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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

AmeriFlux FLUXNET-1F US-xSP NEON Soaproot Saddle (SOAP)

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xSP NEON Soaproot Saddle (SOAP). This is the FLUXNET version of the carbon flux data for the site US-xSP NEON Soaproot Saddle (SOAP) produced by applying the standard ONEFlux (1F) software. Site Description - The Soaproot Saddle is a complex terrain of coarse hills, steep slopes and narrow drainages. With an elevation of 3274 - 4537’ this site encompasses 1438 acres of mixed conifer forests that are experiencing high levels of mortality due to native Pine beetles. At the core of this site stands a 171’ tall flux tower that collects physical and chemical properties of atmosphere and related process. Soaproot Saddle also hosts an array of sensor measurements along with field observations collected by highly trained NEON staff. The automated instrument measurements and some of the terrestrial observational safor this field site are colocted with NEON's aquatic site, Upper Big Creek, which is located just north of Soaproot Saddle's site boundaries.

Network), NEON (National Ecological Observatory [N↗
DOE OSTI2023

Multi-technique structural analysis of zinc carboxylates (soaps)

A series of medium- and long-chain zinc carboxylates (zinc octanoate, zinc nonanoate, zinc decanoate, zinc undecanoate, zinc dodecanoate, zinc pivalate, zinc stearate, zinc palmitate, zinc oleate, and zinc azelate) was analyzed by ultra-high-field 67 Zn NMR spectroscopy up to 35.2 T, as well as 13 C NMR and FTIR spectroscopy. We also report the single-crystal X-ray diffraction structures of zinc nonanoate, zinc decanoate, and zinc oleate—the first long-chain carboxylate single-crystals to be reported for zinc. The NMR and X-ray diffraction data suggest that the carboxylates exist in three distinct geometric groups, based on structural and spectroscopic parameters. The ssNMR results presented here present a future for dynamic nuclear polarization (DNP)-NMR-based minimally invasive methods for testing artwork for the presence of zinc carboxylates.

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

In situ probing of interfacial roughness and transient phases during ceramic cold sintering process

The ceramic cold sintering process (CSP) offers an eco-friendly approach to producing fully dense ceramics at low temperatures. However, an incomplete mechanistic understanding hinders its optimization and widespread adoption. In this study, we analyze the microstructural and structural changes in ZnO, a model CSP system, using in situ synchrotron-based high-energy small-angle X-ray scattering and X-ray diffraction techniques. Our results reveal the time evolution of ZnO particles' surface area and roughness, reflecting the dissolution and re precipitation processes that enable densification. The in situ measurements supply valuable kinetic data for these stages of CSP. Alongside microstructural changes and densification, we observed the evolution of secondary phases representing reaction products between ZnO and acetic acid, the solvent used. The initial ZnO/solvent mixture's dominant secondary phase is attributed to zinc acetate, which is gradually replaced by a zinc soap-type structure during CSP. This structure has a large (≈ 21 Å) lattice parameter and is assumed to have a layered nature. The formation of this soap phase, which is retained in the sintered product as an intergranular component, appears to be a signature of successful cold sintering as it facilitates mass transport, leading to densification. Here, our study underscores the potential of in situ synchrotron characterization for revealing microstructural and phase-evolution details during CSP. These findings, which would be challenging to obtain through ex situ measurements, provide crucial data to guide and validate theoretical models, ultimately enhancing CSP's effectiveness and adoption.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nano- to microscale three-dimensional morphology relevant to transport properties in reactive porous composite paint films

The quantitative evaluation of the three-dimensional (3D) morphology of porous composite materials is important for understanding mass transport phenomena, which further impact their functionalities and durability. Reactive porous paint materials are composites in nature and widely used in arts and technological applications. In artistic oil paintings, ambient moisture and water and organic solvents used in conservation treatments are known to trigger multiple physical and chemical degradation processes; however, there is no complete physical model that can quantitatively describe their transport in the paint films. In the present study, model oil paints with lead white (2PbCO 3 ·Pb(OH) 2 ) and zinc white (ZnO) pigments, which are frequently found in artistic oil paintings and are associated with the widespread heavy metal soap deterioration, were studied using synchrotron X-ray nano-tomography and unilateral nuclear magnetic resonance. This study aims to establish a relationship among the paints’ compositions, the 3D morphological properties and degradation. This connection is crucial for establishing reliable models that can predict transport properties of solvents used in conservation treatments and of species involved in deterioration reactions, such as soap formation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

FLC Notable Technology Award Oleo-Furan Surfactant

It’s a well-kept secret that laundry detergents are harmful to the environment. The important chemical in laundry detergent, called a surfactant. Surfactants are the workhorses of laundry detergent and soaps, and they're attracted to things like grease on your hands and dirt in your clothes to wash them away. Surfactants are used in everything from shampoo, hand soap, laundry detergent, and cosmetics. Many current surfactants are made from petroleum feedstock. These petroleum-derived building blocks come with a big carbon footprint. As the need for greener feedstock alternatives for synthesizing surfactants became obvious, manufacturers turned to natural oils, such as palm kernel and coconut oil. However, these oils are costly, particularly in areas where they are not domestically produced, and using them for surfactant synthesis competes with the food supply chain. Surfactants deactivate and do not work in hard water. Hard water is common - 85% of the U.S. water supply is considered hard because of the presence of small metal ions. These metal ions cover the surfactants making them useless. To solve this problem, many manufacturers add chemicals called chelators to bind the metal ions and make the water less hard, enhancing the effectiveness of the laundry detergent. However, this solution leads to more problems, including increased cost of detergent production and a large output of environmentally harmful chemicals. There remains a strong need for effective laundry detergents that do not negatively impact the environment and are available to consumers at an affordable cost. The American Cleaning Institute (ACI), a surfactant manufacturer organizing body, is emphasizing commitment to developing bio-renewable alternatives to current surfactants in laundry detergents. That’s why chemists at SIRONIX Renewables partnered with Los Alamos National Laboratory to further develop Oleo-furan surfactants (OFS). OFS are a new class of non-toxic, non-irritating surfactants for laundry detergent. It is the only surfactant that performs effectively in cold and hard water without chelators to bind the metal ions in hard water. OFS can be produced readily from sustainable, bio-derived molecules. They are multi-functional molecules - performing the role of a surfactant and a chelator in one - and are completely bio-degradable. OFS does not rely on petroleum feedstock and is synthesized from non-food, bio-renewable cellulosic feedstock. OFS solves each of the pertinent problems facing detergents and our environment today.

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

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs), key components of crucial nutrients, soaps, industrial chemicals, and fuels. Yet, our ability to control the specificity of microbially synthesized FFAs is still limited for medium-chain fatty acids (MCFAs), which is mainly due to lack of high-throughput approaches for FFA analysis. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. Specially, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and the m/z peak ratio at 730 and 758 was used as an index for improved MCFA production. This yeast colony-based method can be performed at a rate of ~2 second per sample, which is much faster than the gold standard gas chromatography (GC)-MS (typically > 30 min per sample). To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified 9 mutants that resulted in improved MCFA production relative to the wild-type strain. This colony-based MALDI-ToF MS screening method offers an alternative way for engineering microbial fatty acid compositions in a high-throughput manner.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Ice Phase Classification Made Easy with Score-Based Denoising

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

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

Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications

Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.

Atomistic models↗
DOE OSTI2024

Unlocking saponin biosynthesis in soapwort

Abstract Soapwort ( Saponaria officinalis ) is a flowering plant from the Caryophyllaceae family with a long history of human use as a traditional source of soap. Its detergent properties are because of the production of polar compounds (saponins), of which the oleanane-based triterpenoid saponins, saponariosides A and B, are the major components. Soapwort saponins have anticancer properties and are also of interest as endosomal escape enhancers for targeted tumor therapies. Intriguingly, these saponins share common structural features with the vaccine adjuvant QS-21 and, thus, represent a potential alternative supply of saponin adjuvant precursors. Here, we sequence the S . officinalis genome and, through genome mining and combinatorial expression, identify 14 enzymes that complete the biosynthetic pathway to saponarioside B. These enzymes include a noncanonical cytosolic GH1 (glycoside hydrolase family 1) transglycosidase required for the addition of d- quinovose. Our results open avenues for accessing and engineering natural and new-to-nature pharmaceuticals, drug delivery agents and potential immunostimulants.

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

Flexible polyurethanes, renewable fuels, and flavorings from a microalgae oil waste stream

Renewable polymers have become an important focus in next-generation materials, and algae biomass offers an environmentally low-impact feedstock that can serve multiple uses. This study aims to develop a scalable methodology for production of microalgae-based polyols for polyurethane synthesis from waste oils derived from algae biomass. Following separation of omega-3 fatty acids from algae oil, residual oils can offer valuable building blocks for petrochemical replacements. However, unlike vegetable oils, algae oils contain organic contaminants, including photosynthetic pigments and hydrophobic cofactors that can complicate preparative methodologies. Here we convert and purify waste streams from omega-3 depleted Nannochloropsis salina algae oil, with major components consisting of palmitic and palmitoleic acid, into azelaic acid (AA) as a building block for flexible polyurethanes, with a simultaneous production of heptanoic acid (HA) as a flavor and fragrance precursor. Conversion of free fatty acid mixtures into a soft soap allows extraction of organic contaminants, and urea complexation provides isolated palmitoleic acid, which is subsequently ozonolyzed to produce AA and HA. Bio-based polyester diols are prepared from AA via esterification to provide a polyol monomer for flexible polyurethane foam preparation. The HA co-product is modified to produce the flavoring agent methyl heptanoate and also decarboxylated to produce hexane as a renewable solvent. This scalable process can be performed on oils from multiple algal species, offering valuable monomers from a highly sustainable source.

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

Bonding similarities and differences between Y–Sb–Te and Sc–Sb–Te phase-change memory materials

The scandium (Sc) – alloyed Sb 2 Te 3 phase-change alloy has recently been found to enable ultrafast crystal nucleation due to the formation of Sc-stabilized octahedral motifs in the amorphous phase, rendering cache-type phase-change memory feasible. When yttrium (Y) is added, however, non-octahedral bonding patterns form in the amorphous Sb 2 Te 3 -based network even though Y has a valence electron configuration similar to that of Sc and also forms perfect octahedral bonding environments with tellurium in the YTe crystal. Here we elucidate the origin of this difference between Sc and Y, by carrying out thorough ab initio simulations and orbital-based bonding analyses on amorphous Y–Sb–Te and Sc–Sb–Te compounds. We also demonstrate how the smooth overlap of atomic positions (SOAP) similarity kernel can be used to quantify the structural similarity of local motifs in the amorphous phase with respect to various crystalline yttrium and scandium tellurides, both in the nearest-neighbor shell and beyond. We discover that the bonding contrast of Y- and Sc-centered structural motifs in amorphous Sb 2 Te 3 stems from their parent crystals at high Te concentrations. The larger atomic radius of Y and the weaker charge transfer when bonded with Te is found to allow more Te neighbors and cause a more open bonding environment, leading to higher coordination numbers and non-octahedral environments. We discuss the implications of the different local environments for practical applications in memory devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Excess Density as a Descriptor for Electrolyte Solvent Design

Electrolytes mediate interactions between the cathode and anode and determine performance characteristics of batteries. Mixtures of multiple solvents are often used in electrolytes to achieve desired properties, such as viscosity, dielectric constant, boiling point, and melting point. Conventionally, multi-component electrolyte properties are approximated with linear mixing, but in practice, significant deviations are observed. Excess quantities can provide insights into the molecular behavior of the mixture and could form the basis for designing high-performance electrolytes. Here we investigate the excess density of commonly used Li-ion battery solvents such as cyclic carbonates, linear carbonates, ethers, and nitriles with molecular dynamics simulations. We additionally investigate electrolytes consisting of these solvents and a salt. The results smoothly vary with mole percent and are fit to permutation-invariant Redlich-Kister polynomials. Mixtures of similar solvents, such as cyclic-cyclic carbonate mixtures, tend to have excess properties that are lower in magnitude compared to mixtures of dissimilar substances, such as carbonate-nitrile mixtures. We perform experimental testing using our automated test stand, Clio, to provide validation to the observed simulation trends. We quantify the structure similarity using SOAP fingerprints to create a descriptor for excess density, enabling the design of electrolyte properties. To a first approximation, this will allow us to estimate the deviation of a mixture from ideal behavior based solely upon the structural dissimilarity of the components.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

COVID-19 global pandemic planning: Performance and electret charge of N95 respirators after recommended decontamination methods

Shortages of N95 respirators for use by medical personnel have driven consideration of novel conservation strategies, including decontamination for reuse and extended use. Decontamination methods listed as promising by the Centers for Disease Control and Prevention (CDC) (vaporous hydrogen peroxide (VHP), wet heat, ultraviolet irradiation (UVI)) and several methods considered for low resource environments (bleach, isopropyl alcohol and detergent/soap) were studied for two commonly used surgical N95 respirators (3M™ 1860 and 1870+ Aura™). Although N95 filtration performance depends on the electrostatically charged electret filtration layer, the impact of decontamination on this layer is largely unexplored. As such, respirator performance following decontamination was assessed based on the fit, filtration efficiency, and pressure drop, along with the relationship between (1) surface charge of the electret layer, and (2) elastic properties of the straps. Decontamination with VHP, wet heat, UVI, and bleach did not degrade fit and filtration performance or electret charge. Isopropyl alcohol and soap significantly degraded fit, filtration performance, and electret charge. Pressure drop across the respirators was unchanged. Modest degradation of N95 strap elasticity was observed in mechanical fatigue testing, a model for repeated donnings and doffings. CDC recommended decontamination methods including VHP, wet heat, and UV light did not degrade N95 respirator fit or filtration performance in these tests. Extended use of N95 respirators may degrade strap elasticity, but a loss of face seal integrity should be apparent during user seal checks. NIOSH recommends performing user seal checks after every donning to detect loss of appropriate fit. Decontamination methods which degrade electret charge such as alcohols or detergents should not be used on N95 respirators. The loss of N95 performance due to electret degradation would not be apparent to a respirator user or evident during a negative pressure user seal check.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Data from A Mass Spectrometry-Based High-Throughput Screening Method for Engineering Fatty Acid Synthases with Improved Production of Medium Chain Fatty Acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs) as key components of crucial nutrients, soaps, industrial chemicals, and fuels. However, our ability to control the composition of microbially synthesized FFAs is still limited, particularly, for producing medium‐chain fatty acids (MCFAs). This is mainly due to the lack of high‐throughput approaches for FFA analysis to engineer enzymes with desirable product specificity. Here we report a mass spectrometry (MS)‐based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. In particular, matrix‐assisted laser desorption/ionization time‐of‐flight (MALDI‐ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and a higher m/z peak ratio at 730 and 758 was used as an indication for improved MCFA production. This colony‐based method can be performed at a rate of ~2 s per sample, representing a substantial improvement over gas chromatography‐MS (typically >30 min per sample) as the gold standard method for FFA detection. To demonstrate the power of this method, we performed site‐saturation mutagenesis of the yeast fatty acid synthase and identified nine missense mutations that resulted in improved MCFA production relative to the wild‐type strain. Colony‐based MALDI‐ToF MS screening provides an effective approach for engineering microbial fatty acid compositions in a high‐throughput manner.

Conversion↗
DOE OSTI2023

Machine Learning for Prediction of Thermodynamic Descriptors

Our objective is to apply machine learning (ML) algorithms for the prediction of molecular catalysis descriptors from geometric properties derived from experimental crystallographic databases. Catalysis is often considered a “low-data” discipline that is poorly suited for ML methods. An exception is the extensive structural information that is available for molecular catalysts through the Cambridge Structural Database (CSD), which contains atomically precise molecular structures from X-ray diffraction analysis for >600K metal complexes. As a proof-of-principle, we targeted the prediction of hydricity, a thermodynamic property that provides understanding and control of catalytic hydride transfer. We built a training set composed of ~100 molecular complexes with a known hydricity and structural information from the CSD. This data set was converted into a machine-readable format using the smooth overlap of atomic positions (SOAP) representation and further labeled with simple electronic descriptors for the metal centers. Multiple different neural networks were trained on this data set, and the accuracy of the hydricity predictions ranged from < 2 kcal/mol to 20 kcal/mol. The accuracy of each model was highly sensitive to which compounds were in the train versus test set, underscoring the challenges associated with small and chemically diverse data sets. Finally, to further augment the data set, we attempted to experimentally measure several new hydricity values, however these experiments were unsuccessful due to undesired chemical reactivity of the selected complexes.

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

Characterization of Oil and Gas Drill Cuttings for Critical Mineral Recovery and Reuse Potential as Soil Supplements

Expansion of unconventional oil and natural gas production over the last decade in the United States has resulted in record production of natural gas and oil in 2024. Millions of tons of drill cuttings generated from shale gas development are currently disposed of in landfills. Converting these cuttings into value product(s) such as critical minerals (CMs) and soil supplements, has the potential of reducing environmental impact of fossil fuel exploration. In this study we characterize the CM distribution and extractability from collected drill cuttings and core samples from major U.S. shale formations. A four-step sequential extraction, consisting of a sonicated soap step, sonicated EDTA step , mildly reducing agent, and a oxidizing agent, was developed to simultaneously extract CMs and convert the drill cuttings into soil supplements. Viability of converted drill cuttings as soil supplement was determined with a seedling growth experiment. Results show high concentrations of vanadium (V) (up to 1575 ppm, barite (up to 5 wt. %), and rare earth elements (REE) concentrations (up to 253 ppm). High extractability of selected critical minerals such as REE (19-50%) and Ba (10-58%) show promising results. Preliminary results show seedling growth in a mixture of converted drill cuttings with soil. These results show the potential for recovery of CMs from drill cuttings as an alternative domestic and the reduction of the environmental impact of fossil fuel production.

Barczok, Maximilian↗
DOE OSTI2024

Field Validation of a Pilot-scale Black Liquor Membrane for Water Removal at Ahlstrom-Munksjö Paper Mill in Mosinee, WI

Project Background: The Industrial Technology Validation (ITV) program aims to identify and demonstrate the performance of new, emerging, and underutilized technologies in the industrial sector to help inform decisions towards accelerating commercialization and deployment. Pulp and paper is considered one of the most energy-intensive industries in the manufacturing sector. There are several methodologies for converting wood into pulp in the paper-making industry. The kraft process is a chemical method for producing wood pulp. The kraft process generates black liquor as a byproduct of pulp production. Traditionally, water is evaporated from the liquor by a set of multi-effect evaporators (MEEs), which concentrate weak black liquor (WBL) into strong black liquor (SBL) to support efficient combustion in a recovery boiler. Concentrating black liquor is an energy-intensive step in recovering pulping chemicals and generating high-pressure steam from dissolved wood solids. About 7% of pulp and paper energy usage, or nearly 164 trillion Btu, is used to remove water from black liquor in U.S. kraft pulp mills per year. The U.S. DOE’s IEDO is interested in the black liquor membrane technology evaluated in this report because it offers the potential for a more energy-efficient and less carbon-intensive kraft pulping process across 99 kraft pulp mills in 24 states (Agenda 2020 Technology Alliance 2016). This ITV project validates an innovative black liquor membrane technology for kraft pulp mills to understand its impact and benefits. Via Separations is a technology vendor that developed a graphene oxide membrane system to remove water from WBL before entering the evaporator set. High-pressure positive displacement (HPR) pumps move the black liquor through membranes that separate water and create a more concentrated black liquor. The vendor claims their pre-commercial technology reduces evaporator steam consumption by dewatering WBL before entering the evaporators. They also claim their technology has non-energy benefits such as enhanced soap collection and improved pulp throughput. This evaluation focuses on validating the energy and carbon dioxide (CO 2 e) emissions benefits associated with the membrane system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗