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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Unconventional Error Cancellation Explains the Success of Hartree–Fock Density Functional Theory for Barrier Heights

Energy barriers, which control the rates of chemical reactions, are seriously underestimated by computationally efficient semilocal approximations for the exchange-correlation energy. The accuracy of a semilocal density functional approximation is strongly boosted for reaction barrier heights by evaluating that approximation non-self-consistently on Hartree–Fock electron densities, which has been known for ~30 years. Here, the conventional explanation is that the Hartree–Fock theory yields the more accurate density. This work presents a benchmark Kohn–Sham inversion of accurate coupled-cluster densities for the reaction H 2 + F → HHF → H + HF and finds a strong, understandable cancellation between positive (excessively overcorrected) density-driven and large negative functional-driven errors (expected from stretched radical bonds in the transition state) within this Hartree–Fock density functional theory. This confirms earlier conclusions (Kaplan, A. D., et al. J. Chem. Theory Comput. 2023, 19, 532–543) based on 76 barrier heights and three less reliable, but less expensive, fully nonlocal density functional proxies for the exact density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Solvent influence on non-adiabatic interfacial electron transfer at conductive oxide electrolyte interfaces

The kinetics for interfacial electron transfer (ET) from a transparent conductive oxide (tin-doped indium oxide, ITO, Sn:In 2 O 3 ) to molecular acceptors 4-[N,N-di(p-tolyl)amino]benzylphosphonic acid, TPA, and [Ru II (bpy) 2 (4,4' -(PO 3 H 2 ) 2 -bpy)] 2+ , RuP, positioned at variable distances within and beyond the electric double layer (EDL), were quantified in benzonitrile and methanol by nanosecond absorption spectroscopy as a function of the thermodynamic driving force, -ΔG ° . Relevant ET parameters such as the rate constant, k et , reorganization energy, λ, and electronic coupling, Hab, were extracted from the kinetic data. Overall, k et increased as the distance between the molecular acceptor and the conductor decreased. For redox active molecules within the Helmholtz planes of the EDL, k et was nearly independent of -ΔG° , consistent with a negligibly small λ value. Rips–Jortner analysis revealed a non-adiabatic electron transfer mechanism consistent with H ab < 1 cm -1 . The data indicate that the barrier for electron transfer is greatly diminished at the conductor–electrolyte interface.

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

Thermodynamic and Kinetic Barriers Limiting Solid-State Reactions Resolved through In Situ Synchrotron Studies of Lithium Halide Salts

Although halide salts such as LiCl and LiBr are routinely used as a source of Li ions during ion exchange reactions, a detailed understanding of the processes controlling the rates of these reactions is presently lacking. Recently, it has been discovered that the rate limiting barriers for ion exchange are commonly associated with these salts rather than the ceramic target of ion exchange, making it important to quantitatively understand of salt processes. Here it is demonstrated that in situ synchrotron studies of ion exchange reactions can be used to precisely quantify the thermodynamic activation energies associated with these solid state reactions in a manner that can be directly compared with predictions from density functional theory (DFT). While the temperature dependence of the LiCl reaction rate is found to be set by a barrier associated with ion hopping, it was discovered that for LiBr it is also affected by the defect formation energy – an energy found to be substantially lower than predicted by DFT. Furthermore, it is shown that when reaction rates for different relative amounts of reactants are varied, it is possible to identify the rate-limiting reagent and to elucidate an overall scaling relationship that controls the concentration-dependence of the reaction rate. Also, it is demonstrated that global fits across doped and undoped salts can be used to probe both intrinsic and extrinsic vacancy concentrations. This improved understanding of ion exchange mechanisms can be used to accelerate ion exchange reaction rates by orders of magnitude. In conclusion, the techniques demonstrated here can be broadly applied to probe the kinetics and thermodynamics of solid state reactions.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Turbulent flow and pumping in the cavity of a chemical laser

The kinetic theory of turbulent flow is employed to estimate an upper bound on the production rates of the lasing species in a free turbulent mixing layer. The maximum production rate is found to be about one third to one half of that predicted by a flame-sheet theory. The effects of turbulence scale and energy on the overall reaction rates are discussed and the gross structure of the combustion zone is determined.

Hayday, A. A.↗
NASA NTRS1980

A study of the cleft region using synoptic ionospheric plasma data obtained by the polar orbiting satellites Aeros-B and Isis-2

The concentrations of O(+) and NO(+) in the dayside high-latitude cleft region of the ionosphere are investigated based on synoptic particle and plasma measurements obtained by the polar orbiting Aeros-B and Isis-2 satellites. At a time when the orbital planes of the satellites are almost at right angles to each other, three maxima in ion temperature are observed, with two of them accompanied by an increased electron temperature and electron density irregularities, and the density of the molecular ions NO(+) and O2(+) is found to increase at the expense of O(+) density. Results are discussed in terms of a theory relating perpendicular electric fields to oxygen atom reaction rates. Systematic analysis of the Aeros data base reveals 14 additional instances of O(+) to NO(+) conversion, with a large variety of forms and structures reflecting the complex structure and dynamics of the high-latitude dayside ionosphere.

Kist, R.↗
DOE OSTI2025

Calculation of ion–ion mutual neutralization rate constants using Landau–Zener theory coupled with trajectory simulations for Ar + –Cl − , Br − , I −

In this computational study, we self-consistently calculate the rate constants of mutual neutralization reactions by incorporating the electron transfer probability, using Landau–Zener state transition theory with inputs derived from ab initio quantum chemistry calculations, into classical trajectory simulations. Electronic structure calculations are done using correlation consistent basis sets with multi-reference configuration interaction to map all the molecular electronic states below the ion-dissociation limit as a function of the distance between the reacting species. Our electronic structure calculations have been significantly improved from our previous work through improved selection of molecular electronic configurations maintaining a fine grid of 1a 0 over a wide range of bond lengths and accurate treatment of spin–orbit couplings. Non-adiabatic coupling matrix elements are calculated with the three-point central difference method near each avoided crossing to estimate the exact crossing point R x and coupling parameter H if , which are inputs to the multi-channel Landau–Zener theory to calculate the electron transition probability. Our approach is applied to estimate the mutual neutralization rate constants for the following ion pairs: Ar + –Cl − , Ar + –Br − , Ar + –I − at ∼133 Pa. Furthermore, our predictions are compared against the experimental data reported. It is seen that the improvement in the electronic structure calculation results in excellent agreement between the simulation results and the available experimental data to within a factor of ∼2 or ∼±50%.

Complete-active space self-consistent field↗
DOE OSTI2022

A matrix completion algorithm for efficient calculation of quantum and variational effects in chemical reactions

This work examines the viability of matrix completion methods as cost-effective alternatives to full nuclear Hessians for calculating quantum and variational effects in chemical reactions. The harmonic variety-based matrix completion (HVMC) algorithm, developed in a previous study (https://doi.org/10.1063/5.0018326), exploits the low-rank character of the polynomial expansion of potential energy to recover, using a small sample, vibrational frequencies (square roots of nuclear Hessian eigenvalues) constituting the reaction path. Furthermore, these frequencies are essential for calculating rate coefficients using variational transition state theory with multidimensional tunneling (VTST-MT). HVMC performance is examined for four SN2 reactions and five hydrogen transfer reactions, with each H-transfer reaction consisting of at least one vibrational mode strongly coupled to the reaction coordinate. HVMC is robust and captures zero-point energies, vibrational free energies, zero-curvature tunneling, and adiabatic ground state and free energy barriers as well as their positions on the reaction coordinate. For medium to large reactions involving H-transfer, with the exception of the most complex Ir catalysis system, less than 35% of total eigenvalue information is necessary for accurate recovery of key VTST-MT observables.

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

Ab Initio Molecular Dynamics Study of the Reduction of Acetone by the Hydrated Electron

We have investigated the reaction dynamics of the reduction of acetone by the solvated electron in water using ab initio molecular dynamics simulations at 298 and 373 K. The rate constants derived from the simulations are consistent with experimental observations that the reaction has a low activation energy. Detailed analyses carried out to shed light on the mechanism of the reaction show that solvent reorganization plays a key role as a reaction coordinate, as expected from the Marcus electron transfer theory. Furthermore, constrained density functional theory calculations indicate that the electronic coupling is large, placing the reaction in the adiabatic limit. Indeed, the activation energies and rate constants of the simulations are in accordance with the predictions of adiabatic Marcus theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Association Kinetics for Perfluorinated n -Alkyl Radicals

Radical-radical reaction channels are important in the pyrolysis and oxidation chemistry of perfluoroalkyl substances (PFAS). In particular, unimolecular dissociation reactions within unbranched n-perfluoroalkyl chains, and their corresponding reverse barrierless association reactions, are expected to be significant contributors to the gas-phase thermal decomposition of families of species such as perfluorinated carboxylic acids and perfluorinated sulfonic acids. Unfortunately, experimental data for these reactions are scarce and uncertain. Furthermore, obtaining reliable theoretical predictions for such reactions is a laborious and computationally intensive task. Here, in this work, the chemical kinetics of the various association/decomposition reactions producing/decomposing the C 2 -C 4 series of unbranched n-perfluoroalkanes (C 2 F 6 , C 3 F 8 , and C 4 F 10 ) are examined using state-of-the-art ab initio transition-state-theory-based master-equation calculations. The variable-reaction-coordinate transition-state theory (VRC-TST) formalism is employed in computing the microcanonical and canonical rates for the association reactions. Reaction thermochemistry is obtained via composite quantum chemistry calculations and the laddering of error-canceling reaction schemes via a connectivity-based hierarchy approach employing ANL1/ANL0-style reference energies. Lennard-Jones collision model parameters for the considered systems were estimated by a direct dynamics approach, and collisional energy transfer parameters were obtained from analogies to systems of similar size and heavy-atom connectivity. A one-dimensional master equation approach was used to convert the microcanonical rate coefficients from the VRC-TST analysis into temperature- and pressure-dependent rate constants for the association reactions and the reverse dissociation reactions. The data are reported in standardized formats for usage in comprehensive chemical kinetic models for PFAS thermal destruction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO 3 , CaZrN 2 , and YMnO 3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Supervised learning and the finite-temperature string method for computing committor functions and reaction rates

A central object in the computational studies of rare events is the committor function. Though costly to compute, the committor function encodes complete mechanistic information of the processes involving rare events, including reaction rates and transition-state ensembles. Under the framework of transition path theory, Rotskoff et al. [Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference, Proceedings of Machine Learning Research (PLMR, 2022), Vol. 145, pp. 757–780] proposes an algorithm where a feedback loop couples a neural network that models the committor function with importance sampling, mainly umbrella sampling, which collects data needed for adaptive training. Here, in this work, we show additional modifications are needed to improve the accuracy of the algorithm. The first modification adds elements of supervised learning, which allows the neural network to improve its prediction by fitting to sample-mean estimates of committor values obtained from short molecular dynamics trajectories. The second modification replaces the committor-based umbrella sampling with the finite-temperature string (FTS) method, which enables homogeneous sampling in regions where transition pathways are located. We test our modifications on low-dimensional systems with non-convex potential energy where reference solutions can be found via analytical or finite element methods, and show how combining supervised learning and the FTS method yields accurate computation of committor functions and reaction rates. We also provide an error analysis for algorithms that use the FTS method, using which reaction rates can be accurately estimated during training with a small number of samples. The methods are then applied to a molecular system in which no reference solution is known, where accurate computations of committor functions and reaction rates can still be obtained.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Automatically generated model for light alkene combustion

Light alkenes are common combustion intermediates for a variety of fuels. Therefore, understanding their oxidation and pyrolysis chemistry is key to building detailed mechanisms for heavier fuels. This work was focused on the development and evaluation of a detailed kinetic mechanism suitable for the combustion of light alkenes up to C 4 without the use of tuned parameters, instead, the parameter values come from first principles or direct measurements. The generated mechanism accurately estimates the laminar burning velocity (S u ) and ignition delay time (IDT) of light alkenes available in the literature, which represent fundamental combustion properties at a wide range of conditions. Because each parameter is thought to have a physically realistic value, not tuned to these measurements, the new model could be used as a sub-mechanism in models for other applications. The reaction network was generated with the open-source Reaction Mechanism Generator (RMG) software. Sensitivity analyses were performed under wide ranges of temperatures and pressures, allowing for the identification of the most impactful species and reactions. Based on these, a comprehensive thermochemistry database, including calculations on 550 molecules performed in this work at the CBS-QB3 level of theory, and a kinetic library, including theoretically-derived reaction rates retrieved from the literature, were built and used in the mechanism generation. The developed mechanism was compared against several existing detailed kinetic mechanisms for ethene, propene, 1-butene, 2-butene, and isobutene. Here, the newly generated model is the most accurate among the ones analyzed, in terms of fractional bias and normalized mean square error. Hence, this new model was used to analyze the chemistry of alkene combustion. Key rate coefficients were compared, to identify the cause of deviations between the models and possible areas for further improvements.

09 BIOMASS FUELS↗
DOE OSTI2020

Marcus–Hush–Chidsey kinetics at electrode–electrolyte interfaces

Electrochemical kinetics at electrode–electrolyte interfaces limit the performance of devices including fuel cells and batteries. While the importance of moving beyond Butler–Volmer kinetics and incorporating the effect of electronic density of states of the electrode has been recognized, a unified framework that incorporates these aspects directly into electrochemical performance models is still lacking. In this work, we explicitly account for the density functional theory-calculated density of states numerically in calculating electrochemical reaction rates for a variety of electrode–electrolyte interfaces. We first show the utility of this for two cases related to Li metal electrodeposition and stripping on a Li surface and a Cu surface (anode-free configuration). The deviation in reaction rates is minor for cases with flat densities of states such as Li, but is significant for Cu due to nondispersive d-bands creating large variation. Finally, we consider a semiconducting case of a solid-electrolyte interphase consisting of LiF and Li2CO3 and note the importance of the Fermi level at the interface pinned by the redox reaction occurring there. We identify the asymmetry in reaction rates as a function of discharge/charge naturally within this approach.

Kurchin, Rachel (ORCID:0000000221474809)↗
NASA NTRS1979

Microscopic phenomena and a modern approach to turbulence

The use of an arc driven shock tube as a technique in the study of turbulence and evidence to support a kinetic theory of turbulence are described. Topics covered include: (1) reaction rate distortion in turbulent flow; (2) turbulent bursts in a shock tube; (3) driver gas flow with fluctuations; (4) improving the Mach number capabilities of arc driver shock tubes; and (5) resonant absorption in an argon plasma at thermal equilibrium.

Johnson, J. A., III↗
NASA NTRS1993

Isomerization reaction dynamics and equilibrium at the liquid-vapor interface of water. A molecular-dynamics study

The gauche-trans isomerization reaction of 1,2-dichloroethane at the liquid-vapor interface of water is studied using molecular-dynamics computer simulations. The solvent bulk and surface effects on the torsional potential of mean force and on barrier recrossing dynamics are computed. The isomerization reaction involves a large change in the electric dipole moment, and as a result the trans/gauche ratio is considerably affected by the transition from the bulk solvent to the surface. Reactive flux correlation function calculations of the reaction rate reveal that deviation from the transition-state theory due to barrier recrossing is greater at the surface than in the bulk water. This suggests that the system exhibits non-Rice-Ramsperger-Kassel-Marcus behavior due to the weak solvent-solute coupling at the water liquid-vapor interface.

Benjamin, Ilan↗
DOE OSTI2024

Hydrogen activation by rhodium under the cover of a copper oxide thin film

Here, the activation of reactants by catalytically active metal sites at metal-oxide interfaces is important for understanding the effect of metal-support interactions on nanoparticle catalysts and for tuning activity and selectivity. Using a combined experimental and theoretical approach, we studied the activation of H 2 and the effect of CO poisoning on isolated Rh atoms completely or partially covered by a copper oxide (Cu 2 O) thin film. Temperature-programmed desorption (TPD) experiments conducted in ultra-high vacuum (UHV) show that neither a partially nor a fully oxidized Cu 2 O layer grown on a Rh/Cu(111) single-atom alloy can activate hydrogen in UHV. However, in situ ambient pressure X-ray photoelectron spectroscopy (AP-XPS) experiments performed at elevated H 2 pressures reveal that Rh significantly accelerates the reduction of these Cu 2 O thin films by hydrogen. Remarkably, the fastest reduction rate is observed for the fully oxidized sample with all Rh sites covered by Cu 2 O. Both TPD and AP-XPS data demonstrate that these covered Rh sites are inaccessible to CO, indicating that Rh under Cu 2 O is active for H 2 dissociation but cannot be poisoned by CO. In contrast, an incomplete oxide film leaves some of the Rh sites exposed and accessible to CO, and hence prone to CO poisoning. Density functional theory calculations demonstrate that unlike many reactions in which hydrogen activation is rate limiting, the rate-determining step in the dissociation of H 2 on thin-film Cu 2 O with Rh underneath is the adsorption of H 2 on the buried Rh site, and once adsorbed, the dissociation of H 2 is barrierless. These calculations also explain why H 2 can only be activated at higher pressures. Together, these results highlight how different the reactivity of atomically dispersed Rh in Cu can be depending on its accessibility through the oxide layer, providing a way to engineer Rh sites that are active for hydrogen activation but resilient to CO poisoning.

36 MATERIALS SCIENCE↗