recognized in facilitating CO 2 methanation, on which recent findings suggest that the CO 2 hydrogenation process can be manipulated to favor the reverse water–gas shift (RWGS) pathway by precisely adjusting the size of Ru particles. However, the size-dependent impact of Ru remains a topic of lively debate. In this work, Ru/MgO catalysts with Ru in the form of single atoms (Ru 1 ) and few-atom cluster (Ru FAC ) structures were prepared for CO 2 hydrogenation. The 1.0Ru/MgO catalyst (with 1 wt.% of Ru), featuring a mixture of Ru 1 and Ru FAC with a size of 0.6–1.0 nm, showed the highest CO yield (38% at 500 °C) with balanced CO 2 conversion and CO selectivity. Transient CO 2 hydrogenation and temperature-programmed surface reaction (TPSR) studies suggested that the adsorbed CO 2 species participated in CO 2 hydrogenation. On Ru 1 sites, CO 2 hydrogenation followed the RWGS pathway, resulting in the production of CO. In contrast, on Ru FAC sites, the enhanced H 2 dissociation ability, along with the presence of adsorbed bidentate and monodentate carbonate species at the Ru-MgO interfaces, facilitated the formation of CH 4 through the CO 2 methanation pathway. In conclusion, this study highlights the critical roles of Ru structure and local environment in defining the CO 2 hydrogenation pathways and provides new design principles for highly active Ru-based catalysts.
Unraveling the Structural Sensitivity of Metal Catalysts in Ethylene Hydroformylation: Insights from Theory and Experiments
Here, in this study, we combined experimental and theoretical methods to investigate the structural sensitivity of metal catalysts in the ethylene hydroformylation reaction. Among Rh, Pt, Ir, Ni, Au, Ag, Pd, and Cu catalysts studied using experimental and theoretical methods, Rh showed the highest selectivity toward the C-C coupling product from CO and C 2 H 5 (i.e., C 2 H 5 CHO). The results from DFT and microkinetic simulations revealed that the activation energy barrier for C-C coupling is lowest on the Rh nanocluster, which explains the experimentally observed highest C 2 H 5 CHO selectivity on the Rh catalyst. Furthermore, DFT results demonstrated that the sites located on the flat surfaces of nanoparticles primarily promote the hydrogenation reaction, leading to the formation of undesired C 2 H 6 . In contrast, undercoordinated edge and corner sites of the nanocluster promote the C-C coupling reaction. Thus, our results illustrate that the selectivity toward C 3 oxygenates in ethylene hydroformylation reaction can be steered by tuning the size of Rh nanoparticles (the best-performing catalyst) to optimize the active (edge and corner) sites that preferentially promote the C-C coupling reaction.
Additive Manufacture of Ultra-Fine Lattice Structures of Green Propulsion Catalysts
Traditional mono-propulsion catalysts consist of coated ceramic or graphite foams that possess anisotropic mechanical and fluid properties limiting design, cost, availability, and operational use. Ultra-fine lattice structures are repeating unit cells with ligament thickness as small as 100 μm produced via Additive manufacture (AM). These lattice structures have the potential to replace coated foams used in a mono-propellant system catalysts. AM ultrafine lattice structures are designed to mimic the operational intent of coated foams but with improved design flexibility, compressive strength, and flow behavior printed from into a single part directly from the preferred platinum metal alloy. The investigation objective was to conduct feasibility studies of AM ultra-fine lattice structures capable of replacing coated foams with superior functionality. NASA MSFC identified desired lattice characteristics and created designs while EOS developed optimized laser powder bed fusion AM parameters to manufacture Ti6Al4V and tungsten specimens. Optimized designs, computational tools, AM parameters, and post-process methods were developed. Specimens underwent x-ray microfocus CT, metallographic inspection, compression testing, and flow testing. Results demonstrate that AM ultra-fine lattices improved geometric and performance repeatability with the potential for significantly increased availability while decreasing cost and lead time.
The significance of the local structure of cobalt-based catalysts on the photoelectrochemical water oxidation activity of BiVO 4
The local structures of the water oxidation catalysts play an important role in reaction kinetics and the performance of the photoanodes. In this study, we deposited cobalt-based catalysts on nanoporous BiVO 4 with controlled thicknesses by atomic layer deposition (ALD). Despite the similar oxidation states of cobalt in all depositions, different water oxidation activities in neutral pH conditions were observed. Here, a dramatic photocurrent raise, lowered kinetic overpotential, and smaller charge transfer resistance across the photoanode/electrolyte interface were achieved when a uniform ultrathin Co(OH) 2 layer was formed on BiVO 4 . Photocurrent density for water oxidation showed a 95% enhancement at 0.6 V vs. RHE when the catalyst was in the form of Co(OH) 2 , while an 80% increase was obtained for CoO. Ideal coordination of Co(OH) 2 on hydroxylated BiVO 4 surface assists the charge transfer between the electrolyte and BiVO 4 without increasing surface recombination. The results of this study emphasize the importance of controlling the local structure of the catalysts in the performance of the water splitting photoanodes.
Real-time dynamics and structures of supported subnanometer catalysts via multiscale simulations
Understanding the performance of subnanometer catalysts and how catalyst treatment and exposure to spectroscopic probe molecules change the structure requires accurate structure determination under working conditions. Experiments lack simultaneous temporal and spatial resolution and could alter the structure, and similar challenges hinder first-principles calculations from answering these questions. Here, we introduce a multiscale modeling framework to follow the evolution of subnanometer clusters at experimentally relevant time scales. We demonstrate its feasibility on Pd adsorbed on CeO 2 (111) at various catalyst loadings, temperatures, and exposures to CO. We show that sintering occurs in seconds even at room temperature and is mainly driven by free energy reduction. It leads to a kinetically (far from equilibrium) frozen ensemble of quasi-two-dimensional structures that CO chemisorption and infrared experiments probe. CO adsorption makes structures flatter and smaller. High temperatures drive very rapid sintering toward larger, stable/metastable equilibrium structures, where CO induces secondary structure changes only.
Finite-Temperature Structures of Supported Subnanometer Catalysts Inferred via Statistical Learning and Genetic Algorithm-Based Optimization
Single-atom catalysts (SACs) minimize noble metal utilization and can alter the activity and selectivity of supported metal nanoparticles. However, the morphology of active centers, including single atoms and subnanometer clusters of a few atoms, remains elusive due to experimental challenges. The computational cost to describe numerous cluster shapes and sizes makes direct first-principles calculations impractical. We present a computational framework to enable structure determination for single-atom and subnanometer cluster catalysts. As a case study, we obtained the low energy structures of Pd n (n = 1-21) clusters supported on CeO 2 (111), which are critical components of automobile three-way catalysts. Trained on density functional theory data, a three-dimensional cluster expansion is established using statistical learning to describe the Hamiltonian and predict energies of supported Pdn clusters of any structure. Low energy stable and metastable structures are identified using a Metropolis Monte Carlo-based genetic algorithm in the canonical ensemble at 300 K. We observe that supported single atoms sinter to form bilayer clusters and large cluster isomers share similarities in both shape and energy, and elucidate the significance of the support and microstructure on cluster stability. We discovered a simple surrogate structure-energy model, where the energy per atom scales with the square root of the average first coordination number, which can be used to estimate energies and compare the stability of clusters. Our framework, applicable to any metal/support system, fills an important methodological gap to predict the stability of supported metal catalysts in the subnanometer regime.
Double-Atom Catalysts Featuring Inverse Sandwich Structure for CO 2 Reduction Reaction: A Synergetic First-Principles and Machine Learning Investigation
Electrocatalytic CO 2 reduction reactions (CO 2 RR) based on scalable and highly efficient catalysis provide an attractive strategy for reducing CO 2 emissions. Here in this work, we combined first-principles density functional theory (DFT) and machine learning (ML) to comprehensively explore the potential of double-atom catalysts (DACs) featuring an inverse sandwich structure anchored on defective graphene (gra) to catalyze CO 2 RR to generate C 1 products. We started with five homonuclear M 2 ⊥gra (M = Co, Ni, Rh, Ir, and Pt), followed by 127 heteronuclear MM'⊥gra (M = Co, Ni, Rh, Ir, and Pt, M' = Sc–Au). Stable DACs were screened by evaluating their binding energy, formation energy, and dissolution potential of metal atoms, as well as conducting first-principles molecular dynamics simulations with and without solvent water molecules. Based on DFT calculations, Rh 2 ⊥gra DAC was found to outperform the other four homonuclear DACs and the Rh-based single- and double-atom catalysts of noninverse sandwich structures. Out of the 127 heteronuclear DACs, 14 were found to be stable and have good catalytic performance. An ML approach was adopted to correlate key factors with the activity and stability of the DACs, including the sum of radii of metal and ligand atoms (d M–M' , d M–C , and d M'–C ), the sum and difference of electronegativity of two metal atoms (P M + P M' , P M – P M '), the sum and difference of first ionization energy of two metal atoms (I M + I M' , I M – I M '), the sum and difference of electron affinity of two metal atoms (A M + A M' , A M – A M '), and the number of d-electrons of the two metal atoms (Nd). The obtained ML models were further used to predict 154 potential electrocatalysts out of 784 possible DACs featuring the same inverse sandwich configuration. Overall, this work not only identified promising CO 2 RR DACs featuring the reported inverse sandwich structure but also provided insights into key atomic characteristics associated with high CO 2 RR activity.
Regulating Electronic Structure of Single-Atom Catalysts toward Efficient Bifunctional Oxygen Electrocatalysis
We report electronic structure of single-atom catalysts (SACs) is critical for bifunctional oxygen electrocatalysis by adjusting the binding energy in oxygen-containing intermediates. However, the regulation of electronic structure has always been a challenge to improve catalytic reactivity. Herein, by introducing a heterogenous metal, the electronic structure through a direct bonding interaction to the active center atom is effectively adjusted. Partial charge transfer between the two atoms optimizes the binding energy of intermediates and reducing the energy barrier of the catalytic reaction. Theoretical calculations confirm these effects and the uniform distribution of 3d orbitals, leading to the improvement of bifunctional oxygen electrocatalytic reactivity. Benefiting from these attributes, the as-constructed bifunctional catalyst enables outstanding electrocatalytic performances in both oxygen reduction and hydrogen oxidation in various energy storage systems. The generality and expandability of this strategy is demonstrated by further successful development of other dual-metal catalysts systems with various active metals.
A Study of How LaFeO 3 and CaTiO 3 Supports Affect the Oxidation, Hydrogenation, and Methane Steam Reforming Activity of Pt and Ni Catalysts
The structure and catalytic properties of Pt and Ni supported on thin films of LaFeO 3 and CaTiO 3 , prepared by Atomic Layer Deposition (ALD), were investigated. X-Ray diffraction (XRD) and scanning transmission electron microscopy (STEM) show that reduction at 1073 K causes Pt-Fe intermetallic compound formation for Pt/LaFeO 3 . For Pt/CaTiO 3 , 1073-K reduction induces local decomposition of the CaTiO 3 with migration of Ti to the Pt. Reduced Pt/LaFeO 3 and Pt/CaTiO 3 exhibited CO-oxidation activity similar to that for Pt supported on MgAl 2 O 4 but were much less active for propane oxidation and hydrogenation of 1-hexene and toluene. In contrast, Ni/CaTiO 3 behaved similarly to a conventional supported Ni catalyst for the methane-steam-reforming (MSR) and 1-hexene hydrogenation. There was also no evidence for local decomposition of the perovskite in the vicinity of the Ni particles. Furthermore, the results obtained in this study demonstrate that metal-perovskite interactions that affect reactivity are specific to each component.
Structural and Reactivity Effects of Secondary Metal Doping into Iron-Nitrogen-Carbon Catalysts for Oxygen Electroreduction
While improved activity was recently reported for bimetallic iron-metal-nitrogen-carbon (FeMNC) catalysts for the oxygen reduction reaction (ORR) in acid medium, the nature of active sites and interactions between the two metals are poorly understood. Here, FeSnNC and FeCoNC catalysts were structurally and catalytically compared to their parent FeNC and SnNC catalysts. While CO cryo-chemisorption revealed a twice lower site density of M-N x sites for FeSnNC and FeCoNC relative to FeNC and SnNC, the mass activity of both bimetallic catalysts is 50–100% higher than that of FeNC due to a larger turnover frequency in the bimetallic catalysts. Electron microscopy and X-ray absorption spectroscopy identified the coexistence of Fe-N x and Sn-N x or Co-N x sites, while no evidence was found for binuclear Fe-M-N x sites. 57 Fe Mössbauer spectroscopy revealed that the bimetallic catalysts feature a higher D 1 /D 2 ratio of the spectral signatures assigned to two distinct Fe-N x sites, relative to the FeNC parent catalyst. Furthermore, the addition of the secondary metal favored the formation of D 1 sites, associated with the higher turnover frequency.