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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Comprehensive Assessment of the Accuracy of the Ideal Adsorbed Solution Theory for Predicting Binary Adsorption of Gas Mixtures in Porous Materials

Quantifying the adsorption of chemical mixtures in porous adsorbents is critical to developing these materials for useful separation applications. The ideal adsorbed solution theory (IAST) is the most widely applied mixing theory for predicting mixture adsorption using single-component adsorption data, but a perceived lack of experimental data has limited previous efforts to explore the accuracy of IAST in a systematic way. In this paper, we take advantage of a large collection of binary experimental data for gas adsorption that became available recently to tackle this issue. Specifically, we identify more than 400 examples in which binary adsorption data and single-component data are available in the same publication and apply IAST to all these examples. This analysis includes experimental data from 63 gas mixtures of 37 different molecular species and 174 different adsorbents. In addition to being the most systematic evaluation to date of the accuracy of IAST for gas adsorption, these data will be valuable for future efforts to test or develop mixing theories that improve upon IAST.

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

Assessment of Molten Eutectic LiF-NaF-KF Density through Experimental Determination and Semiempirical Modeling

Molten salts have favorable material properties for use in high-temperature energy systems, including thermal energy storage systems, concentrating solar power plants, nuclear reactors, and various industrial manufacturing processes. Knowledge of chemical and thermophysical property data is essential for the design and optimization of these systems, yet data are often limited or uncertain for many candidate salts due to the difficulty of thermophysical property measurements at relevant temperatures (e.g., 500–900 °C). Here, the density of molten LiF-NaF-KF eutectic is reassessed through review of previous experimental data, new density measurements from 470 to 800 °C, and semiempirical modeling. Additionally, the density was measured using the displacement technique. Compositional and temperature-dependent density estimates were calculated with a multidimensional Redlich–Kister model. The results of the new experimental measurements agree within 2% of the modeled density of molten eutectic LiF-NaF-KF. The Redlich–Kister model’s prediction shows a near-ideal density behavior for the LiF-NaF-KF system and is promising for the estimation of off-eutectic LiF-NaK-KF densities. Finally, through review of the existing literature and comparison to new measurements, recommendations are made for the density of LiF-NaF-KF.

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

On the Rate Constant for NH 2 +HO 2 and Third-Body Collision Efficiencies for NH 2 +H(+M) and NH 2 +NH 2 (+M)

In low-temperature flash photolysis of NH 3 /O 2 /N 2 mixtures, the NH 2 consumption rate and the product distribution is controlled by the reactions NH 2 + HO 2 → products (R1), NH 2 + H (+M) → NH 3 (+M) (R2), and NH 2 + NH 2 (+M) → N 2 H 4 (+M) (R3). In the present work, published flash photolysis experiments by, among others, Cheskis and co-workers, are re-interpreted using recent direct measurements of NH 2 + H (+N 2 ) and NH 2 + NH 2 (+N 2 ) from Altinay and Macdonald. To facilitate analysis of the FP data, relative third-body collision efficiencies compared to N 2 for R2 and R3 were calculated for O 2 and NH 3 as well as for other selected molecules. We report results were in good agreement with the limited experimental data. Based on reported NH 2 decay rates in flash photolysis of NH 3 /O 2 /N 2 , a rate constant for NH 2 + HO 2 → NH 3 + O 2 (R1a) of $k_{1\text{a}}$ = 1.5(±0.5) × 10 14 cm 3 mol –1 s –1 at 295 K was derived. This value is higher than earlier determinations based on the FP results but in good agreement with recent theoretical work. Kinetic modeling of reported N 2 O yields indicates that NH 2 + HO 2 → H 2 NO + O (R1c) is competing with R1a, but perturbation experiments with addition of CH4 indicate that it is not a dominating channel. Measured HNO profiles indicate that this component is formed directly by NH 2 + HO 2 → HNO + H 2 O (R1b), but theoretical work indicates that R1b is only a minor channel. Based on this analysis, we estimate $k_{1\text{c}}$ = 2.5 × 10 13 cm 3 mol –1 s –1 and $k_{1\text{b}}$ = 2.5 × 10 12 cm 3 mol –1 s –1 at 295 K, with significant uncertainty margins.

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

Localization of Nonblinking Point Sources Using Higher-Order-Mode Detection and Optical Heterodyning: Developing a Strategy for Extending the Scope of Molecular, Super-resolution Imaging

While the stochastic, “blinking” nature of fluorescent systems has enabled the super-resolution of their localization by the fitting of their point-spread functions (PSFs), this strategy cannot be exploited for similar resolution of “nonblinking” systems, such as those that might be encountered in a coherent Raman experiment. An alternative method for subdiffraction-limited imaging lies in the exploitation of optical heterodyning. For example, if a Gaussian PSF (a TEM00 mode) of a point emitter is displaced with respect to the origin of the optical system, photons in the higher-order TEM modes carry information about that displacement. Information concerning the displacement can be extracted from photons in these higher-order modes. These photons can be collected by optical heterodyning, which exploits the large gain in a detector’s response to an optical signal from an emitter coupled to a local oscillator, which is prepared in the TEM of interest, e.g., TEM10. We have generalized and developed the heterodyning technique to localize point emitters via the detection of higher-order spatial modes. We have developed a theoretical approach to find a practical estimation limit of the localization parameters using a realistic model that accounts for shot noise, background noise, and Gaussian noise. To demonstrate the applicability of the method, we designed experiments in which a laser is a surrogate for one and two point emitters. Using the Fisher information and its accompanying Cramér-Rao lower bound, we demonstrate super-resolution localization in these cases: we show that objects can be localized to roughly 2–3 orders of magnitude of their point-spread function’s size for a given optical system. Finally and most importantly, it is suggested that the results will ultimately be generalizable to multiple emitters and, most importantly, to “nonblinking” molecular systems, which will be essential for broadening the scope of super-resolution measurements beyond the limits of fluorescence-based techniques.

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

Analyzing Production Rate and Carbon Utilization Trade-offs in CO 2 RR Electrolyzers

Carbon utilization is a crucially important performance characteristic of CO 2 electrolyzers; however, applications of simple and accurate descriptive models to experimental data on this topic are currently lacking. Here, we apply a simple analytical reactor model to parameterize single-pass conversion as a function of feed-gas flow rate and show that it captures a wide body of experimental data in the literature exceptionally well. In doing so, we demonstrate that this simple conceptual approach can characterize progress and capture recent innovations in reactor design. To validate the extracted parameters, we employ a well-established comprehensive model to confirm that the results agree with physical expectations. Lastly, we explore the implications of the descriptive model by examining the inherent trade-off between single-pass conversion efficiency and reactor productivity. Understanding such trade-offs is crucially important for advancing this electrolyzer technology toward commercial applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å –1 from the first 6 Å –1 (≈100 eV) of the absorption spectrum of Cu 2 + substitutional defects in the Fe 3+ mineral hematite (α-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. In conclusion, to encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.

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

Combining High-Throughput Experiments and Active Learning to Characterize Deep Eutectic Solvents

The high tunability of deep eutectic solvents (DESs) stems from the ease of changing their precursors and relative compositions. However, measuring the physicochemical properties across large composition and temperature ranges, necessary to properly design target-specific DESs, is tedious and error-prone and represents a bottleneck in the advancement and scalability of DES-based applications. As such, active learning (AL) methodologies based on Gaussian processes (GPs) were developed in this work to minimize the experimental effort necessary to characterize DESs. Owing to its importance for large-scale applications, the reduction of DES viscosity through the addition of a low-molecular-weight solvent was explored as a case study. A high-throughput experimental screening was initially performed on nine different ternary DESs. Then, GPs were successfully trained to predict DES viscosity from its composition and temperature, showcasing the ability of these stochastic, nonparametric models to accurately describe the physicochemical properties of complex mixtures. Finally, the ability of GPs to provide estimates of their own uncertainty was leveraged through an AL framework to minimize the number of data points necessary to obtain accurate viscosity modes. This led to a significant reduction in data requirements, with many systems requiring only five independent viscosity data points to be properly described.

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

Temperature Dependence of Nuclear Quadrupole Resonance and the Observation of Metal–Ligand Covalency in Actinide Complexes: 35 Cl in Cs 2 UO 2 Cl 4

We report a study of the temperature dependence of 35 Cl nuclear quadrupole resonance (NQR) transition energies and spin–lattice relaxation times (T 1 ) for 235 U-depleted dicesium uranyl tetrachloride (Cs 2 UO 2 Cl 4 ) aimed at elucidating electronic interactions between the uranium center and atoms in the equatorial plane of the UO 2 2+ ion. The transition frequency decreases slowly with temperature below 75 K and with a more rapid linear dependence above this temperature. The spin–lattice relaxation time becomes shorter with temperature, and as temperatures increase, the T 1 decrease becomes nearly quadratic. The observed trends are reproduced by a model that assumes phonon-induced fluctuations of the electric field gradient tensor and partial electron delocalization from Cl to U. The fit of the theoretical model to experimental data allows a Debye temperature of 96 K to be estimated. Finally, the generalization of this approach to investigations of covalency in actinide–ligand bonding is examined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1996

Theoretical Chemistry At NASA Ames Research Center

The theoretical work being carried out in the Computational Chemistry Branch at NASA Ames will be overviewed. This overview will be followed by a more in-depth discussion of our theoretical work to determine molecular opacities for the TiO and water molecules and a discussion of our density function theory (DFT) calculations to determine the harmonic frequencies and intensities to the vibrational bands of polycyclic aromatic hydrocarbons (PAHs) to assess their role as carriers to the unidentified infrared (UIR) bands. Finally, a more in-depth discussion of our work in the area of computational molecular nanotechnology will be presented.

Langhoff, Stephen↗
DOE OSTI2023

Re-Examination of the N 2 O + O Reaction

The reaction of N 2 O with O is a key step in consumption of nitrous oxide in thermal processes. It has two product channels, NO + NO (R2) and N 2 + O 2 (R3). The rate constant for R2 has been measured both in the forward and the reverse direction at elevated temperature and is well established. However, the rate constant for the N 2 + O 2 channel (R3) has been difficult to quantify and has significant error limits. Here, the direct reaction on the triplet surface has a barrier of around 40 kcal mol –1 , and it is too slow for the N 2 + O 2 channel to have any practical significance. Recently, Pham and Lin (2022) suggested an alternative low activation energy reaction path that involves intersystem crossing and reaction on the singlet surface. In the present work, we re-examined a wide range of experiments relevant for the N 2 O + O reaction through kinetic modeling, paying attention to the impact of artifacts such as impurities and surface reactions. Experimental results from shock tubes and batch reactors on the final NO yield in N 2 O decomposition, covering temperatures of 973–2200 K and pressures of 0.013–11.5 atm, support k 3 ~ 0, consistent with the high activation energy for reaction on the triplet surface and a low probability of ISC.

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

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

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

Inhibition of Extracellular Enzyme Activity by Reactive Oxygen Species upon Oxygenation of Reduced Iron-Bearing Minerals

The dual roles of minerals in inhibiting and prolonging extracellular enzyme activity in soils and sediments are governed by enzyme adsorption to mineral surface. Oxigenation of mineral-bound Fe(II) generates reactive oxygen species (ROS), yet it is unknown whether and how this process alters the activity and functional lifespan of extracellular enzymes. Here, the effect of mineral-bound Fe(II) oxidation on the hydrolytic activity of a cellulose-degrading enzyme ß-glucosidase (BG) was studied using two pre-reduced Fe-bearing clay minerals (nontronite and montmorillonite) and one pre-reduced iron oxide (magnetite) at pH 5 and 7. Under anoxic condition, BG adsorption to mineral surface decreased its activity but prolonged its lifespan. Under oxic condition, ROS was produced, with the amount of •OH, the most abundant ROS, being positively correlated with the extent of structural Fe(II) oxidation in reduced minerals. •OH decreased BG activity and shortened its lifespan via conformational change and structural decomposition of BG. These results suggest that under oxic condition the ROS-induced inhibitory role of Fe(II)-bearing minerals outweighed their adsorption-induced protective role in controlling enzyme activity. These results disclose a previously unknown mechanism of extracellular enzyme inactivation, which have pivotal implications for predicting the active enzyme pool in redox-oscillating environments.

58 GEOSCIENCES↗
DOE OSTI2022

PemNet: A Transfer Learning-Based Modeling Approach of High-Temperature Polymer Electrolyte Membrane Electrochemical Systems

Widespread adoption of high-temperature electrochemical systems such as polymer electrolyte membrane fuel cells (HT-PEMFCs) requires models and computational tools for accurate optimization and guiding new materials for enhancing fuel cell performance and durability. Furthermore, while robust and better suited for extrapolation, knowledge-based modeling has limitations as it is time-consuming and requires information about the system that is not always available (e.g., material properties and interfacial behavior between different materials). Data-driven modeling, on the other hand, is easier to implement but often necessitates large datasets that could be difficult to obtain. In this contribution, knowledge-based modeling and data-driven modeling are combined by implementing a few-shot learning (FSL) approach. A knowledge-based model originally developed for a HT-PEMFCs was used to generate simulated data (887,735 points) and used to pretrain a neural network source model tuned via a genetic algorithm-based AutoML. Then, experimental datasets from HT-PEMFCs with different materials and operating conditions (~50 points each) were used to train six target models via FSL. Models for the unseen data reached high accuracies in all cases (rRMSE < 10%).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗
DOE OSTI2023

TransPolymer: a Transformer-based language model for polymer property predictions

Accurate and efficient prediction of polymer properties is of great significance in polymer design. Conventionally, expensive and time-consuming experiments or simulations are required to evaluate polymer functions. Recently, Transformer models, equipped with self-attention mechanisms, have exhibited superior performance in natural language processing. However, such methods have not been investigated in polymer sciences. Herein, we report TransPolymer, a Transformer-based language model for polymer property prediction. Our proposed polymer tokenizer with chemical awareness enables learning representations from polymer sequences. Rigorous experiments on ten polymer property prediction benchmarks demonstrate the superior performance of TransPolymer. Moreover, we show that TransPolymer benefits from pretraining on large unlabeled dataset via Masked Language Modeling. Experimental results further manifest the important role of self-attention in modeling polymer sequences. We highlight this model as a promising computational tool for promoting rational polymer design and understanding structure-property relationships from a data science view.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Theoretical Calculation Study of Nanomaterials

In recent years, theoretical simulations using computational physics and chemistry models have been used widely to predict the physical, chemical, and structural properties of nanomaterials. To reflect such rapid progress, two Special Issues on the theoretical study of nanomaterials were published. The 36 articles collected cover broad re-search fields and applications. In this Editorial, we provide an overview of these pub-lications and summarize the main findings. Based on these research outcomes, we lay out some future research directions.

First-principles calculations↗
DOE OSTI2023

Electronic properties and optical spectra of donor–acceptor conjugated organic polymers

Organic semiconductors based on conjugated donor-acceptor (D–A) polymers are a unique platform for electronic, spintronic, and energy-harvesting devices. Understanding the electronic structure of D–A polymers with a small band gap is essential for developing next-generation technologies. Here, we investigate the electronic structure and optical spectra of cyclopentadithiophene-based closed/open-shell D–A polymers using density functional theory and the Bethe–Salpeter equation based on G 0 W 0 approximation. We explored the role of different acceptor units and chemical substitutions on the structural changes and, more importantly, electronic, optical, and dielectric behavior. We found that the computed first exciton peak of the polymers agreed well with the available experimentally measured optical gap. Furthermore, D–A polymers with open-shell character display higher dielectric constant than the closed-shell polymers. We show that the exceptional performance of polycyclopentadithiophene-thiophenylthiadiazoloquinoxaline (PCPDT-TTQ) as a scalable n-type material for Faradaic supercapacitors can be partly ascribed to its elevated dielectric constant. Consequently, these D–A polymers, characterized by their high dielectric constants, exhibit significant potential for various applications, including energy storage, organic electronics, and the production of dielectric films.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗