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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 OSTI2023

The Good, the Bad, and the Ugly: Pseudopotential Inconsistency Errors in Molecular Applications of Density Functional Theory

The pseudopotential (PP) approximation is one of the most common techniques in computational chemistry. Despite its long history, the development of custom PPs has not tracked with the explosion of different density functional approximations (DFAs). As a result, the use of PPs with exchange/correlation models for which they were not developed is widespread, although this practice is known to be theoretically unsound. The extent of PP inconsistency errors (PPIEs) associated with this practice has not been systematically explored across the types of energy differences commonly evaluated in chemical applications. Here, we evaluate PPIEs for a number of PPs and DFAs across 196 chemically relevant systems of both transition-metal and main-group elements, as represented by the W4-11, TMC34, and S22 data sets. Near the complete basis set limit, these PPs are found to cleanly approach all-electron (AE) results for noncovalent interactions but introduce root-mean-squared errors (RMSEs) upwards of 15 kcal mol –1 into predictions of covalent bond energies for a number of popular DFAs. We achieve significant improvements through the use of empirical atom- and DFA-specific PP corrections, indicating considerable systematicity of the PPIEs. The results of this work have implications for chemical modeling in both molecular contexts and for DFA design, which we discuss.

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

De Novo Design of High‐Affinity Miniprotein Binders Targeting Francisella Tularensis Virulence Factor

Abstract Francisella tularensis poses considerable public health risk due to its high infectivity and potential for bioterrorism. Francisella‐like lipoprotein (Flpp3), a key virulence factor unique to Francisella, plays critical roles in infection and immune evasion, making it a promising target for therapeutic development. However, the lack of well‐defined binding pockets and structural information on native interactions has hindered structure‐guided ligand discovery against Flpp3. Here, we used a combination of physics‐based and deep‐learning methods to design high‐affinity miniprotein binders targeting two distinct sites on Flpp3. We identified four binders for site I with binding affinities ranging between 24–110 nM. For the second site, an initial binder showed a dissociation constant ( K D ) of 81 nM, and subsequent site saturation mutagenesis yielded variants with sub‐nanomolar affinities. Circular dichroism confirmed the topology of designed miniproteins. The X‐ray crystal structure of Flpp3 in complex with a site I binder is nearly identical to the design model (Cα root‐mean‐square deviation (RMSD): 0.9 Å). These designed miniproteins provide research tools to explore the roles of Flpp3 in tularemia and should enable the development of new therapeutic candidates.

Gokce‐Alpkilic, Gizem [Molecular Engineering and S↗
DOE OSTI2023

Mixing in Low Reynolds Number Reacting Impinging Jets in Crossflow

Previous efforts to model uranyl fluoride formation in an impinging jet gas reactor underpredicted spatial mixing and overpredicted chemical conversion into particulates. The previous fluid dynamics model was based on the solution of the Reynolds Averaged Navier Stokes equations. After simulating fluid dynamics, aerosol dynamics were superimposed onto CFD-simulated gas reactant species concentrations. The current work explores the influence of complex unsteady flow features on the overall flow physics and chemistry for a low Reynolds number, opposed flow, impinging jet gas reactor where there is a low Reynolds number cross flow. The objective of this study was to assess the impact of model formulation on scalar mixing and transport. Here, transient flow simulations were performed using Scale Resolving Simulations. Large-Eddy Simulations with the dynamic Smagorinsky turbulence model were performed along with simulations which directly resolved the flow. Average and root-mean-square (RMS) velocities and species concentrations were computed along with modeled and resolved turbulence kinetic energy (TKE), modeled turbulence dissipation, and modeled turbulent viscosity. Lagrangian flow tracers were also used to quantify species concentrations along path lines emanating from the jet tips. Transient simulation data were compared to results from RANS simulations using the k-ω shear stress transport (SST) model and Reynolds Stress Model (RSM). Transient simulations showed spatial mixing patterns which were more consistent with experimental data and helped elucidate the process of particle formation observed in experiments.

42 ENGINEERING↗
DOE OSTI2021

Vegetation Warming Experiment: 15N Uptake Experiment Water-Extractable Soil Nutrients, Utqiagvik (Barrow), Alaska, 2018

This dataset consists of measured water-extractable carbon and nutrients measured on soils collected from warming experiment enclosures and paired control plots. Harvest types include both natural abundance and enriched soils. Vegetation warming chambers (Zero Power) were deployed on the Barrow Environmental Observatory (BEO), Utqiagvik, Alaska. These chambers (Figure 1) consistently elevated air temperatures by approximately 4°C using a self-venting system described by Lewin et al (2017). Five chambers were deployed from June 17, 2018 to September 24, 2018 on the BEO within a 1 km2 area centered on 71.275N, -156.641W. Each chamber was co-located with an ambient plot where temperatures were not manipulated on patches of tundra containing the target species Arctagrostis latifolia. An intensive field campaign in late July investigated the impact of warming had on A. latifolia biomass, chemistry, and uptake of 15N labeled ammonia that was injected into the surface soils for one week. Initial measurements were taken on July 21, 2018. Harvest occurred on July 27, 2018. Water-extractable nutrients in soils were measured in July following harvests of A. latifolia plants and underlying soils. Availability of ammonia, nitrate, and phosphate throughout the growing season was measured by extracting nutrients bound to anion and cation binding resins deployed from July through September. Environmental variables (thaw depth, surface soil temperatures, surface soil moisture) were measured. Leaf traits and root traits of A. latifolia were also measured. The data package includes one *.csv data file and one *.pdf user guide. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Low-Lying Electronic States of NO 2 : Potential Energy and Dipole Surfaces, Bound States, and Electronic Absorption Spectrum

Nitrogen dioxide, NO 2 , is a free radical composed of the two most abundant elements in Earth’s atmosphere, nitrogen and oxygen, and is relevant to atmospheric and combustion chemistry. The electronic structure of even its lowest-lying states is remarkably complex, with various conical intersections and Renner–Teller pairings, giving rise to complex and perturbed vibronic states. Here we report some analysis of the 18 molecular states of doublet spin-multiplicity formed by combining ground-state N( 4 S u ) and O( 3 P g ) atoms. In this work, three-dimensional potential energy surfaces were fit at the MRCI(Q)-F12/VTZ-F12 level, describing the lowest four ($\tilde X$, $\tilde A$, $\tilde B$, and $\tilde C$) electronic states. A properties-based diabatization procedure was applied to accommodate the intersections, producing energies in a quasidiabatic representation and yielding couplings that were also fit into surfaces. The low-lying vibrational levels on the ground $\tilde X$ state were computed and compared with experimental measurements. Compared to experiment, the lowest 125 calculated vibrational levels (up to 8500 cm –1 above the zero-point energy) have a root-mean-squared error of 16.5 cm –1 . In addition, dipole moments for each of the lowest four electronic states—and the transition dipoles between them—were also computed and fit. With the coupled energy and dipole surfaces, the electronic spectrum was calculated in absolute intensity and compared with experimental measurements. Detailed structure in the experimental spectrum was successfully reproduced, and the total integrated intensity matches experiment to an accuracy of ~1.5% with no empirical adjustments.

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

Thermodynamic analysis of hydrogel swelling in aqueous sodium chloride solutions

Here, a comprehensive thermodynamic model is presented for swelling and salt partitioning of poly (n-isopropyl acrylamide) hydrogels in aqueous sodium chloride (NaCl) solutions at 298 K. Together with a Helmholtz energy expression for network elastic energy, a modified electrolyte Nonradom Two-Liquid activity coefficient model is used to represent both the long-range electrostatic interactions and the short-range van der Waals interactions present in the gel phase. The model parameters include one network parameter per hydrogel system plus two binary parameters per interaction pair in the polymer–solvent-salt systems. With semiquantitave agreement with experimental data for both hydrogel swelling and salt partitioning up to salt saturation, the analysis suggests the ion hydration between NaCl and water is the root cause for hydrogel deswelling in aqueous NaCl solution at high NaCl concentrations, i.e., NaCl weight fraction >0.03. On the other hand, the strong repulsive interaction between NaCl and hydrogel polymer is the dominant factor for salt partitioning.

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

Number of sites-based solver for determining coverages from steady-state mean-field micro-kinetic models

Kinetic models parameterized by ab-initio calculations have led to significant improvements in understanding chemical reactions in heterogeneous catalysis. These studies have been facilitated by implementations which determine steady-state coverages and rates of mean-field micro-kinetic models. As implemented in the open-source kinetic modeling program, CatMAP, the conventional solution strategy is to use a root-finding algorithm to determine the coverage of all intermediates through the steady-state expressions, constraining all coverages to be non-negative and to properly sum to unity. Though intuitive, this root-finding strategy causes issues with convergence to solution due to these imposed constraints. In this work, we avoid explicitly imposing these constraints, solving the mean-field steady-state micro-kinetic model in the space of number of sites instead of solving it in the space of coverages. Here, we transform the constrained root-finding problem to an unconstrained least-squares minimization problem, leading to significantly improved convergence in solving micro-kinetic models and thus enabling the efficient study of more complex catalytic reactions.

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

Dynamics-Based Peptide–MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen–major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Finally, our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

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

Lanthanide transport in angstrom-scale MoS 2 -based two-dimensional channels

Rare earth elements (REEs), critical to modern industry, are difficult to separate and purify, given their similar physicochemical properties originating from the lanthanide contraction. Here, we systematically study the transport of lanthanide ions (Ln 3+ ) in artificially confined angstrom-scale two-dimensional channels using MoS 2 -based building blocks in an aqueous environment. The results show that the uptake and permeability of Ln 3+ assume a well-defined volcano shape peaked at Sm 3+ . This transport behavior is rooted from the tradeoff between the barrier for dehydration and the strength of interactions of lanthanide ions in the confinement channels, reminiscent of the Sabatier principle. Molecular dynamics simulations reveal that Sm 3+ , with moderate hydration free energy and intermediate affinity for channel interaction, exhibit the smallest dehydration degree, consequently resulting in the highest permeability. Our work not only highlights the distinct mass transport properties under extreme confinement but also demonstrates the potential of dialing confinement dimension and chemistry for greener REEs separation.

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

Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop

Abstract Background Despite their widespread distribution and ecological importance, protists remain one of the least understood components of the soil and rhizosphere microbiome. Knowledge of the roles that protists play in stimulating organic matter decomposition and shaping microbiome dynamics continues to grow, but there remains a need to understand the extent to which biological and environmental factors mediate protist community assembly and dynamics. We hypothesize that protists communities are filtered by the influence of plants on their rhizosphere biological and physicochemical environment, resulting in patterns of protist diversity and composition that mirror previously observed diversity and successional dynamics in rhizosphere bacterial communities. Results We analyzed protist communities associated with the rhizosphere and bulk soil of switchgrass (SG) plants ( Panicum virgatum ) at different phenological stages, grown in two marginal soils as part of a large-scale field experiment. Our results reveal that the diversity of protists is lower in rhizosphere than bulk soils, and that temporal variations depend on soil properties but are less pronounced in rhizosphere soil. Patterns of significantly prevalent protists groups in the rhizosphere suggest that most protists play varied ecological roles across plant growth stages and that some plant pathogenic protists and protists with omnivorous diets reoccur over time in the rhizosphere. We found that protist co-occurrence network dynamics are more complex in the rhizosphere compared to bulk soil. A phylogenetic bin-based null model analysis showed that protists’ community assembly in our study sites is mainly controlled by homogenous selection and dispersal limitation, with stronger selection in rhizosphere than bulk soil as SG grew and senesced. Conclusions We demonstrate that environmental filtering is a dominant determinant of overall protist community properties and that at the rhizosphere level, plant control on the physical and biological environment is a critical driver of protist community composition and dynamics. Since protists are key contributors to plant nutrient availability and bacterial community composition and abundance, mapping and understanding their patterns in rhizosphere soil is foundational to understanding the ecology of the root-microbe-soil system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Battery aging mode identification across NMC compositions and designs using machine learning

A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing durable batteries and optimizing use protocols. Although battery lifetime prediction methods are flourishing, diagnosis of the root causes of aging and degradation have not yet been well developed nor studied for a broad mixture of designs and use cases. Here, we create a machine-learning (ML)-based framework that distinguishes aging modes using multiple electrochemical signatures recorded cycle-by-cycle. The predominant aging behaviors include a combination of loss of active materials in cathode (LAMPE) and a loss of Li inventory (LLI) in Li plating or solid electrolyte interphase (SEI) formation, manifested from 44 batteries representing two cathode chemistries, two electrode loadings, and five charging rates. Here, the aging mode classification accuracy is 86% using features within the first 50 cycles and increases to 88% beyond 225 cycles. The same features can quantify the percentage of end-of-life LAMPE with only 4.3% of error.

25 ENERGY STORAGE↗
DOE OSTI2022

Electron-Affinity Time-Dependent Density Functional Theory: Formalism and Applications to Core-Excited States

The lack of particle-hole attraction and orbital relaxation within time-dependent density functional theory (TDDFT) lead to extreme errors in the prediction of K-edge X-ray absorption spectra (XAS). We derive a linear-response formalism that uses optimized orbitals of the n - 1-electron system as the reference, building orbital relaxation and a proper hole into the initial density. Here, our approach is an exact generalization of the static-exchange approximation that ameliorates the particle-hole interaction error associated with the adiabatic approximation and reduces errors in TDDFT XAS by orders of magnitude. With a statistical performance of just 0.5 eV root-mean-square error and the same computational scaling as TDDFT under the core-valence separation approximation, we anticipate that this approach will be of great utility in XAS calculations of large systems.

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

Influence of tritium exposure route on vegetation types at the Savannah River Site

Plant, soil, water, and other media from various locations at the Savannah River Site were measured for total tritium (T) content and T speciation to characterize T in these areas, as well as investigate its uptake behavior and the transport of T species in these media. This characterization included the isolation and measurement of T in tritiated water (HTO), and (when possible) exchangeable organic bound T (E-OBT) and non-exchangeable organic bound T (NE-OBT). Two areas of interest were investigated: (1) a holding pond with T-contaminated water and (2) open basins or streams with low to background levels of T. Water in the holding pond is used to irrigate forest plots in the local area as a T remediation approach. This study compares the analytical data for water, soil/sediment, plants, and lichens from these locations. Here, the results indicate that the behavior of T in plants from these areas can be a function of one or more of the following: seasonal precipitation, the plant's primary route of access to the T-contamination (such as water uptake through the root vs. shoot), plant physical location (relative to T-contaminated water sources), plant rooting depth, pond water level, and plant height above the ground. Total T concentrations were lowest in the un-irrigated forest plants, followed by irrigated forest plants, shallow rooting plants near the pond, deep rooting plants further from the pond, and then water-saturated plants. The OBT:HTO and NE-OBT:E-OBT ratios were always greater for plants from irrigated forest plots compared to those from the holding pond.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid in situ nutrient element distribution in plants and soils using laser-induced breakdown spectroscopy ($\mathrm{LIBS}$)

The aim of this study is to develop and test the applicability of a rapid in situ plant chemistry profiling technique to determine elemental composition of small-volume plant and soil samples obtained from a woody bioenergy crop species, Populus trichocarpa. Expanding the research tools available to characterize the nutrient element correlations among plant tissue types and soil depths is a critical need in the path of understanding productivity and adaptation of plants to variations in external abiotic and biotic factors and developing sustainable perennial bioenergy crops that are co-optimized for biomass valorization aboveground and carbon sequestration belowground. Several plant root, stem, and soil samples were tested using laser-induced breakdown spectroscopy (LIBS) to evaluate the presence and distribution of nutrient elements. Samples were tested as collected and after being dried and cross sectioned to evaluate the effectiveness of using LIBS for in situ analysis on plant samples. The collected LIBS spectra show the elemental peaks were the same in both the as collected and prepared samples for roots and stems. Qualitative amounts of elements such as H, C, N, O, Li, Na, Mg, K, Ca, Fe, Al, and Si were able to be identified rapidly in raw samples. Here in this report we demonstrate suitability of LIBS in obtaining rapid, in situ, elemental distribution in plant and soil samples, utilizing only small sample volumes and minimal sample preparation. This demonstration opens up a new rapid phenotyping avenue necessary to fill the asymmetrical knowledge gaps in belowground performance of plant systems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material’s structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal–organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25–30% decrease in root-mean-squared-deviation and an average increase of 40–50% in R 2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure–property relationships for materials design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

SPRUCE FT-ICR MS, Bulk Chemistry, and Mass Loss from Litter Decomposition Study in Experimental Plots, Marcell Experimental Forest, Minnesota, 2015-2017

This dataset contains molecular, bulk chemical, and mass loss measurements from a litter decomposition study at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. This site is in a Sphagnum spp. ombrotrophic bog forest. Litterbags were deployed into the peat in September 2015 across three warming levels (+0, +4.5, and +9°C) under ambient and elevated carbon dioxide (CO₂ - +500 ppm) and retrieved after roughly 0.5, 1, and 2 years of field incubation (2015-09-23 to 2017-08-02). Litterbags containing six peatland litter types: black spruce needles (Picea mariana - SPL), spruce fine roots (SPR), Sphagnum angustifolium (ANG), Sphagnum magellanicum (MAG), Labrador tea leaves (Rhododendron groenlandicum - LTL), and Labrador tea roots (LTR). Molecular composition of water-soluble organic matter extracts was characterized using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) at 9.4 Tesla, operated in negative ion mode with electrospray ionization, providing molecular formula assignments and compound-class distributions across the decomposition time series. Bulk chemical characterization included elemental analysis (percent carbon, nitrogen, and phosphorus) and Fourier Transform Infrared Spectroscopy (FTIR) to quantify functional group composition. Litter mass loss was tracked gravimetrically at each retrieval interval, expressed as percent mass remaining relative to initial dry mass for each litter type and treatment combination. These data are valuable for understanding how vegetation shifts driven by increased atmospheric CO2 and temperature in peatlands alter litter inputs and organic matter stabilization trajectories, with implications for projecting and modeling peatland carbon cycling. This dataset contains two data files in comma-separated value (.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma separate (.csv) format and a user guide in PDF (*.pdf) format.

decomposition↗