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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
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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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Mean field model parameterization to recover coverage-dependent kinetics

Lateral interactions between adsorbates introduce coverage dependence into adsorption energies and activation barriers of surface reactions. Lattice-based kinetic Monte Carlo (kMC) simulations can capture these interactions quantitatively but are laborious to parameterize and solve. Mean field models are more tractable, but protocols to construct and parameterize them are unclear. Here we explore the ability of a coverage-aware mean-field model to map to a lattice-kMC model of a generic two-step reaction network, including quasi-equilibrated adsorption and rate-limiting dissociation steps. We derive expressions for mean-field and coverage-dependent adsorption energies and dissociation barriers and parameterize against lattice-kMC predictions. We show that the parameterized mean-field rates correlate with ground truth lattice-kMC results across a wide range of reaction conditions and identify regions where the mean field fails. The mean field model similarly captures kMC-derived rate-order, Arrhenius and Sabatier plots at a greatly reduced computational cost. Further, the results provide guidance for parameterizing mean-field models, benchmarked against explicit lattice-based approaches for incorporating the influence of coverage effects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Consequences of adsorbate-adsorbate interactions for apparent kinetics of surface catalytic reactions

Lateral adsorbate interactions at catalyst surfaces are known to influence adsorption energies and reaction rates. Lattice-based kinetic Monte Carlo (kMC) simulations are able to capture these influences, but such models are typically parameterized for a specific reaction network and catalyst surface. Here we report kMC simulations to probe the influence of lateral interactions on simulated rates, rate orders, apparent activation energies, and Sabatier plots. We construct a simple, two-step reaction network involving a single adsorbate and rate- limiting diatomic dissociation, employ a generic repulsive lateral interaction model consistent with known adsorbate-adsorbate interactions on metal surfaces, and a rate model consistent with known Brønsted-Evans-Polyani relationships for diatomic dissocations. Furthermore, we juxtapose reaction kinetics over a wide range of reaction conditions and catalyst binding energies, as a function of interaction strength. We find that at a given zero-coverage binding energy and external conditions, adsorbate coverage decreases monotonically with increasing interaction strength, but absolute rates can vary linearly or nonlinearly. Interactions flatten the Sabatier volcano and shift the maximum towards stronger binding. Influences on apparent rate orders and activation energies are modest and are sensitive to interaction-induced adsorbate ordering. Model predictions are sensitive to lattice size effects at high coverages. The results, modelled for a simple reaction system, highlight the generic consequences of lateral interactions and guidance for identifying their signatures in observed kinetics.

kinetic Monte Carlo↗
DOE OSTI2022

Kinetic Model for the Reduction of Cu II Sites by NO + NH 3 and Reoxidation of NH 3 -Solvated Cu I Sites by O 2 and NO in Cu-SSZ-13

In this work, a kinetic model is developed for the reduction of Cu II sites by NO + NH 3 and the reoxidation of NH 3 -solvated Cu I sites by O 2 and NO in Cu-SSZ-13. Fourier transform infrared (FTIR) spectroscopy and spatially resolved capillary inlet mass spectrometry (SpaciMS) measurements during transient reactor experiments are utilized to identify the rate parameters associated with NO + NH 3 RHC (reduction half-cycle), proposed to occur via two distinct pathways involving adsorbed NH 3 and gas-phase NH 3 . The resulting NO + NH 3 RHC model is validated using spatiotemporal N 2 measurements covering a wide range of temperatures (200–450 °C) and space velocities (53,000–640,000 h –1 ). N 2 O formation is observed and modeled during NO + NH 3 RHC, with quantitative validation under standard selective catalytic reduction (SCR) conditions. Experimentally measured enthalpic and entropic changes associated with O 2 adsorption on NH 3 -solvated Cu I (ZCu(NH 3 ) 2 ) complexes [ Kamasamudram, K. Catal. Today 2010 , 151 (3–4), 212-222], along with activation energies estimated computationally for the intercage diffusion of ZCu(NH 3 ) 2 complexes [ Paolucci, C. Science 2017 , 357 (6 354), 898-903], are incorporated into a mean field kinetic model for the low-temperature oxidation half-cycle (OHC). Significant NH 3 release is observed during the isothermal oxidation of Cu I sites, attributed to desorption of NH 3 ligands from NH 3 -solvated Cu II dimers (Z 2 Cu 2 (NH 3 ) 4 O 2 ). Reduction of these dimeric complexes leads to the consumption of one NO/Cu II , contradicting the expected reduction stoichiometry. Inclusion of a global Arrhenius rate for the NO titration of Z 2 Cu 2 (NH 3 ) 4 O 2 complexes provides accurate representations of standard SCR on reduced and oxidized catalysts, predicting transient NO and NH 3 consumption between 150 and 250 °C as a function of hydrothermal aging. Deactivation of low-temperature standard SCR by NH 3 is observed at high NH 3 pressures, modeled via the formation of superoxo amino (ZCu(NH 3 ) 3 OO*) complexes during NH 3 titration of Z 2 Cu 2 (NH 3 ) 4 O 2 complexes [ Negri, C. J. Am. Chem. Soc. 2020 , 142 (37), 15884-15896]. The redox kinetic model presented here provides a foundational description of active site redox during low-temperature standard SCR, combining the recent kinetic, spectroscopic, and computational findings on the mechanism of standard SCR over Cu-SSZ-13.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Catalytic descriptors and electronic properties of single-site catalysts for ethene dimerization to 1-butene

Six first-row transition metal cations (Mn 2+ , Fe 2+ , Co 2+ , Ni 2+ , Cu 2+ , Zn 2+ ) were evaluated as catalysts for ethene dimerization to 1-butene. This is an important reaction in the chemistry of C—C bond formation and in the conversion of natural gas to higher hydrocarbons. Two related classes of transition metal cation catalysts were investigated: 1) single transition metal cations supported on zirconium oxide nodes of the metal–organic framework NU-1000 and 2) small metal hydroxide clusters with two metal atoms (M 2 ) that could be grown by atomic layer deposition on a support exhibiting isolated hydroxyl groups. Using scaling relations, the free energies of co-adsorbed hydrogen and ethene (i.e., (H/C 2 H 4 )*) and adsorbed ethyl (i.e., C 2 H 5 *) were identified as descriptors for ethene dimerization catalysis. Using degree of rate control analysis, it was determined that the rate controlling steps are either ethene insertion (C—C bond forming) or β-hydride elimination (C—H bond breaking), depending on the metal. Using degree of catalyst control analysis, it was determined that activity on all the catalysts studied could be improved by tuning the free energy of C 2 H 5 *.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Degrees of rate control and AutoDiff-driven direct sensitivity analysis in heterogeneous catalysis

Despite the wide application and benefits of the degree of rate control (DRC) analysis, several details remain argued, particularly about the conservation of DRCs at transient (TR) and steady-state (SS) conditions, especially for complex reaction networks. This work argues that previous proofs about the conservation properties of DRCs have been incomplete, and we provide new mathematical proofs at TR and SS conditions. In addition, we use both analytical (automatic differentiation) and numerical (finite difference) approaches to compute DRCs for the case study of ethane hydrogenolysis (EH) over Pt(111). This work confirms that at both TR and SS conditions, the sum of all DRCs, i.e., sum of the degrees of kinetic (DKRC) and thermodynamic rate control (DTRC), is conserved at zero. At SS conditions, the sum of DKRC is conserved at 1 while the sum of DTRC is conserved at −1. In corroboration of previous works, we show that the DTRC for any adsorbate at SS is equal to the product of the species coverage and a constant. In contrast, at TR conditions, the individual sums of both DTRC and DKRC are not conserved and can be any real number, with potential implications for the novel field of dynamic catalysis. Finally, we show that the conventional finite difference (FD) approach, only useful at SS, is prone to inaccuracy and very sensitive to the value of the differential change applied. The optimal differential value also varies significantly with system and rate definition. Consequently, we describe and illustrate in this work the application of the automatic differentiation (AD) approach for the more accurate determination of DRCs at both TR and SS conditions.

Automatic differentiation↗
DOE OSTI2023

Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. Here, we consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Prediction of Adsorption Energies for Chemical Species on Metal Catalyst Surfaces Using Machine Learning

Computational catalyst screening has the potential to significantly accelerate heterogeneous catalyst discovery. Typically, this involves developing microkinetic reactor models that are based on parameters obtained from density functional theory and transition-state theory. To reduce the large computational cost involved in computing various adsorption and transition-state energies of all possible surface states on a large number of catalyst models, linear scaling relations for surface intermediates and transition states have been developed that only depend on a few, typically one or two descriptors, such as the carbon atom adsorption energy. As a result, only the descriptor values have to be computed for various active site models to generate volcano curves in activity or selectivity. Unfortunately, for more complex chemistries the predictability of linear scaling relations is unknown. Also, the selection of descriptors is essentially a trial and error process. Here, using a database of adsorption energies of the surface species involved in the decarboxylation and decarbonylation of propionic acid over eight monometalic transition-metal catalyst surfaces (Ni, Pt, Pd, Ru, Rh, Re, Cu, Ag), we tested if nonlinear machine learning (ML) models can outperform the linear scaling relations in prediction accuracy when predicting the adsorption energy for various species on a metal surface based on data from the rest of the metal surfaces. We found linear scaling relations to hold well for predictions across metals with a mean-absolute error of 0.12 eV, and ML methods being unable to outperform linear scaling relations when the training dataset contains a complete set of energies for all of the species on various metal surfaces. Only when the training dataset is incomplete, namely, contains a random subset of species’ energies for each metal, a currently unlikely scenario for catalyst screening, do kernel-based ML models significantly outperform linear scaling relations. We also found that simple coordinate-free species descriptors, such as bond counts, achieve as good results as sophisticated coordinate-based descriptors. Finally, we propose an approach for automatic discovery of appropriate metal descriptors using principal component analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Moving from Fundamental Knowledge of Kinetics and Mechanisms on Surfaces to Prediction of Catalyst Performance in Reactors

Catalysis is defined by kinetics, but after over a century of catalyst development our kinetic and mechanistic knowledge governing real-world catalysis is still severely lacking. This Perspective considers how we can use the precise knowledge available from both theoretical and fundamental experimental techniques to understand and even predict catalytic performance under operational conditions. Here, we describe advances that link fundamental and "real-world" measurements, crucial elements required to successfully recreate a reaction network, and avenues which appear to offer viable routes to success.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Transport of Delocalized Excitons through DNA-Based Molecular Photonic Wires

Molecular photonic wires conduct electronic energy via their rapid transport properties. In photosynthesis, nature achieves efficient transport across large distances using delocalized excitons, generated by strong excitonic coupling between chromophores. How, or even whether, delocalization facilitates long-distance energy transport in synthetic systems has been challenging to experimentally test and optimize. Thus, far, studies have been limited to strongly coupled, heterogeneous chromophore aggregates or weakly coupled chromophore monomers. Here, in this work, we employed DNA nanostructures to engineer molecular photonic wires constructed from a series of excitonically coupled indocarbocyanine chromophores─achieving the intermediate and strong coupling regimes. Using time-resolved fluorescence spectroscopy and complementary simulations, we demonstrated that an intermediate intermolecular electronic coupling (∼ k B T ) enables up to 40% faster exciton transport as compared to strongly coupled chromophores. The delocalized excitons generated in the intermediate coupling regime exhibited properties conducive to rapid diffusivity, similar to their monomeric counterparts. Thus, intermediate excitonic coupling, analogous to natural systems, achieves long-distance exciton transport with the high chromophore density required for energy capture.

DNA origami↗
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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Ethane Dehydrogenation on Single and Dual Centers of Ga-modified γ-Al 2 O 3

Density-functional theory calculations and microkinetic analysis are used to investigate the efficacy of Ga-modified γ-Al 2 O 3 (110) surfaces for the catalytic dehydrogenation of ethane and elucidate the synergy between Ga and Al sites. The model surfaces are modified by either Ga grafting or doping. We consider and analyze numerous active sites and rank them using microkinetic analysis. The kinetic parameters obtained from microkinetic modelling are compared with experimental values for ethane dehydrogenation over Ga 2 O 3 -Al 2 O 3 mixed oxides prepared by co-precipitation. The dominant reaction pathway proceeds via heterolytic C-H bond dissociation to a surface proton and a metal-carbanion intermediate that undergoes β-hydride elimination. We find that grafted Ga sites are catalytically inactive. In contrast, Ga-doped sites exhibit fivefold enhancement in catalytic activity when compared to the sites on pristine Al 2 O 3 , owed to the synergy between neighboring AlIII and GaIV sites. Furthermore, we model and investigate the effect of surface hydroxylation, demonstrate how surface water interferes with the aforementioned synergy between AlIII and GaIV sites and discuss the implications for the catalytic activity of the modified surfaces. Here, an increase in the partial pressure of H 2 O significantly increases the apparent activation energies of dehydrogenation and interestingly changes the most active site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mechanistic understanding of pH effects on the oxygen evolution reaction

The oxygen-evolution reaction (OER) is pivotal in many energy-conversion technologies as it is an important counter reaction to others that convert stable chemicals to higher-value products using electrochemistry. The local microenvironment and pH for the anode OER can vary from acidic to neutral to alkaline depending on the system being explored, making definitive mechanistic insights difficult. In this paper, we couple experiments, first-principles calculations based on density functional theory, microkinetics, and transport modeling to explore the entire pH range of the OER. At low current densities, neutral pH values unexpectedly perform better than the acidic and alkaline conditions, and this trend is reversed at higher current densities (> 20 mA cm -2 ). Using multiscale modeling, this switch is rationalized by a change from a dual-reaction mechanism to a single rate-determining step. The model also shows how the alkaline reaction rates dominate in the middle to high pH range. Furthermore, we explore that the local pH for near-neutral conditions is much different (e.g., 2.4 at the reaction surface vs. 9 in the bulk) than the pH extremes, demonstrating the criticality that transport phenomena plays in kinetic activity.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Continuum Microkinetic Rate Theory of Lattice Systems: Formalization, Current Limitations, and a Possible Basis for Continuum Rate Theory

A broad class of reaction networks are controlled not by any thermodynamic ensemble but by rates and are referred to as “kinetically constrained”. Here in this paper, microkinetic rate theory is extended into a model of arbitrarily complex reaction networks. This microkinetic rate theory takes a form where the constraints imposed by kinetically constrained systems apply naturally. This model is compared to numerically complete results and is shown to be quantitatively predictive. Model deviations are shown to emerge for systems with incomplete mixing. Finally, this model is analytical and may serve as a basis for device and continuum scale rate modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗