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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

CWMD Nuclear Forensics Quarterly Report

Analysis and evaluation of nuclear debris is a key component to identify forensic signatures in a post-detonation environment. Currently, methods exist and are practiced in obtaining signatures from post-detonation debris. However, efforts to reduce the timeline in obtaining signatures is continuous within nuclear forensics research and will be forwarded by this research. This education and research project will explore combined material chemistry and separations of radionuclides to provide actionable signatures in less time than standard methods. The research will use the phase distribution of radionuclides in debris to develop targeted and rapid dissolution methods. These dissolution methods will be coupled with microfluidics to achieve samples suitable for analysis within reduced timelines.

and nuclear chemistry↗
DOE OSTI2022

Review of recent developments in iodine wasteform production

Radioiodine capture and immobilization is not only important to consider during the operation of reactors (i.e., I-131), during nuclear accidents (i.e., I-131 and I-129) or nuclear fuel reprocessing (i.e., I-131 and I-129), but also during disposal of nuclear wastes (i.e., I-129). Most disposal plans for I-129-containing waste forms (including spent nuclear fuel) propose to store them in underground repositories. Here, iodine can be highly mobile and, given its radiotoxicity, needs to be carefully managed to minimize long-term environmental impacts arising from disposal. Typically, any process that has been used to capture iodine from reprocessing or in a reactor is not suitable for direct disposal, rather conversion into a wasteform for disposal is required. The objectives of these materials are to use either chemical immobilization or physical encapsulation to reduce the leaching of iodine by groundwaters. Some of the more recent ideas have been to design capture materials that better align with disposal concepts, making the industrial processing requirements easier. Research on iodine capture materials and wasteforms has been extensive. This review will act as both an update on the state of the research since the last time it was comprehensively summarized, and an evaluation of the industrial techniques required to create the proposed iodine wasteforms in terms of resulting material chemistry and applicability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Pressure-Induced Phase Transformations

The study of phase transitions in solids under high pressure conditions is a very active and vigorous research field. In recent decades, thanks to the development of experimental techniques and computer simulations, a plethora of important discoveries has been made under high-pressure conditions. Many of the achievements accomplished in recent years a affect various research fields, from solid-state physics to chemistry, materials science, and geophysics. They not only contribute to a deeper understanding of solid-sold phase transitions but also to a better understanding of melting under compression.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design and Performance of lithium-Ion Batteries for Achieving Electric Vehicle Takeoff, Flight, and Landing

Today, the burgeoning drive towards global urbanization with over half the earth’s population living in cities, has created major challenges with regards to intracity and intercity transit and mobility. This problem is compounded due to the fact that almost always urbanization and increase in standard of living drives individual automobile ownerships. Over 95% of automobiles are presently powered by some form of fossil fuel and as an unintended consequence, urban centers have also been centers for peak greenhouse gas emissions, a major contributor to global climate change. A revolutionary solution to this conundrum is flight capable electric automobiles or electric aerial vehicles that can tackle both urban mobility and climate change challenges. For such advanced electric platforms, energy storage and delivery component is the vital component towards achieving takeoff, flight, cruise, and landing. The requirements and duty cycle demands on the energy storage system is drastically different when compared to the performance metrics required for terrestrial electric vehicles. As the widely deployed lithium ion-based battery systems are often the primary go-to energy storage choice in electric vehicle related applications, it is imperative that performance metrics and specifications for such batteries towards areal electric vehicles need to be established. In this nascent field, there exists ample opportunities for battery material innovations, understanding degradation mechanism, battery design, development and deployment of battery control and management systems. Thus, this chapter comprehensively discusses battery requirements and identifies battery material chemistries suitable for handling aerial electric automobile duty cycles. The chapter also discusses the battery cell-level metrics pertaining to electrochemical, chemical, mechanical, and structural parameters. Furthermore, specific models for battery degradation, state of health (SOH), capacity and models for full cell performance and degradation are also discussed here. Finally, the chapter also discusses battery safety and future directions of batteries that would power these next generation urban electric aircrafts.

Amin, Ruhul↗
DOE OSTI2020

Multiple-objective Reinforcement Learning for Inverse Design and Identification

The aim of inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning (DL) networks are considered as the state-of-the-art in inverse chemical design and have achieved early success in generating molecular structures with desired properties in the pharmaceutical and material chemistry fields. However, satisfying a large number (> 10 objectives) of molecular objectives is a limitation of current generative models. To improve the model’s ability to handle a large number of molecule design objectives, we developed a Reinforcement Learning (RL) based generative framework to optimize chemical molecule generation. Our use of Curriculum Learning (CL) to fine-tune the pre-trained generative network allowed the model to satisfy up to 21 objectives and increase the generative network’s robustness. The experiments show that the proposed multiple-objective RL-based generative model can correctly identify unknown molecules with an 83% to 100% success rate, compared to the baseline approach of 0%. Additionally, this proposed generative model is not limited to just chemistry research challenges; we anticipate that problems that utilize RL with multiple objectives will benefit from this framework.

reinforcement learning, generative model, inverse ↗
DOE OSTI2021

Towards Efficient Uncertainty estimation in deep learning for robust energy prediction in crystal materials

In material science, recent studies have started to explore the potential of using deep learning to improve property prediction from high-fidelity simulations, e.g, density functional theory (DFT). However, the design spaces are sometimes too large and intractable to sample completely. This results in a critical question that is how to evaluate the confidence and robustness of the prediction. In this paper, we propose an efficient approach to estimate uncertainty in deep learning using a single forward pass and then apply it for robust prediction of the total energy in crystal lattice structures. Our approach is built upon the deep kernel learning (DKL) that originally introduces to leverage the expressiveness of deep neural networks as input with a probabilistic prediction of Gaussian processes (GPs) as output. Existing DKL methods have difficulties in the accuracy of predictive uncertainty, training stability, and scaling to large datasets, which lead to significant barriers in real-world applications. We propose to address these challenges by using an inducing point approximate GP in feature space combined with spectral normalization as a regularization. We finally demonstrate our robust performance on an artificial example and a real-world application from materials chemistry.

Bi, Sirui↗
DOE OSTI2022

Hierarchical printed product and composition and method for making the same

Disclosed herein are embodiments of a printable composition that can be used to make printed products of a chosen material chemistry that have different levels of porosity within the printed product's structure Also disclosed herein are embodiments of a printed product that has multiple levels of porosity throughout its structure, which can include a macroscale level of porosity, a microscale level of porosity, a nanoscale level of porosity and any combination thereof. These printed products can be made using a 3-D printer and can be made from a single printable composition without the need to add different structural components during the production process. Also disclosed herein are embodiments of a method for making and using a printed product.

Lee, Matthew N.↗
DOE OSTI2023

Lithium battery cathode

A novel lithium battery cathode, a lithium ion battery using the same and processes and preparation thereof are disclosed. The battery cathode is formed by force spinning. Fiber spinning allows for the formation of core-shell materials using material chemistries that would be incompatible with prior spinning techniques. A fiber spinning apparatus for forming a coated fiber and a method of forming a coated fiber are also disclosed.

Bell, Nelson S.↗
DOE OSTI2025

An MLCommons Scientific Benchmarks Ontology

Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transformative applications of machine learning to critical scientific use-cases more fragmented and less clear in pathways to impact. This paper introduces an ontology for scientific benchmarking developed through a unified, community-driven effort that extends the MLCommons ecosystem to cover physics, chemistry, materials science, biology, climate science, and more. Building on prior initiatives such as XAI-BENCH, FastML Science Benchmarks, PDEBench, and the SciMLBench framework, our effort consolidates a large set of disparate benchmarks and frameworks into a single taxonomy of scientific, application, and system-level benchmarks. New benchmarks can be added through an open submission workflow coordinated by the MLCommons Science Working Group and evaluated against a six-category rating rubric that promotes and identifies high-quality benchmarks, enabling stakeholders to select benchmarks that meet their specific needs. The architecture is extensible, supporting future scientific and AI/ML motifs, and we discuss methods for identifying emerging computing patterns for unique scientific workloads. The MLCommons Science Benchmarks Ontology provides a standardized, scalable foundation for reproducible, cross-domain benchmarking in scientific machine learning. A companion webpage for this work has also been developed as the effort evolves: https://mlcommons-science.github.io/benchmark/

Hawks, Ben [Fermilab] (ORCID:0000000157000288)↗
DOE OSTI2020

Mechanistic Insight into Lignin Slow Pyrolysis by Linking Pyrolysis Chemistry and Carbon Material Properties

As a highly abundant renewable carbon source, lignin can be converted to a variety of advanced carbon materials with tailorable properties through slow pyrolysis. In this study, slow pyrolysis of kraft lignin, for the first time, was investigated with a commercial pyrolysis–gas chromatography–mass spectrometry (Py–GC–MS) system through evolved gas analysis-MS (EGA-MS) and heart-cutting-GC–MS (HC-GC–MS) analyses. These analyses allow recovery and examination of the multiphased gas products generated from thermal decomposition of lignin during slow pyrolysis at a controlled heating rate over a long time course, thus making it possible to link operation conditions, pyrolysis chemistry, and carbon material properties. The overall product distributions, including volatiles and solid products, were quantitatively tracked at different heating rates (2, 20, and 40 °C/min) and different temperature regions (100–200, 200–300, and 300–600 °C). Solid residues were further characterized using a suite of analytical tools, in correlation with the investigation of formation mechanisms of volatiles to reveal the reaction chemistry of lignin during slow pyrolysis and to determine the morphology, pore structure, and interfacial chemical properties. This study provides critical insights into the slow pyrolysis chemistry of lignin and the properties of the resulting carbon material. These results will facilitate a better design and control of the lignin slow pyrolysis process for synthesizing functional carbon materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Resurgence of a Nation’s Radiation Science Driven by Its Nuclear Industry Needs

This article describes the radiation facilities and associated sample preparation, management, and analysis equipment currently in place at the Dalton Cumbrian Facility, a facility which opened in 2011 to support the UK’s nuclear industry. Examples of measurements performed using these facilities are presented to illustrate their versatility and the breadth of research they make possible. Results are presented from research which furthers our understanding of radiation damage to polymeric materials, radiolytic yield of gaseous products in situations relevant to nuclear materials, radiation chemistry in light water reactor cooling systems, material chemistry relevant to immobilization of nuclear waste, and radiation-induced corrosion of fuel cladding elements. Applications of radiation chemistry relevant to health care are also described. Research concerning the mechanisms of radioprotection by dietary carotenoids is reported. An ongoing open-labware project to develop a suite of modular sample handling components suited to radiation research is described, as is the development of a new neutron source able to provide directional beams of neutrons.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Direct Recycling of End-of-Life Cathode Material Through Redox Chemistry Mediators

Lithium-ion batteries (LIBs) are ideal for electric vehicles and electronic devices because of their high-power density and outstanding cycle life. The need for recycled LIBs material is pivotal for the sustainability of the renewable energy industry. The recycling process needs to be both economically and environmentally conscious. Direct recycling is cheaper and generates the least amount of waste compared to pyrometallurgical and hydrometallurgical processes. Direct recycling explored in this work is useful for reclaiming precious minerals from the End-of-Life (EOL) LIBs material. The EOL material has varying lithium deficiency, hence, redox mediator relithiation restores the lithium content rapidly and at low cost. The redox mechanism relithiate EOL cathode material by shuttling charges very fast between lithium metal and EOL cathode material. The redox mediator is oxidized to create lithium rich solvent and reduced to relithiate EOL cathode material. However, the process is sensitive to pH changes. The redox reaction creates acidic solvent, which may cause lithium leaching. Therefore, use of lithium hydroxide (LiOH) to remove impurities on the surface of the EOL material and as a lithium source creates a basic solution to prevent lithium leaching and relithiate EOL material. This approach will potentially pave way for fast quality cathode material recovery at a low cost.

cathode material↗
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Embedding Methods for Quantum Chemistry: Applications from Materials to Life Sciences

Quantum mechanical embedding methods hold the promise to transform not just the way calculations are performed, but to significantly reduce computational costs and improve scaling for macro molecular systems containing hundreds if not thousands of atoms. The field of embedding has grown increasingly broad with many approaches of different intersecting flavors. Here, we lay out the methods into two streams: QM:MM and QM:QM, showcasing the advantages and disadvantages of both. We provide a review of the literature, the underpinning theories including our contributions, and we highlight current applications with select examples spanning both materials and life sciences. Lastly, we conclude with prospects and future outlook on embedding, and our view on the use of universal test case scenarios for cross comparisons of the many available (and future) embedding theories.

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

Predictive coarse-grained (CG) modeling of morphologies in polymer nanocomposites with specific and directional intermolecular interactions (Final Report)

The overarching goal of the proposed work was to develop predictive models for investigating structure and dynamics in soft materials with chemistries that have specific and directional molecular interactions. The motivation behind studying materials with specific and directional interactions lies in the many desirable features these interactions provide when designing novel soft materials. Soft materials with specific and direction interactions (such as hydrogen bonds or H-bonds) can have a) thermally reversible phase behavior with different functions with varying temperature, b) precisely tuned nanostructure with desirable geometries that afford unique physical properties (e.g., color response, mechanical properties) and c) well-mixed/blended morphologies that are useful for variety of applications in energy field (e.g., materials for batteries require use of blended polymers where one domain gives superior mechanical properties and one domain promotes electrical conduction). Notably, biology makes extensive use of specific and directional interactions, in many cases based on H-bonds, to construct materials with precisely defined architectures and properties. Engineered soft materials with precisely tuned nanostructures and improved processiblity through thermoresponsive phase behavior are useful in numerous applications that are relevant to the Department of Energy (DOE) including high efficiency electronic devices, light-weight high-strength composite materials for batteries and fuel cells, and polymer membranes for separations, etc. While past computational studies have been tremendously useful in understanding molecular phenomena and guiding synthesis of new macromolecular soft materials for a wide variety of applications, the inability to capture small scale specific and directional interactions alongside macromolecular length and time scales represented a key limitation of most studies to date. Our work in this project addressed this grand challenge in computational materials chemistry, i.e., ability to model the anisotropic, directional, and specific interactions that govern the behavior of many macromolecular soft matter systems of interest, thus, has greatly expanded the predictive potential of simulations. Specifically the key outcomes were: successful development of new coarse-grained (CG) polymer models to study generic and specific polymer chemistries in which hydrogen-bonding interactions are dominant. These CG models were then used in molecular simulations to study structure and thermodynamics in polymer nanocomposites and blends; some studies were conducted in collaboration with experimentalists. We also published a perspective and a viewpoint which included some of the work we completed in this DOE project; we believe these perspective and viewpoint articles guide other researchers in the soft materials community on how to extend the computational approaches and models we have developed for the purposes of their studies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Perspectives of 2D MXene Tribology

Abstract The large and rapidly growing family of 2D early transition metal carbides, nitrides, and carbonitrides (MXenes) raises significant interest in the materials science and chemistry of materials communities. Discovered a little more than a decade ago, MXenes have already demonstrated outstanding potential in various applications ranging from energy storage to biology and medicine. The past two years have witnessed increased experimental and theoretical efforts toward studying MXenes’ mechanical and tribological properties when used as lubricant additives, reinforcement phases in composites, or solid lubricant coatings. Although research on the understanding of the friction and wear performance of MXenes under dry and lubricated conditions is still in its early stages, it has experienced rapid growth due to the excellent mechanical properties and chemical reactivities offered by MXenes that make them adaptable to being combined with other materials, thus boosting their tribological performance. In this perspective, the most promising results in the area of MXene tribology are summarized, future important problems to be pursued further are outlined, and methodological recommendations that could be useful for experts as well as newcomers to MXenes research, in particular, to the emerging area of MXene tribology, are provided.

2D materials↗