CO₂ Methanation on Cu-Cluster Decorated Zirconia Supports with Different Morphology: A Combined Experimental In Situ GIXANES/GISAXS, Ex Situ XPS and Theoretical DFT Study

Abstract

Subnanometer copper tetramer−zirconia catalysts turn out to be highly efficient for CO 2 hydrogenation and its conversion to methane. The cluster size and substrate morphology are controlled to optimize the catalytic performance. The two types of zirconia supports investigated are prepared by atomic layer deposition (∼3 nm thick film) and supersonic cluster beam deposition (nanostructured film, ∼100 nm thick). The substrate plays a crucial role in determining the activity of the catalyst as well as its cyclability over repeated thermal ramps. A temperature-programmed reaction combined with in situ X-ray characterization reveals the correlation between the evolution in the oxidation state and catalytic activity. Ex situ photoelectron spectroscopy indicates Cu clusters with stronger interactions with the nanostructured film, which can be the cause for the higher activity of this catalyst. Density functional theory calculations based on the Cu 4 O 2 cluster supported on a ZrOx subunit reveal low activation barriers and provide mechanism for CO 2 hydrogenation and its conversion to methane. Altogether, the results show a new way to tune the catalytic activity of CO 2 hydrogenation catalysts through controlling the morphology of the support at the nanoscale.

Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis

“…The first challenge could be solved if spectra that correspond to unique structures could be isolated. To solve the first part, several approaches have been proposed, ranging from forward modelling 38,39 to spectral deconvolution methods, such as principal component analysis (PCA), 40 linear combination analysis, 41 and multivariate curve resolution-alternating least square (MCR-ALS). 40 The latter approaches are particularly attractive if a series of spectra are available, such as when the sample is exposed to reaction conditions, where the speciation of different structures of catalytically active metal complexes changes with the reaction.…”

Probing Active Sites in CuxPdy Cluster Catalysts by Machine-Learning-Assisted X-ray Absorption Spectroscopy

“…MCR-ALS. Because of the possible presence of mixed states of Cu electronic structure in the experimental XANES at all stages of the reaction, as evident from our previous work 40 on CO 2 methanation using CuO x clusters, the task of XANES data analysis is 2-fold: (1) speciation and (2) structural analysis of each species. Linear combination fit (LCF) and MCR-ALS are the most commonly used techniques for studying the composition of mixtures.…”

“…Because of the possible presence of mixed states of Cu electronic structure in the experimental XANES at all stages of the reaction, as evident from our previous work on CO 2 methanation using CuO x clusters, the task of XANES data analysis is 2-fold: (1) speciation and (2) structural analysis of each species. Linear combination fit (LCF) and MCR-ALS are the most commonly used techniques for studying the composition of mixtures. ,, LCF requires the use of known standards for the fitting, which is a limitation for nanomaterial studies because their XANES spectra change significantly between the bulk and nanomaterial for which there are often no standard spectra available.…”

“…GIXANES data analysis was limited, until recently, to the linear algebra methods, such as linear combination analysis with experimental standards. A recently developed machine learning-assisted interpretation of XANES spectra (ML-XANES) in monometallic Cu clusters and their oxides was shown to be useful for their structural analysis. − For bimetallic catalysts at the size scale of a few nanometers, both ML-XANES and ML-EXAFS were successfully used for their structural analysis. , In this work, and in other such systems and processes, where the catalysts undergo phase changes during the in situ XANES experiments, a combination of several structures that may coexist in reaction conditions cannot be quantitatively interpreted by the currently available ML-XANES methods.…”

Recent Advances in Theoretical Development of Thermal Atomic Layer Deposition: A Review

“…Table 2 summarizes the theoretical-study-only ALD articles found in the Web of Science and covered in this review. It is worth mentioning that this review mostly focuses on the theoretical-study-only ALD articles and does not cover the combined experimental and theoretical ones, which are cited here [ 75 , 76 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ]. Figure 5 displays the publications, summarized in Table 2 , that are covered in this study.…”