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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Computational and Experimental Study for the Denitrification of Biomass-Derived Hydrothermal Liquefaction Oil

Hydrothermal liquefaction (HTL) is a promising method for processing wet biomass and waste feedstock to produce biofuels. During the HTL process, proteins and other biomolecules in certain feedstock get converted into nitrogenous compounds in produced biocrude, which represents a major challenge to further upgrading them into fuels. One promising approach is to separate nitrogenous compounds from the biocrude using polymeric resins. In this study, experiments were conducted to down-select sorbent and resin systems using a nitrogen-compound-containing surrogate biocrude. We model the binding interactions between an Amberlyst polymeric resin with various compounds present in the biocrude mixture such as nitrogenous compounds like pyrrole, pyridine, hexanamide, and representative co-existing compounds like phenol and dodecanoic acid. To ascertain the efficiency of various resins in the denitrogenation process, we have developed a quantitative structure–function model for the interacting components in the mixture. Our results suggest that the Amberlyst resin is a viable candidate for efficient removal of target nitrogen-containing compounds (such as pyridine) from the biocrude as a result of favorable interactions. The computational studies provide some insight into how and why the identified resin (Amberlyst) works in selective extraction of nitrogenous compound(s).

09 BIOMASS FUELS↗
DOE OSTI2024

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a “few-shot learning” challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets.

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

A unified understanding of minimum lattice thermal conductivity

Here, we propose a first-principles model of minimum lattice thermal conductivity ($κ^{min}_L$) based on a unified theoretical treatment of thermal transport in crystals and glasses. We apply this model to thousands of inorganic compounds and find a universal behavior of $κ^{min}_L$ in crystals in the high-temperature limit: The isotropically averaged $κ^{min}_L$ is independent of structural complexity and bounded within a range from ~0.1 to ~2.6 W/(m K), in striking contrast to the conventional phonon gas model which predicts no lower bound. We unveil the underlying physics by showing that for a given parent compound, $κ^{min}_L$ is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder. Moreover, we propose that the diffuson-dominated $κ^{min}_L$ in complex and disordered compounds might be effectively approximated by the phonon gas model for an ordered compound by averaging out disorder and applying phonon unfolding. With these insights, we further bridge the knowledge gap between our model and the well-known Cahill–Watson–Pohl (CWP) model, rationalizing the successes and limitations of the CWP model in the absence of heat transfer mediated by diffusons. Finally, we construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured ultralow κ L . Our work offers a unified understanding of $κ^{min}_L$, which can guide the rational engineering of materials to achieve .

42 ENGINEERING↗
NASA NTRS2024

Assessing Acetone for the GISS ModelE2.1 Earth System Model

Acetone is an abundant volatile organic compound (VOC) in the atmosphere, with important influences on ozone and oxidation capacity. Direct sources include chemical production from other VOCs and anthropogenic emissions, terrestrial vegetation, biomass-burning emissions, and ocean production. Sinks include chemical loss, deposition onto the land surface, and ocean uptake. Acetone also has a lifetime that is long enough to allow transport and reactions with other compounds remote from its sources. The NASA Goddard Institute for Space Studies (GISS) Earth system model ModelE2.1 simulates a variety of Earth system interactions. Previously, acetone had a very simplistic representation in the ModelE chemical scheme. This study assesses a more sophisticated acetone scheme in which acetone is a full 3-dimensional tracer with explicit sources, sinks, and atmospheric transport. We first evaluate the new global acetone budget in the context of past literature. Estimated source and sink fluxes fall within the range of previous models, although total atmospheric burden and lifetime are at the lower end of the published literature. Acetone's new representation in ModelE2.1 also results in more realistic spatial and vertical distributions, which we compare against previous models and field observations. The seasonality of acetone-related processes was also studied in conjunction with field measurements, and these comparisons show promising agreement but also shortcomings at high-emission urban locations, where the model's resolution is too coarse to capture the true behavior. Finally, we conduct a variety of sensitivity studies that explore the influence of key parameters on the acetone budget and its global distribution. An impactful finding is that the production of acetone from precursor hydrocarbon oxidation has strong leverage on the overall chemical source, indicating the importance of accurate molar yields. Overall, our implementation is one that corroborates with previous studies and marks a significant improvement in the development of the acetone tracer in GISS ModelE2.1.

Acetone↗
DOE OSTI2020

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V D,ss ) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V D,ss , accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V D,ss directly and to predict input parameters required for mechanistic and empirical V D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V D,ss predictions was also investigated. In predicting V D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r 2 = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V D,ss predictions significantly (81% within 3-fold, r 2 = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V D,ss but was limited in estimating the compounds with low V D,ss .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

Computational methods have demonstrated success in identifying virucidal agents, effectively contributing to the discovery of novel virucidal molecules. In this study, we developed a machine learning (ML) model, trained on a small dataset, to predict inhibitors of human enterovirus 71 (EV71), a pathological agent that causes severe disease in children and immunocompromised adults. Despite the dataset’s limitation, comprising of only 36 compounds tested, our ML framework demonstrated significant predictive capability. Notably, experimental validation revealed that five out of the eight compounds predicted by our model from the Chinese cosmetic material list exhibited virucidal activity. The inhibitor effects displayed by the main active compounds were further confirmed by molecular dynamics simulation. This underscores the potential of our AI-driven approach to bypass data constraints in identifying active molecules against viral pathogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions

Understanding protein-protein interactions (PPIs) between viruses and host organisms is crucial for uncovering infection mechanisms and identifying potential therapeutic targets. The ability to generalize PPI predictive models across understudied viruses presents a significant challenge. In this work, we use arenavirus-human PPIs to illustrate the difficulties associated with model generalization, which are compounded by a lack of both positive and negative data. We employ a Transfer Learning approach to investigate arenavirus-human PPIs by utilizing models trained on better-studied virus-human and human-human PPIs. Additionally, we curate and assess four types of negative sampling datasets to evaluate their impact on model performance. Despite the overall high accuracies (93–99 %) and AUPRC scores (0.8–0.9) appearing promising, further analysis indicates that these performance metrics can be misleading due to data leakage, data bias, and overfitting, especially concerning under-represented viral proteins. We reveal these gaps and assess the impact of data imbalance using standard k-fold cross-validation and Independent Blind Testing with a Balanced Dataset, resulting in a drop in accuracy below 50 %. We propose a viral protein-specific evaluation framework that categorizes viral proteins into majority and minority classes based on their representation in the dataset, enabling comparison of model performance across these groups using balanced accuracies. This framework offers a more robust evaluation of model generalizability, addressing biases inherent in standard evaluation techniques and paving the way for more reliable PPI prediction models for understudied viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Examining the Reactions of Ethanolamine’s Thermal Degradation Compounds in Carbon Capture through 1 H NMR and 13 C NMR

In amine scrubbing carbon capture, concerns about amine solvent degradation include whether it can affect the ability of the solvent to capture CO 2 . This study examines the interactions between the three most reported monoethanolamine (MEA) thermal degradation compounds namely, oxazolidine-2-one (OZD), N-(2-hydroxyethyl)-ethylenediamine (HEEDA), and N-(2- hydroxyethyl)-imidazoline-2-one (HEIA) with CO 2 in the presence and absence of MEA. We compared 1 H NMR, 13 C NMR, and heteronuclear single-quantum coherence (HSQC) NMR for neat OZD, HEEDA, and HEIA samples with CO 2 -loaded samples to observe changes in protonation and unique carbamate species formation. We found that OZD and HEIA did not directly react with CO 2 or undergo proton shifting based on our comparison of the neat OZD and HEIA samples with CO 2 -loaded spectra. However, we observed that the OZD can protonate when sparged with CO 2 in the presence of MEA, which suggests that the OZD acts as an intermediate. The NMR spectra for HEEDA indicated that HEEDA directly reacts with CO 2 at both amino groups and can protonate. In the presence of MEA, HEEDA and MEA can act like a solvent blend, resulting in multiple carbamates forming within the solvent. The neat HEIA spectrum, compared with CO 2 -loaded HEIA spectra, revealed similar results as OZD, where it does not react with CO 2 or protonate directly. However, HEIA does not protonate in the presence of MEA. These degradation compound reactions can increase the number of general equilibrium reactions during carbon capture and impact the model MEA solvent. This work helps provide a more complete picture of the reactions as the solvent degrades. Although this study examines CO 2 effects on thermal degradation products for MEA, other amines such as piperazine or 1- amino-3-propanol will degrade, and the degradation may speciate similarly with CO 2 . Furthermore, this study can impact and improve process models by assessing the degradation compounds’ reactions with CO 2 and potentially incorporating them into the model based on a neat solvent.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1999

Chemistry Experiments

The purpose of the chemistry component of the model comparison is to assess to what extent differences in the formulation of chemical processes explain the variance between model results. Observed concentrations of chemical compounds are used to estimate to what degree the various models represent realistic situations. For readability, the materials for the chemistry experiment are reported in three separate sections. This section discussed the data used to evaluate the models in their simulation of the source gases and the Nitrogen compounds (NO(y)) and Chlorine compounds (Cl(y)) species.

Brasseur, Guy↗
DOE OSTI2026

Alginate–Amorphous Calcium Carbonate Hydrogels for Controlled Therapeutic Release

Alginate hydrogels are widely explored as biocompatible matrices for transdermal delivery of therapeutic compounds but burst release and mechanical stability remain persistent challenges in drug delivery systems. This experimental study investigated alginate–amorphous calcium carbonate (ACC) hydrogel composites designed to regulate release of model anti-inflammatory compound, ibuprofen. Hydrogels containing 1.6–2.0 wt% sodium alginate were crosslinked with CaCl₂ and combined with ACC through two incorporation pathways: (i) separate addition of ACC and ibuprofen or (ii) co-precipitation of ACC onto ibuprofen prior to hydrogel incorporation. Hydrogels without ACC served as Control. Biocomposite structure and properties were characterized and release profiles quantified using Korsmeyer–Peppas (KP) model.Burst release was curbed as crosslinking time increased, highlighting importance of network density in diffusion control. Co-precipitating ACC with ibuprofen prior to incorporating into the hydrogel suppressed burst release and sustained release for > ~72 h. Rheological measurements indicate ACC reinforces hydrogel network, increasing storage modulus while maintaining hydration and flexibility. KP model indicates release is diffusion-controlled, with deviations reflecting contributions from diffusion barriers and morphologic/structural changes near the ACC coated ibuprofen. ACC within alginate hydrogels provides a strategy for tuning drug release while preserving mechanical properties relevant to transdermal applications.

36 MATERIALS SCIENCE↗
NASA NTRS2007

Statistical Model of Evaporating Multicomponent Fuel Drops

An improved statistical model has been developed to describe the chemical composition of an evaporating multicomponent- liquid drop and of the mixture of gases surrounding the drop. The model is intended for use in computational simulations of the evaporation and combustion of sprayed liquid fuels, which are typically mixtures of as many as hundreds of different hydrocarbon compounds. The present statistical model is an approximation designed to afford results that are accurate enough to contribute to understanding of the simulated physical and chemical phenomena, without imposing an unduly large computational burden.

Harstad, Kenneth↗
NASA NTRS1986

Rainout lifetimes of highly soluble aerosols and gases as inferred from simulations with a general circulation model

The rainout-determined lifetimes of highly soluble particulate and gaseous atmospheric compounds are investigated using general circulation model simulations in which removal is explicitly calculated in terms of the local, model-produced precipitation rates. The calculations indicate that because of the episodic and asymmetric nature of rainout, species' lifetimes depend not only on the amount of precipitation but also on the characteristics of the precipitation regime (such as duration and frequency of the precipitation events) and on the direction of the tracer main flow (determined by the species' average mixing ratio gradient). For this reason, averaged rainout lifetimes of tracers flowing downstream from the stratosphere are found to differ substantially from those of tracers of surface origin flowing upward or tracers of a more ubiquitous tropospheric source. These results imply that the use of a first-order parameterization to simulate rainout in a photochemical model that does not explicitly calculate precipitation can be inadequate in representing this process. A computationally efficient parameterization that includes the effects of intermittence and asymmetry of rainout is proposed, and it is shown how this parameterization can be used to estimate rainout-determined tropospheric residence times from observational data sets. A review of published estimates of submicron aerosol tropospheric residence times based on observations shows that these are consistent with the model results.

Giorgi, Filippo↗
DOE OSTI2026

Machine-learning guided search for phonon-mediated superconductivity in boron and carbon compounds

We present a workflow that iteratively combines ab-initio calculations with a machine-learning (ML) guided search for superconducting compounds with both dynamical stability and instability from imaginary phonon modes, the latter of which have been largely overlooked in previous studies. Electron-phonon coupling (EPC) properties and critical temperature (T c ) of 417 boron, carbon, and borocarbide compounds have been calculated with density functional perturbation theory (DFPT) and isotropic Eliashberg approximation. Our study addresses T c convergence of Brillouin zone sampling with an ansatz test, stabilizing imaginary phonon modes for significant EPC contributions, and comparing the performance of two ML models, especially when including compounds of dynamical instability. We predict a few promising superconducting compounds with formation energy just above the ground state convex hull, such as Ca 5 B 3 N 6 (35 K), TaNbC 2 (28.4 K), Nb 3 B 3 C (16.4 K), Y 2 B 3 C 2 (4.0 K), Pd 3 CaB (7.0 K), MoRuB 2 (15.6 K), RuVB 2 (15.0 K), RuSc 3 C 4 (6.6 K) among others.

Nepal, Niraj K. [Ames Laboratory (AMES), Ames, IA ↗
DOE OSTI2020

Computational micromechanics model based failure criteria for chopped carbon fiber sheet molding compound composites

Chopped carbon fiber sheet molding compound has a great potential in lightweight automotive, marine, and aerospace applications. One of the most challenging tasks is to predict the failure strength of the material due to its anisotropy and heterogeneity, as well as complex stress states in real-world working conditions. In this work, a novel constitutive model of carbon fiber chip is proposed to capture the pre- and post-failure behaviors under different loading modes. On this basis, we propose a new computational micromechanics model, which is calibrated and validated by uniaxial tensile, compressive, and in-plane shear experiments. Furthermore, a set of microstructures representative volume element (RVE) models under complex loading conditions are reconstructed to understand the relationship between the microstructure characteristics and the failure envelopes. Finally, several modified versions of classical failure criteria are proposed for anisotropic materials with consideration of the fiber orientation tensor. The modified Tsai-Wu failure criterion, which shows the best accuracy among all failure criteria, is highlighted in the comparative study.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Thermodynamic description of molten salt systems: KCl-LiCl-NaCl and KCl-LiCl-NdCl 3

The mixture of KCl and LiCl has been used as electrolyte in the electrorefining process to recover uranium from used nuclear fuels due to the low melting point. However, lanthanides and sodium in the reactor waste continuously dissolve into it and thus alter its thermodynamic properties. To understand the thermodynamic behavior evolution of the electrolyte with the accumulation of impurities, thermodynamic modeling for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and four constituent binary systems in the entire composition space was performed using the CALPHAD (CALculation of PHAse Diagrams) approach. The ionic liquid was described by the two-sublattice model, where neutral species were introduced to consider short-range ordering (SRO) within the melt, whereas the solid solution was modeled based on the Compound Energy Formalism. Literature data on phase equilibria and thermochemical properties were critically evaluated and used during the optimization of thermodynamic parameters for KCl-LiCl-NaCl and KCl-LiCl-NdCl 3 and their subsystems. The calculated phase diagrams and mixing enthalpies are in good agreement with the experimental data. The thermodynamic modeling for the KCl-LiCl-NdCl 3 system was carried out for the first time. To fill the gap in experimental measurement, enthalpy of mixing for the KCl-LiCl-NdCl 3 melt was estimated using the surrounded-ion model. These data then served as critical inputs for thermodynamic optimization. Furthermore, the present study can provide insights into thermodynamic property evolution of the electrolyte and solubility limit of various impurities during the electrorefining process.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗