Structural trends in the dehydrogenation selectivity of palladium alloys

Purdy, Stephen; Seemakurthi, Ranga Rohit; Mitchell, Garrett; Davidson, Mark E.; Lauderback, Brooke A; Deshpande, Siddharth; Wu, Zhenwei; Wegener, Evan C.; Greeley, Jeffrey; Miller, Jeffrey T.

doi:10.1039/d0sc00875c

Cited by 35 publications

(50 citation statements)

References 69 publications

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“…Importantly, it suggests that the aggregated iron species (such as Fe 2 O 3 , FeO x S y , FeS) are not as active as dispersed Fe sites. It is generally accepted that propane conversion to propylene can occur over an individual active site of noble metal/metal alloys (such as Pt or Pd) and is a structure-insensitive reaction, whereas large ensembles of active sites can also induce structure-sensitive side reactions. ,− It implies that the rate of PDH reaction strongly depends on the number of the active sites, and thus, the rate is directly proportional to the number of exposed atoms. Previously, Kim and Wachs studied vanadium oxide catalysts with different VO x loading for selective methanol oxidation to formaldehyde .…”

Section: Resultsmentioning

confidence: 99%

Sulfur Tolerant Subnanometer Fe/Alumina Catalysts for Propane Dehydrogenation

Sharma

Purdy

Page

et al. 2021

ACS Appl. Nano Mater.

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A series of Al 2 O 3 -supported Fe-containing catalysts were synthesized by incipient wetness impregnation. The iron surface density was varied from 1 to 13 Fe atoms/nm 2 spanning submonolayer to above-monolayer coverage. The resulting supported Fe-catalysts were characterized by N 2 physisorption, ex situ X-ray diffraction (XRD), X-ray pair distribution function (PDF), X-ray absorption spectroscopy (XAS), aberration corrected scanning transmission electron microscopy (AC-STEM) and chemically probed by hydrogen temperature-programmed reduction (H 2 -TPR). The results suggest that over this entire range of loadings, Fe was present as dispersed species, with only a very small fraction of Fe 2 O 3 aggregates, at the highest Fe loading in oxide phase. The in situ sulfidation of Fe/Al 2 O 3 resulted in the formation of a highly active and selective PDH catalyst. The highest activity with 52% propane conversion and ∼99% propylene selectivity at 560 °C was obtained for the 6.4 Fe/Al 2 O 3 -S catalyst, suggesting that this is the highest amount of Fe that could be fully dispersed on the support in sulfided form. XRD and AC-STEM indicated the absence of any crystalline iron sulfide aggregates after sulfidation and reaction. H 2 -TPR results indicated that the amount of the reducible Fe sites in the sulfided catalyst remained constant above monolayer coverage, and increasing loading did not increase the number of reducible Fe sites. Consistent with these results, the reactivity per gram of catalyst showed no increase with Fe loading above monolayer coverage, suggesting that additional Fe remains conformal to the alumina surface.

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Section: Resultsmentioning

confidence: 99%

Sulfur Tolerant Subnanometer Fe/Alumina Catalysts for Propane Dehydrogenation

Sharma

Purdy

Page

et al. 2021

ACS Appl. Nano Mater.

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“…However, as the intermediates become more deeply dehydrogenated, they prefer to maximize their interaction with Pd atoms by stabilizing themselves on Pd–Pd bridge sites. These conclusions for deeply dehydrogenated intermediates are in agreement with results reported for 1:1 PdZn and PdIn terrace surfaces in our previous work . On the other hand, on the Pd-step(210)_PdIn, all intermediates, regardless of the degree of dehydrogenation, prefer to bind to the Pd atoms along the undercoordinated edge.…”

Section: Resultsmentioning

confidence: 65%

“…The model is run as a CSTR at a temperature of 873 K and a pressure of 1 atm. The reactor dimensions and volumetric flow rates (Table S18) are chosen to match experimental conditions, , while the catalyst surface area is varied to adjust the conversion of propane. Further information regarding the details of the microkinetic model can be found in the Supporting Information (SI section 12).…”

Section: Methodsmentioning

confidence: 99%

“…Finally, it should be noted that the quantity of promoter elements in alloys can influence the properties of PDH catalysts, and 1:1 alloys of both Pt and Pd have recently been shown to exhibit similar catalytic performance to, and in some cases even greater selectivity than, the commonly studied 3:1 alloys . The present study begins with a detailed analysis of one such alloy, a 1:1 PdIn intermetallic, which has been shown to convert propane to propylene with much higher selectivity than pure Pd. , DFT calculations, in conjunction with microkinetic modeling, are used to study the complete PDH reaction network on both steps and terrace surfaces of the alloy, and a similar analysis is subsequently performed on pure Pd. The results demonstrate that Pd-terminated steps on the PdIn alloy exhibit approximately 5 orders of magnitude higher activity than the PdIn terraces, while both steps and terraces have very high selectivity compared to corresponding structures on Pd.…”

Section: Introductionmentioning

confidence: 99%

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Identification of a Selectivity Descriptor for Propane Dehydrogenation through Density Functional and Microkinetic Analysis on Pure Pd and Pd Alloys

Seemakurthi

Canning

et al. 2021

ACS Catal.

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First principles periodic density functional theory (DFT) calculations, in conjunction with detailed microkinetic modeling and experimental characterization, are employed to elucidate the structure sensitivity and identify key selectivity descriptors for nonoxidative propane dehydrogenation (PDH) on intermetallic alloys. A comprehensive theoretical treatment of 1:1 PdIn surfaces demonstrates that the Pd-terminated steps have 5 orders of magnitude higher rates than do the (110) terraces, with nearly complete selectivity to propylene formation. Pure Pd steps and terraces, in contrast, have considerably lower propylene selectivity and higher coverages of adsorbed intermediates, suggesting that Pd may experience more coking and reduced lifetimes compared to the alloys. A degree of rate and selectivity control analysis on the optimized microkinetic model demonstrates that propane C–H bond scission to yield 1-propyl is the most kinetically relevant step for propylene formation, while the C–C bond breaking barriers are important for byproduct formation. From these analyses, a simplified rate expression is derived for the step surface of the alloy, leading to the identification of a selectivity descriptor expressed in terms of effective free energy barriers of the rate controlling transition states, propane C–H bond breaking, and propyne C–C bond breaking. This descriptor is subsequently generalized to evaluate the propylene production selectivities for a series of Pd-containing alloys. The results show enhanced agreement with experimentally measured selectivity trends compared to traditional selectivity descriptors, suggesting a general strategy for identification of highly selective, nonoxidative PDH alloy catalysts.

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“…This flexibility is demonstrated in the context of two catalytic systems that are relevant to practical electrocatalytic applications and that represent the typical complexities encountered when developing computational models of heterogeneous catalysts. The first case treats high coverage configurations of the adsorbate NO* on a Pt3Sn(111) terrace surface, wherein a vast surface configurational space resulting from both the reduction in the catalyst surface symmetry due to alloying [27][28][29][30] and the strong binding nature of NO* yields rich catalytic behavior.…”

Section: Introductionmentioning

confidence: 99%

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

Ghanekar

Deshpande

Greeley

2021

Preprint

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Heterogeneous catalytic reactions are influenced by a subtle interplay of atomic-scale factors, ranging from the catalysts’ local morphology to the presence of high adsorbate coverages. Describing such phenomena via computational models requires generation and analysis of a large space of surface atomic configurations. To address this challenge, we present the Adsorbate Chemical Environment-based Graph Convolution Neural Network (ACE-GCN), a screening workflow that can account for atomistic configurations comprising diverse adsorbates, binding locations, coordination environments, and substrate morphologies. Using this workflow, we develop catalyst surface models for two illustrative systems: (i) NO adsorbed on a Pt3Sn(111) alloy surface, of interest for nitrate electroreduction processes, where high adsorbate coverages combine with the low symmetry of the alloy substrate to produce a large configurational space, and (ii) OH* adsorbed on a stepped Pt(221) facet, of relevance to the Oxygen Reduction Reaction, wherein the presence of irregular crystal surfaces, high adsorbate coverages, and directionally-dependent adsorbate-adsorbate interactions result in the configurational complexity. In both cases, the ACE-GCN model, having trained on a fraction (~10%) of the total DFT-relaxed configurations, successfully ranks the relative stabilities of unrelaxed atomic configurations sampled from a large configurational space. This approach is expected to accelerate development of rigorous descriptions of catalyst surfaces under in-situ conditions.

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Structural trends in the dehydrogenation selectivity of palladium alloys

Abstract: Alloying is well-known to improve the dehydrogenation selectivity of pure metals, but there remains considerable debate about the structural and electronic features of alloy surfaces that give rise to this behavior.

Cited by 35 publications

References 69 publications

Sulfur Tolerant Subnanometer Fe/Alumina Catalysts for Propane Dehydrogenation

Sulfur Tolerant Subnanometer Fe/Alumina Catalysts for Propane Dehydrogenation

Identification of a Selectivity Descriptor for Propane Dehydrogenation through Density Functional and Microkinetic Analysis on Pure Pd and Pd Alloys

Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis

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