Extended X-ray absorption fine structure (EXAFS) spectroscopy has been used to study short range order in heterometallic alloys for almost four decades. In this critical review, experimental, ...theoretical and data analytical approaches are revisited to examine their power, and limitations, in studies of bimetallic nanocatalysts. This article covers the basics of EXAFS experiments, data analysis, and modelling of nanoscale clusters. It demonstrates that, in the best case scenario, quantitative information about the nanocatalyst's size, shape, details of core-shell architecture, as well as static and dynamic disorder in metal-metal bond lengths can be obtained. The article also emphasizes the main challenge accompanying such insights: the need to account for the statistical nature of the EXAFS technique, and discusses corrective strategies.
Oxide-supported noble metal catalysts have been extensively studied for decades for the water gas shift (WGS) reaction, a catalytic transformation central to a host of large volume processes that ...variously utilize or produce hydrogen. There remains considerable uncertainty as to how the specific features of the active metal-support interfacial bonding-perhaps most importantly the temporal dynamic changes occurring therein-serve to enable high activity and selectivity. Here we report the dynamic characteristics of a Pt/CeO
system at the atomic level for the WGS reaction and specifically reveal the synergistic effects of metal-support bonding at the perimeter region. We find that the perimeter Pt
- O vacancy-Ce
sites are formed in the active structure, transformed at working temperatures and their appearance regulates the adsorbate behaviors. We find that the dynamic nature of this site is a key mechanistic step for the WGS reaction.
The rapid growth of methods emerging in the past decade for synthesis of “designer” catalystsranging from the size and shape-selected nanoparticles to mass-selected clusters, to precisely engineered ...bimetallic surfaces, to single site and pair site catalystshas opened opportunities for tailoring the catalyst structure for the desired activity and selectivity. It has also sharpened the need for developing approaches to the operando characterization, ones that identify the catalytic active sites and follow their evolutions in reaction conditions. Commonly used methods for determination of the activity descriptors in the nanocatalysts, based on the correlation between the changes in catalyst performance and evolution of its structural and electronic properties, are hampered by the paucity of experimental techniques that can detect such properties with high accuracy and in reaction conditions. Out of many such techniques, X-ray absorption spectroscopy (XAS) stands out as an element-specific method that is very sensitive to the local geometric and electronic properties of the metal atoms and their surroundings and, therefore, is able to track catalyst structure modifications in operando conditions. Despite the vast amount of structure-specific information (such as, e.g., the charge states and radial distribution function of neighbors of selected atomic species) stored in the XAS data of catalysts, extracting it from the spectra is challenging, especially in the conditions of low metal weight loading, nanoscale dimensions, heterogeneous size and composition distributions, and harsh reaction environment. In this Perspective, we discuss the recent developments in XAS data analysis achieved by employing supervised and unsupervised machine learning (ML) methods for structural characterization of catalysts. By benefiting from the sensitivity of ML methods to subtle variations in experimental data, a previously “hidden” relationship between the X-ray absorption spectrum and descriptors of material’s structure and/or composition can be found, as illustrated on representative examples of mono-, hetero-, and nonmetallic catalysts. In the case of supervised ML, the experimental spectra can be rapidly “inverted”, and the structure of the catalyst can be tracked in real time and in reaction conditions. Emerging opportunities for catalysis research that the ML methods enable, such as high-throughput data analysis, and their applications to other experimental probes of catalyst structure are discussed.
Ceria and its solid solutions play a vital role in several industrial processes and devices. These include solar energy-to-fuel conversion, solid oxide fuel and electrolyzer cells, memristors, ...chemical looping combustion, automotive 3-way catalysts, catalytic surface coatings, supercapacitors and recently, electrostrictive devices. An attractive feature of ceria is the possibility of tuning defect-chemistry to increase the effectiveness of the materials in application areas. Years of study have revealed many features of the long-range, macroscopic characteristics of ceria and its derivatives. In this review we focus on an area of ceria defect chemistry which has received comparatively little attention - defect-induced local distortions and short-range associates. These features are non-periodic in nature and hence not readily detected by conventional X-ray powder diffraction. We compile the relevant literature data obtained by thermodynamic analysis, Raman spectroscopy, and X-ray absorption fine structure (XAFS) spectroscopy. Each of these techniques provides insight into material behavior without reliance on long-range periodic symmetry. From thermodynamic analyses, association of defects is inferred. From XAFS, an element-specific probe, local structure around selected atomic species is obtained, whereas from Raman spectroscopy, local symmetry breaking and vibrational changes in bonding patterns is detected. We note that, for undoped ceria and its solid solutions, the relationship between short range order and cation-oxygen-vacancy coordination remains a subject of active debate. Beyond collating the sometimes contradictory data in the literature, we strengthen this review by reporting new spectroscopy results and analysis. We contribute to this debate by introducing additional data and analysis, with the expectation that increasing our fundamental understanding of this relationship will lead to an ability to predict and tailor the defect-chemistry of ceria-based materials for practical applications.
Doped and oxygen deficient ceria exhibits local bonding patterns that deviate from the average fluorite symmetry found in XRD.
Conspectus To improve the reactivity of catalysts, two goals that are perhaps the most obvious but at the same time the most elusive ones are (1) to increase the number of active sites and/or (2) to ...enhance the intrinsic activity of each active site. Both seem realizable in single-atom catalysts (SACs), in which in principle all of the metal sites could be active sites. The enhanced reactivity of SACs and their unique reaction mechanisms originate from their unique structures and interactions with supports. The details of these structures are therefore the focus of intense investigation and debates. Among the factors hindering the progress in their investigation is the complexity of SAC systems, which is primarily related to the heterogeneity in their structures within the same sample. In this Account, we outline strategies that we have found to be useful for selected systems we have studied that can also be applied to many other SACs. As an example of the most uniformly distributed SAC system, we focus on a Pt SAC support on nanoceria. A combination of imaging and spectroscopic techniques confirmed the atomic dispersion of Pt and the uniform distribution of Pt2+ single-atom sites. That uniformity was a prerequisite for determining the three-dimensional structure of Pt single atoms on the support surface. Our work illuminated the dependence of the structure and dynamics of Pt single atoms on the type of support. For Pt/ceria SACs, upon breaking of the Pt–O–Ce interaction at high temperatures under reductive conditions, the SACs aggregated into Pt nanoparticles that were active for the water gas shift reaction. In contrast, when Pt single atoms were anchored on the surface of a Co3O4 support, the removal of O in H2 at high temperatures resulted in the formation of Pt1Co m /Co3O4 single-atom alloys (SAAs), which showed high N2 selectivity for NO reduction. In SAAs with increased complexity, when the interparticle distribution of compositions of catalytically active species is narrow, advanced methods of X-ray absorption near-edge structure (XANES) analysis, e.g., those employing machine learning, allow their placements within “representative” particles to be deciphered and their changes in reaction conditions to be tracked. Increasing the level of heterogeneity in the binding sites available to SACs blurs the resolution of spectroscopic methods such as X-ray absorption fine structure (XAFS) spectroscopy for detecting the details of their environments. We illustrate the effects of heterogeneity of the distribution of singly dispersed metal active sites using the PtNi/SBA-15 bimetallic catalyst as an example. In this system, the fact that Ni atoms existed in two types of species (the silicate phase and the PtNi nanoclusters) complicated the XAFS analysis, although when corrections for the silicate phase were applied, the results obtained from extended XAFS (EXAFS) data analysis helped to determine the three-dimensional structure of the PtNi nanoclusters. While not a review of the field, this Account is aimed to share with the readers our efforts to resolve challenges due to many forms of structural complexity existing in most heterogeneous single-atom systems and obtain insights into the unique atomic structures, as inferred from the correlative use of multimodal characterization tools and advances in data analysis and modeling methods that we developed.
Framework nitrogen atoms of carbon nitride (C3N4) can coordinate with and activate metal sites for catalysis. In this study, C3N4 was employed to harvest visible light and activate Co2+ sites, ...without the use of additional ligands, in photochemical CO2 reduction. Photocatalysts containing single Co2+ sites on C3N4 were prepared by a simple deposition method and demonstrated excellent activity and product selectivity toward CO formation. A turnover number of more than 200 was obtained for CO production using the synthesized photocatalyst under visible-light irradiation. Inactive cobalt oxides formed at relatively high cobalt loadings but did not alter product selectivity. Further studies with X-ray absorption spectroscopy confirmed the presence of single Co2+ sites on C3N4 and their important role in achieving selective CO2 reduction.
Photo–thermo catalysis, which integrates photocatalysis on semiconductors with thermocatalysis on supported nonplasmonic metals, has emerged as an attractive approach to improve catalytic ...performance. However, an understanding of the mechanisms in operation is missing from both the thermo‐ and photocatalytic perspectives. Deep insights into photo–thermo catalysis are achieved via the catalytic oxidation of propane (C3H8) over a Pt/TiO2‐WO3 catalyst that severely suffers from oxygen poisoning at high O2/C3H8 ratios. After introducing UV/Vis light, the reaction temperature required to achieve 70 % conversion of C3H8 lowers to a record‐breaking 90 °C from 324 °C and the apparent activation energy drops from 130 kJ mol−1 to 11 kJ mol−1. Furthermore, the reaction order of O2 is −1.4 in dark but reverses to 0.1 under light, thereby suppressing oxygen poisoning of the Pt catalyst. An underlying mechanism is proposed based on direct evidence of the in‐situ‐captured reaction intermediates.
Photo–thermo catalytic oxidation of propane (C3H8) on Pt/TiO2‐WO3 catalyst proceeds with excellent activity at high O2/C3H8 ratios and low temperatures. An investigation of the apparent activation energy and reaction orders (relative to thermal catalysis) reveals that oxygen poisoning of the supported Pt catalyst is mitigated and performance is consequently enhanced.
The knowledge of the coordination environment around various atomic species in many functional materials provides a key for explaining their properties and working mechanisms. Many structural motifs ...and their transformations are difficult to detect and quantify in the process of work (operando conditions), due to their local nature, small changes, low dimensionality of the material, and/or extreme conditions. Here we use an artificial neural network approach to extract the information on the local structure and its in situ changes directly from the x-ray absorption fine structure spectra. We illustrate this capability by extracting the radial distribution function (RDF) of atoms in ferritic and austenitic phases of bulk iron across the temperature-induced transition. Integration of RDFs allows us to quantify the changes in the iron coordination and material density, and to observe the transition from a body-centered to a face-centered cubic arrangement of iron atoms. This method is attractive for a broad range of materials and experimental conditions.
Catalytic transformation of CH
under a mild condition is significant for efficient utilization of shale gas under the circumstance of switching raw materials of chemical industries to shale gas. ...Here, we report the transformation of CH
to acetic acid and methanol through coupling of CH
, CO and O
on single-site Rh
O
anchored in microporous aluminosilicates in solution at ≤150 °C. The activity of these singly dispersed precious metal sites for production of organic oxygenates can reach about 0.10 acetic acid molecules on a Rh
O
site per second at 150 °C with a selectivity of ~70% for production of acetic acid. It is higher than the activity of free Rh cations by >1000 times. Computational studies suggest that the first C-H bond of CH
is activated by Rh
O
anchored on the wall of micropores of ZSM-5; the formed CH
then couples with CO and OH, to produce acetic acid over a low activation barrier.
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.
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