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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul [National Renewable Energy Lab. (NREL↗
DOE OSTI2023

Review of Carbon Support Coordination Environments for Single Metal Atom Electrocatalysts (SACS)

This topical review focuses on the distinct role of carbon support coordination environment of single-atom catalysts (SACs) for electrocatalysis. The article begins with an overview of atomic coordination configurations in SACs, including a discussion of the advanced characterization techniques and simulation used for understanding the active sites. A summary of key electrocatalysis applications is then provided. These processes are oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), nitrogen reduction reaction (NRR), and carbon dioxide reduction reaction (CO 2 RR). The review then shifts to modulation of the metal atom-carbon coordination environments, focusing on nitrogen and other non-metal coordination through modulation at the first coordination shell and modulation in the second and higher coordination shells. Representative case studies are provided, starting with the classic four-nitrogen-coordinated single metal atom (M$-$N 4 ) based SACs. Bimetallic coordination models including homo-paired and hetero-paired active sites are also discussed, being categorized as emerging approaches. The theme of the discussions is the correlation between synthesis methods for selective doping, the carbon structure–electron configuration changes associated with the doping, the analytical techniques used to ascertain these changes, and the resultant electrocatalysis performance. In conclusion, critical unanswered questions as well as promising underexplored research directions are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Effect of Fe on Co-Based SiO2Al2O3 Mixed Support Catalyst for Fischer–Tropsch Synthesis in 3D-Printed SS Microchannel Microreactor

This research explores the effect of a composite support of SiO2 and Al2O3 with Fe and Co incorporated as catalysts for Fischer–Tropsch synthesis (FTS) using a 3D-printed stainless steel (SS) microchannel microreactor. Two mesoporous catalysts, FeCo/SiO2Al2O3 and Co/SiO2Al2O3, were synthesized via a one-pot (OP) method and extensively characterized using N2 physisorption, XRD, SEM, TEM, H2-TPR, TGA-DSC, FTIR, and XPS. H2-TPR results revealed that the synthesis method significantly affected the reducibility of metal oxides, thereby influencing the formation of active FTS sites. SEM-EDS and TEM further revealed a well-defined hexagonal matrix with a porous surface morphology and uniform metal ion distribution. FTS reactions, carried out in the 200–350 °C temperature range at 20 bar with a H2/CO molar ratio of 2:1, exhibited the highest activity for FeCo/SiO2Al2O3, with up to 80% CO conversion. Long-term stability was evaluated by monitoring the catalyst performance for 30 h on stream at 320 °C under identical reaction conditions. The catalyst was initially active for the methanation reaction for up to 15 h, after which the selectivity for CH4 declined. Correspondingly, the C4+ selectivity increased after 15 h of time-on-stream, indicating a shift in the product distribution toward longer-chain hydrocarbons. This trend suggests that the catalyst undergoes gradual activation or restructuring under reaction conditions, which enhances chain growth over time. The increase in C4+ products may be attributed to the stabilization of the active sites and suppression of methane or light hydrocarbon formation.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Identification of potent inhibitors of JUN N-terminal kinases for treatment of endometriosis and associated pain

Endometriosis, defined as the ectopic growth of endometrial tissue outside of the uterine cavity, is an inflammatory and hormone-dependent disease that causes excruciating pelvic pain, infertility, and significantly decreases quality of life in affected patients. The JUN N-terminal kinases (JNKs) are a leading class of nonhormonal therapeutic targets that have been validated in preclinical models of endometriosis and in a Phase 1/2 clinical trial. Despite their therapeutic potential, JNK inhibitors with increased potency and specificity are needed to address the inflammatory pathology of endometriosis and to prevent disease progression. Leveraging a DNA-encoded chemical library collection of ~4 billion compounds, we identified lead inhibitor CDD-2428 and optimized derivatives, CDD-2728 and CDD-3013, with excellent binding affinity to JNK1-3 (K d = 0.12 to 3.7 nM), enhanced selectivity, metabolic stability, and cellular permeability. Crystallographic and biochemical studies confirmed that CDD-3013 exhibited superior kinase selectivity with improved efficacy compared to existing JNK inhibitors. In primary endometriosis cell models, CDD-2728 and CDD-3013 suppressed JNK-dependent inflammatory signaling, dampening pathways linked to pain, invasion, angiogenesis, and macrophage recruitment. In an endometriosis mouse model, both CDD-2728 and CDD-3013 reduced endometriotic lesion size, macrophage infiltration, and cellular proliferation, showing in vivo efficacy. When tested in a lipopolysaccharide-induced hyperalgesia model, CDD-2728 and CDD-3013 decreased markers of induced pain, as measured by changes in a dynamic weight bearing test and Grimace scores. These findings nominate CDD-2728 and CDD-3013 as potent, nonhormonal therapeutic candidates for endometriosis with broad anti-inflammatory and analgesic activity, addressing a critical unmet clinical need.

Madasu, Chandrashekhar [Department of Pathology an↗
DOE OSTI2024

The SRG/eROSITA All-Sky Survey: Optical identification and properties of galaxy clusters and groups in the western galactic hemisphere

The first SRG/eROSITA All-Sky Survey (eRASS1) provides the largest intracluster medium-selected galaxy cluster and group catalog covering the western Galactic hemisphere. Compared to samples selected purely on X-ray extent, the sample purity can be enhanced by identifying cluster candidates using optical and near-infrared data from the DESI Legacy Imaging Surveys. Using the red-sequence-based cluster findereROMaPPer, we measured individual photometric properties (redshiftz λ , richnessλ, optical center, and BCG position) for 12000 eRASS1 clusters over a sky area of 13 116 deg 2 , augmented by 247 cases identified by matching the candidates with known clusters from the literature. The median redshift of the identified eRASS1 sample isz= 0.31, with 10% of the clusters atz> 0.72. The photometric redshifts have an accuracy ofδz/(1 +z) ≲ 0.005 for 0.05 specand velocity dispersionσ) were measured a posteriori for a subsample of 3210 and 1499 eRASS1 clusters, respectively, using an extensive compilation of spectroscopic redshifts of galaxies from the literature. We infer that the primary eRASS1 sample has a purity of 86% and optical completeness >95% forz> 0.05. For these and further quality assessments of the eRASS1 identified catalog, we applied our identification method to a collection of galaxy cluster catalogs in the literature, as well as blindly on the full Legacy Surveys covering 24069 deg 2 . Using a combination of these cluster samples, we investigated the velocity dispersion-richness relation, finding that it scales with richness as log(λ norm ) = 2.401 × log(σ) − 5.074 with an intrinsic scatter ofδ in = 0.10 ± 0.01 dex. The primary product of our work is the identified eRASS1 cluster catalog with high purity and a well-defined X-ray selection process, opening the path for precise cosmological analyses presented in companion papers.

Astronomy & Astrophysics↗
DOE OSTI2025

Intensified atomic utilization efficiency of single-atom catalysts for nitrate conversion via electrified nanoporous membrane

Conventional electrochemical reactors for nitrate reduction typically suffer from limited reaction efficiency when applied for real-world water treatment due to poor utilization of electrocatalytic active sites. Here, we applied nanoporous electrofiltration to intensify atomic utilization by incorporating single-atom catalysts into an electrified membrane for reducing low-concentration nitrate to ammonia under realistic water conditions. We enhance the exposure of single atoms in nanopores by coating the catalysts on a carbon nanotube–interwoven membrane framework. Electrofiltration intensifies the transport and adsorption of nitrate in confined nanopores with highly exposed single-atom active sites to enhance reduction. The membrane enables a superior ammonia turnover frequency of 15.1 grams of nitrogen per gram of metal per hour, up to four orders of magnitude higher than that reported in the literature, under both high removal efficiency and Faradaic efficiency of over 86% when treating influents with a low nitrate concentration of 100 milligrams of nitrogen per liter in a residence time on the order of seconds.

Science & Technology - Other Topics↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗
DOE OSTI1978

An Assessment of the Nevada Test Site for Low-Level Waste Management

This report presents a summary of description information, facilities, and operating practices at the Nevada Test Site. It is concluded that NTS is not only a feasible location for surface and near-surface storage of low-level wastes, but also a potential site for underground storage of higher level wastes.

052002* -- Nuclear Fuels-- Waste Disposal & Storag↗
DOE OSTI2026

Small-molecule modulation of β-arrestins

β-Arrestins are multifunctional regulators of G-protein-coupled receptor (GPCR) signalling and orchestrate diverse downstream signalling events and physiological responses across the GPCR superfamily. Although GPCR pharmacology has advanced to target orthosteric and allosteric sites, as well as G proteins and GPCR kinases, direct chemical tools to modulate β-arrestin activities have remained conspicuously absent. Here we report the identification of small-molecule inhibitors that selectively target β-arrestins and delineate their mechanism of action through integrated pharmacological, biochemical, biophysical and structural analyses. These inhibitors disrupt β-arrestin engagement with agonist-activated GPCRs, impairing desensitization, internalization and β-arrestin-dependent physiological functions while sparing G protein–receptor coupling. Cryo-electron microscopy, molecular dynamics simulations and structure-guided mutagenesis reveal that one modulator, Cmpd-5, engages a pocket within the central crest of β-arrestin1 formed by the middle, C and lariat loops, a critical receptor-binding interface, stabilizing a distinct conformation that is incompatible with full β-arrestin–receptor engagement. Together, these findings establish a mechanistic framework for β-arrestin modulation, reveal a novel allosteric site for structure-based drug design, and open new avenues for transducer-targeted, pathway-specific GPCR therapeutic agents.

Kahsai, Alem W. [Duke University, Durham, NC (Unit↗
DOE OSTI2025

Research and Development on Advanced Electrochemical Energy Storage Devices Enabling a Spectrum of Electrified Vehicles

The USABC Advanced Battery development plan had the following three focus areas: 1. Existing technology validation, implementation, and cost reduction 2. Identification of the next viable technology with emphasis on the potential to meet USABC cost and energy density goals. 3. Support high-risk, high-reward battery technology R&D The primary aim of this project was focused on the barriers that most impede wider public adoption of electrification in vehicles-cost, energy density, low-temp performance, calendar life, and improvements in abuse tolerance. The specific objectives are as follows: Cost Reduction: drive to significantly reduce battery cost to be in-line with the $\$$100/kWh or less (by the end of calendar year 2020) DOE cost goals. Additionally, goals of $\$$75/KWh were set for 2023 aligned with fast charge and some offset of energy density. Energy Density Increase: focus on improving the cell-level specific energy and energy densities to >350 watt hours per kilogram (Wh/kg) and >750Wh/L. Low Temperature Performance Increase: strive toward developments that improve the discharge power and eliminate or dramatically reduce the life limiting lithium plating associated with regenerative braking at low temperatures, during the program. Calendar Life Increase: drive to achieve a 15-year calendar life. Abuse Tolerance Improvement: strive to develop improvements in Li-ion abuse tolerance and/or development of electrochemical energy storage technologies with inherently better response to abuse circumstances. Emerging Areas: initiate new programs that address emerging technologies that arise during the contract period.

25 ENERGY STORAGE↗
DOE OSTI2025

Monitoring Depolymerization in Mesopores Using Dynamic Properties of Polymeric Melt Accessed via Dielectric Spectroscopy

Traditional design principles for heterogeneous catalysis guide the use of catalytic particles with mesosized (∼2–50 nm) pores to increase the number of surface-active sites by way of an increased surface area. However, the entry of long-chain polymers into such pores may be significantly limited by the size and entanglement of polymers in the melt state, thereby decreasing the number of accessible sites. Assessment of catalyst performance from traditional reactor-based studies averages over intrapore reaction events as well as reactions on the surface of a particle, resulting in an inability to distinguish between differences in site accessibility and activity. Techniques that assess the intrapore performance can inform the design of future heterogeneous catalysts for polymer upcycling. In this work, we demonstrate the use of broadband dielectric spectroscopy to monitor depolymerization of a polymer melt within mesopores via changes in the segmental relaxation time scale of amorphous polymer chains. In particular, we highlight the use of an anodic aluminum oxide (AAO) membrane as a readily available model for catalyst pores with a well-characterized pore morphology. The decrease in the segmental relaxation (α-relaxation) time of the melt with increasing chain scission emerges as a measure of the extent of polymer deconstruction inside mesopores. To demonstrate the utility of this technique, we demonstrate the decomposition of two commercial poly(propylene carbonate) polymers with different decomposition rates within mesopores. As the polymers depolymerize, their segmental relaxation time decreases as the molecular weight decreases (as predicted by the Fox–Flory equation). The BDS-measured change in segmental relaxation time mirrors the expected trend based on change in molecular weight measured by size exclusion chromatography.

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

Flexible and Extensive Platinum Ion Gel Condensers for Programmable Catalysis

Catalytic condensers composed of ion gels separating a metal electrode from a platinum-on-carbon active layer were fabricated and characterized to achieve more powerful, high surface area dynamic heterogeneous catalyst surfaces. Ion gels comprised of poly(vinylidene difluoride)/1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl) imide were spin coated as a 3.8 μm film on a Au surface, after which carbon sputtering of a 1.8 nm carbon film and electron-beam evaporation of 2 nm Pt clusters created an active surface exposed to reactant gases. Electronic characterization indicated that most charge condensed within the Pt nanoclusters upon application of a potential bias, with the condenser device achieving a capacitance of ∼20 μF/cm 2 at applied frequencies of up to 120 Hz. The maximum charge of ∼10 14 |e – | cm –2 was condensed under stable device conditions at 200 °C on catalytic films with ∼10 15 sites cm –2 . Grazing incidence infrared spectroscopy measured carbon monoxide adsorption isobars, indicating a change in the CO* binding energy of ∼19 kJ mol –1 over an applied potential bias of only 1.25 V. Condensers were also fabricated on flexible, large area Kapton substrates allowing stacked or tubular form factors that facilitate high volumetric active site densities, ultimately enabling a fast and powerful catalytic condenser that can be fabricated for programmable catalysis applications.

Catalysis↗