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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging [Slides]

Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.

09 BIOMASS FUELS↗
DOE OSTI2023

Achieving giant electrostrain of above 1% in (Bi,Na)TiO 3 -based lead-free piezoelectrics via introducing oxygen-defect composition

Piezoelectric ceramics have been extensively used in actuators, where the magnitude of electrostrain is key indicator for large-stroke actuation applications. Here, we propose an innovative strategy based on defect chemistry to form a defect-engineered morphotropic phase boundary and achieve a giant strain of 1.12% in lead-free Bi 0.5 Na 0.5 TiO 3 (BNT)–based ceramics. The incorporation of the hypothetical perovskite BaAlO 2.5 with nominal oxygen defect into BNT will form strongly polarized directional defect dipoles, leading to a strong pinning effect after aging. The large asymmetrical strain is mainly attributed to two factors: The defect dipoles along crystallographic [001] direction destroy the long-range ordering of the ferroelectric and activate a reversible phase transition while promoting polarization rotation when the dipoles are aligned along the applied electric field. Our results not only demonstrate the potential application of BNT-based materials in low-frequency, large-stroke actuators but also provide a general methodology to achieve large strain.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Upcycling of post-consumer mixed polyolefin feedstock: An economic and technical evaluation

Here, this study reports techno-economic and life cycle analyses to evaluate the economic and environmental impacts of mechanically recycled PE/PP blends in the presence of rheology modifiers. Additionally, fiber-reinforced composites derived from the compatibilized blends were prepared and evaluated for their performance compared with virgin plastics. Results suggest that compatibilized PE/PP blends exhibit a 70% lower selling price compared to virgin PE. Furthermore, these blends achieved a 74% reduction in greenhouse gas emissions or climate change impact compared to the virgin counterpart. Fiber-reinforced composites from compatibilized PE/PP blends demonstrated improved or comparable mechanical properties relative to composites made from virgin PE/PP blends. Based on their favorable cost and environmental impact, along with performance comparable to virgin composites, compatibilized PP/PE composites made from post-consumer plastics can find applications in large-scale composite manufacturing.

Fiber reinforced plastics (FRP)↗
DOE OSTI2023

ABF Industry Engagement Lab Call with Danimer Scientific

This Agile BioFoundry Directed Funding Opportunity project with Danimer Scientific focuses on the development of a strain and a corresponding bioprocess to convert bio-based feedstocks to mixed composition polyhydroxyalkanoates (PHAs). Danimer Scientific produces PHAs today at industrial scale in proprietary strains and for many applications where bio-based, biodegradable materials are advantaged. The project team consists of NREL to lead the strain engineering efforts, PNNL to conduct systems biology experiments that will inform further strain engineering, and Danimer to conduct bioprocess development and materials development. To date, we have on-boarded Danimer strains and demonstrated successful engineering thereof. We anticipate conducting systems biology studies in Spring 2023. Overall, the impact of this DFO project could be improved material properties accessed through the Design-Build-Test-Learn cycle for designer PHA production.

bio-based feedstocks↗
DOE OSTI2024

Nonvolatile electrochemical memory at 600°C enabled by composition phase separation

Silicon-based microelectronics are limited to ~150°C and therefore not suitable for the extremely high temperatures in aerospace, energy, and space applications. While wide-band-gap semiconductors can provide high-temperature logic, nonvolatile memory devices at high temperatures have been challenging. In this work, we develop a nonvolatile electrochemical memory cell that stores and retains analog and digital information at temperatures as high as 600°C. Through correlative scanning transmission electron microscopy, we show that this high-temperature information retention is a result of composition phase separation between the oxidized and reduced forms of amorphous tantalum oxide. This result demonstrates a memory concept that is resilient at extreme temperatures and reveals phase separation as the principal mechanism that enables nonvolatile information storage in these electrochemical memory cells.

42 ENGINEERING↗
DOE OSTI2023

Chitosan as a Canvas for Studies of Macromolecular Controls on CaCO 3 Biological Crystallization

A mechanistic understanding of how macromolecules, typically as an organic matrix, nucleate and grow crystals to produce functional biomineral structures remains elusive. Advances in structural biology indicate that polysaccharides (e.g., chitin) and negatively charged proteoglycans (due to carboxyl, sulfate, and phosphate groups) are ubiquitous in biocrystallization settings and play greater roles than currently recognized. This review highlights studies of CaCO 3 crystallization onto chitinous materials and demonstrates that a broader understanding of macromolecular controls on mineralization has not emerged. With recent advances in biopolymer chemistry, it is now possible to prepare chitosan-based hydrogels with tailored functional group compositions. By deploying these characterized compounds in hypothesis-based studies of nucleation rate, quantitative relationships between energy barrier to crystallization, macromolecule composition, and solvent structuring can be determined. Finally, this foundational knowledge will help researchers understand composition-structure-function controls on mineralization in living systems and tune the designs of new materials for advanced applications.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Composite biopolymer electrolytes for high-performance reversible zinc-air batteries

Zinc-air batteries (ZABs) are regarded as one of the most promising candidates for next-generation energy storage systems due to their inherent safety, high energy density, and low cost. In this study, we optimized the composition of gel polymer electrolytes (GPEs), which play a crucial role in enhancing ZAB reversibility due to their water retention capabilities. The GPEs are synthesized using a combination of polyacrylic acid (PAA), polyacrylamide (PAM), and biopolymer kappa-carrageenan (KC), with and without additional additives. Among these, the composite GPE containing KC, PAA, and PAM demonstrated superior ionic conductivity (6.68 mS/cm at 30 °C) and enhanced water retention compared to the PAA–PAM-based GPE. Furthermore, this composite electrolyte operates within a wider electrochemical window of −1.0 V–2.2 V. A bifunctional catalyst, nickel–iron layered double hydroxide (NiFe-LDH), is electrochemically synthesized on a modified activated carbon cloth (CC). The catalyst loading is optimized based on oxygen evolution reaction (OER) performance, showing an overpotential of 555 mV. The oxygen reduction reaction (ORR) occurred at a half-wave potential of 0.620 V, which is lower than that of Pt/C (0.852 V), indicating better catalytic activity. The KC based cell exhibit better cycling stability than PAA–PAM-based GPE. The electrochemical performance of ZAB cells is evaluated under various atmospheres: ambient air and humid conditions and temperatures. Notably, in a humid environment, the cells achieved extended cycling stability, operating for over 500 h—significantly longer than under open-air conditions. Overall, this study highlights the importance of tailored GPE design and efficient bifunctional catalysts to enhance the performance and longevity of zinc-air batteries for long-duration energy storage applications.

Catalyst↗
DOE OSTI2020

A Graphene Enhanced SiC-SiC Ceramic Composite Bonding Solution for Generation IV Nuclear Reactors

The feasibility of an effective bonding methodology for SiC/SiC Ceramic Matrix composites using a graphene base brazing compound was established under pressure less conditions. The feasibility demonstration during this program identified the further need to improve the composition of the brazing compound and the processing conditions to enhance the joint strength.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Pseudo-Polymorphism in Layered FeS Intercalates: A Competition between Charged and Neutral Guest Species

Systematic synthesis studies of the formation of tetrahedral FeS-ethylenediamine intercalates resulted in the synthesis of a new compound, [Fe 9.4(2) S 10 ][Fe(en) 3 ] 0.6(1) ·en 0.9 ( 3 ). The composition and complex crystal structure were determined based on a synergistic combination of elemental composition, decomposition behavior, high-resolution synchrotron X-ray diffraction and total scattering, 57 Fe Mössbauer spectroscopy, and electron diffraction. The structural model was derived based on a systematic comparison to the previously reported structures [Fe 8 S 10 ][Fe(en) 3 ] 1 ·en 0.5 and tetragonal FeS. The new compound has flat Fe 9.4 S 10 layers, analogous to those in superconducting binary FeS. In the crystal structure of [Fe 9.4 S 10 ][Fe(en) 3 ] 0.6 ·en 0.9 , the interlayer space is occupied by [Fe(en) 3 ] 2+ complexes and neutral ethylenediamine molecules in a ~2:3 ratio. Interlayer species are not randomly oriented but ordered as evidenced by superstructural diffraction peaks in both high-resolution X-ray diffraction and electron diffraction patterns. Magnetic studies reveal no superconducting transition down to 2 K, indicating that the presence of minute amounts (~6%) of iron vacancies at the Fe-S layer in [Fe 9.4 S 10 ][Fe(en) 3 ] 0.6 ·en 0.9 is still sufficient to shift the position of the Fermi level resulting in an adjustment of the properties. Here, our work shows the importance of detailed characterization of the crystal structure of intercalated compounds to understand the origin of the observed properties and develop proper structure–property relationships.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Development of green-colour-emitting pyrotechnics as a core for 3D temperature imaging sensors inside coal boilers

Efficiency in the control operation of the boilers for coal and coal with biomass can be further improved if the flue gas temperature distribution can be better characterized. This is very difficult in these harsh environmental systems, where spatially resolved measurements are nearly impossible with solid-state sensors. Here, in this work, we evaluate the development of pyrotechnic compounds that would serve as the basis for a novel optical mapping of the temperature inside coal boilers. For this purpose, various green-colour-emitting pyrotechnics using BaCl 2 · 2H 2 O and Ba(NO 3 ) 2 as the green light source were prepared, as this colour offers a distinct signal from the combustion-based background in the boiler. These pyrotechnics were characterized using thermogravimetric analysis (TGA) and X-ray diffraction (XRD) and tested using a flat-flame burner. Furthermore, the composition was varied to evaluate the effect of different metal fuels such as Sn, Co, and Mg, as well as various binders such as ethylcellulose, shellac, parlon, and PVC on green light emission. The emission intensity and the apparent ignition temperature were strongly dependent on the metal type, with Mg showing higher intensities. On the other hand, the effect of the binder showed that the ignition behaviour, emission intensity, and spectral purity were influenced by the nature and exothermicity of the binder. The addition of other potential green light-producing materials, such as boric acid, increased the intensity of emission by 17% for a BaCl 2 · 2H 2 O-based composition. This study identified prospective compositions with intense and bright green-colour emissions that have high spectral purities.

42 ENGINEERING↗
DOE OSTI2024

Self-healing carbon fiber/epoxy laminates with particulate interlayers of a low-melting-point alloy

In order to prolong the service life of fiber-reinforced polymer composites, the implementation of self-healing ability with the micro-encapsulated healing agent has been extensively studied. However, such microcapsule-based self-healing composites typically suffer from degraded mechanical properties due to the liquid-phase inclusions, thereby limiting their proliferation. Here, in this study, a low-melting-point alloy is utilized as the particulate inclusions of carbon fiber/epoxy laminated composites. Field's Metal particles (melting point: 62 °C) are distributed between woven carbon fiber preforms followed by the resin impregnation to realize laminated composites with a Field's Metal-enhanced interlayer(s). The resulting laminated composites demonstrate the autonomic repair of interlaminar failure with a 40 % of healing efficiency. Most of all, the mechanical properties of these self-healing laminated composites are comparable to the conventional laminated composites attributed to the rigid inclusions that can be compressed to increase the fiber volume. Since the Field's Metal particle inclusions can bestow polymer composites with self-healing ability and the potential increase in mechanical properties, Field's Metal-enhanced fiber-reinforced polymer composites are expected to unlock the practical utility of self-healing composites.

A. polymer-matrix composites (PMCs)↗
DOE OSTI2023

A Multicontinuum-Theory-Based Approach to the Analysis of Fiber-Reinforced Polymer Composites with Degraded Stiffness and Strength Properties Due to Moisture Absorption

Marine energy generation technologies such as wave and tidal power have great potential in meeting the need for renewable energy in the years ahead. Yet, many challenges remain associated with marine-based systems because of the corrosive environment. Conventional materials like metals are subject to rapid corrosive breakdown, crippling the lifespan of structures in such environments. Fiber-reinforced polymer composites offer an appealing alternative in their strength and corrosion resistance, but can experience degradation of mechanical properties as a result of moisture absorption. An investigation is conducted to test the application of a technique for micromechanical analysis of composites, known as multicontinuum theory and demonstrated in past works, as a mechanism for predicting the effects of prolonged moisture absorption on the performance of fiber-reinforced composites. Experimental tensile tests are performed on composite coupons with and without prolonged exposure to a salt water solution to obtain stiffness and strength properties. Multicontinuum theory is applied in conjunction with micromechanical modeling to deduce the effects of moisture absorption on the behavior of constituent materials within the composites. The results are consistent with experimental observations when guided by known mechanisms and trends from previous studies, indicating multicontinuum theory as a potentially effective tool in predicting the long-term performance of composites in marine environments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Design and scale-up of 3D printed bat houses with biomass-derived polymer composites

Biomass (e.g., pine sawdust, especially high–ash content pine sawdust) is commonly disposed of as waste. Combining biomass with polymers to make composite feedstocks for 3D printing has been explored as a method to reduce or repurpose the biomass waste. Although not all biocomposite properties are known, the wood-based polylactic acid (PLA) composite has promising qualities for applications in ecological settings. In this work, pine wood–PLA composite feedstock was used to 3D print supplemental roost structures for endangered tree-roosting bats, which often face a paucity of suitable naturally occurring roosts. This material combination was selected because it is estimated to degrade faster than the synthetic material systems that are used widely in supplemental bat roosting structures to aid in the conservation of tree roosting bats. The layered, rough surface created by the 3D printing process serves as a surface that bats can grip while roosting. Computer-aided design (CAD) models were generated based on natural roost structures, and a full-size bat house was successfully additively manufactured using a pellet-fed large-scale 3D printing system. The 3D printed hexagon exhibited a tensile strength of 22–23 MPa and a Young’s modulus of 3202–3218 MPa in the x-direction. It has been demonstrated that the 3D printed bat house can be installed on a tree in a stable fashion. This successful demonstration of a bat roost manufactured using a bioderived composite should promote its use in other fish and wildlife structures and broader industrial applications such as construction and automobiles.

3D printing↗
DOE OSTI2021

The interpenetration polymer network in a cement paste–waterborne epoxy system

The formation of the interpenetrating polymer network (IPN) structure within the cement-polymer system has been revealed by experiments from 2 dimensional to 3-dimensional scales. However, the microstructure design and performance prediction of IPN as a function of specific C-S-H/polymer components or ingredients' parameters e.g. water to cement ratios (w/c), polymer to cement ratios (p/c), monomer components, degree of polymerization (DP), etc. is by far not available. Here we developed a mesoscale model for IPN visualization based on the Flory-Huggins interaction theory which was applied to cement science for the first time. All the ingredients in the micro-structure were considered as soluble beads with rational sizes based on their properties obtained by molecular dynamic methods. The interaction parameters of each bead were then determined based on their element ratios and chemical backbones. The model was validated with the waterborne epoxy-cement material (WECM) in which a novel waterborne epoxy resin (WEP) was prepared and impregnated. The verification results on the 2D-3D scale show that the developed model predicts the WECM's IPN structures by rule and line concerning DP, w/c, and p/c in the mixture. The C-S-H beads were progressively scaled in size which alters the C-S-H texture to have completely different dissolution characteristics. Beads of diameter ~6 Å are more unstable and soluble which enable them to form a continuous phase in water or a composite structure with WEP. In contrast, beads with diameters larger than 10 Å have different properties with stronger nucleation effects. The results also suggest the impregnation content of WEP in cement-based material should be limited to 10% vol. to prevent the polymer IPN from decomposing into discrete clusters. The application of Flory-Huggins theory in cement-based composites demonstrates great potential in performance prediction and microstructure design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Uncertainty-Based Design: Finite Element and Explainable Machine Learning Modeling of Carbon–Carbon Composites for Ultra-High Temperature Solar Receivers

Design under uncertainty has significantly grown in research developments during the past decade. Additionally, machine learning (ML) and explainable ML (XML) have offered various opportunities to provide reliable predictable models. The current article investigates the use of finite element modeling (FEM), ML and XML predictions, and uncertain-based design of carbon-carbon (C-C) composites for use in ultra-high temperatures. A C-C composite concentrating solar power (CSP) as a microvascular receiver is considered as a case study. These C-C composites are fiber composites with directly integrated carbonized microchannels to form a lightweight, high-absorptivity material that includes an embedded microvascular network of channels. The topology of these microchannels is engineered to optimize heat transfer to a supercritical carbon dioxide (sCO2) heat transfer fluid. The mechanical characterization of C-C composites is highly challenging. Thus, designing every component made of C-C composites for ultra-high temperature applications needs an uncertainty-based analysis. As a part of a comprehensive project on the development of a novel carbonized microvascular C-C composite, this paper explores C-C composite sensitivity analysis, FEM, ML prediction, and XML analysis. The resulting composite can then be carbonized and coated with an oxidation-resistant coating to form a thermally efficient and mechanically robust C-C composite. An ANSYS 3-D-FE model was used to analyze the CSP’s stress/strain. To consider the variability in the mechanical and thermal properties of C-C composites, various mechanical properties are considered as the ANSYS FEM’s input. A synthetic dataset from 730 ANSYS runs was produced to feed into the ML and XML algorithms for uncertainty analysis and prediction. The ML and XML algorithms could accurately predict the CSP stresses/strains.

Daghigh, Vahid (ORCID:0000000298941620)↗
DOE OSTI2020

Deformation behavior of metallic glass composites and plasticity accommodation at microstructural length-scales

Bulk metallic glass matrix composites represent a unique microstructural design strategy for overcoming the strength/ductility trade-off in structural alloys. Site-specific mechanical behavior of a Ti-based bulk metallic glass composite was evaluated at microstructural length-scale. The micro-pillars on the amorphous matrix showed on average a yield point of 1.9 GPa followed by serrated plastic deformation characteristic of shear banding. In contrast, micro-pillars on the crystalline dendritic phase showed a much lower yield strength of 0.72 GPa on average for the four different crystallographic orientations chosen, smooth plastic flow with no recognizable load burst, and stable strain-hardening after yield point. A number of micro-pillars were made at the interface between the glassy matrix and the crystalline dendrite. The mixed micro-pillars showed homogeneous deformation and greater plasticity which was attributed to their smaller stored elastic energy. Shear bands initiated in the amorphous matrix were arrested by the crystalline dendrite, which accommodated plasticity through slip bands and dislocations pile-ups. The interface remained intact after plastic deformation, with no observable signs of devitrification in the amorphous phase.

Bulk metallic glass matrix composite, Micro-pillar↗
DOE OSTI2021

Evaluation of the rheological and electrical percolation of high‐density polyethylene/carbon black composites using mathematical models

Abstract In this work, conductive polymer composites (CPCs) of bio‐based polyethylene (BioPe) containing different concentrations of carbon black (CB) were developed. By using oscillatory rheology analysis, a Newtonian plateau was observed in BioPe, and all BioPe/CB composites had a behavior of a pseudo‐solid and that composites with volume fractions ranging from 0.24 to 0.56 presented higher viscosity, storage, and loss modulus. This suggests the formation of a percolated network and by using the power‐law models, it was observed that the electrical percolation threshold was higher than the rheological percolation threshold. The electrical conductivity was measured using the four‐point probe method and a sigmoid model was used to predict the CPCs' electrical conductivity percolation threshold. The results indicated that the four‐point probe method presented satisfactory results according to the calculated standard deviations and voltage–current characteristics for each round of measurements considering the same ranging as used in rheology analysis. The analytical model used showed a coefficient of determination ( R 2 ) higher than 95%, allowing the prediction of the electrical conductivity of the CPC and the percolation threshold as a function of the volumetric fraction of the CB.

da SiIva, Moacy P.↗
DOE OSTI2025

Application of Machine Learning and Data Augmentation Algorithms in the Discovery of Metal Hydrides for Hydrogen Storage

The development of efficient and sustainable hydrogen storage materials is a key challenge for realizing hydrogen as a clean and flexible energy carrier. Among various options, metal hydrides offer high volumetric storage density and operational safety, yet their application is limited by thermodynamic, kinetic, and compositional constraints. In this work, we investigate the potential of machine learning (ML) to predict key thermodynamic properties—equilibrium plateau pressure, enthalpy, and entropy of hydride formation—based solely on alloy composition using Magpie-generated descriptors. We significantly expand an existing experimental dataset from ~400 to 806 entries and assess the impact of dataset size and data augmentation, using the PADRE algorithm, on model performance. Models including Support Vector Machines and Gradient Boosted Random Forests were trained and optimized via grid search and cross-validation. Results show a marked improvement in predictive accuracy with increased dataset size, while data augmentation benefits are limited to smaller datasets and do not improve accuracy in underrepresented pressure regimes. Furthermore, clustering and cross-validation analyses highlight the limited generalizability of models across different material classes, though high accuracy is achieved when training and testing within a single hydride family (e.g., AB2). The study demonstrates the viability and limitations of ML for accelerating hydride discovery, emphasizing the importance of dataset diversity and representation for robust property prediction.

augmentation↗