Metal-exchanged zeolites have been widely used in industrial catalysis and separation, but fundamental understanding of their structure-property relationships has remained challenging, largely due to the lack of quantitative information concerning the atomic structures and reactionrelevant adsorption properties of the embedded metal active sites. We report on the use of lowtemperature chemisorption to titrate Cu-exchanged ZSM5. Quantitative descriptors of the atomic structures and adsorption properties of Cu-ZSM5 are established by combining atomistic simulation, DFT calculations, operando molecular spectroscopy, chemisorption and titration measurements. These descriptors are then applied to interpret the catalytic performance of Cu-ZSM5 for NO decomposition. Linear correlations are established to bridge the low-temperature adsorption analytics and high-temperature reaction kinetics, which are demonstrated to be generally applicable for understanding the structure-property relationships of metal exchanged zeolites and foregrounded for guiding the development of advanced catalytic materials.
Catalysts composed of platinum dispersed on zeolite supports
are
widely applied in industry, and coking and sintering of platinum during
operation under reactive conditions require their oxidative regeneration,
with the platinum cycling between clusters and cations. The intermediate
platinum species have remained only incompletely understood. Here,
we report an experimental and theoretical investigation of the structure,
bonding, and local environment of cationic platinum species in zeolite
ZSM-5, which are key intermediates in this cycling. Upon exposure
of platinum clusters to O2 at 700 °C, oxidative fragmentation
occurs, and Pt2+ ions are stabilized at six-membered rings
in the zeolite that contain paired aluminum sites. When exposed to
CO under mild conditions, these Pt2+ ions form highly uniform
platinum gem-dicarbonyls, which can be converted
in H2 to Ptδ+ monocarbonyls. This conversion,
which weakens the platinum–zeolite bonding, is a first step
toward platinum migration and aggregation into clusters. X-ray absorption
and infrared spectra provide evidence of the reductive and oxidative
transformations in various gas environments. The chemistry is general,
as shown by the observation of platinum gem-dicarbonyls
in several commercially used zeolites (ZSM-5, Beta, mordenite, and
Y).
Natural gas remains an essential energy source for the industrial and residential sectors. However, selective valorization of methane (the main component of natural gas) into more mobile liquid energy carriers such as methanol remains challenging. Inspired by pMMO enzymes, many recent studies have examined Cu-exchanged zeolites as promising catalysts, specifically through [CuOCu]2+ sites. These efforts, in part, have been motivated by the possibility of finding an elusive “Goldilocks” active site or topology that can outperform known catalysts while also maintaining selectivity towards methanol. As large-scale experiments with 1000s of material variations are impossible, theory will likely play an important role. Although computational screening studies are now routine for metals and alloys, similar studies for zeolites are not as straightforward due to the diversity of local chemical environments, and the aforementioned studies are not trivial using the traditional density functional theory (DFT)-based approach. Therefore, the overarching goal of this study is to leverage large-scale DFT calculations to develop a reactive machine learning-based potential (rMLP) capable of systematically sampling the stability and reactivity of all [CuOCu]2+ sites within a representative set of zeolites. Specifically, using methane activation as a prototypical example of an industrially relevant zeolite-catalyzed reaction, we have developed a novel multistage active learning algorithm that preferentially samples the potential energy surface of the system near the transition state of methane activation. We show that the resulting rMLP replaces the expensive DFT-based NEB calculations without any appreciable loss in accuracy (within 0.07 eV of the DFT computed energy barriers) – we evaluate C-H bond activation energies for 5,400 distinct sites across 52 zeolites and obtain 3,356 valid sites suitable for methane activation. By replacing the expensive DFT-based NEB calculations with rMLPs, we now report an exhaustive high-throughput screening study of thousands of [CuOCu]2+ sites in zeolites, comparing the maximum rates of methane activation across 52 zeolite topologies and more than 3,000 sites. To the best of our knowledge, this work represents the first example of using reactive MLPs to identify the transition state geometries and screen the catalytic performance of thousands of zeolite-based active sites at DFT accuracies.
Copper-based zeolites have been widely explored as promising catalysts for the methane valorization reaction to form methanol. These studies are motivated by the hope of finding an elusive ‘Goldilocks’ topology or an active site that shows high methanol selectivity at reasonable methane conversions. As large-scale screening studies with density functional theory (DFT) remain challenging for zeolite catalysts, we now show that a reactive and interpretable machine learning-based potential (rMLP), developed using multistage active learning algorithm and a curriculum-based training (CBT) approach can be used to overcome this bottleneck. Our rMLP approach replaces expensive DFT-based NEB calculations without appreciable accuracy loss. We calculate methane activation barriers for all possible [CuOCu]2+ sites across 52 zeolites with an MAE of 0.07 eV versus DFT. By comparing with known experimental measurements, our approach establishes the limits of methane activation performance across 52 zeolite topologies. Finally, we show that our curriculum-based training (CBT) approach, which relies on several different types of calculations, gradually “teaches” the model about different relevant parts of the PES. This progressive training approach has important implications for the interpretability of emerging machine learning-based approaches.
Metal-exchanged zeolites have been widely used in industrial catalysis and separation, but fundamental understanding of their structure-property relationships has remained challenging, largely due to the lack of quantitative information concerning the atomic structures and reaction-relevant adsorption properties of the embedded metal active sites. We report on the use of low-temperature chemisorption to titrate Cu-exchanged ZSM5. Quantitative descriptors of the atomic structures and adsorption properties of Cu-ZSM5 are established by combining atomistic simulation, DFT calculations, operando molecular spectroscopy, chemisorption and titration measurements. These descriptors are then applied to interpret the catalytic performance of Cu-ZSM5 for NO decomposition. Linear correlations are established to bridge the low-temperature adsorption analytics and high-temperature reaction kinetics, which are demonstrated to be generally applicable for understanding the structure-property relationships of metal exchanged zeolites and foregrounded for guiding the development of advanced catalytic materials.
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