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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hourly Load Profile Dataset for Electric Transit Bus Depots in the United States

Transit buses operate primarily in dense urban areas, where nearby populations face increased exposure to fine particulates, nitrogen oxides, and other harmful pollutants. Electrifying transit buses presents a clear opportunity to reduce greenhouse gas emissions and improve urban air quality. However, widespread adoption may pose significant energy and infrastructure challenges, which can be mitigated through proactive planning and investment. This report presents a robust modeling framework and an initial estimation of the hourly electricity demand at transit bus depots across the United States. The resulting depot-level dataset, available at data.nrel.gov/submissions/282, provides valuable insights for infrastructure planning and electricity demand forecasting, supporting the scalable electrification of transit bus fleets nationwide.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Monitoring Operational States of a Nuclear Reactor Using Seismoacoustic Signatures and Machine Learning

Monitoring nuclear reactors is an important safety and security task with growing requirements. We explore the possibility of using seismic and acoustic data for inferring the power level of an operating reactor. Continuous data recorded at a single seismoacoustic station that is located about 50 m away from a research reactor was visualized and analyzed. The data show a clear correlation between seismoacoustic features and reactor main operational states. We designed a workflow that includes two machine learning (ML) models to classify the reactor operational states (OFF, transition, and ON) and estimate reactor power levels (10%, 30%, 50%, 70%, and 90%). We applied and compared five ML algorithms for the reactor OFF-transition-ON and four approaches for the power level classification. We also compared the performance of ML models trained with seismic-only, acoustic-only, and both types of data. Five-fold cross validations were implemented to assure a thorough evaluation of the model performances. Additionally, the results show the extreme boosting gradient algorithm worked best for the first model, whereas random forests performed best for the second model. Combining seismic and acoustic data leads to better performance than using a single type of data. Seismic data contributed more than acoustic data for both models. We reached an accuracy of 0.98 for reactor OFF and ON. The accuracies for the transition state and power levels are less optimal with a minimum accuracy of 0.66. However, our results suggest seismic and acoustic data contain useful information about the transition state as well as power levels. Seismic and acoustic data could be integrated with other observations to improve monitoring performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Thermally sensitive state change ionic redox transistor

A thermally sensitive ionic redox transistor comprises a channel, a reservoir layer, and an electrolyte layer disposed between the channel and the reservoir layer. A conductance of the channel is varied by changing concentration of ions in the channel layer. The electrolyte layer is configured to undergo a state change at a state transition temperature. Below the state transition temperature, ions in the electrolyte layer are substantially immobile. Above the state transition temperature, ions can move freely between the reservoir layer and the channel across the electrolyte layer in response to a voltage being applied between the channel and the reservoir layer. When the device is cooled below the state transition temperature or temperature range, the ions are trapped in one or more of the layers because the electrolyte layer loses its ionic conductivity. A state of the redox transistor can be read by measuring the conductance of the channel.

Ashby, David Scott↗
DOE OSTI2022

Cybersecurity vulnerability mitigation framework

Systems, methods, and computer media for mitigating cybersecurity vulnerabilities of systems are provided herein. A current cybersecurity maturity of a system can be determined based on maturity criteria. The maturity criteria can be ranked based on importance. Solution candidates for increasing the cybersecurity maturity of the system can be determined based on the ranking. The solution candidates specify cybersecurity levels for the maturity criteria. A present state value reflecting the current cybersecurity maturity of the system can be calculated. For the solution candidates, an implementation state value and a transition state value can be determined. The implementation state value represents implementation of the maturity levels of the solution candidate, and the transition state value represents a transition from the present state value to the implementation state value. Based on the transition state values, a solution candidate can be selected for the system, and the system can be modified accordingly.

Gourisetti, Sri Nikhil Gupta↗
DOE OSTI2021

Analysis and prediction of reaction kinetics using the degree of rate control

“Degree of rate control” (DRC) analysis provides a quantitative approach for analysing the kinetics of multi-step reaction mechanisms that has been widely applied to both heterogeneous and homogeneous catalysis research, as well as electrocatalysis. The DRC of any given transition state or intermediate is defined as a partial derivative such that it approximately equals the fractional increase in net rate to the product of interest per differential decrease in its standard-state free energy for that species (÷RT), holding constant the standard-state free energies of all other transition states and intermediates. Even very complex mechanisms usually have only a few species with non-zero DRCs and are thus the species whose interactions with the catalyst most strongly affect the net rate. These key DRC values thus offer a simple and intuitive route to optimize catalyst materials, especially with the assistance of computational methods like density functional theory (DFT). These high-DRC species are also the species whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. In simple cases with a single “rate-determining step”, the DRC for its transition state (TS) is + 1. Catalyst-bound intermediates, on the other hand, often have negative DRCs equal to a small integer times their fractional occupancy of catalyst sites. The apparent activation energy equals a weighted average of the standard-state enthalpies (relative to reactants) of all the species (intermediates, transition states and products) in the reaction mechanism, each weighted by its DRC (+RT). It has been shown that the apparent transfer coefficient in electrocatalysis, an inverted form of the Tafel slope, is a weighted average of the number of electrons transferred to generate each intermediate or product species in the mechanism, weighted again by the DRC. Quantitative analysis of kinetic isotope effects (KIEs, or the ratio of net rates for different reactant isotopes) in complex mechanisms has shown that the logarithm of the KIE equals the weighted average over all species in the mechanism of the difference between the two isotopes in their standard-state free energies (÷RT), again weighted by the DRC. The reaction orders with respect to fluid-phase concentrations of reactants, products and intermediates have also been proven to be directly related to DRCs. Thus, there are numerous experimental observables which equate to short linear combinations of DRCs, so that combinations of experimental measurements might provide access to DRC values. Since its invention for transition states in 1994 and its generalization to include intermediates in 2009, the DRC has thus far mainly been calculated for microkinetic models based either entirely on DFT or on DFT with the key energies (i.e., those for high-DRC species) being fine-tuned to match experiments. The relationships summarized in this work provide new opportunities for using experiments earlier in the development and optimization of microkinetic models that require input from computational catalysis.

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

The Role of Protons and Hydrides in the Catalytic Hydrogenolysis of Guaiacol at the Ruthenium Nanoparticle-water Interface

The mechanistic roles of free hydronium ions, surface hydrides, and interfacial protons during guaiacol hydrodeoxygenation (HDO) on ruthenium nanoparticles are established. As guaiacol adsorbs on Ru, it loses its strong aromaticity and undergoes a rapid H-shift from its hydroxyl to meta carbons (in relation to its hydroxyl group), leading enol and keto surface isomers to exist in chemical equilibrium. HDO occurs via a hydridic H-adatom (H*) attack to the enol, followed by a kinetically relevant C-O bond rupture step, during which water shuttles the hydroxyl proton, enabling its intramolecular attack to the methoxy, evolving a high charged [Ru(s)-(C6H5O-)…(H+)…OCH3]† transition state. The competing HYD begins with a rapid H* attack to the keto, before a second, kinetically relevant H* attack, without proton involvement. Water, despite shifting the thermodynamics towards the more polar surface keto, promotes HDO to a much greater extent than HYD, because of its dual catalytic roles—it mobilizes hydroxyl proton (Brønsted acid) to cleave the strong C-O bond, synchronizing with the Ru metal surface (base) function that stabilizes the resulting [Ru(s)-(C6H5O-)…(H+)…OCH3]† transition state, and the water layers solvate this charged transition state, further reducing its free energy. Free hydronium ions do catalyze a separate homogeneous enol-keto isomerization, but this reaction is kinetically unrelated to HDO catalysis. This mechanistic picture explains the strong effects of polar protic solvent in hydrodeoxygenation, highlighting (i) the requirements of surface hydrides and interfacial protons acting in tandem to complete a HDO turnover and (ii) the cooperative role of protic solvent and metal surface in breaking the aromaticity and stabilizing charged reactive precursors and transition states.

Shangguan, Junnan↗
DOE OSTI2023

Radical–Radical Reactions in Molecular Weight Growth: The Phenyl + Propargyl Reaction

The mechanism for hydrocarbon ring growth in sooting environments is still the subject of considerable debate. The reaction of phenyl radical (C 6 H 5 ) with propargyl radical (H 2 CCCH) provides an important prototype for radical–radical ring-growth pathways. We studied this reaction experimentally over the temperature range of 300–1000 K and pressure range of 4–10 Torr using time-resolved multiplexed photoionization mass spectrometry. We detect both the C 9 H 8 and C 9 H 7 + H product channels and report experimental isomer-resolved product branching fractions for the C 9 H 8 product. We compare these experiments to theoretical kinetics predictions from a recently published study augmented by new calculations. Here, these ab initio transition state theory-based master equation calculations employ high-quality potential energy surfaces, conventional transition state theory for the tight transition states, and direct CASPT2-based variable reaction coordinate transition state theory (VRC-TST) for the barrierless channels. At 300 K only the direct adducts from radical–radical addition are observed, with good agreement between experimental and theoretical branching fractions, supporting the VRC-TST calculations of the barrierless entrance channel. As the temperature is increased to 1000 K we observe two additional isomers, including indene, a two-ring polycyclic aromatic hydrocarbon, and a small amount of bimolecular products C 9 H 7 + H. Our calculated branching fractions for the phenyl + propargyl reaction predict significantly less indene than observed experimentally. We present further calculations and experimental evidence that the most likely cause of this discrepancy is the contribution of H atom reactions, both H + indenyl (C 9 H 7 ) recombination to indene and H-assisted isomerization that converts less stable C 9 H 8 isomers into indene. Especially at low pressures typical of laboratory investigations, H-atom-assisted isomerization needs to be considered. Regardless, the experimental observation of indene demonstrates that the title reaction leads, either directly or indirectly, to the formation of the second ring in polycyclic aromatic hydrocarbons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1998

Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures

Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.

Liang, Shoudan↗
DOE OSTI2020

Reactivity descriptors in acid catalysis: acid strength, proton affinity and host–guest interactions

Brønsted acids mediate chemical transformations via proton transfer to bound species and interactions between the conjugate anion and bound cationic intermediates and transition states that are also stabilized by van der Waals forces within voids of molecular dimensions in inorganic hosts. This Feature Article describes the relevant descriptors of reactivity in terms of the properties of acids and molecules that determine their ability to donate and accept protons and to reorganize their respective charges to optimize their interactions at bound states. The deprotonation energy (DPE) of the acids and the protonation energy (Eprot) of the gaseous analogs of bound intermediates and transition states reflect their respective properties as species present at non-interacting distances. These properties accurately describe the reactivity of acids of a given type, such as polyoxometalates (POM) with a given type of addenda atom but different central atoms and heterosilicates, for different families of reactions. They do not fully capture, however, differences among acid types (e.g., Mo and W POM, heterosilicates, mineral acids) for diverse types of chemical transformations (e.g., elimination, isomerization, dimerization, condensation). The incompleteness of such descriptors reflects their inability to describe how protonated molecular species and conjugate anions restructure their respective charges when present as a binding pair at interacting distances. Such interaction energies represent electrostatic forces that depend on charge distributions in the cations and anions and the ability to reorganize the distributions to maximize the interactions. In the case of deprotonation, the electrostatic and charge reorganization components of DPE for various acids solely reflect the ability of the conjugate anion to accept and distribute the negative charge, a characteristic unique of each type of solid acid and specifically of the composition of its extended conjugate anion framework. The energy required to accept and rearrange the positive charge in bound intermediates and transition states reflects, in turn, their respective ability to recover the ionic and covalent components of DPE, the energy required to detach proton from conjugate anions. The DPE components and the recovery fractions together lead to a modified DPE, which captures only the part of DPE that remains unrecovered by the ion-pair interactions at bound intermediates and transition states, as the unifying descriptor for broad families of acids and reactions. The electrostatic and charge reorganization energies involved in these general descriptors are placed in historical context by assessing their connections to the heuristics of hard–soft acid–base displacements. Further development of these concepts requires benchmarking and extension of electrostatic and reorganization components of energies for a more diverse set of reaction types and acid families and advancement of methods for more efficient calculations of electrostatic interactions. Reactivity descriptors must also account for dispersive interactions between host cavities and guest molecules, requiring a framework analogous to the one described here for ion-pair interactions; these dispersive interactions depend on the fit between their shapes and sizes as well as their ‘‘structural stiffness’’ that determines the ability to modify the shapes of molecules and voids to minimize free energy. Entropy considerations and estimates of their dependence on properties of catalysts and molecules are also required for accurately determining Gibbs free energies that ultimately determine reaction rates.

Deshlahra, Prashant↗
DOE OSTI2025

Elucidation of Marcus Relationships for Hydride Transfer Reactions Involving Transition Metal Hydrides

The rate of hydride transfer from three Ir hydride complexes of the type Cp*Ir( R bpy)H + (Cp* = C 5 Me 5 ; R bpy = 4,4′-R-2,2′-bipyridine, R = OMe, H, CO 2 Me) to six N-methylacridinium ( R Acr + ) acceptors with electronically different substituents in the 2- or 2,7-positions were measured. Using the thermodynamic hydricity of the donors and the hydride affinity of the acceptors the thermodynamic driving forces for hydride transfer were determined. Brønsted plots, which correlate kinetic and thermodynamic hydricity, demonstrate distinct linear free energy relationships for each complex, with different Brønsted α values. Thus, at the same driving force hydride transfer from Cp*Ir( OMe bpy)H + is faster than for Cp*Ir(bpy)H + or Cp*Ir( CO2Me bpy)H + . Experimental and computational analyses are consistent with a concerted hydride transfer mechanism for all Ir complexes. As the thermodynamic driving force increases an earlier transition state is observed and all transition states also include π-stacking interactions between the donor and acceptor, which likely contribute to the different α values. The experimental data fits well to the Marcus model, enabling the determination of reorganization energies (λ) that range from 58 to 69 kcal mol -1 . These are lower than λ values for hydride transfer reactions involving organic donors and acceptors. This work provides a rare example of the correlation of kinetic and thermodynamic hydricity using only experimental data and shows that hydride transfer reactions involving metal hydrides can follow Marcus theory. Furthermore, the findings offer insight into controlling metal-catalyzed hydride transfer reactions, which is valuable for designing improved systems for a range of transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗