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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Carbonation, strength development, and characterization of calcined limestone as a potential construction material

The emergence of the modern Portland cement has led to the downfall of lime as a construction material. Consequentially, its use has been limited to repair and restoration of historical buildings. Yet, its lower calcination temperature (~950 °C) and the ability to gain strength through carbonation implies its potential as a sustainable alternative to cement. In this study, calcined limestone was studied concerning its carbonation and strength development. Superplasticizer, raw limestone, and different carbonation regimes were explored as techniques to improve the composite strength development, a shortfall that hinders its re-adoption. The results indicated that the raw limestone as a partial replacement of the calcined limestone significantly improved the carbonation degree and mechanical properties. The average compressive strength reached 20 MPa under 20% CO{sub 2} due to the formation of an interconnected network of calcite as the dominant carbonation product. Supercritical CO{sub 2}, in contrast, formed disjoint particles or agglomerates of carbonates.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Designing Nuclear Fuels with a Multi-Principal Element Alloying Approach

Previous research has shown that multi-principal element alloys (MPEAs) using chromium, molybdenum, niobium, tantalum, titanium, vanadium, and zirconium can form stable body-centered-cubic (BCC) structures across a large temperature region (25°C to 1000°C). This is the same crystal structure as γ-uranium (U), which has shown desirable thermal and irradiation behavior in previous alloy fuel research. It is hypothesized then that the MPEA alloying approach can be used to produce a stable BCC uranium-bearing alloy and to retain its stability throughout anticipated operating regimes of power-producing reactors. Candidate elements were assessed using Monte Carlo N-Particle (MCNP) analysis to determine uranium densities necessary to make the alloy an economically viable fuel compared to conventional fuel forms. Following neutronic considerations, materials property databases and empirical predictors were used to determine the compositions with a high potential to form a BCC solid solution alloy. The final four alloys were MoNbTaU 2 , MoNbTiU 2 , NbTaTiU 2 , and NbTaVU 2 , which were cast using arc melting of raw elemental foils and chunks. Characterization of the fabricated alloys included scanning electron microscopy, X-ray diffraction, energy dispersive X-ray spectroscopy, and transmission electron microscopy. The results showed a two-phase system with a U-rich matrix phase surrounding the refractory precipitates. The U phase was found to contain varying concentrations of the alloying elements and was a BCC γ-U phase. These results warrant further research to identify ideal compositions for use as an advanced alloy fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

ATLAS-MAP: An Automated Test Station for Gated Electronic Transport Measurements

The diversification of electronic materials in devices provides a strong incentive for methods to rapidly correlate device performance with fabrication decisions. In this work, we present a low-cost automated test station for gated electronic transport measurements of field-effect transistors. Utilizing open-source PyMeasure libraries for transparent instrument control, the “ATLAS-MAP” system serves as a customizable interface between sourcemeters and samples under test and is programmed to conduct transfer curve and van der Pauw methods with static and sweeping gate voltages. Zinc oxide transistors of variable thickness (5, 10, and 20 nm) and channel size (50 μm to 3 mm, of equal length and width) were fabricated to validate the design. Standardization of testing procedures and raw data formatting enabled automated data analysis. A detailed list of parts and code files for the system are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Broad range material-to-system screening of metal–organic frameworks for hydrogen storage using machine learning

Hydrogen is pivotal in the transition to sustainable energy systems, playing major roles in power generation and industrial applications. Metal–organic frameworks (MOFs) have emerged as promising mediums for efficient hydrogen storage. However, identifying potential candidates for deployment is challenging due to the vast number of currently available synthesized MOFs. This study integrates molecular simulations, machine learning, and techno-economic analysis to evaluate the performance of MOFs across broad operation conditions for hydrogen storage applications. While previous screenings of MOF databases have predominantly emphasized high hydrogen capacities under cryogenic conditions, this study reveals that optimal temperatures and pressures for cost minimization depend on the raw price of the MOF. Specifically, when MOFs are priced at $15/kg, among the 9720 MOFs tested, 9692 MOFs achieve the lowest cost at temperatures between 170 K and 250 K and a pressure of 150 bar. Under these optimal conditions, 362 MOFs deliver a lower levelized cost of storage than 350 bar compressed gas hydrogen storage. Furthermore, this study reveals key material properties that result in low system cost, such as high surface areas (>3000 m2/g), large void fractions (>0.78), and large pore volumes (>1.1 cm3/g).

Hydrogen storage↗
DOE OSTI2024

Robust Machine Learning Inference from X-ray Absorption Near Edge Spectra through Featurization

X-ray absorption spectroscopy (XAS) is a commonly employed technique for characterizing functional materials. In particular, X-ray absorption near edge spectra (XANES) encode local coordination and electronic information, and machine learning approaches to extract this information are of significant interest. To date, most ML approaches for XANES have primarily focused on using the raw spectral intensities as input, overlooking the potential benefits of incorporating spectral transformations and dimensionality reduction techniques into ML predictions. Here, in this work, we focused on systematically comparing the impact of different featurization methods on the performance of ML models for XAS analysis. We evaluated the classification and regression capabilities of these models on computed data sets and validated their performance on previously unseen experimental data sets. Our analysis revealed an intriguing discovery: the cumulative distribution function feature achieves both high prediction accuracy and exceptional transferability. This remarkably robust performance can be attributed to its tolerance to horizontal shifts in the spectra, which is crucial when validating models using experimental data. While this work exclusively focuses on XANES analysis, we anticipate that the methodology presented here will hold promise as a versatile asset to the broader spectroscopy community.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Robust estimation of charge carrier diffusivity using transient photoluminescence microscopy

Transient microscopy has emerged as a powerful tool for imaging the diffusion of excitons and free charge carriers in optoelectronic materials. In many excitonic materials, extraction of diffusion coefficients can be simplified because of the linear relationship between signal intensity and local excited state population. However, in materials where transport is dominated by free charge carriers, extracting diffusivities accurately from multidimensional data is complicated by the nonlinear dependence of the measured signal on the local charge carrier density. To obtain accurate estimates of charge carrier diffusivity from transient microscopy data, statistically robust fitting algorithms coupled to efficient 3D numerical solvers that faithfully relate local carrier dynamics to raw experimental measurables are sometimes needed. Here, we provide a detailed numerical framework for modeling the spatiotemporal dynamics of free charge carriers in bulk semiconductors with significant solving speed reduction and for simulating the corresponding transient photoluminescence microscopy data. To demonstrate the utility of this approach, we apply a fitting algorithm using a Markov chain Monte Carlo sampler to experimental data on bulk CdS and methylammonium lead bromide (MAPbBr 3 ) crystals. Parameter analyses reveal that transient photoluminescence microscopy can be used to obtain robust estimates of charge carrier diffusivities in optoelectronic materials of interest, but that other experimental approaches should be used for obtaining carrier recombination constants. Additionally, simplifications can be made to the fitting model depending on the experimental conditions and material systems studied. Our open-source simulation code and fitting algorithm are made freely available to the scientific community.

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

Composite Sorbents: Enabling Economical Biomethane Production (CRADA Final Report)

The research performed under this CRADA enhanced the understanding of the performance of LLNL’s composite sorbent technology in the presence of raw biogas with H 2 S contamination, the long-term CO 2 removal performance stability of the composite sorbent in simulated biogas, and highlighted some of the challenges to overcome for further scale-up of material production and system design for deployment. Further development of the technology is of benefit to the public by enabling biogas upgrading from small sources for which existing commercial technologies are not suited, providing additional sources of renewable natural gas and diversifying our energy supply.

09 BIOMASS FUELS↗
DOE OSTI2020

Assessment of Impurities in Neptunium Metal Using Double-Focusing SF-ICP-MS

In the transmutation process, minor actinides such as neptunium, americium and curium obtained from spent nuclear fuel are incorporated into new fuel that can be placed into a fast reactor, where these elements fission into products with shorter half-lives which reduces the health and safety hazards. In order to better understand the all the involved processes, fundamental physical properties of actinides should be investigated, and for that reason, high-purity materials need to be produced. One of the main challenges in characterizing these materials is the lack of pure metal available for study, and lack of analytical methods to determine the purity. Trans-uranic elements are challenging to study because of the need to be handled in specialized facilities, safeguards concerns and they are not easily purchased and/or produced. High-purity Np metal is being produced at the Materials and Fuels Complex (MFC) of Idaho National Laboratory (INL), in a process of direct oxide reduction developed by Squires and Lessing, with a target purity of 99.999%. The goal of this research is to develop methods to determine the impurities in the Np metal, with detection limits low enough to achieve this purity level. In this work, we have used several ICP-MS and ICP OES techniques, but the focus was the method development using a recently acquired Double-Focusing Sector Field Inductively Coupled Plasma Mass Spectrometer (SF-ICP-MS). To produce the Np metal, approximately 20 g of the neptunium oxide powder was calcined at 800 oC for 6 h in a magnesium oxide crucible, which is placed inside a stainless steel crucible, that goes into the Hot Uniaxial Press (HUP) furnace insert. After the calcining step, the Np oxide was mixed with approximately 9 g of calcium metal and 100 g of ultra-dry calcium chloride. The HUP furnace was heated in a ramp rate of 50 oC min -1 up to 1000 oC. When the reaction mixture reached 850 oC, a tantalum stirrer was activated at 100 rpm. After the tantalum stirrer was removed and the mixture cooled overnight, the MgO crucible was opened with a hammer and chisel to retrieve the Np metal button at the bottom of the reaction mixture. Prior to the dissolution process, the surface contaminants of the sample were removed. The Np button was washed in 10 mL of ultra-pure water for 1 min, with a faint reaction observed on the surface, indicating the presence of Ca metal. The water-washed sample was placed in a tube with 2 M HCl for only 3 s, as a vigorous reaction in the button surface was observed. The leachates were saved for further analysis. The sample was then rinsed with ultra-pure water and air-dried prior to dissolution. The cleaned sample was placed in a PTFE beaker and 25 mL of 6 M HCl and 150 µL of 24 M HF were added. After 10 minutes of reaction, the beaker was placed on a hot plate for 1 h and 30 min, followed by the addition of 1 mL of 16 M HNO3 and allowed to react for a further 1 h and 20 min. The sample was removed from heat and 10 mL of HNO3 16 M was added to react with the HCl. The beaker was covered and left for overnight reaction, producing a solution with approximately 20 mL of final volume. A Quadrupole Inductively Coupled Plasma Mass Spectrometer (Q-ICP-MS, Perkin Elmer ELAN DRC II Q-ICP-MS) was used to obtain qualitative mass spectra for the dissolution blank, each of the leachates and the dissolved sample. In the water leachate spectrum mainly Ca and Mg were observed, while the acid wash had a similar peak pattern to the one observed for the Np metal sample, indicating that the process not only removed surface contamination, but also reacted with the sample itself. In the Np material spectrum, Ta and actinides were identified as candidates for major contaminants, as the most intense peaks corresponded to the mass-to-charge ratios of these elements. Minor contaminants included Al, Au, Ca, Cr, Fe, I, Na, Nb, Pb, Sn, Ti, Tl, V, Y. The presence of impurities in the material could have originated from several sources, such as the materials used for the casting process, the use of impure NpO2 or contamination of reagents and materials used for the analysis. Some of the isotopes listed as potential contaminants are susceptible to spectral interferences, so Double-Focusing SF-ICP-MS (Nu Instruments Attom ES HR-ICP-MS) was used for the quantitative analysis. For each isotope determined, several items were evaluated: raw signal of a blank solution, sensitivity and determination coefficient of calibration curves, possibility of interferences and comparison of results obtained for at least two resolving powers (RP): 300 and 4000. The evaluation of the signal in a blank solution can indicate if a spectral interference from the plasma gas and/or the solvent was successfully resolved (commonly observed for 52Cr+/40Ar12C+, for example). It is important to highlight that a signal reduction is expected when a higher RP is used, as the ion beam transmission is decreased by the use of narrower slits

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

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa (for corresponding manuscript: DOI: 10.1002/bbb.2148) PDF Files: Images of 1H NMR spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum). Suppression of the water peak was achieved using a NOESY-1D with presaturation, a recycle delay of 5 s, and a total of 64 scans. Spectra were acquired at 298 K and processed with automatic phase correction, baseline correction, and chemical shift referencing to TSP-d4. Images show all 1H data from 10 to 1ppm with inset spectra of region of interest (4.0 to 3.1 ppm). Text Files: Spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum) were converted into text files for plotting. Files contain 8192 points of raw spectral data from 12.23 to -2.78 ppm. The text file contains 4 columns of data and includes: Point number, Intensity, Hz, and ppm. Xcel Spreadsheet: HPLC measured monomeric sugar concentrations and bucketed 1H NMR data used to build monomeric sugar composition prediction models. Sugar composition in biomass determined from HPLC analyses are given in mg sugar/mg of biomass. Spectral bucketing was performed using Bruker’s AMIX software. Spectra were divided into 0.005 ppm buckets in the region of 3.10– 4.15 ppm for a total of 210 buckets. Headers for the bucketed data are the chemical shift in ppm of the center of the bucket. Bucketed data was used to build partial least squares models for subsequent predictions in The Unscrambler v. 10.5(CAMO A/S, Trondheim, Norway). The formation of methanol during hydrolysis interferes with the quantitative NMR analysis of sugars, so the methanol peak centered at 3.37 ppm and spanning four buckets (3.2925 – 3.2775 ppm) was set to zero for all spectra.

09 BIOMASS FUELS↗
DOE OSTI2022

Gamma-ray Spectra from a 3x3 CsPbBr 3 Array

CsPbBr 3 is a promising semiconductor material for X- and γ-ray detection at room temperature due to a wide bandgap of 2.3eV, high stopping power, and relatively high mobility-lifetime products of electrons and holes, and the capability of growing large crystals. In this paper we report on fabrication and testing of a 3.8 mm thick, 3 X 3 array with 1.2 mm pitch. Raw 137 Cs spectra were acquired with applied bias of 400 V, 500 V, and 600 V. Energy resolution of between 2 and 3 % FWHM at 662 keV was obtained from most pixels without any correction at room temperature. After 15 days under continuous bias at 600 V, no polarization effects were observed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

CNCS raw data for CoGeO3

Dataset collected at Cold Neutron Chopper Spectrometer at the Spallation Neutron Source, a DOE Office of Science User Facility operated by Oak Ridge National Laboratory.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

14 SOLAR ENERGY↗
DOE OSTI2026

Graphitization of banana peel – synergistic effect of Ca and other elements of the biomass on carbon structure transformation

Recently, growing interest has been observed in using calcium as an effective catalyst for graphitizing disordered carbon materials derived from biomass and bioprecursors, such as cellulose and lignin, at relatively low tem peratures (<2000 °C). Herein, it is demonstrated that in the presence of elements such as calcium, silicon, sulfur, and potassium, which are abundant in banana peel composed of cellulose and lignin base, graphitization occurs much more effectively. The addition of external calcium source, in the form of calcium carbonate, to the banana peel-derived carbon and heat-treatment of the mixture under protective atmosphere up to 1750 °C results in a significant increase in the degree of the graphitization order and local formation of graphite crystals, whereas the same preparation and heat-treatment procedure applied to carbon material derived from pristine cellulose does not lead to such effective graphitization. It is indicated that the graphitization of banana peel carbon occurs due to the synergistic effect of the external Ca-based catalyst and the internal elements present in the raw biomass, supporting the transformation of the disordered graphene-like layers into graphitic structures. These findings are important for the future production of green graphite from biomass

09 BIOMASS FUELS↗
DOE OSTI2026

Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy

High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Archive of AGR-5/6/7 Particle Radiographs for Identification of Particles with Defective IPyC

As a part of fuel quality control characterization, 2D radiographs of large numbers of particles produced by the Advanced Gas Reactor Fuel Development and Qualification (AGR) Program’s AGR-5/6/7 irradiation were acquired. These radiographs were used for the identification of particles with excessive uranium dispersion from the kernel into the surrounding buffer layer caused by chlorine infiltration through a defective inner pyrolytic carbon (IPyC) layer during silicon carbide (SiC) deposition. Additional features of interest associated with fabrication anomalies were also catalogued. Raw radiography images, along with the noted defective IPyC defects found by analysis at Oak Ridge National Laboratory are reported herein.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗