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DOE OSTI2026

Carbon source–driven metabolic and regulatory remodeling defines phenomic states in Lipomyces starkeyi

Lipomyces is a genus of oleaginous yeasts with potential for contributing to reliable biomanufacturing supply chains. However, progress in advanced strain designs and engineering efforts are still constrained by a lack of understanding of the underlying molecular drivers of Lipomyces phenotypes. To address this gap, we collected a suite of multi-omic data to dissect how carbon source availability reshapes the metabolic network, lipid allocation, and regulatory architecture of Lipomyces starkeyi. We observed that glucose promotes biosynthetic and proliferative processes supported by abundant energy and carbon intermediates, xylose enhances redox-balancing mechanisms centered on the pentose phosphate pathway, and glycerol activates respiratory metabolism, ß-oxidation, and the glyoxylate cycle. Lipid species distributions remained consistent in both nitrogen replete and depleted conditions across the carbon sources, indicating robust production mechanisms. Regulatory protein identification and network analysis revealed glycerol-driven respiratory growth favors regulatory programs integrating stress tolerance, redox balance, and lipid-associated metabolism, whereas xylose growth activates compensatory transcriptional responses aimed at maintaining mitochondrial function. Nitrogen limitation modulates the strength of these responses but does not fundamentally alter their direction, reinforcing carbon source as the dominant driver of regulatory architecture. Taken together, this data enhances the understanding of Lipomyces molecular rearrangements and provides a foundation for further development of predictive phenotypic tools in this genus.

Biotechnology↗

NASA NTRS2026

Impact Testing of 410 Stainless Steel for Material Impact Model Development

A project is underway to develop a consistent set of material properties, impact test data, and failure analysis for a variety of metallic aircraft materials that can be used to develop improved impact failure and deformation models. This project is jointly funded by the NASA Glenn Research Center and the Federal Aviation Administration William J. Hughes Technical Center. Particular features of this set of data are that all material property and impact test data are obtained using traceable material, the test methods and procedures are extensively documented, and all the raw data are available. Four parallel efforts are currently underway: measurement of material deformation and failure response over a wide range of strain rates and temperatures, failure analysis of material property specimens and impact test articles, development of improved numerical modeling techniques for deformation and failure, and impact testing of flat panels and substructures for model validation. This report describes impact testing performed on 410 stainless steel sheet and plate samples of different thicknesses with two different types of projectiles, one a regular cylinder and one with a more complex geometry incorporating features representative of a jet engine fan blade. Data from this testing will be used in validating material models developed under this program. The material tests and the material models developed in this program will be published in separate reports.

410 Stainless Steel↗

NASA NTRS1991

Nickel hydrogen battery expert system

The Hubble Telescope Battery Testbed at MSFC uses the Nickel Cadmium (NiCd) Battery Expert System (NICBES-2) which supports the evaluation of performance of Hubble Telescope spacecraft batteries and provides alarm diagnosis and action advice. NICBES-2 provides a reasoning system along with a battery domain knowledge base to achieve this battery health management function. An effort is summarized which was used to modify NICBES-2 to accommodate Nickel Hydrogen (NiH2) battery environment now in MSFC testbed. The NICBES-2 is implemented on a Sun Microsystem and is written in SunOS C and Quintus Prolog. The system now operates in a multitasking environment. NICBES-2 spawns three processes: serial port process (SPP); data handler process (DHP); and the expert system process (ESP) in order to process the telemetry data and provide the status and action advice. NICBES-2 performs orbit data gathering, data evaluation, alarm diagnosis and action advice and status and history display functions. The adaptation of NICBES-2 to work with NiH2 battery environment required modification to all of the three component processes.

Shiva, Sajjan G.↗

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↗

NASA NTRS2026

High Temperature Dielectric Properties and Differential Scanning Calorimetry of Lunar Simulants

To guide development of microwave process technology that could be used during in situ construction on the Moon, we measured the high-temperature basic dielectric properties (εʹ and εʺ) of 17 lunar simulants and related materials. In order to confidently use these data one needs to understand the data’s strengths and weaknesses. Therefore, a goal of this publication is to provide insights into the comparative effects of sample composition, pre-treatments, experimental variables, high temperatures, and other factors on the measured response. The dielectric measurements were performed using the cavity perturbation method over a temperature range between room temperature to 1000 °C, or higher, and provided the real and imaginary components of permittivity at six frequencies. The utility of the original values was limited by the varying density of the pellets used in the measurement. Therefore, all of the εʹ and εʺ measurements at the frequency of 2466 MHz have been scaled to a constant density, 1.75 g/cm 3 . Here the data are presented as graphs chosen to aid analysis within and across simulant groups. To gain additional insight into the processes happening at the elevated temperatures in the dielectric measurements, heat capacity data was obtained using differential scanning calorimetry (DSC) on several of the simulant materials. Our data show that over the frequency range 397 MHz – 2985 MHz a material’s behavior does not greatly change, as compared to the scale of differences observed between lunar mare and highland simulants at high temperatures. For example at 1000 °C, the mare simulant JSC-1A absorbs 10 times more power than the highland simulant NUW-LHT-5M. We observe that as melting temperatures are reached both permittivity and dielectric loss rise non-linearly, helping to explain thermal runaway during microware heating. Our data show that even less than a few weight % of many non-lunar minerals, and the use of mixtures in simulants can affect the dielectric behavior at higher temperatures. A comparison of our results with published dielectric data for Apollo samples and with remote sensing of the Moon supports the conclusion the simulants and lunar material at room temperature have very similar dielectric values.

Differential Scanning Calorimetry↗

NASA NTRS1984

RCA SATCOM Battery in Orbit Performance Update and Accelerated Life Test Results

No significant degradation of nickel cadmium battery performance in SATCOM F1 and F2 after almost 8 and 7-3/4 years in orbit was shown. Battery minimum discharge voltage data are presented for these spacecraft. In addition, 2 groups of nickel cadmium cells which are representative of those in orbit are undergoing real time eclipse-reduced suntime cycling in the laboratory. These groups of cells, which are being cycled at a maximum of 53% and 62% depth of discharge (based on actual capacity), have completed 14 and 15 eclipse seasons, respectively. Data for these groups of cells are presented and are compared with the in-orbit battery data.

Gaston, S. J.↗

DOE OSTI2025

Large language model-driven database for thermoelectric materials

Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here, in this work, we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Database↗

NASA NTRS1970

Development of a silver-zinc battery system

Summary report is described of historical documentation and detailed design data for development of silver-zinc battery for use on Surveyor spacecraft. Electrical and physical characteristics of battery models are included, along with data on qualification, acceptance, solar-thermal-vacuum, mission simulation testing, and actual flight performance.

Moses, A. J.↗

NASA NTRS1977

Remote sensing of hurricane waves

A program was undertaken to obtain data on wind generated ocean waves in hurricanes by remote sensing techniques. The sensor, a synthetic aperture radar, has collected the first data ever on the directional wave climate throughout a hurricane. This information was found to have inconsistencies with present hurricane wave generation models.

David B King↗

NASA NTRS2011

Thermal Cycling and Isothermal Deformation Response of Polycrystalline NiTi: Simulations vs. Experiment

A recent microstructure-based FEM model that couples crystal-based plasticity, the B2<-> MB190 phase transformation and anisotropic elasticity at the grain scale is calibrated to recent data for polycrystalline NiTi (49.9 at.% Ni). Inputs include anisotropic elastic properties, texture and differential scanning calorimetry data, as well as a subset of recent isothermal deformation and load-biased thermal cycling data. The model is assessed against additional experimental data. Several experimental trends are captured - in particular, the transformation strain during thermal cycling monotonically increases and reaches a peak with increasing bias stress. This is achieved, in part, by modifying the martensite hardening matrix proposed by Patoor et al. [Patoor E, Eberhardt A, Berveiller M. J Phys IV 1996;6:277]. Some experimental trends are underestimated - in particular, the ratcheting of macrostrain during thermal cycling. This may reflect a model limitation that transformation-plasticity coupling is captured on a coarse (grain) scale but not on a fine (martensitic plate) scale.

Phase Transformations↗

NASA NTRS2002

The Design and Use of Calorimeters For Characterization of High-Enthalpy Flows in Arc-Heated Test Facilities

High-enthalpy, arc-heated flows used for aerothermal testing present a challenging environment for flow characterization. This paper describes the design and use of cooled dwell probes used for heat-transfer and pressure characterization of the NASA and United States Department of Defense (DoD) arc facilities located at Moffett Field, CA, and Arnold Air Force Base, TN. Dwell calorimeters designed, fabricated, and tested by Thermal Technologies, Inc. (TTI) in the NASA IHF and the USAF H1 and H2 arc test facilities provide heat flux and pressure data with excellent repeatability. These calorimeters use standard Gardon calorimeter gages and pressure ports with unique cooling systems enabling them to withstand the severe arc-heated environments for durations sufficient for acquisition of steady-state data. The TTI probes have proven very robust in the extreme aerothermal environments of these facilities. This paper provides an overview of calorimeter instruments typically used in arc facilities, discusses design features of the TTI dwell probes, presents selected data acquired by the TTI calorimeters, and provides comparisons with data acquired from using measurement techniques.

D Mark Smith↗

DOE OSTI2026

Electron temperature relations and the direct N, O, Ne, S, and Ar abundances of 49 959 star-forming galaxies in DESI data release 2

We present the largest direct-method abundance catalogue of galaxies to date, containing measurements of 49 959 star-forming galaxies at z<0.96 from DESI (Dark Energy Spectroscopic Instrument) data release 2. By directly measuring electron temperatures across multiple ionization zones, we provide constraints on a number of electron temperature relations. Using the temperature measurements, we derive reliable abundances for N, O, Ne, S, and Ar, and measure the evolution of abundances and abundance ratios of as a function of metallicity and other galaxy properties. Our measurements include direct oxygen abundances for 49 507 galaxies, leading to the discovery of the two most metal-poor galaxies in the nearby Universe, with oxygen abundances of 12+log⁡(O/H)=6.77−0.03+0.03 dex (1.2 per cent Z⊙⁠) and 12+log⁡(O/H)=6.81−0.04+0.04 dex (1.3 per cent Z⊙⁠). We identify a rare outlier population of 24 galaxies with high-N/O ratios at low metallicity, reminiscent of galaxy abundances observed in the early Universe. We find the Ne/O ratio is constant at low metallicity but increases gradually at 12+log(O/H)>8.105±0.004 dex. We show that the S/O and Ar/O abundance ratios are strongly correlated, consistent with the expected additional Type Ia enrichment channel for S and Ar. In this work, we present an initial survey of the key properties of the sample, with this data set serving as a foundation for extensive future work on galaxy abundances at low redshift.

Scholte, D. [Edinburgh U., Inst. Astron.] (ORCID:0↗

NASA NTRSDate not supplied

Aerothermal Shape Optimization of Actively-Cooled Battery Packs Using Conjugate Heat Transfer

Thermal management for battery is important for electric aircraft because battery temperature is critically important to vehicle safety, and it also has direct impact on the efficiency of the battery system. Because ambient air is a readily available resource for aircraft, this paper considers an active cooling concept with forced convection of ambient air through the battery pack. Conjugate heat transfer analysis is used to solve the coupled aero-thermal problem, which consists of a finite-volume computational fluid dynamics solver for the fluid domain, and a conduction heat transfer solver for the solid domain. A mixed Neumann and Dirichlet boundary condition is developed for the fluid-solid interface, which allows the solid domain to completely submerge in the fluid domain. A gradient-based optimization method is adopted, and the discrete adjoint approach implemented in DAFoam is used to efficiently compute the gradients. The aero-thermal coupling for primal analysis and gradient computation is handled using the OpenMDAO-based MPhys framework. A constant heat source is prescribed for the battery cells, and the battery shape (design variable) is optimized to minimize cooling pump power and battery weight (composite objective function) while keeping the battery temperature below a threshold (constraint). The optimized design achieves a 44.6% and 1.5% reduction in the cooling pump power and battery weight, respectively, and the maximal temperature constraint is satisfied. This work has the potential to reduce battery-pack weight, improve performance, and reduce the weight of thermal management systems for electric vertical take-off and landing aircraft.

heat transfer↗

DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗

NASA NTRSDate not supplied

Aerothermal Shape Optimization of Actively-Cooled Battery Packs using Conjugate Heat Transfer

thermal management↗

DOE OSTI2026

Commutative Algebra Modeling in Materials Science – A Case Study on Metal–Organic Frameworks (MOFs)

Metal-organic frameworks (MOFs) are a class of important crystalline and highly porous materials whose hierarchical geometry and chemistry hinder interpretable predictions in materials properties. Commutative algebra is a branch of abstract algebra that has been rarely applied in data and material sciences. We introduce the first ever commutative algebra modeling and prediction in materials science. Specifically, category-specific commutative algebra (CSCA) is proposed as a new framework for MOF representation and learning. It integrates element-based categorization with multiscale algebraic invariants to encode both local coordination motifs and global network organization of MOFs. These algebraically consistent, chemically aware representations enable compact, interpretable, and data efficient modeling of MOF properties such as Henry’s constants and uptake capacities for common gases. Compared to traditional geometric and graph-based approaches, CSCA achieves comparable or superior predictive accuracy while substantially improving interpretability and stability across data sets. By aligning commutative algebra with the chemical hierarchy, the CSCA establishes a rigorous and generalizable paradigm for understanding structure and property relationships in porous materials and provides a nonlinear algebra-based framework for data-driven material discovery.

Khaemba, Caleb S.↗

DOE OSTI2026

A Probabilistic Approach to Load Modeling for Central HVAC Systems in Large Commercial Buildings for Retrofit Decisions Under Uncertainty

Retrofitting central HVAC systems in large commercial buildings with advanced technologies like heat recovery chillers (HRCs) offers a significant opportunity to enhance energy efficiency. However, analyzing these retrofits is challenging with traditional whole-building simulation tools, which require intensive calibration and struggle to model innovative system configurations and controls. To overcome these limitations, this study proposes a load profilebased retrofit analysis framework that provides better decisions under uncertainty. The main focus of this paper is the development of a probabilistic load profile model that can be used in the framework by using exploratory data analysis (EDA) of measured building data to properly quantify its inherent variability. A non-parametric Gaussian Process (GP) model was employed to capture the time- and weather-dependent characteristics of the heating load while explicitly modeling its uncertainty. The model's effectiveness is demonstrated through strong predictive performance on unseen data and physically interpretable insights into load behavior. This data-driven, probabilistic load profile serves as a robust and flexible input for subsequent system simulations, enabling a more confident and statistically sound analysis of retrofit potential.

Ham, S W↗

DOE OSTI2024

Efficient Leaching of Metal Ions from Spent Li-Ion Battery Combined Electrode Coatings Using Hydroxy Acid Mixtures and Regeneration of Lithium Nickel Manganese Cobalt Oxide

Extensive use of Li-ion batteries in electric vehicles, electronics, and other energy storage applications has resulted in a need to recycle valuable metals Li, Mn, Ni, and Co in these devices. In this work, an aqueous mixture of glycolic and lactic acid is shown as an excellent leaching agent to recover these critical metals from spent Li-ion laptop batteries combined with cathode and anode coatings without adding hydrogen peroxide or other reducing agents. An aqueous acid mixture of 0.15 M in glycolic and 0.35 M in lactic acid showed the highest leaching efficiencies of 100, 100, 100, and 89% for Li, Ni, Mn, and Co, respectively, in an experiment at 120 °C for 6 h. Subsequently, the chelate solution was evaporated to give a mixed metal-hydroxy acid chelate gel. Pyrolysis of the dried chelate gel at 800 °C for 15 h could be used to burn off hydroxy acids, regenerating lithium nickel manganese cobalt oxide, and the novel method presented to avoid the precipitation of metals as hydroxide or carbonates. The Li, Ni, Mn, and Co ratio of regenerated lithium nickel manganese cobalt oxide is comparable to this metal ratio in pyrolyzed electrode coating and showed similar powder X-ray diffractograms, suggesting the suitability of α-hydroxy carboxylic acid mixtures as leaching agents and ligands in regeneration of mixed metal oxide via pyrolysis of the dried chelate gel.

Electrochemistry↗