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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Thermionic microwave gun for terahertz and synchrotron light sources

Conventional thermionic microwave and radiofrequency (RF) guns can offer high average beam current, which is important for synchrotron light and terahertz (THz) radiation source facilities, as well as for industrial applications. For example, the Advanced Photon Source at Argonne National Laboratory is a national synchrotron radiation light source research facility that utilizes thermionic RF guns. Yet, these existing thermionic guns are bulky, difficult to handle and install, easily detuned, very sensitive to thermal expansion, and are due for a major upgrade and replacement. Here, we present the design of a new, more stable and reliable gun with optimized electromagnetic performance, improved thermal engineering and a more robust cathode mounting technique, which is a critical step to improve the performance of existing and future light sources, industrial accelerators, and electron beam-driven terahertz sources. We will also present a fabricated gun prototype and show results of high-power and beam tests.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Leverage modern artificial intelligence (AI) enabled systems for waste reduction

Manufacturing industries continue to face challenges in reducing waste, as upstream strategies such as source reduction and product redesign require a deeper understanding of processes compared to conventional recycling methods. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new opportunities to integrate modern computational techniques with traditional waste minimization strategies. This paper explores AI-enabled approaches for product redesign, source reduction, and recycling that can significantly reduce waste generation while improving efficiency and sustainability. AI-driven material substitution and lightweighting in product design enable discovery of novel materials with optimized properties, reducing waste without compromising performance. Reinforcement learning models optimize process parameters, raw material specifications, and machine sequencing to minimize production losses, while Industrial Internet of Things (IIoT) systems paired with AI analytics enhance real-time waste tracking, predictive maintenance, and quality inspection. Furthermore, AI-based demand forecasting and production planning reduce overproduction and excess inventory, as demonstrated in industrial applications. In recycling, ML-powered pattern recognition and robotic sorting technologies achieve higher accuracy in waste segregation, directly improving recycling efficiency. Complementary solutions such as smart bins and AI-enabled waste pickup scheduling optimize collection logistics, reducing both costs and emissions. Although implementation requires upfront investment in infrastructure and training, the long-term benefits include higher material efficiency, reduced waste, improved product quality, and stronger sustainability outcomes across the supply chain. By leveraging AI-enabled systems, manufacturers can align waste minimization efforts with circular economy principles, creating scalable solutions for both industry and society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Techno-Economic Comparison of Molten-Salt Electrolysis and Carbothermic Reduction for the Production of Metallurgical-Grade Silicon

Metallurgical-grade silicon (MG-Si) is an important source material for many industrial applications, including the manufacture of alloys, solar photovoltaics, and electronics. The process to refine raw materials into MG-Si is energy-intensive, with the predominant method of submerged-arc furnaces requiring energy consumption of approximately 11–13 kWh/kg Si. Recent research has discussed promising methods for reducing the energy required for the silicon production process, including the use of molten-salt electrolysis (MSE), a technique that offers potential savings in energy consumption without requiring carbon inputs for the process. This paper presents a techno-economic study of a potential industrial-scale MSE plant for MG-Si production to evaluate the trade-offs between capital and operating costs of the system. Capital costs are sourced from recent MG-Si plants and an existing cost model developed for MSE processes that includes the size of the plant and the operating temperature among its inputs. The results show that MSE technology has the potential to be an economically cost-competitive option for MG-Si production if the technology successfully scales to industrial production and matures enough to allow for financing costs similar to that of a comparably sized submerged-arc furnace plant.

14 SOLAR ENERGY↗
DOE OSTI2025

Co-Catalyst Enabled Biotransformation of Polyunsaturated Fatty Acids for Biobased Monomers

Enzymatic lipid epoxidation offers a promising approach to obtain renewable intermediates for biomaterials, but regiochemical control of these reactions has remained elusive. Here we report the discovery and application of artificial cocatalysts to direct the regioselectivity of catalytic epoxidation with the lipase CpLIP2. The methyl esters of alkyl dicarboxylic acids show the unique ability to direct regioselective epoxidation of polyunsaturated fatty acids, major components of plant and algae-based oils. We apply this transformation to the conversion of linoleic acid into sebacic acid, a dicarboxylic acid precursor valuable for biobased polyester polyurethanes, through a six-step pathway involving a regioselective Meinwald rearrangement. To highlight the route’s significance, sebacic acid was used to prepare a 100% biobased thermoplastic polyurethane, illustrating the relevance of this pathway to industrial applications. Furthermore, this regioselective chemoenzymatic oxidation and rearrangement process can be used to access multiple dicarboxylic acids that have remained previously unexplored as biobased monomers.

09 BIOMASS FUELS↗
DOE OSTI2020

Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases

Glycosylation of secondary metabolites involves plant UDP-dependent glycosyltransferases (UGTs). UGTs have shown promise as catalysts in the synthesis of glycosides for medical treatment. However, limited understanding at the molecular level due to insufficient biochemical and structural information has hindered potential applications of most of these UGTs. In the absence of experimental crystal structures, we employed advanced molecular modeling and simulations in conjunction with biochemical characterization to design a workflow to study five Group H Arabidopsis thaliana (76E1, 76E2, 76E4, 76E5, 76D1) UGTs. Based on our rational structural manipulation and analysis, we identified key amino acids (P129 in 76D1; D374 in 76E2; K275 in 76E4), which when mutated improved donor substrate recognition than wildtype UGTs. Molecular dynamics simulations and deep learning analysis identified structural differences, which drive substrate preferences. The design of these UGTs with broader substrate specificity may play important role in biotechnological and industrial applications. These findings can also serve as basis to study other plant UGTs and thereby advancing UGT enzyme engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Editorial: Lignocellulose valorization: Fractionation, conversion and applications

Lignocellulosic biomass is the most abundant form of renewable feedstock on the Earth. Chemicals, energies, and materials derived from lignocellulosic biomass are renewable and sustainable, and have the potential to replace fossil feedstocks. Biorefinery, a process to fractionate lignocellulose into the three major components, is considered the most pivotal step for bioenergy, biomaterials and biochemicals. However, due to the complex hierarchy and chemical structures, only limited lignocellulose is valorized into value-added products, and most of them are burnt or just discarded. Therefore, there is a need to investigate the heterogeneous structure of lignocellulose, improve its efficient fractionation into cellulose, hemicelluloses and lignin, and develop the technology that can convert the lignocellulose into high-value chemical products and high-performance functional materials in a sustainable and promising configuration. In short, the ultimate goal of biomass utilization is that the production of biofuels and biomaterials in industrial applications must be cost-, and performance-competitive with petroleum-derived equivalents.

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

Design and synthesis of diazirine‐containing dyes for polypropylene fibre: A study on the effect of alkyl chain

Abstract The dyeing of polypropylene fabrics utilising conventional‐disperse dyes often meets poor colour yield and fastness problems, fundamentally caused by the low affinity between dyes and polypropylene fibres. In this article, a new class of hydrophobic reactive dyes that can react with polypropylene were designed using diazirine moiety as a reactive group, azo structure as a chromophore, and an ester group as a linking bridge. The difference among these dyes was the alkyl chain length on the azo chromophore, which was used to fine‐tune the affinity between the dyes and fibre. The structures of the synthesised dyes and intermediates were characterised and confirmed using nuclear magnetic resonance, mass spectrometry, and infrared spectrometry. The absorption and thermal properties of the dyes were also studied to ensure their feasibility for dyeing application. In addition, the diazirine dyes were successfully applied to dye polypropylene fabrics using a two‐step process by dyeing in a non‐aqueous solvent and fixing it under heating. The dyes and polypropylene fibre were demonstrated to react, where the fixation values were in the range 64.5–76.4%. The systematic study of the structure–property relationship demonstrated the positive effect of the alkyl chain in promoting colour yield, however, accompanied by a slightly reduced fixation value. Due to the chemical bonding of the dye molecules to the fibre, the dyed polypropylene fabrics exhibited excellent colour fastness property, making the dyeing technology very promising for future industrial applications.

Guo, Guangluo↗
DOE OSTI2024

Abstract for CRADA between National Energy Technology Laboratory and OCO Chem

The National Energy Technology Laboratory (NETL) and OCO Chem (Participant) will collaborate in the development and scaling of electrochemical technologies that convert carbon dioxide (CO 2 ) into formic acid. This CO 2 -derived product is an industrially relevant chemical with various agricultural and industrial applications, as well as an emerging liquid hydrogen carrier 53 g of H 2 per liter. The use of formic acid as a liquid hydrogen carrier eliminates the challenges of long-distance H 2 transport and storage, which could lead to additional use as a precursor in the “green” energy and chemical sectors. Conventional formic acid production relies on carbon-intensive processes that use fossil fuel-derived starting materials. The collaboration will facilitate the deployment of technologies that convert captured CO 2 , water, and excess renewable electricity into carbon-neutral formic acid. The anticipated results will reduce the carbon footprint of conventional industrial processes, allow more sustainable use of our Nation’s fossil energy resources, and advance NETL’s ongoing CO 2 utilization efforts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Advanced blade-shaped thermal energy storage device: Development and application

Thermal energy storage (TES) using phase change materials (PCMs) is a promising approach for capturing and reusing excess thermal energy, yet widespread adoption is limited by low thermal conductivity, bulky configurations, and inadequate scalability. Here, this study presents a modular, blade-shaped TES prototype designed to address these challenges. The device integrates a lightweight aluminum shell, an embedded serpentine coil for active or passive heat exchange, and a cost-effective corrugated metal mesh for enhanced PCM thermal conductivity. With thickness-to-length and thickness-to-width ratios of 0.03 and 0.08, respectively, the blade-shaped TES achieves a compact, modular form factor suitable for space-constrained applications. Experimental testing demonstrated the efficient charge and discharge behavior of blade-shaped TES, capturing PCM superheating, phase-change transitions, and subcooling dynamics, with charging and discharging efficiencies of 94.9% and 94.6%, respectively. Also, the system can potentially achieve higher energy density than that of conventional TES designs. When integrated into a household refrigerator during the study, three blade-shaped TES modules successfully shifted 100% of peak-time compressor operation to off-peak hours, reducing energy consumption while maintaining more stable compartment temperatures. The blade-shaped TES's thin geometry, modularity, and enhanced thermal performance support scalable deployment across residential, commercial, and industrial applications, providing a versatile, cost-effective solution for high-efficiency, demand-flexible thermal energy management.

Blade-shaped↗
DOE OSTI2023

NdFeB Permanent Magnet Uses, Projected Growth Rates and Nd Plus Dy Demands across End-Use Sectors through 2050: A Review

Rare earth element (REE) permanent magnets (NdFeB) are a critical element in a vast and growing number of industrial applications. In consumer electronics, a broad category encompassing computer, CD, and DVD hard drives, in addition to the ubiquitous cell phones, the nominal NdFeB magnet content may be small, but the global market share for this sector accounts for almost 30% of NdFeB demand, due to a large and continually increasing consumer base. It is estimated that wind turbines that primarily employ permanent magnets will add roughly 110 GW annually of on- and off-shore capability over the next few years. Electric vehicles (EVs) and E-bicycles (EBs) equipped with permanent magnet motors comprise the transportation contribution. Permanent magnet motors have garnered nearly 100% of the market share among EV manufacturers worldwide. Industrial, professional service, and personal robots, most using permanent magnets, are also included in the projected global need for rare earths, particularly Nd and Dy. The sector projects significant growth of approximately 10% across robotic categories. In this paper, we calculate the future demand for Nd and Dy through 2050 across these sectors using a compounded annual growth rate coupled with magnet weight and rare earth content. Uncertainties in the estimates, such as the true global production of Nd, a range of end-product scales and/or unit types in each sector, varied magnet compositions, and the variety of uses within a sector, are all considered.

Geochemistry & Geophysics↗
DOE OSTI2020

Methanobactin from methanotrophs: genetics, structure, function and potential applications

ABSTRACT Aerobic methane-oxidizing bacteria of the Alphaproteobacteria have been found to express a novel ribosomally synthesized post-translationally modified polypeptide (RiPP) termed methanobactin (MB). The primary function of MB in these microbes appears to be for copper uptake, but MB has been shown to have multiple capabilities, including oxidase, superoxide dismutase and hydrogen peroxide reductase activities, the ability to detoxify mercury species, as well as acting as an antimicrobial agent. Herein, we describe the diversity of known MBs as well as the genetics underlying MB biosynthesis. We further propose based on bioinformatics analyses that some methanotrophs may produce novel forms of MB that have yet to be characterized. We also discuss recent findings documenting that MBs play an important role in controlling copper availability to the broader microbial community, and as a result can strongly affect the activity of microbes that require copper for important enzymatic transformations, e.g. conversion of nitrous oxide to dinitrogen. Finally, we describe procedures for the detection/purification of MB, as well as potential medical and industrial applications of this intriguing RiPP.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗
DOE OSTI2024

Photo-accelerated oxidation of spiro-OMeTAD for efficient carbon-based perovskite solar cells

Metal halide perovskite thin film solar cell (PSC) technology demonstrates promising power conversion efficiency and paves the way to commercialization. So far, the best device performance originates from the n-i-p structure with FTO/SnO 2 /perovskite/Spiro-OMeTAD/Au device architecture. Traditionally, to achieve high efficiency, several hours of oxidization of a Li-doped Spiro-OMeTAD hole transport layer (HTL) is necessary, which hinders the application of Spiro-OMeTAD for rapid large-scale manufacturing. In particular, the oxidization process significantly relies on ambient conditions, including oxygen and moisture levels. In this work, we report a fast technique to accomplish rapid oxidation in several mins of Spiro-OMeTAD HTL in perovskite solar cells by illuminating in visible light with one Sunlight intensity (100 mW cm −2 ). Compared to the traditional approach, our photo-accelerated oxidization of Spiro-OMeTAD approach can deliver a high-power conversion efficiency. We employ this photo-accelerated oxidization for the planar carbon-based perovskite solar cell and achieve a champion device efficiency of 17.47%. This strategy overcomes the barriers for a simple and rapid means toward mass manufacturing and industrial applications by utilizing Spiro-OMeTAD as the HTL for the perovskite solar module.

14 SOLAR ENERGY↗
DOE OSTI2025

Engineering the green algae Chlamydomonas incerta for recombinant protein production

Chlamydomonas incerta , a genetically close relative of the model green alga Chlamydomonas reinhardtii , shows significant potential as a host for recombinant protein expression. Because of the close genetic relationship between C. incerta and C. reinhardtii , this species offers an additional reference point for advancing our understanding of photosynthetic organisms, and also provides a potential new candidate for biotechnological applications. This study investigates C. incerta ’s capacity to express three recombinant proteins: the fluorescent protein mCherry, the hemicellulose-degrading enzyme xylanase, and the plastic-degrading enzyme PHL7. We have also examined the capacity to target protein expression to various cellular compartments in this alga, including the cytosol, secretory pathway, cytoplasmic membrane, and cell wall. When compared directly with C. reinhardtii , C. incerta exhibited a distinct but notable capacity for recombinant protein production. Cellular transformation with a vector encoding mCherry revealed that C. incerta produced approximately 3.5 times higher fluorescence levels and a 3.7-fold increase in immunoblot intensity compared to C. reinhardtii . For xylanase expression and secretion, both C. incerta and C. reinhardtii showed similar secretion capacities and enzymatic activities, with comparable xylan degradation rates, highlighting the industrial applicability of xylanase expression in microalgae. Finally, C. incerta showed comparable PHL7 activity levels to C. reinhardtii , as demonstrated by the in vitro degradation of a polyester polyurethane suspension, Impranil® DLN. Finally, we also explored the potential of cellular fusion for the generation of genetic hybrids between C. incerta and C. reinhardtii as a means to enhance phenotypic diversity and augment genetic variation. We were able to generate genetic fusion that could exchange both the recombinant protein genes, as well as associated selectable marker genes into recombinant offspring. These findings emphasize C. incerta ’s potential as a robust platform for recombinant protein production, and as a powerful tool for gaining a better understanding of microalgal biology.

cell membranes↗
DOE OSTI2024

7 Innovations in high-rate composite manufacturing: integrating additive manufacturing with compression molding process

Advanced composites play a pivotal role in modern engineering, offering exceptional strength-to-weight ratios and tailored properties, essential for various industries. High-rate composite manufacturing techniques have rapid production capabilities, which are essential for meeting the demands of industries requiring cost-saving, efficiency, and quick turnaround times. This chapter explores the Additive Manufacturing- Compression Molding (AM-CM) system developed by Oak Ridge National Laboratory (ORNL) for advanced composites manufacturing. The AM-CM system integrates additive manufacturing with compression molding, facilitating the production of polymer composite parts with superior mechanical properties and meticulously controlled microstructures. This innovative system not only ensures precise material deposition but also operates as a fast composite manufacturing process, enhancing productivity and performance, which are needed attributes across industrial applications. Through comprehensive mechanical testing and microstructural analysis, AM-CM promotes remarkable fiber alignment and reduced porosity in composite parts compared to alternative thermoplastic high-rate composite manufacturing methods. Furthermore, AM-CM enables overmolding reinforcement using continuous carbon fiber and supports selective reinforcement through customizable toolpaths. It also facilitates the production of hybrid materials to achieve tailored mechanical properties. Future advancements in AM-CM technology aim to enhance process efficiency, broaden material versatility, and improve part performance. This involves exploring novel materials, advancing process monitoring, implementing automation technologies, and integrating artificial intelligence (AI) and machine learning (ML) for predictive modeling and real-time optimization in composite manufacturing. These developments will establish the AM-CM system as a transformative technology in composite manufacturing, driving innovation across industries.

Hassen, Ahmed [ORNL] (ORCID:0000000328521222)↗
DOE OSTI2026

Quantum-inspired weight-constrained neural network: Reducing variable numbers by 100× compared to standard neural networks

Although quantum machine learning has shown great promise, the practical application of quantum computers remains constrained in the noisy intermediate-scale quantum era. To take advantage of quantum machine learning, we investigate the underlying mathematical principles of these quantum models and find that the quantum neural network with amplitude encoding is equivalent to a weight-constrained neural network. Motivated by this discovery, we develop a classical weight-constrained neural network. We find that this approach can reduce the number of variables in a classical neural network by a factor of 135 while preserving its accuracy. In addition, we develop a dropout method to enhance the robustness of quantum machine learning models, which are highly susceptible to adversarial attacks. This technique can also be applied to improve the adversarial robustness of the classical weight-constrained neural network, which is essential for industry applications, such as self-driving vehicles. Our work offers an approach to reduce the complexity of large classical neural networks, addressing a critical challenge in machine learning.

quantum algorithms & computation↗
DOE OSTI2023

A Review of Biodegradable Plastics: Chemistry, Applications, Properties, and Future Research Needs

Environmental concerns over waste plastics’ effect on the environment are leading to the creation of biodegradable plastics. Biodegradable plastics may serve as a promising approach to manage the issue of environmental accumulation of plastic waste in the ocean and soil. Biodegradable plastics are the type of polymers that can be degraded by microorganisms into small molecules (e.g., H 2 O, CO 2 , and CH 4 ). However, there are misconceptions surrounding biodegradable plastics. For example, the term “biodegradable” on product labeling can be misconstrued by the public to imply that the product will degrade under any environmental conditions. Such misleading information leads to consumer encouragement of excessive consumption of certain goods and increased littering of products labeled as “biodegradable”. Here this review not only provides a comprehensive overview of the state-of-the-art biodegradable plastics but also clarifies the definitions and various terms associated with biodegradable plastics, including oxo-degradable plastics, enzyme-mediated plastics, and biodegradation agents. Analytical techniques and standard test methods to evaluate the biodegradability of polymeric materials in alignment with international standards are summarized. The review summarizes the properties and industrial applications of previously developed biodegradable plastics and then discusses how biomass-derived monomers can create new types of biodegradable polymers by utilizing their unique chemical properties from oxygen-containing functional groups. The terminology and methodologies covered in the paper provide a perspective on directions for the design of new biodegradable polymers that possess not only advanced performance for practical applications but also environmental benefits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Predictive Chemical Kinetic Modeling: Where We Succeed, Where We Struggle, and What Comes Next

Chemical kinetic modeling plays a foundational role in fields ranging from energy to environmental science, pharmaceuticals, and advanced materials. The past two decades have seen remarkable progress, particularly in modeling gas-phase reactions for thermochemical processes, leading to impactful industrial applications such as steam cracking and air quality management. However, new challenges are emerging. The successful development of systematic methodologies for the description of gas-phase kinetics opens the possibility to apply the same approach to the study of more challenging systems. Here, we review recent advances, including ab initio transition state theory-based master equation estimation of elementary rates, automated mechanism generation, machine-learning-assisted kinetics, and uncertainty quantification, and discuss the advances needed to apply the same methodological approach in areas such as heterogeneous catalysis, electrochemistry, liquid-phase and solid-state reactivity, and multiscale model integration. We advocate for the development of targeted tools, especially methods that go beyond empirical tuning toward first-principles-based predictions. We highlight the need for accessible software and AIaugmented workflows to democratize modeling for industry and academia alike. In this perspective, we call attention to not only what has worked but also what remains unsolved, advocating to avoid overemphasizing successes in scientific works at the expense of realism. The next decade should focus on predictive capability, physical accuracy, and community infrastructure (e.g., databases and services) to enable innovation across diverse fields. We argue that kinetic modeling, properly equipped, can accelerate discovery far beyond its traditional domains.

ab initio calculations↗