Spectral Decomposition of X-ray Absorption Spectroscopy Datasets: Methods and Applications

Synergizing Fe2O3 Nanoparticles on Single Atom Fe-N-C for Nitrate Reduction to Ammonia at Industrial Current Densities

The electrochemical reduction of nitrates (NO3 -) enables a pathway for the carbon neutral synthesis of ammonia (NH3), via the nitrate reduction reaction (NO3RR), which has been demonstrated at high selectivity. However, to make NH3 synthesis cost-competitive with current technologies, high NH3 partial current densities (jNH3) must be achieved to reduce the levelized cost of NH3. Here, the high NO3RR activity of Fe-based materials is leveraged to synthesize a novel active particle-active support system with Fe2O3 nanoparticles supported on atomically dispersed Fe-N-C. The optimized 3×Fe2O3/Fe-N-C catalyst demonstrates an ultrahigh NO3RR activity, reaching a maximum jNH3 of 1.95 A cm-2 at a Faradaic efficiency (FE) for NH3 of 100% and an NH3 yield rate over 9 mmol hr-1 cm-2. Operando XANES and post-mortem XPS reveal the importance of a pre-reduction activation step, reducing the surface Fe2O3 (Fe3+) to highly active Fe0 sites, which are maintained during electrolysis. Durability studies demonstrate the robustness of both the Fe2O3 particles and Fe-Nx sites at highly cathodic potentials, maintaining a current of -1.3 A cm-2 over 24 hours. This work exhibits an effective and durable active particle-active support system enhancing the performance of the NO3RR, enabling industrially relevant current densities and near 100% selectivity.

Revealing the structure of the active sites for the electrocatalytic CO2 reduction to CO over Co single atom catalysts using operando XANES and machine learning

Transition-metal nitrogen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co-N-C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co-N-C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co-N-C catalysts, and further optimization of this class of electrocatalytic systems.

Operando UV-vis spectroscopy for real-time monitoring of nanoparticle size in reaction conditions: a case study on rWGS over Au nanoparticles

We propose the use of surface plasmon resonance (SPR) as a distinctive marker for real-time monitoring in reaction conditions of gold nanoparticles supported on α-Al2O3. The study leverages the SPR shape-and-size dependency to monitor metal nanoparticles in reaction conditions, evidencing an influence of both dimensions and agglomerations on the SPR peak position. Operando measurements, coupling UV-vis spectroscopy and catalytic testing, allows to follow the dynamics during nanoparticle formation (Au3+ to Au0 reduction) and during the reverse water gas shift reaction (CO2 + H2 → CO + H2O). The catalyst structure and stability in reaction conditions was further confirmed by operando X-ray spectroscopy and PXRD data. Overall, this approach enables the direct acquisition of information on the structure-activity relationship of metal-based supported catalysts under actual reaction conditions.

Understanding C-H activation in light alkanes over Cu-MOR zeolites by coupling advanced spectroscopy and temperature-programmed reduction experiments

The direct activation of methane to methanol (MTM) proceeds through a chemical-looping process over Cu-oxo sites in zeolites. Herein, we extend the overall understanding of oxidation reactions over metal-oxo sites and C-H activation reactions by pinpointing the evolution of Cu species during reduction. To do so, a set of temperature-programmed reduction experiments were performed with CH4, C2H6, and CO. With a temperature ramp, the Cu reduction could be accelerated to detect changes in Cu speciation that are normally not detected due to the slow CH4 adsorption/interaction during MTM (∼200 °C). To follow the Cu-speciation with the three reductants, X-ray absorption spectroscopy (XAS), UV-vis and FT-IR spectroscopy were applied. Multivariate curve resolution alternating least-square (MCR-ALS) analysis was used to resolve the time-dependent concentration profiles of pure Cu components in the X-ray absorption near edge structure (XANES) spectra. Within the large datasets, as many as six different CuII and CuI components were found. Close correlations were found between the XANES-derived CuII to CuI reduction, CH4 consumption, and CO2 production. A reducibility-activity relationship was also observed for the Cu-MOR zeolites. Extended X-ray absorption fine structure (EXAFS) spectra for the pure Cu components were furthermore obtained with MCR-ALS analysis. With wavelet transform (WT) analysis of the EXAFS spectra, we were able to resolve the atomic speciation at different radial distances from Cu (up to about 4 Å). These results indicate that all the CuII components consist of multimeric CuII-oxo sites, albeit with different Cu-Cu distances.

Tracking the Evolution of Single-Atom Catalysts for the CO2 Electrocatalytic Reduction Using Operando X-ray Absorption Spectroscopy and Machine Learning

Transition metal-nitrogen-doped carbons (TMNCs) are a promising class of catalysts for the CO2 electrochemical reduction reaction. In particular, high CO2-to-CO conversion activities and selectivities were demonstrated for Ni-based TMNCs. Nonetheless, open questions remain about the nature, stability, and evolution of the Ni active sites during the reaction. In this work, we address this issue by combining operando X-ray absorption spectroscopy with advanced data analysis. In particular, we show that the combination of unsupervised and supervised machine learning approaches is able to decipher the X-ray absorption near edge structure (XANES) of the TMNCs, disentangling the contributions of different metal sites coexisting in the working TMNC catalyst. Moreover, quantitative structural information about the local environment of active species, including their interaction with adsorbates, has been obtained, shedding light on the complex dynamic mechanism of the CO2 electroreduction.

Intermediates during the Nucleation of Platinum Nanoparticles by a Reaction with Ethylene Glycol: Operando X-ray Absorption Spectroscopy Studies with a Microfluidic Cell

Using operando X-ray absorption spectroscopy in a continuous-flow microfluidic cell, we have investigated the nucleation of platinum nanoparticles from aqueous hexachloroplatinate solution in the presence of the reducing agent ethylene glycol. By adjusting flow rates in the microfluidic channel, we resolved the temporal evolution of the reaction system in the first few seconds, generating the time profiles for speciation, ligand exchange, and reduction of Pt. Detailed analysis of the X-ray absorption near-edge structure and extended X-ray absorption fine structure spectra with multivariate data analysis shows that at least two reaction intermediates are involved in the transformation of the precursor H₂PtCl₆ to metallic platinum nanoparticles, including the formation of clusters with Pt-Pt bonding before complete reduction to Pt nanoparticles.

Deciphering the Structural and Chemical Transformations of Oxide Catalysts during Oxygen Evolution Reaction Using Quick X-ray Absorption Spectroscopy and Machine Learning

Bimetallic transition-metal oxides, such as spinel-like CoxFe3-xO4 materials, are known as attractive catalysts for the oxygen evolution reaction (OER) in alkaline electrolytes. Nonetheless, unveiling the real active species and active states in these catalysts remains a challenge. The coexistence of metal ions in different chemical states and in different chemical environments, including disordered X-ray amorphous phases that all evolve under reaction conditions, hinders the application of common operando techniques. Here, we address this issue by relying on operando quick X-ray absorption fine structure spectroscopy, coupled with unsupervised and supervised machine learning methods. We use principal component analysis to understand the subtle changes in the X-ray absorption near-edge structure spectra and develop an artificial neural network to decipher the extended X-ray absorption fine structure spectra. This allows us to separately track the evolution of tetrahedrally and octahedrally coordinated species and to disentangle the chemical changes and several phase transitions taking place in CoxFe3-xO4 catalysts and on their active surface, related to the conversion of disordered oxides into spinel-like structures, transformation of spinels into active oxyhydroxides, and changes in the degree of spinel inversion in the course of the activation treatment and under OER conditions. By correlating the revealed structural changes with the distinct catalytic activity for a series of CoxFe3-xO4 samples, we elucidate the active species and OER mechanism.

XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications

Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The existing methods of experimental characterization of nanocatalysts, which make it possible to assess the possibility of using these materials in specific chemical reactions or applications, generate significant amounts of heterogeneous data. The acceleration of new functional materials, including nanocatalysts, directly depends on the speed and quality of extracting hidden dependencies and knowledge from the obtained experimental data. Usually, such experiments involve different characterization techniques and different types of X-ray absorption spectroscopy (XAS) too. Using the machine learning (ML) methods based on XAS data, we can study and predict the atomic-scale structure and another bunch of parameters for the nanocatalyst efficiently. However, before using any ML model, it is necessary to make sure that the XAS raw experimental data is properly pre-processed, cleared, and prepared for ML application. Usually, the XAS preprocessing stage is vaguely presented in scientific studies, and the main efforts of researchers are devoted to the ML description and implementation stage. However, the quality of the input data influences the quality of ML analysis and the prediction results used in the future. This paper fills the gap between the stage of obtaining XAS data from synchrotron facilities and the stage of using and customizing various ML analysis and prediction models. We aimed this study to develop automated tools for the preprocessing and presentation of data from physical experiments and the creation of deposited datasets on the basis of the example of studying palladium-based nanocatalysts using synchrotron radiation facilities. During the study, methods of preliminary processing of XAS data were considered, which can be conditionally divided into X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). This paper proposes a software toolkit that implements data preprocessing scenarios in the form of a single pipeline. The main preprocessing methods used in this study proposed are principal component analysis (PCA); z-score normalization; the interquartile method for eliminating outliers in the data; as well as the k-means machine learning method, which makes it possible to clarify the phase of the studied material sample by clustering feature vectors of experiments. Among the results of this study, one should also highlight the obtained deposited datasets of physical experiments on palladium-based nanocatalysts using synchrotron radiation. This will allow for further high-quality data mining to extract new knowledge about materials using artificial intelligence methods and machine learning models, and will ensure the smooth dissemination of these datasets to researchers and their reuse.

Copper Pairing in the Mordenite Framework as a Function of the CuI /CuII Speciation

A series of gas‐phase reactants is used to treat a Cu‐exchanged mordenite zeolite with the aim of studying the influence of the reaction environment on the formation of Cu pairs. The rearrangement of Cu ions to form multimeric sites as a function of their oxidation state was probed by X‐ray absorption spectroscopy (XAS) and also by applying advanced analysis through wavelet transform, a method able to specifically locate Cu–Cu interactions also in the presence of overlapping contributions from other scattering paths. The nature of the Cu‐oxo species formed upon oxidation was further crosschecked by DFT‐assisted fitting of the EXAFS data and by resonant Raman spectroscopy. Altogether, the Cu^I/Cu^II speciation clearly correlates with Cu proximity, with metal ion pairs quantitatively forming under an oxidative environment.

Insights into the Preparation of Copper Catalysts Supported on Layered Double Hydroxide Derived Mixed Oxides for Ethanol Dehydrogenation

Acetaldehyde is an important chemical commodity and a building block for producing several other high-value products in the chemical industry. This has motivated the search for suitable, efficient, stable, and selective catalysts, as well as renewable raw materials such as ethanol. In this work, supported copper catalysts were prepared from CuZnAl layered double hydroxides (LDHs) with different copper contents (5, 10, and 20 wt %) for application in the ethanol dehydrogenation reaction (EDR). The samples were thoroughly characterized by a series of techniques, which allowed for analysis of all of the copper and zinc species involved in the different catalyst preparation steps and during the EDR. The results obtained by in situ quick extended X-ray absorption fine structure (EXAFS) measurements, combined with multivariate data analysis, showed that the copper content in the pristine LDH influenced the phase composition of the mixed oxide support, which consequently affected the dispersion of copper nanoparticles. The higher the copper content, the higher are the ZnAl₂O₄ and zinc tetrahedral prenuclei (TPN) contents, to the detriment of the ZnO content. All the samples showed high selectivity (>97%) and stability in the catalytic reactions at 300 and 350 °C, with no observed deactivation during 6 h on-stream. Although the samples with lower copper content presented higher copper dispersion and reactivity, the sample containing 20 wt % of copper outperformed the others, with greater conversion and higher activity toward acetaldehyde.

Activation of C-H bonds by a nonheme iron(IV)-oxo complex: mechanistic evidence through a coupled EDXAS/UV-Vis multivariate analysis

The understanding of reactive processes involving organic substrates is crucial to chemical knowledge and requires multidisciplinary efforts for its advancement. Herein, we apply a combined multivariate, statistical and theoretical analysis of coupled time-resolved X-ray absorption (XAS)/UV-Vis data to obtain detailed mechanistic information for on the C-H bond activation of 9,10-dihydroanthracene (DHA) and diphenylmethane (Ph2CH2) by the nonheme FeIV-oxo complex [N4Py·FeIV(O)]2+ (N4Py = N,N-bis(2-pyridylmethyl)-N-bis(2-pyridyl)methylamine) in CH3CN at room temperature. Within this approach, we determine the number of key chemical species present in the reaction mixtures and derive spectral and concentration profiles for the reaction intermediates. From the quantitative analysis of the XAS spectra the transient intermediate species are structurally determined. As a result, it is suggested that, while DHA is oxidized by [N4Py·FeIV(O)]2+ with a hydrogen atom transfer-electron transfer (HAT-ET) mechanism, Ph2CH2 is oxidized by the nonheme iron-oxo complex through a HAT-radical dissociation pathway. In the latter process, we prove that the intermediate FeIII complex [N4Py·FeIII(OH)]2+ is not able to oxidize the diphenylmethyl radical and we provide its structural characterization in solution. The employed combined experimental and theoretical strategy is promising for the spectroscopic characterization of transient intermediates as well as for the mechanistic investigation of redox chemical transformations on the second to millisecond time scales.

Direct structural and mechanistic insights into fast bimolecular chemical reactions in solution through a coupled XAS/UV–Vis multivariate statistical analysis

A combined multivariate and theoretical analysis of coupled XAS/UV–Vis data was proven to be an innovative method to obtain direct structural and mechanistic evidence for bimolecular reactions in solution involving organic substrates.