Speciation of Ru Molecular Complexes in a Homogeneous Catalytic System: Fingerprint XANES Analysis Guided by Machine Learning

Abstract

X-ray absorption spectroscopy is a powerful tool for the characterization of local atomic structure. Commonly, bond lengths and coordination numbers are extracted from the extended energy region of the spectrum (extended X-ray absorption fine structure, EXAFS). However, for many diluted systems, such as homogeneous catalysts, with a low concentration of the active component and under in situ or operando conditions, one cannot collect sufficient EXAFS data for a quantitative analysis. Considering the case of a homogeneous ruthenium-based catalyst, where the ligand surrounding the ruthenium atoms can change from Br to CO depending on the reaction conditions, we establish here an effective machine learning approach based on the descriptor analysis of spectral features. After the training procedure, the algorithm predicts both the ligand surrounding ruthenium and the distances to Br and CO ligands. The prediction quality of the approach was verified by means of a cross-validation procedure applied to the mixture of compounds and was validated for experimental spectra of reference RuBr3 and [RuBr2(CO)3]2 complexes. This work describes a practical route to improve classical fingerprint analysis and linear combination fit by more sophisticated data science algorithms.

In Situ Generated Dispersed Catalysts Based on Molybdenum and Tungsten Phosphides in Hydroprocessing of Guaiacol

“…These peaks are attributed to contributions of W-C and W-O, the interatomic distances of which (2.05±0.01 and 3.22±0.02 Å, respectively) were derived from an R-space model. The large amplitude of the second peak can be explained by the contribution of single and combined multiple scattering paths (W → O → C → W, W → C → O → W, and W → C → O → C → W), as additionally confirmed by the presence of a carbonyl structure in the tungsten catalyst precursor [7,8]. The spectrum of the in situ prepared catalyst (Fig.…”

Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra

“…Then, more intuitive descriptors were utilized. Recently, we successfully applied such an approach to reduce the dimensionality problem in ML application to X-ray absorption spectra, , where such descriptors as peak positions and their intensities and curvatures were used. For the current problem, we constructed the non-convoluted dataset based on the full database, where instead of convoluted spectra, only a set of 36 frequencies of the most intensive vibrational modes was used.…”

Evolution of the active species of homogeneous Ru hydrodeoxygenation catalysts in ionic liquids

“…The fact that only three components were identified also indicates that no other Ru bromide carbonyl complex besides [RuBr 2 (CO) 3 ] 2 is present in the system. Indeed, a complex with different Br/CO coordination numbers would result in similar spectral features but shifted edge position due to changes in Ru oxidation state, 9 which would result in an additional component in PCA.…”

“…S7 †) also provided the 3D atomic structure of the complex. 9,10 Ex situ XAS data collection Experimental Ru K-edge XAS data were measured at the BM23 beamline of ESRF (Grenoble, France) and CLAESS beamline of ALBA (Barcelona, Spain). The powdered reference samples were pressed into pellets to optimize the absorption jump at the Ru K-edge.…”