Strategy to improve catalytic trend predictions for methane oxidation and reforming

Baek, Byeongjin; Aboiralor, Abraham; Wang, Shengguang; Kharidehal, Purnima; Grabow, Lars C.; Massa, Jacob D.

doi:10.1002/aic.15404

Cited by 16 publications

(22 citation statements)

References 64 publications

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“…Additionally, the TOFs of CO and H 2 O were also strongly increased in this region, yet the authors did not study oxygenate formation. 42 Although the aforementioned theoretical studies provide useful knowledge on either plasma or catalyst surface chemistry, they are developed to simulate plasma-only or thermal-catalytic POX of CH 4 , respectively. As a result, these studies do not incorporate any effects of plasma species on surface chemistry.…”

Section: Introductionmentioning

confidence: 99%

Plasma-Catalytic Partial Oxidation of Methane on Pt(111): A Microkinetic Study on the Role of Different Plasma Species

2021

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We use microkinetic modeling to examine the potential of plasma-catalytic partial oxidation (POX) of CH 4 as a promising new approach to produce oxygenates. We study how different plasma species affect POX of CH 4 on the Pt(111) surface, and we discuss the associated kinetic and mechanistic changes. We discuss the effect of vibrationally excited CH 4 and O 2 , as well as plasma-generated radicals and stable intermediates. Our results show that vibrational excitation enhances the turnover frequency (TOF) of catalytic CH 4 dissociation and has good potential for improving the selectivities toward CH 3 OH, HCOOH, and C 2 hydrocarbons. Nevertheless, when also considering plasma-generated radicals, we find that these species mainly govern the surface chemistry. Additionally, we find that plasma-generated radicals and stable intermediates enhance the TOFs of CO x and oxygenates, increase the selectivity toward oxygenates, and make the formation of HCOOH more significant on Pt(111). We also briefly illustrate the potential impact of Eley− Rideal reactions that involve plasma-generated radicals. Finally, we reveal how various radicals affect the catalyst surface chemistry and we link this to the formation of different products. This allows us to make suggestions on how the plasma composition should be altered to improve the formation of desired products.

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Section: Introductionmentioning

confidence: 99%

Plasma-Catalytic Partial Oxidation of Methane on Pt(111): A Microkinetic Study on the Role of Different Plasma Species

2021

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“…The value of descriptor-based screening studies lies in the ability to rank order catalysts depending on their activity or selectivity and to extract mechanistic trend information across the periodic table [36]. This information is to be obtained using the smallest possible number of reactivity descriptors.…”

Section: Trend Comparison Between Mkm and Kmcmentioning

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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“…The value of descriptor-based screening studies lies in the ability to rank order catalysts depending on their activity or selectivity and to extract mechanistic trend information across the periodic table [36]. Given the many approximations in this approach it is certainly unrealistic to expect reliable absolute reaction rates or coverages.…”

Section: Trend Comparison Between Mkm and Kmcmentioning

confidence: 99%

Evaluating the Benefits of Kinetic Monte Carlo and Microkinetic Modeling for Catalyst Design Studies in the Presence of Lateral Interactions

Li¹,

Grabow

2021

Preprint

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<div><p>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.</p>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 mean-field 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.<br></div>

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Strategy to improve catalytic trend predictions for methane oxidation and reforming

Cited by 16 publications

References 64 publications

Plasma-Catalytic Partial Oxidation of Methane on Pt(111): A Microkinetic Study on the Role of Different Plasma Species

Plasma-Catalytic Partial Oxidation of Methane on Pt(111): A Microkinetic Study on the Role of Different Plasma Species

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

Evaluating the Benefits of Kinetic Monte Carlo and Microkinetic Modeling for Catalyst Design Studies in the Presence of Lateral Interactions

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