Using microkinetic analysis to search for novel anhydrous formaldehyde production catalysts

Li, Han-Jung; Lausche, Adam C.; Peterson, Andrew A.; Hansen, Heine Anton; Studt, Felix; Bligaard, Thomas

doi:10.1016/j.susc.2015.04.028

Cited by 29 publications

(19 citation statements)

References 30 publications

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“…10). Although water-catalyzed dehydrogenation of methanol has been observed on the Cu(111) surface under UHV conditions [12], it is the first time that this reaction has been observed under realistic reaction conditions. The promotion effect of water can be explained by the co-operative interactions of water and methanol facilitating the dehydrogenation of methanol to methoxy, thereby, increasing the formation of methoxy species.…”

Section: Resultsmentioning

confidence: 97%

“…In other words, the reductive environment in the methanol dehydrogenation reaction leads to fast deactivation of oxygen-containing catalysts. Once these catalysts lose their oxygen, they are not active for the non-oxidative dehydrogenation of methanol [12]. Therefore, a different metal particle preparation is in order to investigate methanol dehydrogenation in the absence of any oxygen, which is the subject of this work.…”

Section: Introductionmentioning

confidence: 99%

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Water co-catalyzed selective dehydrogenation of methanol to formaldehyde and hydrogen

et al. 2016

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The non oxidative dehydrogenation of methanol to formaldehyde is considered a promising method to produce formaldehyde and clean hydrogen gas. Although Cu-based catalysts have excellent catalytic activity in the oxidative dehydrogenation of methanol, metallic Cu is commonly believed to be unreactive for the dehydrogenation of methanol in the absence of oxygen adatoms or oxidized copper. Herein we show that metallic Cu can catalyze the dehydrogenation of methanol in the absence of oxygen adatoms by using water as a co-catalyst both under realistic reaction conditions using silica-supported PtCu nanoparticles in a flow reactor system at temperatures below 250 o C, and in ultra-high vacuum using model PtCu (111) catalysts. Adding small amounts of isolated Pt atoms into the Cu surface to form PtCu single atom alloys (SAAs) greatly enhances the dehydrogenation activity of Cu. Under the same reaction conditions, the yields of formaldehyde from PtCu SAA nanoparticles are seven times higher than on the Cu nanoparticles, indicating a significant promotional effect of individual, isolated Pt atoms. Moreover, this study also shows the unexpected role of water in the activation of methanol. Water, a catalyst for methanol dehydrogenation at low temperatures, becomes a

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Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Water co-catalyzed selective dehydrogenation of methanol to formaldehyde and hydrogen

et al. 2016

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“…For example, these interactions do not seem to be crucial in formaldehyde synthesis from methanol. 132 However, in the case of CO methanation, coadsorption interactions between key intermediates may change the reaction rate by several orders of magnitude but only when the catalysts become densely covered during the reaction. 142 In addition, such interactions may cause the volcano relationship to be less steep, although they did not change the position of the volcano peak.…”

Section: Volcano Relationshipmentioning

confidence: 99%

Constructing Bridges between Computational Tools in Heterogeneous and Homogeneous Catalysis

2018

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In this perspective, we review concepts and tools in theoretical and computational chemistry that can help to accelerate the rational design of homogeneous and heterogeneous catalysts. In particular, we focus on the following three topics: 1) identification of key intermediates and transition states in a reaction using the energetic span model, 2) disentanglement of factors influencing the relative stability of the key species using energy decomposition analysis and the activation strain model, and 3) discovery of new catalysts using volcano relationships. To facilitate wider use of these techniques across different areas, we illustrate their potentials and pitfalls when applied to the study of homogeneous and heterogeneous catalysts.

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“…In addition to the reaction conditions (T, Pi), the ODEs contain a pair of rate coefficients for all elementary steps, causing the number of parameters to quickly increase with mechanistic complexity. But when employed in conjunction with Brønsted−Evans−Polanyi (BEP) and scaling relationships based on catalytic descriptors, the complexity of the MKM can be reduced, allowing the study of activity and selectivity trends across large material spaces [3][4][5][6][7][8][9]. Optimal catalysts for a given set of reaction conditions are located near the top of the resulting volcano curve in descriptor space.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Grabow

2022

Catalysis Today

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Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Brønsted−Evans−Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate−adsorbate lateral interactions. Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. The meanfield implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.

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Using microkinetic analysis to search for novel anhydrous formaldehyde production catalysts

Cited by 29 publications

References 30 publications

Water co-catalyzed selective dehydrogenation of methanol to formaldehyde and hydrogen

Water co-catalyzed selective dehydrogenation of methanol to formaldehyde and hydrogen

Constructing Bridges between Computational Tools in Heterogeneous and Homogeneous Catalysis

Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

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