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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A comprehensive study on three typical photoacid generators using photoelectron spectroscopy and ab initio calculations

Conducting a comprehensive molecular-level evaluation of a photoacid generator (PAG) and its subsequent impact on lithography performance can facilitate the rational design of a promising 193 nm photoresist tailored to specific requirements. In this study, we integrated spectroscopy and computational techniques to meticulously investigate the pivotal factors of three prototypical PAG anions, p-toluenesulfonate (pTS - ), 2-(trifluoromethyl)benzene-1-sulfonate (TFMBS - ), and triflate (TF - ), in the lithography process. Our findings reveal a significant redshift in the absorption spectra caused by specific PAG anions, attributed to their involvement in electronic transition processes, thereby enhancing the transparency of the standard PAG cation, triphenylsulfonium (TPS + ), particularly at ~193 nm. Furthermore, the electronic stability of PAG anions can be enhanced by solvent effects with varying degrees of strength. Here we observed the lowest vertical detachment energy of 6.6 eV of pTS - in PGMEA solution based on the polarizable continuum model, which prevents anion loss at 193 nm lithography. In addition, our findings indicate gas-phase proton affinity values of 316.4 kcal/mol for pTS - , 308.1 kcal/mol for TFMBS - , and 303.2 kcal/mol for TF - , which suggest the increasing acidity strength, yet even the weakest acid pTS - is still stronger than strong acid HBr. The photolysis of TPS + -based PAG, TPS + ·pTS - , generated an excited state leading to homolysis bond cleavage with the lowest reaction energy of 83 kcal/mol. Overall, the PAG anion pTS - displayed moderate acidity, possessed the lowest photolysis reaction energy, and demonstrated an appropriate redshift. These properties collectively render it a promising candidate for an effective acid producer.

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

Deciphering decomposition pathways of high explosives with cryogenic X-ray Raman spectroscopy

We employed cryogenic X-ray Raman spectroscopy to investigate the early-stage decomposition of the high explosive molecule hexanitrohexaazaisowurtzitane (CL-20). By systematically varying the radiation dose under cryogenic conditions, we induced the decomposition of the molecule using ionizing radiation and observed the evolution of spectral features at the carbon, nitrogen, and oxygen K edges. Through extensive first-principles calculations, we identified key intermediates in the early stages of the decomposition process, resulting from C–C and C–N bond cleavage which leads to the opening of the internal cage structure. A detailed analysis of spectral trends and fingerprints provided evidence supporting N–NO 2 homolytic cleavage as the primary initial decomposition pathway. The combination of advanced core-level spectroscopy methods and state-of-the-art theoretical calculations enabled a comprehensive characterization of the molecular changes induced by controlled radiation dose exposures. In conclusion, our findings establish a benchmark for understanding the decomposition chemistry of high-explosive materials, offering important insights into their stability and reactivity under extreme conditions.

X-ray Raman spectroscopy↗
DOE OSTI2025

Integrated low-temperature PVC and polyolefin upgrading

Polyolefins and their chlorinated derivatives such as polyvinyl chloride (PVC) are among the most prevalent plastics in global production and waste streams. Traditional waste-to-energy methods such as incineration and pyrolysis, as well as most chemical upcycling methods for PVC utilization, require thorough, high-temperature dechlorination to prevent the release of toxic chlorinated compounds. Here, we present here a strategy for upgrading discarded PVC into chlorine-free fuel range hydrocarbons and hydrogen chloride in a single-stage process catalyzed by chloroaluminate ionic liquids. This approach offsets endothermic dechlorination and carbon-carbon bond cleavage with exothermic alkylation and hydrogen transfer by isobutane or isopentane in a low-temperature tandem process. The light isoalkanes are available from refinery processes and partly from recycling of the product stream. This process is suitable for handling real-world mixed and contaminated PVC and polyolefin waste streams.

Zhang, Wei [East China Normal Univ. (ECNU), Shangh↗
DOE OSTI2024

From Waste to Clean Fuel: Using Microwave Chemistry to Achieve Process Decarbonization

Microwave co-gasification of plastics and biomass has several advantages over conventional gasification, including enhanced hydrogen production and greater solid-to-gas conversion, as opposed to undesirable char and tar -- via process electrification route. A previous study at NETL has revealed the efficient role of microwave heating, which can liberate the oxygenated free radicals and encourage bond cleavage in non-microwave active plastics, if combined with microwave-active corn stover. However, further reduction of the unwanted tar must be achieved for future process scale-up and higher energy efficiency, which can be realized via a microwave-assisted catalytic approach. This continuous study investigates a series of microwave-active Fe catalysts that can efficiently enhance H2 production and reduce tar for plastic-corn stover co-gasification. The results suggest that in presence of magnetite, hydrogen yield increase 100% as compared to non-catalytic system at similar reaction conditions.

Abedin, Ashraf↗
DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1974

Dual photon effects in nitrogen dioxide photolysis

A previous study demonstrated two-photon consecutive absorption as being the most probable mechanism for the photodissociation of NO2 using a pulsed ruby laser at 6943 A. Additional data discussed here confirmed this and also examined an associated phenomenon, that of multiphoton induced fluorescence. The dissociation of NO2 by ON-O bond cleavage requires 3.4 eV, while the laser energy corresponds to 1.785 eV. The pooling of the energy of two photons would give more than enough energy to dissociate the NO2 into NO + O. Several mechanisms including (1) simultaneous absorption of two photons; (2) reaction of two singly excited NO2; (3) reaction of a singly excited NO2 with a ground state NO2; and (4) consecutive absorption of two photons were examined.

Hakala, D.↗
NASA NTRS1976

Photoionization and ion cyclotron resonance studies of the ion chemistry of ethylene oxide

The formation of the ethylene oxide molecular ion and its subsequent ion-molecule reactions leading to the products C2H5O(+) and C3H5O(+) have been studied using time-resolved photoionization mass spectroscopy, ion cyclotron resonance spectroscopy, and photoelectron spectroscopy. An examination of the effects of internal energy on reactivity shows that the ratio of C3H5O(+) to C2H5O(+) increases by an order of magnitude with a single quantum of vibrational energy. The formation of (C2H4O/+/)-asterisk in a collision-induced isomerization is found which yields a ring-opened structure by C-C bond cleavage. The relaxed ring-opened C2H4O(+) ion reacts with neutral ethylene oxide by CH2(+) transfer to yield an intermediate product ion C3H6O(+) which gives C3H5O(+) by loss of H.

Corderman, R. R.↗
NASA NTRS1982

Pulsed radiolysis of model aromatic polymers and epoxy based matrix materials

Models of primary processes leading to deactivation of energy deposited by a pulse of high energy electrons were derived for epoxy matrix materials and polyl-vinyl naphthalene. The basic conclusion is that recombination of initially formed charged states is complete within 1 nanosecond, and subsequent degradation chemistry is controlled by the reactivity of these excited states. Excited states in both systems form complexes with ground state molecules. These excimers or exciplexes have their characteristics emissive and absorptive properties and may decay to form separated pairs of ground state molecules, cross over to the triplet manifold or emit fluorescence. ESR studies and chemical analyses subsequent to pulse radiolysis were performed in order to estimate bond cleavage probabilities and net reaction rates. The energy deactivation models which were proposed to interpret these data have led to the development of radiation stabilization criteria for these systems.

Gupta, A.↗
NASA NTRS1982

Radiation-induced defects in SiO2 as determined with XPS

An analytical method based on oxide/substrate intensity ratios is used to deduce the product of the atomic number density and electron mean free path as a function of depth, for the cases of device-quality gate oxides grown on Si substrates and irradiated with zero to 20 eV electrons during in situ X-ray photoelectron spectroscopy. These structures had been thinned stepwise to 25-60 A by means of a wet chemical depth-profiling procedure. Si(3+) species are formed in the Si/SiO2 interface region, and their observation during their relaxation and annihilation is correlated with strained Si-O-Si bonds. The observation of bond of cleavage and bond strain gradients in these samples is used to extend silica devitrification models for the explanation of fixed oxide charge generation and interface states.

Grunthaner, F. J.↗
NASA NTRS1973

Reductive cleavage of the peptide bond

In many biological research efforts, long chain organic molecules are studied by breaking large molecules into smaller components. Cleavage technique of recent interest is the use of solvated electrons. These are formed when aqueous solutions are bombarded with gamma radiation. Solvated electron is very reactive and can reduce most any species present, even to form free radicals.

Holian, J.↗
DOE OSTI2025

Photochemistry of Hypervalent Iodoazide Derivatives

The photochemical properties and reaction mechanisms of a series of hypervalent iodoazide compounds (R–IN3) were investigated, with substituents (−CH 3 , −H, −CF 3 ) tuning the electronic density of the phenyl ring. With the help of UV irradiation, ultrafast time-resolved spectroscopy, and density functional theory (DFT) calculations, we elucidated the mechanisms of azide radical (N 3 • ) release and its subsequent reactivity. UV–vis spectroscopy reveals that the photoconversion rates follow the trend CH3 > H > CF3, aligning with DFT-calculated ΔG values for ring-opening transitions. Homolytic cleavage of the I–N bond is identified as the dominant pathway for N3• generation upon UV irradiation, occurring within 400 fs. The released azide radicals react with the solvent molecules, while the iodo radical R–I C • fragments undergo a thermodynamically uphill lactone ring-opening (RO) reaction and subsequent hydrogen atom abstraction from the solvent to form carboxylic acids R–I–COOH, as validated by NMR and IR spectroscopy. The study also highlights the role of substituents in influencing reaction kinetics and intermediate stability, with electron-donating groups accelerating the release of the N 3 • species. This work bridges experimental observations with computational predictions, offering a foundation for future advancements in azide-based reactions and materials.

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

Energetic and Electronic Properties of AcX and LaX (X = O and F)

The bonding and spectroscopic properties of LaX and AcX (X = O and F) diatomic molecules were studied by highlevel ab initio CCSD(T) and SO-CASPT2 electronic structure calculations. Bond dissociation energies (BDEs) were calculated at the Feller−Peterson−Dixon (FPD) level. Potential energy curves and spectroscopic constants for the lowest-lying spin−orbit Ω states were obtained at the SO-CASPT2/aQ-DK level. A dense manifold of excited states was described for the monofluorides with the ground states well separated from the excited states. The spectroscopic parameters were in good agreement with those reported experimentally for LaO and LaF. For the diatomic molecules containing actinides, no experimental data of these parameters was found, but the results were consistent with other high-level calculations. The BDEs calculated at the FPD level were 791.3 (LaO), 705.2 (AcO), 650.0 (LaF), and 678.6 (AcF) kJ/ mol. The NBO analysis showed that the monofluorides are essentially ionic, which explains why the BDE(AcF) is higher than BDE(LaF); for the monoxides, covalent contributions involving the d orbitals of the metal and the p orbitals of the oxygen are stronger for LaO than AcO, which explains the higher BDE for LaO. Finally, the bond orders are predicted to be 2 for LaF and AcF, 3 for AcO, and higher than 3 for LaO.

Basis sets↗
NASA NTRS1995

Cleavage of an amide bond by a ribozyme

A variant form of a group I ribozyme, optimized by in vitro evolution for its ability to catalyze magnesium-dependent phosphoester transfer reactions involving DNA substrates, also catalyzes the cleavage of an unactivated alkyl amide when that linkage is presented in the context of an oligodeoxynucleotide analog. Substrates containing an amide bond that joins either two DNA oligos, or a DNA oligo and a short peptide, are cleaved in a magnesium-dependent fashion to generate the expected products. The first-order rate constant, kcat, is 0.1 x 10(-5) min-1 to 1 x 10(-5) min-1 for the DNA-flanked substrates, which corresponds to a rate acceleration of more than 10(3) as compared with the uncatalyzed reaction.

NASA Discipline Exobiology↗
DOE OSTI2025

Characterizing the Potential for Sustainable Azelaic Acid Production from High-Oleic Vegetable Oil Using Two-Step Oxidative Cleavage

Azelaic acid is a renewable monomer conventionally produced via the energy-intensive ozonolysis of oleic acid. Recent advancements have enabled the use of high-oleic vegetable oils (rather than tallow-derived oleic acid) and replaced ozonolysis with two-step oxidative cleavage using hydrogen and oxygen. Although this shift would improve process safety, the financial viability and environmental implications remain uncertain. In this study, we characterized the sustainability of azelaic acid production from high-oleic vegetable oil using two-step oxidative cleavage. Process design, simulation, technoeconomic analysis (TEA), and life cycle assessment (LCA) were executed under uncertainty using BioSTEAM. The modeled system produces azelaic acid at a market-competitive minimum selling price (MSP) of 8.32 [4.93−13.34] $\$kg$ −1 (median 5th−95th percentiles), below the minimum estimated market price of 9.93 $\$kg$ −1 . Further, it has the potential to approach carbon neutrality (0.0 [−5.5 to 5.6] kg of CO 2 -eq kg −1 ) under displacement allocation. Improvements to dihydroxylation (86 to 99%) and oxidative cleavage conversions (93 to 99%) would reduce MSP to $\$5.24$ kg −1 and carbon intensity to −1.90 kg of CO 2 -eq kg −1 (displacement). Additionally, increasing the feedstock triolein content (75 to 85%) lowers MSP by $\$0.82$ kg −1 . Overall, this research demonstrates the potential for financially viable production of azelaic acid from vegetable oils and the utility of agile TEA/LCA.

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

QC-GN 2 oMS 2 : a Graph Neural Net for High Resolution Mass Spectra Prediction

Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. Throughout this work, we evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.

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

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. Further, this physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. In conclusion, not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations.

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

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

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

Including Physics-Informed Atomization Constraints in Neural Networks for Reactive Chemistry

Machine learning interatomic potentials (MLIPs) have emerged as powerful tools for investigating atomistic systems with high accuracy and a relatively low computational cost. However, a common and unaddressed challenge with many current neural network (NN) MLIP models is their limited ability to accurately predict the relative energies of systems containing isolated or nearly isolated atoms, which appear in various reactive processes. To address this limitation, we present a mathematical technique for modifying any existing atom-centered NN architecture to account for the energies of isolated atoms. The result produces a consistent prediction of the atomization energy (AE) of a system using minimal constraints on the model. Using this technique, we build a model architecture that we call hierarchically interacting particle neural network (HIP-NN)-AE, an AE-constrained version of the HIP-NN, as well as ANI-AE, the AE-constrained version of the accurate NN engine for molecular energies (ANI). Our results demonstrate AE consistency of AE-constrained models, which drastically improves the AE predictions for the models. We compare the AE-constrained approach to unconstrained models as well as models from the literature in other scenarios, such as bond dissociation energies, bond dissociation pathways, and extensibility tests. These results show that the constraints improve the model performance in some of these tasks and do not negatively affect the performance on any tasks. The AE constraint approach thus offers a robust solution to the challenges posed by isolated atoms in energy prediction tasks.

74 ATOMIC AND MOLECULAR PHYSICS↗