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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Counterintuitive Catalytic Reactivity of the Aluminum Oxide “Passivation” Shell of Aluminum Nanoparticles Facilitating the Thermal Decomposition of exo -Tetrahydrodicyclopentadiene (JP-10)

High energy density aluminum nanoparticles (AlNP) have been at the center of attention as additives to hydrocarbon jet fuels like exo-tetrahydrodicyclopentadiene (JP-10, C 10 H 16 ) aiming at a superior performance of volume-limited air-breathing propulsion systems. However, a fundamental understanding of the ignition and combustion chemistry of JP-10 in presence of AlNPs has been elusive. Exploiting an isomer-selective comprehensive identification of the decomposition products in a newly designed high temperature chemical microreactor coupled to vacuum ultraviolet photoionization, we reveal an active low temperature heterogeneous surface chemistry commencing at 650 K involving the alumina (Al 2 O 3 ) shell. Contrary to textbook knowledge of an “inactive alumina surface”, this unconventional reactivity, where oxygen is transferred from alumina to JP-10, leads to generating cyclic, oxygenated organics like phenol (C 6 H 5 OH) and 2,4-cyclopentadiene-1-one (C 5 H 4 O) - key tracers of an alumina-mediated interfacial chemistry. Furthermore, this counterintuitive reactivity transforms our knowledge on the (catalytic) processes of alumina-coated AlNPs on the molecular level.

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

Electron Transfer in Microemulsion-Based Electrolytes

The use of flowing electrochemical reactors, for example, in redox flow batteries and in various electrosynthesis processes, is increasing. This technology has the potential to be of central significance in the increased deployment of renewable electricity for carbon-neutral processes. A key element of optimizing efficiency of electrochemical reactors is the combination of high solution conductivity and reagent solubility. In this work, we show a substantial rate of charge transfer for an electrochemical reaction occurring in a microemulsion containing electroactive material is loaded inside the nonpolar (toluene) subphase of the microemulsion. The measured rate constant translates to an exchange current density comparable to that in redox flow batteries. The rate could be controlled by the surfactant, which maintains partitioning of reactants and products by forming an interfacial region with ions in the aqueous phase in close proximity. The hypothesized echanism is evocative of membrane-bound enzymatic reactions. Achieving sufficient rates of electrochemical reaction is the product of an effort designed to establish a reaction condition that meets the requirements of electrochemical reactors using microemulsions to realize a separation of conducting and reactive elements of the solution, opening a door to the broad use of microemulsions to effect controlled electrochemical reactions as steps in more complex processes.

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

Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole–dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. In conclusion, this study enables the ability to predict the structure–property relations for PAN and analogues with sustainable ab initio accuracy across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Dynamic self-assembly of compartmentalized DNA nanotubes

Abstract Bottom-up synthetic biology aims to engineer artificial cells capable of responsive behaviors by using a minimal set of molecular components. An important challenge toward this goal is the development of programmable biomaterials that can provide active spatial organization in cell-sized compartments. Here, we demonstrate the dynamic self-assembly of nucleic acid (NA) nanotubes inside water-in-oil droplets. We develop methods to encapsulate and assemble different types of DNA nanotubes from programmable DNA monomers, and demonstrate temporal control of assembly via designed pathways of RNA production and degradation. We examine the dynamic response of encapsulated nanotube assembly and disassembly with the support of statistical analysis of droplet images. Our study provides a toolkit of methods and components to build increasingly complex and functional NA materials to mimic life-like functions in synthetic cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Molecular engineering of dispersed nickel phthalocyanines on carbon nanotubes for selective CO 2 reduction

Electrochemical reduction of CO 2 is a promising route for sustainable production of fuels. A grand challenge is developing low-cost and efficient electrocatalysts that can enable rapid conversion with high product selectivity. Here we design a series of nickel phthalocyanine molecules supported on carbon nanotubes as molecularly dispersed electrocatalysts (MDEs), achieving CO 2 reduction performances that are superior to aggregated molecular catalysts in terms of stability, activity and selectivity. The optimized MDE with methoxy group functionalization solves the stability issue of the original nickel phthalocyanine catalyst and catalyses the conversion of CO 2 to CO with >99.5% selectivity at high current densities of up to -300 mA cm -2 in a gas diffusion electrode device with stable operation at -150 mA cm -2 for 40 h. The well-defined active sites of MDEs also facilitate the in-depth mechanistic understandings from in situ/operando X-ray absorption spectroscopy and theoretical calculations on structural factors that affect electrocatalytic performance.

42 ENGINEERING↗
DOE OSTI2021

Guidelines for performing lignin-first biorefining

With these guidelines, we aim to unite the lignin-first biorefining research field around best practices for performing or reporting feedstock analysis, reactor design, catalyst performance, and product yields.

09 BIOMASS FUELS↗
DOE OSTI2021

Selection of the silicon sensor thickness for the Phase-2 upgrade of the CMS Outer Tracker

During the operation of the CMS experiment at the High-Luminosity LHC the silicon sensors of the Phase-2 Outer Tracker will be exposed to radiation levels that could potentially deteriorate their performance. Previous studies had determined that planar float zone silicon with n-doped strips on a p-doped substrate was preferred over p-doped strips on an n-doped substrate. The last step in evaluating the optimal design for the mass production of about 200 m$^{2}$ of silicon sensors was to compare sensors of baseline thickness (about 300 μm) to thinned sensors (about 240 μm), which promised several benefits at high radiation levels because of the higher electric fields at the same bias voltage. This study provides a direct comparison of these two thicknesses in terms of sensor characteristics as well as charge collection and hit efficiency for fluences up to 1.5 × 10$^{15}$ n$_{eq}$/cm$^{2}$. The measurement results demonstrate that sensors with about 300 μm thickness will ensure excellent tracking performance even at the highest considered fluence levels expected for the Phase-2 Outer Tracker.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Characterizing and communicating uncertainty: lessons from NASA’s Carbon Monitoring System

Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advanced Signal Decomposition Analysis and Anomaly Detection in Photovoltaic Systems

With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.

14 SOLAR ENERGY↗
DOE OSTI2026

Optimal Methods for Estimating Cactus Pear Biomass Using Cladode Dimensions of Morphologically Diverse Accessions

Current allometric methods for photosynthetic-stem (cladode) plants, such as cactus pear (Opuntia spp.), require refinement to be used in field settings in which diverse accessions are grown. We analysed cladode dimensional data using 14 accessions representing four species and two hybrids to quantify statistically significant morphological differences among accessions and derived cross-accession models to approximate cladode fresh weight. A Box model using cladode dimensions (e.g., length, width, thickness and diameter) and factorial combinations of these measures (e.g., length*width*thickness*diameter vs. fresh weight) resulted in the highest coefficient of determination (R 2 = 0.95 general fit) across all accessions for estimating fresh weight along with parsimony estimates using the Schwarz–Bayes Criterion (SBC), which assesses the most consistent performance on individual accessions. A Fitting-box modelling approach used the measured cladode area captured using ImageJ (R 2 = 0.93 general fit). Lastly, an Elliptical model used an elliptical approximation for the measured area and performed well over all accessions (R 2 = 0.94 general fit) while avoiding extensive manual measurements. These models meet or exceed the performance of previously published approaches when applied across morphologically diverse accessions, providing efficient tools for nondestructive estimation of cactus pear biomass under the conditions tested.

Opuntia↗
DOE OSTI2023

Metal-semiconductor-metal structured p-i-n GaN/AlGaN ultrafast detectors for deep-UV photodetection

We report the productive outcome of the design, fabrication, and testing of a new class of GaN/AlGaN-based ultrafast, sensitive photodiodes for deep-UV solar blind photodetection. Pt was employed as the interdigitated metal electrode to establish that while a Schottky contact was formed at the metal/semiconductor heterojunction, it was the quantum wells in the p-i-n vertical structure that produced the diode characteristics of the detectors. The metal–semiconductor–metal design retained its ultrafast property as expected in the p-i-n structure because the spacing between the interdigitated fingers was asymmetrically implemented to optimize the photogenerated carriers’ transit time from the quantum well to the external circuitry. Also, the vertical p-i-n structure provided its renowned efficient photocarrier generation, which was significantly enhanced by the delta quantum well architecture of the vertical epitaxial structure. The active area of the device was 300 μm × 300 μm with 5-μm finger width, and asymmetric electrode spacing of 2 μm, 3 μm, and 5 μm. Here, the best device had peak responsivity of 3.5 A/W under 262-nm illumination with full width half maximum of 337.2-ps and 37.5-ps rise times when biased with 20 V.

efficient↗
DOE OSTI2022

Rhythm of the Night (and Day): Predictive Metabolic Modeling of Diurnal Growth in Chlamydomonas

We have developed the first transient metabolic model for diurnal growth of algae based on experimental data and capable of predicting phenotype from genotype. This model enables us to evaluate the impact of genetic and environmental changes on the growth, biomass composition and intracellular fluxes of the model green alga, Chlamydomonas reinhardtii . The availability of this model will enable faster and more efficient design of cells for production of fuels, chemicals, and pharmaceuticals.

09 BIOMASS FUELS↗
DOE OSTI2022

Tuning the Porous Structure in PMMA-Templated Mesoporous MoO 2 for Pseudocapacitive Li-Ion Electrodes

MoO 2 is an exciting candidate for next-generation energy storage. It can be used for fast-charging applications in nanoscale form, but its kinetic performance is often limited by insulating MoO 3 surface oxide layers. Here, we developed methods to produce polymer-templated porous MoO 2 powders where electrical conductivity was well-maintained throughout the structure, even in the presence of some surface oxidation. Porosity, pore size, and crystallite size were controlled by varying the amount and size of the colloidal templates and through calcination temperature. The electrochemical performance was correlated with nanoscale structure: samples with high porosity, medium pore sizes, and good crystallinity display optimal rate capabilities, with over 100 mAh g −1 delivered in 3 min and 93% capacity retention after 1000 cycles. Kinetic studies were performed on samples with the largest and smallest crystallite sizes to understand the charge storage mechanism. In the sample with the smallest crystallite size, 85% of the total stored charge was capacitive, compared to 60% for the largest crystallite size. Sloping voltage profiles in materials with smaller domain sizes further suggest suppression of intercalation-induced phase transitions. This work thus provides insights into the mechanisms of charge storage in nanoscale MoO 2 and design parameters for the production of fast charging materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Stable, Efficient Iron Electrodeposition via Anion-Directed Control of Fe(II) Coordination

Traditional steelmaking processes consume about 7% of the world’s energy supply, with reduction of iron oxides into iron via blast furnaces representing the most energy-demanding and capital-intensive step. To economize and modularize iron reduction processes, we aim to develop an electrodeposition technique to reduce aqueous iron ions to metallic iron. However, the hydrogen reduction reaction (HER) occurs at a more positive standard reduction potential than the iron reduction reaction. In addition, aqueous Fe(II) cations easily precipitate at mildly acidic conditions (pH ≥ 3), which limits the deposition efficiency and degrades deposit quality. To address these challenges, we first search for anions that have intermediate coordination strength with Fe(II) based on the hard-soft acid-base theory, trading a slightly more negative Fe(II) reduction potential for a considerably broader pH stability range. We select citrate with predicted intermediate coordination strength, in combination with more weakly coordinating anions (e.g., SO 4 2- , Cl - ) to control the coordination structure of Fe 2+ for improved electrolyte stability and electrodeposition behavior. We find that citrate coordination stabilizes Fe 2+ -based electrolytes at higher pH conditions (4.8–5.5), significantly extending their shelf life while also suppressing HER during Fe electrodeposition by orders of magnitude. To measure Faradaic efficiencies (FE), we developed a straightforward, titration-based methodology to quantify the amount of deposited iron regardless of the rate of concurrent HER. Although coordination between citrate and Fe 2+ decreases the reduction potential of Fe(II), high FE (≥98%) was achieved at 10 mA cm -2 . FE and achievable deposition rates are tunable by both concentration and the ratio of Fe 2+ to citrate. In all cases, Raman spectroscopy and X-ray diffraction (XRD) reveal that iron deposition in citrate-containing electrolytes suppresses iron oxide/hydroxide precipitation, in contrast to deposits generated in citrate-free electrolytes. Altogether, this work demonstrates that citrate-mediated anion coordination enables high-purity iron electrodeposition with increased FE, high current density, and improved electrolyte stability. This multi-anion coordination strategy provides a versatile framework for designing stable electrolyte and efficient metal electrodeposition.

coordination↗
DOE OSTI2023

ShadeLab/PAPER_Howe_2023_switchgrass_MetaT

The raw data (metagenomes and metatranscriptomes) for this study are available in the Joint Genomes Institute Genome Portal (https://genome.jgi.doe.gov/portal/ Project ID 503249) with projects designated by year and product type. The MAG genomes analyzed in this paper are available on NCBI, as bioproject PRJNA800073. Plants and microorganisms form beneficial associations. Understanding plant-microbe interactions will inform microbiome management to enhance crop productivity and resilience to stress. Here, we apply a genome-centric approach to identify ecologically important leaf microbiome members on field-grown switchgrass and miscanthus and to quantify their activities for switchgrass over two growing seasons. We integrate metagenome and metatranscriptome sequencing from 192 leaf samples collected over representative time points in crop phenology. We curated 40 medium- and high-quality metagenome-assembled-genomes (MAGs) and focused analysis on seasonal transcript recruitment to them. Classes represented by these focal MAGs (Actinomycetia, Alpha- and Gamma- Proteobacteria, and Bacteroidota) were active and had increases in transcripts for short-chain dehydrogenase, molybdopterin oxidoreductase, and polyketide cyclase in the late season. The majority of MAGs had activated stress-associated pathways, including trehalose metabolism, indole acetic acid degradation, betaine biosynthesis, and reactive oxygen species degradation, suggesting direct engagement with the host environment. We also detected seasonally activated biosynthetic pathways for terpenes (carotenoids and isoprenoids) and for various non-ribosomal peptide pathways that were poorly annotated. Overall, this study overcame laboratory and bioinformatic challenges associated with field-based leaf metatranscriptome analysis to inform both general and likely specialized activities of these phyllosphere populations. These activities collectively support that leaf-associated bacterial populations are seasonally dynamic, responsive to host cues, and interactively engage in feedback with the plant. This analysis represented quality filtering of metagenomes and metatranscriptomes (data-preparation folder), metagenome assemblies (metagenome-assembly folder) and metagenome-assembled genome binning, curation, refinement, annotation (mag-evaluation folder). Abundances of sequencing libraries were calculated based on reads mapped (mapping folder). Additionallly, analysis of our annotated results are also included (analysis folder).

Howe, Adina↗
DOE OSTI2025

Osprey Framework v0.2.2

The Alpha Berkeley Framework is a software architecture for building agentic AI systems that coordinate multi-step workflows in scientific and industrial environments. It is based on a plan-first orchestration model, where natural language requests are translated into execution plans with explicit dependencies and optional human approval. The framework includes capability classification, which selects relevant tools on a per-task basis to keep orchestration efficient as the number of available tools grows. It incorporates task extraction methods that compress conversational context and integrate external resources such as databases, APIs, and knowledge bases into structured, machine-readable tasks. Execution is supported by modular services with checkpointing, artifact management, and error handling, allowing workflows to be paused, inspected, and resumed. The system is designed for deployment in production environments, supporting both local and containerized execution as well as integration with HPC clusters. Interfaces include command-line tools, browser-based workflows, and containerized services. The framework has been demonstrated in tutorial examples and deployed at the Advanced Light Source, where it coordinates accelerator control and analysis workflows.

Hellert, Thorsten [Lawrence Berkeley National Labo↗
DOE OSTI2025

QuESt PCM

SAND2025-14369O QuESt PCM is a tool for modeling power system production cost. It is designed for high-fidelity representation of energy storage systems (ESS) and is part of QuESt 2.0: Open-source Platform for Energy Storage Analytics. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Nguyen, Tu Anh [Sandia National Lab. (SNL-CA), Liv↗