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Heyden, Andreas

Publications and source records attributed to Heyden, Andreas.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Probing surface-adsorbate interactions through active particle dynamics

Adsorbate molecules present in a reaction mixture may bind to and block catalytic sites. Measurement of the surface coverage of these molecules via adsorption isotherms is critical for modeling and design of catalytic reactions on surfaces. However, it is challenging to measure isotherms in solution in a way that is directly relevant to catalytic activity under reaction conditions, particularly since adsorbates may bind with an enormous range of surface affinity parameters. Here we used the motion of self-propelled catalytic Janus particles, which employ the decomposition of hydrogen peroxide fuel as a propulsion mechanism, to determine the effective surface coverage of thioglycerol, furfural, and ethanol on a platinum surface as a function of concentration in aqueous solution by measuring the decrease in active motion due to the blocking of active sites. For strongly adsorbing thioglycerol, this effective coverage was compared and contrasted to the total adsorbed amount measured using inductively-coupled plasma analysis. Demonstrating the broad applicability of this approach, the surface affinity of the three adsorbates spanned more than four orders of magnitude. For each species, the adsorbate-mediated attenuation of active motion occurred over a wide concentration range and was well-described by a Langmuir isotherm. The strongly interacting thioglycerol had the highest affinity towards the surface (K a = 15.5 ± 4.3 mM –1 ) and fully deactivated the active particle motion at surface saturation. Furfural had an intermediate affinity (K a = 0.42 ± 0.07 mM –1 ) but did not fully block H 2 O 2 access to the surface at apparent saturation, consistent with a maximum fractional surface coverage of θ max = 0.67. Ethanol exhibited even lower affinity (K a = 0.0025 ± 2x10 -4 mM –1 ) and its coverage saturated at only θ max = 0.38. Analysis of isotherms at elevated temperatures enabled direct extraction of the enthalpies of adsorption. The degree of surface coverage at adsorbate saturation appeared to correlate with the relative energies of adsorption for the different adsorbate species and was consistent with adsorbate saturation of one of multiple active site populations towards H 2 O 2 decomposition. Furthermore, computational investigations into solvent effects on furfural adsorption showed good quantitative agreement with the experimental results. This work leverages unique properties of active particles to explore fundamental catalysis questions and demonstrates a novel paradigm for significant and experimentally accessible multidisciplinary research.

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DOE OSTI2021

Aqueous-phase effects on ethanol decomposition over Ru-based catalysts

The effects of an aqueous phase on ethanol decomposition for hydrogen production over a Ru(0001) catalyst surface model have been investigated from first principles. Solvent effects on the reaction mechanism and kinetic parameters have been quantified with the help of a microkinetic reactor model, density functional theory, and an implicit solvation scheme (iSMS). Our calculations indicate that in both vapor- and aqueous-phase reaction environments, the ethanol decomposition starts with acetaldehyde formation on the surface, some of which further dehydrogenates to ketenyl species (CHCO), where the C–C bond cleaves to form methylidyne (CH) and CO. In the vapor phase, adsorbed CH gets hydrogenated to methane, and CO desorbs or undergoes methanation reducing the amount of hydrogen produced. In contrast, under aqueous phase reaction conditions, the methanation is inhibited, and the water–gas shift (WGS) reaction is accelerated, leading to complete conversion of CO to CO 2 and H 2 . Furthermore, calculations indicate that the observed reaction behavior under aqueous phase reforming conditions originates primarily from the higher water chemical potential, and implicit solvent models predict only a small solvation effect.

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DOE OSTI2021

Computational Investigation of the Catalytic Hydrodeoxygenation of Propanoic Acid over a Cu(111) Surface

Cu-based alloy catalysts have recently been investigated experimentally for the hydrodeoxygenation (HDO) of biomass-derived organic acids. Here, the HDO of propanoic acid (PAc) has been studied over Cu(111) by mean-field microkinetic modeling based on parameters obtained from first-principles calculations. Models were developed for the gas- and liquid-phase HDO in condensed water and 1,4-dioxane. In agreement with experimental observations, the gas-phase PAc conversion rate is low at 573 K and increases in liquid water by 1 order of magnitude. In all reaction environments, the decarboxylation mechanism is dominant at low hydrogen partial pressures less than 0.1 bar, and the C–COO bond dissociation is the rate-controlling elementary step. This observation contrasts with the rate-controlling step identified over most group VIII metal surfaces, which is the C–OH bond dissociation in the decarbonylation mechanism. At high hydrogen (H 2 ) partial pressures greater than 10 bar, the HDO of PAc produces propionaldehyde that can readsorb and further react through decarbonylation to produce C 2 alkane products, which is conceptually different from the low H 2 partial pressure scenario. At high H 2 partial pressures, the initial hydrogenation at the carbonyl carbon of PAc becomes the rate-controlling elementary step.

Catalysts↗
DOE OSTI2021

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2020

Dependency of solvation effects on metal identity in surface reactions

Solvent interactions with adsorbed moieties involved in surface reactions are often believed to be similar for different metal surfaces. However, solvents alter the electronic structures of surface atoms, which in turn affects their interaction with adsorbed moieties. To reveal the importance of metal identity on aqueous solvent effects in heterogeneous catalysis, we studied solvent effects on the activation free energies of the O–H and C–H bond cleavages of ethylene glycol over the (111) facet of six transition metals (Ni, Pd, Pt, Cu, Ag, Au) using an explicit solvation approach based on a hybrid quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface. A significant metal dependence on aqueous solvation effects was observed that suggests solvation effects must be studied in detail for every reaction system. The main reason for this dependence could be traced back to a different amount of charge-transfer between the adsorbed moieties and metals in the reactant and transition states for the different metal surfaces.

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DOE OSTI2020

Catalytic upcycling of high-density polyethylene via a processive mechanism

The overconsumption of single-use plastics is creating a global waste catastrophe, with widespread environmental, economic and health-related consequences. In this work we show that the benefits of processive enzyme-catalysed conversions of biomacromolecules can be leveraged to affect the selective hydrogenolysis of high-density polyethylene into a narrow distribution of diesel and lubricant-range alkanes using an ordered, mesoporous shell/active site/core catalyst architecture that incorporates catalytic platinum sites at the base of the mesopores. Solid-state nuclear magnetic resonance revealed that long hydrocarbon macromolecules readily move within the pores of this catalyst, with a subsequent escape being inhibited by polymer–surface interactions, a behaviour that resembles the binding and translocation of macromolecules in the catalytic cleft of processive enzymes. Accordingly, the hydrogenolysis of polyethylene with this catalyst proceeds processively to yield a reliable, narrow and tunable stream of alkane products.

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DOE OSTI2020

Investigation of the reaction mechanism of the hydrodeoxygenation of propionic acid over a Rh(1 1 1) surface: A first principles study

Microkinetic models based on first principles calculations have been used to study the vapor and liquid phase hydrodeoxygenation of propionic acid on a Rh(111) surface. Calculations suggest that both decarboxylation and decarbonylation do not occur at an appreciable rate under all reaction environments. Propanol and propionaldehyde are the main products on this surface and they are produced at similar rates in both vapor and liquid phase environments. While a condensed phase can shift the reaction rate, the dominant pathways and selectivity towards the various products are hardly affected. At 473 K, the turnover frequency is increased by about a factor of 1.5 in liquid water relative to the gas phase. In liquid 1,4-dioxane, the turnover frequency is also slightly increased relative to the gas phase. Here, given the uncertainty in Rh cavity radius in the liquid phase calculations, computations with different cavity radii have been performed. With larger Rh cavity radius, the promotional effect of the solvents on the turnover frequency becomes more significant, while practically no changes are observed for a smaller cavity radius.

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DOE OSTI2020

Theoretical Investigation of Solvent Effects on the Hydrodeoxygenation of Propionic Acid over a Ni(111) Catalyst Model

The effect of two solvents, liquid water and 1,4-dioxane, has been studied from first principles on the hydrodeoxygenation of propionic acid over a Ni (111) catalyst surface model. A mean-field microkinetic model was developed to investigate these effects at a temperature of 473 K. Under all reaction conditions, a decarbonylation mechanism is favored significantly over a decarboxylation pathway. Although no significant solvent effects were observed on the decarbonylation rate, a substantial solvent stabilization of two key surface intermediates in the decarboxylation mechanism, CH 3 CCOO and CH 3 CHCOO, lead to a notable increase of the decarboxylation rate by two orders of magnitude in liquid water and by one order of magnitude in liquid 1,4-dioxane. Furthermore, a significant solvent stabilization of the transition state of C-H bond cleavage of the α-carbon of CH 3 CHCO, relative to the stabilization of the C-C bond cleavage of the α-carbon of CH 3 CHCO, leads to a change in dominant pathway in the liquid phase environments. Finally, a sensitivity analysis shows that the C-OH bond cleavage of propionic acid and C-C bond cleavage of the α-carbon of CH 3 CHCO are the most rate controlling states in the gas phase. In contrast, in solvents the dehydrogenation of CH 3 CHCO becomes the most influential step. This shift in rate controlling state is attributed to the solvent effect on the dehydrogenation of CH 3 CHCO, which is facilitated in aqueous phase. Altogether, it is likely that the investigated (111) facet of Ni is not active for the hydrodeoxygenation of propionic acid in neither the gas nor liquid phase and other Ni facets or phases must be responsible for the experimentally observed kinetics.

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DOE OSTI2020

Oxidative dehydrogenation of propane on the oxygen adsorbed edges of boron nitride nanoribbons

Metal-free boron nitride has recently been reported to exhibit high catalytic activity and selectivity towards oxidative dehydrogenation of propane (ODHP). Despite several experimental studies, the exact nature and function of the active sites in this emerging catalyst are unknown. In the present work, density functional theory calculations are combined with microkinetic modeling to systematically explore the ODHP on oxygen passivated boron nitride nanoribbons (BNNRs). Here, the relative stabilities of different edge structures of BNNR were examined using ab initio thermodynamic analysis, and the most stable oxygen passivated edge structure was used for a mechanistic study. Microkinetic analysis revealed that a N 2 O or NO x -type active site is active and selective for the conversion of propane to propene. In addition to a heterogeneous catalytic cycle, the proposed N 2 O/NO x -type active sites are expected to be able to generate gas phase C 3 H 7 ˙, which in turn can trigger gas phase reactions of ODHP as experimentally speculated.

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DOE OSTI2020

Selective activation of methane C–H bond in the presence of methanol

Direct methane to methanol (MTM) conversion over heterogeneous catalysts is a promising route for valorization of methane. The methane C-H bond activation is considered as the key step of the MTM and is the focus of considerable research activity. However, the formed methanol typically suffers from overoxidation largely due to the cleavage of a methanol C-H bond, whose bond dissociation energy is ca. 0.5 eV lower than that of the methane C-H bond, which usually translates to a transition state energy of the methanol C-H bond cleavage that is ca. 0.55 eV lower than that of methane whenever the reactions proceed through a radical mechanism. Furthermore, we propose a general approach for decreasing the transition state energy difference between the CH 4 and CH 3 OH C-H bond dissociation. When a metal-oxide supported cationic transition metal atom and a neighboring oxygen on the oxide surface serve as the active site, the transition state energy difference through a surface-stabilized pathway can be noticeably narrowed as compared with that of a radical pathway. For Ir, Pt and Rh-doped anatase TiO 2 (101), the CH 4 C-H bond activation can be preferred over that of CH 3 OH at significant methanol mole fraction. Also, for PdAu alloys containing adsorbed oxygen and positively charged Pd, calculations suggest in agreement with recent experiments (Science 367 (6474), 193-197) that the CH 4 C-H bond can be selectively activated in the presence of CH 3 OH.

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DOE OSTI2020

A Multiple Filter Based Neural Network Approach to the Extrapolation of Adsorption Energies on Metal Surfaces for Catalysis Applications

Computational catalyst discovery involves the development of microkinetic reactor models based on estimated parameters determined from density functional theory (DFT). For complex surface chemistries, the number of reaction intermediates can be very large, and the cost of calculating the adsorption energies by DFT for all surface intermediates even for one active site model can become prohibitive. In this paper, we have identified appropriate descriptors and machine learning models that can be used to predict a significant part of these adsorption energies given data on the rest of them. Furthermore, our investigations also included the case when the species data used to train the predictive model are of different size relative to the species the model tries to predict—this is an extrapolation in the data space which is typically difficult with regular machine learning models. Due to the relative size of the available data sets, we have attempted to extrapolate from the larger species to the smaller ones in the current work. Here, we have developed a neural network based predictive model that combines an established additive atomic contribution based model with the concepts of a convolutional neural network that, when extrapolating, achieves a statistically significant improvement over the previous models.

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DOE OSTI2019

Prediction of Transition-State Energies of Hydrodeoxygenation Reactions on Transition-Metal Surfaces Based on Machine Learning

Computational catalyst discovery involves identification of a meaningful model and suitable descriptors that determine the catalyst properties. We study the impact of combining various descriptors (e.g., reaction energies, metal descriptors, and bond counts) for modeling transition-state energies (TS) based on a database of adsorption and TS energies across transition-metal surfaces for the decarboxylation and decarbonylation of propionic acid, a chemistry characteristic for biomass conversion. Results of different machine learning models for more than 1572 descriptor combinations suggest that there is no statistically significant difference between linear and nonlinear models when using the right combination of reactant energies, metal descriptors, and bond counts. However, linear models are inferior when not including bond count and metal descriptors. In conclusion, when there are missing data for reaction steps on all metals, conventional linear scaling is inferior to linear and nonlinear models with proper choice of descriptors that are surprisingly robust.

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DOE OSTI2019

Investigation of solvent effects in the hydrodeoxygenation of levulinic acid to γ-valerolactone over Ru catalysts

Liquid phase, reductive deoxygenation of biomass derived platform chemicals over transition metal surfaces constitutes an efficient scheme for upgrading lignocellulosic biomass. The solvation effects on the reaction kinetics of the hydrodeoxygenation (HDO) of levulinic acid (LA) towards the formation of γ-valerolactone (GVL) over Ru(0001) has been studied in three condensed phase media, i.e., liquid water, methanol, and 1,4-dioxane. Detailed microkinetic models have been developed incorporating various catalytic pathways including formation of 4-hydroxypentanoic acid (HPA) and α-angelicalactone (AGL) to simulate the catalytic activity of Ru(0001) under various reaction conditions of solvent, temperature, and partial pressures. Our microkinetic models suggest that direct catalytic conversion with alkoxy formation is the preferred reaction mechanism in all reaction environments. Here, we find that water facilitates the reaction kinetics significantly and that the solvent effect is strongest at lower temperatures (T<373 K). Here, rate increases due to liquid water solvation effects of 2-4 orders of magnitude are observed. All solvents increase the rate of reaction relative to the gas phase; however, solvation effects decrease with decrease in polarity. 1,4-dioxane increases the rate only minimally due to competitive adsorption of the solvent molecules despite facilitating the partially rate controlling step of the LA hydrogenation to an alkoxy intermediate.

1,4-Dioxane↗
DOE OSTI2019

Investigation of solvent effects on the hydrodeoxygenation of guaiacol over Ru catalysts

The effects of a liquid phase environment on the hydrodeoxygenation of guaiacol, a prototypical lignin derived compound, have been investigated over a Ru catalyst from first principles. A microkinetic reactor model with parameters obtained from density functional theory and implicit solvation schemes was developed to study the effects of condensed phases on the reaction mechanism and kinetic parameters. Phenol was found to be the major aromatic product across all reaction environments. Our model predicts that less protic solvents such as 1-butanol, diethyl ether, and n-hexane have a positive effect on the reaction kinetics for the production of phenolics relative to vapor and aqueous reaction environments. The dominant reaction mechanism for aromatics production remains unchanged across all reaction media. Next, we investigated the possibility of cycloalkane production through hydrogenation of phenol in vapor and liquid phase environments. Our calculations indicated that the reaction pathway for cycloalkane production from phenol will most likely follow an initial dehydrogenation of the hydroxyl group. Based on the vapor phase density functional theory calculations, we proposed a probable reaction pathway and calculated the condensed phase effects along this reaction route. We observed that an aqueous phase has a more favorable effect for cycloalkane production from phenol relative to vapor phase and other less protic solvent environments.

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DOE OSTI2019

Computational Investigation of Aqueous Phase Effects on the Dehydrogenation and Dehydroxylation of Polyols over Pt(111)

Prediction of solvation effects on the kinetics of elementary reactions occurring at metal–water interfaces is of high importance for the rational design of catalysts for the biomass and electrocatalysis communities. A lack of knowledge of the reliability of various computational solvation schemes for processes on metal surfaces is currently a limiting factor. Using a multilevel quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface, we determined characteristic time and length scales for typical free-energy perturbation (FEP) calculations of bond cleavages in ethylene glycol, a sugar surrogate molecule, over Pt(111). Our approach is based on our explicit solvation model for metal surfaces and the repetition of FEP calculations to estimate confidence intervals. Results indicate that aqueous phase effects on the free energies of elementary processes can be determined with 95% confidence intervals from limited configuration space sampling and the fixed charge approximation used in the QM/MM-FEP methodology of smaller 0.1 eV. Next, we computed the initial O–H, C–H, and C–OH bond cleavages in ethylene glycol over Pt(111) in liquid water utilizing two different metal–water interaction potentials. Our calculations predict that aqueous phase effects are small (<0.1 eV) for the C–H bond cleavage and the activation barrier of the C–OH bond cleavage. In contrast, solvation effects are large (>0.35 eV) for the O–H bond cleavage and the reaction free energy of the C–OH bond scission. While the choice of a different Pt–water force field can lead to differences in predicted solvation effects of up to 0.2 eV, the differences are usually smaller (<0.1 eV), and the trends are always the same. In contrast, implicit solvation methods appear to currently not be able to reliably describe solvation effects originating from hydrogen bonding for metal surfaces even qualitatively.

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DOE OSTI2019

Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis

Chemical kinetic modeling in heterogeneous catalysis is gaining ground in its ability to provide qualitatively or even quantitatively accurate prediction of real-world behavior because of new advances in the physical and chemical representations of catalytic systems, estimation of relevant kinetic parameters, and capabilities in kinetic modeling. This Perspective chronicles current trends and future areas of advancement in chemical kinetic modeling, simulation, and parameter estimation: ranging from elementary step calculations to multiscale modeling to the role of advanced statistical methods for incorporating uncertainties in predictions. Multiple new or growing methodologies are covered, examples are provided, and forward-looking topics for advancement are noted.

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