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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2024

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design

Background: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. Results: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass (R 2 = 0.82) and root surface area (R 2 = 0.8). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. Conclusion: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An Overview of Behind-the-Meter Solar-Plus-Storage Regulatory Design: Approaches and Case Studies to Inform International Applications

Behind-the-meter energy storage systems paired with distributed photovoltaic (DPV) - with the capability to act as both generation and load - represent a unique and disruptive power sector technology capable of providing a range of important services to customers, utilities, and the broader power system. How should regulators, utilities, and policymakers manage the range of challenges and opportunities that increased behind-the-meter energy storage deployment will bring to the power system, in particular when these systems are paired with DPV? This report is intended to offer key regulatory considerations for facilitating DPV-plus-storage programs for retail customers; relevant cases from U.S. states are provided as examples of how novel regulatory issues related to behind-the-meter energy storage systems paired with DPV are being addressed in practice. At a high level, designing a regulatory framework that aligns DPV-plus-storage deployment with larger policy objectives requires thoughtful deliberation across a range of technical and economic issues. While this report attempts to segment many of these issues into distinct topics to enhance reader understanding, in reality, DPV-plus-storage regulatory issues are all closely integrated, and design decisions on a single aspect often have broader implications. With that in mind, this report outlines a series of steps that can be employed by regulators to approach DPV-plus-storage regulatory design.

14 SOLAR ENERGY↗
DOE OSTI2026

A System-Level Cost Modeling Framework for Design for Remanufacturing: A Case Study of an Agricultural Machine Transmission

Remanufacturing offers significant environmental and economic benefits by restoring end-of-life products to as-new conditions. Although extensive research has been conducted on the topic, the adoption of remanufacturing practices remains limited across various industries. A primary barrier to broader implementation is the substantial upfront investment required, which necessitates reliable cost modeling to justify potential future savings. Most existing models treat components independently and ignore inter-component dependencies. We develop a probabilistic, system-level cost modeling framework that integrates reliability, reusability, and a dependency matrix to capture cascading effects across components over multiple life cycles. Our model identifies those critical components that maximize remanufacturing benefits across a product's many lives. A toy example and an industry case study (John Deere PowrQuad transmission subassembly) illustrate how design alternatives affect cumulative cost. Using a Monte Carlo simulation (MCS) to perform life cycle cost analysis on the system with different design changes, we show the normalized average cost savings after three remanufacturing cycles. Accounting for dependencies meaningfully alters cost projections and ignoring them underestimates accumulated cost by up to 20% in our examples. Furthermore, the results of our study confirm that accounting for component interdependencies is necessary to produce cost estimates that meet industry standards.

Life Cycle Analysis and Design↗
DOE OSTI2024

Proof-of-concept studies with a computationally designed M pro inhibitor as a synergistic combination regimen alternative to Paxlovid

As the SARS-CoV-2 virus continues to spread and mutate, it remains important to focus not only on preventing spread through vaccination but also on treating infection with direct-acting antivirals (DAA). The approval of Paxlovid, a SARS-CoV-2 main protease (M pro ) DAA, has been significant for treatment of patients. A limitation of this DAA, however, is that the antiviral component, nirmatrelvir, is rapidly metabolized and requires inclusion of a CYP450 3A4 metabolic inhibitor, ritonavir, to boost levels of the active drug. Serious drug–drug interactions can occur with Paxlovid for patients who are also taking other medications metabolized by CYP4503A4, particularly transplant or otherwise immunocompromised patients who are most at risk for SARS-CoV-2 infection and the development of severe symptoms. Developing an alternative antiviral with improved pharmacological properties is critical for treatment of these patients. By using a computational and structure-guided approach, we were able to optimize a 100 to 250 μM screening hit to a potent nanomolar inhibitor and lead compound, Mpro61. In this study, we further evaluate Mpro61 as a lead compound, starting with examination of its mode of binding to SARS-CoV-2 M pro . In vitro pharmacological profiling established a lack of off-target effects, particularly CYP450 3A4 inhibition, as well as potential for synergy with the currently approved alternate antiviral, molnupiravir. Development and subsequent testing of a capsule formulation for oral dosing of Mpro61 in B6-K18-hACE2 mice demonstrated favorable pharmacological properties, efficacy, and synergy with molnupiravir, and complete recovery from subsequent challenge by SARS-CoV-2, establishing Mpro61 as a promising potential preclinical candidate.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Survey of Advanced Generation IV Reactor Parameters for Integrated Energy System Modeling Capabilities

This paper is to categorize and characterize advanced Generation IV nuclear reactors that are not water based according to the key parameters for the Integrated Energy Systems (IES) project[1]. A key goal of the IES project is to assess the economic viability of including an advanced nuclear reactor into an IES. The landscape of advanced reactors is heterogeneous: several unique designs with different heat profiles are currently being studied and designed. This report provides a high-level perspective of advanced reactor concepts, including primary loop nuclear output temperatures and operation schemes that could affect secondary side power production or storage. For example, this report addresses the type of thermodynamic system (Brayton, supercritical CO 2 [S-CO 2 ], etc.) and the various pressures and temperatures on the secondary side that are expected to be provided by the various primary side reactor types. Based on these characteristics, it may be possible to determine which type of nuclear power plant could be the most economical for analysis in the IES framework for power production coupled with energy storage and waste heat utilization. The results of this effort will serve as input for model development of various secondary side components, concentrating on off-the-shelf capabilities for current and near future secondary side power generation and heat rejection as simulated in TRANSFORM. Generic models can be created which are agnostic of the nuclear energy supply without violating intellectual property rights of the developer. These models can be used to make informed decisions on the economics of integrating various energy system components with nuclear plant designs. Once complete, conclusions may be drawn regarding (1) the economics, efficiency, and reliability of certain nuclear reactors, (2) certain industry processes and energy storage systems, and (3) prioritization of certain reactor types with the studied industry types.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Progress towards the FRIB-EDM 3 -Frontend: A tool to provide radioactive molecules from isotope harvesting for fundamental symmetry studies

The under-construction FRIB-EDM 3 -instrument was designed to study polar radioactive molecules (such as RaF) in transparent cryogenic solids by laser spectroscopy. The instrument is divided into a frontend- and a backend section. The frontend accepts an aqueous sample from isotope harvesting and provides a mass-separated molecular ion beam in an ultra-high vacuum environment. In the backend, the ions are guided into alkali-metal vapor and the resulting neutrals are co-deposited in a solid argon matrix to perform laser spectroscopy. Here, this work addresses the frontend of the instrument. The efficient ionization of harvested radioisotopes from aqueous samples is achieved with a spray-ionization method. Subsequently, the molecular ion beam is analyzed by mass-to-charge ratio by a quadrupole mass filter. To verify the feasibility of the approach, numerical simulations with the COMSOL and SIMION packages have been conducted. While the former was applied to study transport in ion funnels, the latter was used to investigate ion beam transmission through the lower pressure sections. Following promising simulation results, a first experimental setup is under construction.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

FEED Study of CarbonCapture Inc DAC and CarbonCure Utilization Technologies Using United States Steel’s Gary Works Plant Waste Heat (Final Report)

The University of Illinois at Urbana-Champaign (UIUC) led this project to produce a front-end engineering design (FEED) study of an advanced Direct Air Capture and Utilization System (DACUS) system that can remove a minimum of 5,000 tonnes/yr net of carbon dioxide from air (based on cradle-to-gate LCA) and utilizing the CO 2 to produce low carbon intensity ready mix concrete. The designed system, if built, would be larger than any currently existing Direct Air Capture (DAC) collector in the U.S. Such carbon capture technologies are critical to meeting the goals of the DOE’s program to accelerate climate-critical technology. In addition to the power sector, industrial facilities for the manufacture of steel and cement/concrete are among the major sources of anthropogenic CO 2 . DAC is a promising new technology for reducing CO 2 , a potent greenhouse gas, in the atmosphere but is expensive, in part due to the energy required to adsorb and desorb captured CO 2 during cycles. By integrating CarbonCapture Inc. (CCI) DAC modules at United States Steel's Gary Works (USS) and utilizing the site's waste heat, energy, and location this project evaluates the feasibility of utilizing the captured CO 2 and the logistics of transportation. CarbonCapture Inc. has developed an innovative DAC system using novel adsorbents to cost-effectively capture CO 2 . The captured, liquified gas will be trucked to ready-mix concrete plants within the region, the closest of which is approximately 3.5 miles away, where CarbonCure will inject it into concrete during the mixing process at the facilities. The carbon dioxide reacts with concrete, mineralizing into calcium carbonate (CaCO 3 ), permanently locking the greenhouse gas into the matrix of the building material. This FEED study demonstrated a full CO 2 value chain for DACUS from industrial facilities. It also provided a means for Visage Energy Corp. (Visage) to assess the impact of this holistic approach on job creation, regional economic impact, and environmental justice. The project team also included Sargent & Lundy (S&L) to provide the constructability review and costing of the integration of the DAC with the steel plant. Ecotek Engineering USA, LLC designed the outside battery limit (OSBL) infrastructure to connect the DAC and the plant. Activities performed during the project included: (1) Project Management Plan; (2) Technology Maturation Plan (TMP); (3) Initial Workforce Readiness Plan; (4) Workforce Readiness Plan; (5) Front-End Engineering Design (FEED) Study; (6) Project Design Basis; (7) Hazards and Operability (HAZOP) Study; (8) Constructability Review; (9) Project Cost Assessment; (10) Logistics Analysis of CO 2 Transportation to the Utilization Site; (11) Business Case Analysis; (12) Life Cycle Analysis (LCA); (13) Environmental Health and Safety (EH&S) Analysis; (14) Environmental Justice Analysis; and (15) Economic Revitalization and Job Creation Outcomes Analysis. This report provides a summary of the outcomes and results of the project, which was performed between Oct. 1, 2022, through Sept. 30, 2024.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

High Flux Isotope Reactor Low Enriched Uranium U-10Mo Fuel Design Parameters

Activities to convert the HFIR from HEU to LEU are ongoing as part of the US Department of Energy (DOE) National Nuclear Security Administration (NNSA) nuclear nonproliferation mission. Design activities to study the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a uranium-molybdenum (U-10Mo) monolithic alloy fuel. This high-density alloy contains 90 wt % uranium and 10 wt % molybdenum and has a uranium density of 15.318gU/cm 3 . The goal of these studies is to generate several candidate HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. Recent advancements in modeling and simulation tools and design methods enabled a thorough analysis of the available design space with U-10Mo fuel. A surrogate model used this analysis as training data to quickly determine the performance of a design given specific design parameters. An optimization module used this surrogate model to quickly search this multidimensional search space given specific desired performance characteristics. This approach was made possible by the large available design space with U-10Mo fuel. Shift, a Monte Carlo tool optimized for high-performance computing (HPC) architectures, was used for faster calculation and better data management for reactor physics simulations. Once most of these design studies were complete, a new suite called the Python HFIR Analysis and Measurement Engine (PHAME) was developed to connect all fuel design analysis steps, making design studies more efficient and reproducible. The post-processing capabilities of these new tools are leveraged for the information provided herein. Leveraging these tools, several candidate fuel designs were selected with varying levels of feature complexity and reactor performance. This report provides design feature details for four selected HFIR LEU U-10Mo fuel designs and their corresponding performance and safety metrics. Nominal best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities, are provided. Simulations show that the high uranium density of U-10Mo fuel provides a large potential design space that enables various LEU designs to meet HEU core performance metrics and safety requirements with a power increase from 85 MW (HEU) to 95 MW or 100 MW (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A Comparative Study of Two BAM Designs for Friction Sensitivity Testing of Explosives

A new commercial design of BAM friction tester that utilizes a programmable servo motor was tested in comparison to the traditional cam-driven model. Displacement and velocity profiles were analyzed for both designs; significant differences were found between the two designs, most notable of which is that the traditional cam-driven unit has plate velocities that can be 50–75 % greater than those seen on the servo-driven model. Five energetic materials were also tested on each machine, including PETN, RDX, HMX, CL-20, and HNAB. Finally, results from the servo-driven model generally showed slightly less sensitivity when compared to the cam driven model, though the magnitude of the difference is not significant enough to require modification of safe handling procedures for the materials tested.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2026

Discovery of hydrogen storage molecules using large language models and machine learning

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.

Harb, Hassan [Argonne National Laboratory (ANL), A↗
DOE OSTI2024

Interplay of freeze-in and freeze-out: Lepton-flavored dark matter and muon colliders

We study a lepton-flavored dark matter model and its signatures at a future muon collider. We focus on the less-explored regime of feeble dark matter interactions, which suppresses the dangerous lepton-flavor-violating processes, gives rise to dark matter freeze-in production, and leads to long-lived particle signatures at colliders. We find that the interplay of dark matter freeze-in and its mediator freeze-out gives rise to an upper bound of around TeV scales on the dark matter mass. The signatures of this model depend on the lifetime of the mediator and can range from generic prompt decays to more exotic long-lived particle signals. In the prompt region, we calculate the signal yield, study useful kinematics cuts, and report tolerable systematics that would allow for a 5 σ discovery. In the long-lived region, we calculate the number of charged tracks and displaced lepton signals of our model in different parts of the detector and uncover kinematic features that can be used for background rejection. We show that, unlike in hadron colliders, multiple production channels contribute significantly, which leads to sharply distinct kinematics for electroweakly charged long-lived particle signals. Ultimately, the collider signatures of this lepton-flavored dark matter model are common among models of electroweak-charged new physics, rendering this model a useful and broadly applicable benchmark model for future muon collider studies that can help inform work on detector design and studies of systematics. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Ko’olauloa Community Resilience Hub Design Trade-off Study

The Ko’olauloa community resilience hub has affordable microgrid options to meet its resilience requirements. Renewables and battery are insufficient without very high cost. Either diesel generation or a hydrogen battery are needed to assure availability and keep cost down.

Villa, Daniel↗