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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 OSTI2024

Mechanochemically responsive polymer enables shockwave visualization

Abstract Understanding the physical and chemical response of materials to impulsive deformation is crucial for applications ranging from soft robotic locomotion to space exploration to seismology. However, investigating material properties at extreme strain rates remains challenging due to temporal and spatial resolution limitations. Combining high-strain-rate testing with mechanochemistry encodes the molecular-level deformation within the material itself, thus enabling the direct quantification of the material response. Here, we demonstrate a mechanophore-functionalized block copolymer that self-reports energy dissipation mechanisms, such as bond rupture and acoustic wave dissipation, in response to high-strain-rate impacts. A microprojectile accelerated towards the polymer permanently deforms the material at a shallow depth. At intersonic velocities, the polymer reports significant subsurface energy absorption due to shockwave attenuation, a mechanism traditionally considered negligible compared to plasticity and not well explored in polymers. The acoustic wave velocity of the material is directly recovered from the mechanochemically-activated subsurface volume recorded in the material, which is validated by simulations, theory, and acoustic measurements. This integration of mechanochemistry with microballistic testing enables characterization of high-strain-rate mechanical properties and elucidates important insights applicable to nanomaterials, particle-reinforced composites, and biocompatible polymers.

Science & Technology - Other Topics↗
DOE OSTI2026

Are Deep Energy Retrofits in Commercial Buildings Including Window Upgrades?

U.S. Commercial buildings account for about 20% of total U.S. energy consumption. Because the thermal performance of windows significantly affects building energy efficiency and HVAC system performance, best practice guidance often includes window and envelope improvements in conjunction with HVAC upgrades to optimize energy use and improve occupant comfort. It is an open question, however, regarding how often these best practices are implemented in the field. This paper aims to address that gap by conducting a literature review and a series of interviews with commercial building auditing and management professionals to explore the factors that drive window retrofits in commercial buildings. The paper explores a range of case studies from deep energy retrofits across the globe, comparing projects with and without window retrofits. The primary goals of this review are to: (1) provide data from real-world case studies illustrating the role of windows in deep energy renovations and HVAC upgrades, (2) conduct retrofit cost analyses for windows and high-performance HVAC systems and (3) offer insights into how window upgrade decisions are made and when they are implemented as part of deep energy retrofits. Most of the retrofit studies focused exclusively on high performance HVAC upgrades without considering how window upgrades might further enhance the overall energy efficiency of commercial buildings. Interviews with building industry experts shed light on the key factors influencing deep energy retrofit decisions and what factors tip the scales in favor of including window measures with more comprehensive retrofit projects.

Cort, Katherine↗
DOE OSTI2023

2,5‐Dimercapto‐1,3,4‐Thiadiazole (DMCT)‐Based Polymers for Rechargeable Metal–Sulfur Batteries

Organosulfur materials are a sustainable alternative to the present-day layered oxide cathodes in lithium-based batteries. One such organosulfur material that was intensely explored from the 1990s to early 2010s is 2,5-dimercapto-1,3,4-thiadiazole (DMCT). However, research interest declined as the electrode reactions with DMCT were assumed to be too sluggish to be practical. Armed with the advances in metal–sulfur batteries, we revisit DMCT-based materials in the form of poly[tetrathio-2,5-(1,3,4-thiadiazole)], referred to as pDMCT-S. With an appropriate choice of electrode design and electrolyte, pDMCT-S cathode paired with a Li-metal anode shows a capacity of 715 mA h g −1 and a Coulombic efficiency of 97.7% at a C/10 rate, thus quelling the concerns of sluggish reactions. Surprisingly, pDMCT-S shows significantly improved long-term cyclability compared to a sulfur cathode. Investigations into the origin of the stability reveals that the discharge product Li-DMCT in its mesomeric form can strongly bind to polysulfides, preventing their dissolution into the electrolyte and shuttling. This unique mechanism solves a critical problem faced by sulfur cathodes. Encouragingly, this mechanism results in a stable performance of pDMCT-S with Na-metal cells as well. In conclusion, this study opens the potential for exploring other organic materials that have inherent polysulfide sequestering capabilities, enabling long-life metal–sulfur batteries.

2,5-dimercapto-1,3,4-thiadiazole↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler [National Renewable Energy Lab. (NR↗
DOE OSTI2026

Powered By SAM [Slides]

The System Advisor Model(TM) (SAM) is a free, open-source desktop application for techno-economic analysis of energy technologies. By combining detailed performance modeling with financial analysis, SAM allows users to assess technology trade-offs, explore future scenarios, and make informed decisions about energy investments. Users also have access to model details and the ability to embed SAM's core models in their own applications. This webinar, hosted by National Laboratory of the Rockies researchers Janine Keith and Matt Prilliman, highlights how this widely used modeling tool supports data-driven decision-making for energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

The phase diagram of quantum chromodynamics in one dimension on a quantum computer

The quantum chromodynamics (QCD) phase diagram, which reveals the state of strongly interacting matter at different temperatures and densities, is key to answering open questions in physics, ranging from the behaviour of particles in neutron stars to the conditions of the early universe. However, classical simulations of QCD face significant computational barriers, such as the sign problem at finite matter densities. Quantum computing offers a promising solution to overcome these challenges. Here, we take an important step toward exploring the QCD phase diagram with quantum devices by preparing thermal states in one-dimensional non-Abelian gauge theories. We experimentally simulate the thermal states of SU(2) and SU(3) gauge theories at finite densities on a trapped-ion quantum computer using a variational method. This is achieved by introducing two features: Firstly, we add motional ancillae to the existing qubit register to efficiently prepare thermal probability distributions. Secondly, we introduce charge-singlet measurements to enforce colour-neutrality constraints. This work pioneers the quantum simulation of QCD at finite density and temperature for two and three colours, laying the foundation to explore QCD phenomena on quantum platforms.

Quantum information↗
DOE OSTI2026

Passive Confirmation of the Presence Of High-Explosive Material Via Neutron Transmission Spectroscopy

The goal of this project was to explore a novel approach for identifying presence and potential types of high explosives (HE) in treaty-controlled items with passive neutron sources by using passive neutron transmission spectroscopy. We predominantly look to measure relative elemental abundance of Carbon, Hydrogen, Oxygen, and Nitrogen (CHON) since most relevant materials are certain mix of these elements, as shown in Table 1. Previously, most studies exploring potential identification of CHON elemental content of targets in the vicinity of passive neutron source were focused solely on gamma spectroscopy using high resolution gamma detectors. Using neutron transmission spectroscopy with pulse-shape discrimination (PSD) capable organic scintillators is not only a novel idea in of itself, but arguably necessary for accurate identification of the CHON elemental content of an interrogated target. While high resolution gamma spectroscopy in principle can determine a presence of hydrogen and nitrogen, it is effectively blind to a quantitatively measuring areal densities of C, O. The initially proposed approach, described in detail in the project proposal, was to leverage previous Monte Carlo study on neutron transmission spectroscopy using slab shaped target interrogated with well-collimated (“pencil”) neutron beam. In this study, a simulated test CO 2 target was interrogated by a pencil neutron beam and transmitted neutron spectra were measured using PSD capable organic scintillator using MLEM based unfolding technique. To establish relative elemental content of carbon and oxygen, the unfolded neutron spectrum was fit with a parametrized combination of their respective elemental spectral templates. The individual spectral template was calculated by convoluting ENDF neutron crosssection with the known resolution of the PSD detector used in the study. This approach in the studied configuration successfully quantitatively established relative elemental composition of carbon and oxygen.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Enhanced Recovery of Critical Minerals and Geological Hydrogen in The Mine of the Future

invited perspective article: The rapid demand for critical minerals (CMs) and hydrogen (H2) necessitates innovative solutions beyond conventional mining. This study explores enhanced mineral recovery (EMR) and geological hydrogen production (GeoH2) by leveraging the Earth’s subsurface as a reactive platform. Using ultramafic rocks and engineered fluids, the approach simultaneously mobilizes critical resources and produces H2 through natural processes like serpentinization. This paradigm offers a transformative pathway to secure essential materials and diversify energy resources, setting a foundation for the Terrestrial Mine of the Future

Yan, Keju↗
DOE OSTI2026

Development of large-scale, 3D Printed high temperature ceramic material

Through this collaborative effort, a new 3D printing platform for ceramics called laser-induced slip casting (LIS) was explored and developed for improving the processing and manufacture of silicon carbide (SiC), a high temperature ceramic material. This method prints layers of ceramic slips/slurries with subsequent selective-laser heating to dry each layer to build a 3D structure. The completed and dried part is then sintered. This manufacturing process is inherently lower cost than alternative ceramic printing methods such as binder jet technology or stereolithography when it comes to the feedstock material but is more expensive than robocasting or direct ink writing. However, it has the potential to make more controlled parts with less defects compared to robocasting. The largest cost is the heating source for the printer. With this technique, there is potential to make large ceramic parts in a near-net shape. Further, there is no known commercial manufacturing of 3D printed ceramics that creates a large range of different high-density ceramics at large scale. The goals and outcomes for the development of the new printing technology were: 1) assessing the technology with alumina by characterizing coupons, 2) printing and sintering of silicon carbide (SiC), and 3) characterization and properties testing of materials printed and a scale-up of SiC part(s). Through this collaborative project, it was sought to provide the best solution to achieving highly dense and near-net shaped ceramic parts at large scale and reduced cost. The goal was to produce high density sintered materials for applications in defense, energy generating systems, armor, and wear parts using 3D printing methods. Variables including powder particle sizes, dispersant molecular weights (MWs), binders, printing parameters, and post processing were explored as well as the characterization of the physical properties (add specifics here).

36 MATERIALS SCIENCE↗
DOE OSTI2026

U.S. ESCO Industry Report: Industry Size and Recent Market Trends, 2022- 2024

The latest edition of the U.S. Energy Service Company (ESCO) Industry Report by Lawrence Berkeley National Laboratory (LBNL) finds that the U.S. ESCO industry continues to show strong growth. The report draws from ESCO industry reported revenue data for the 2022-2024 period, detailing the current size and characteristics of the U.S. ESCO industry. Following 20 years of ESCO industry reports, the 2024 report explores significant revenue trends across market segments, geographic regions, ESCO size, financing structures, and business activities. New analysis in this report outlines customer priorities and non-energy benefit drivers of Energy Savings Performance Contract projects, adjusted revenue analysis detailing the impacts of inflation on industry growth, and project challenges by market segment.

Chelminski, Kathryn↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗
DOE OSTI2024

Converting Second‐Order Saddle Points to Transition States: New Principles for the Design of 4π Photoswitches

Abstract Molecular photoswitches have demonstrated potential for storing solar energy at the molecular level, with power densities comparable to commercial batteries and hydroelectric energy storage. However, development of efficient photoswitches is hindered by limitations in cyclability and optical properties of existing materials. We here demonstrate that certain limitations in photoswitches based on electrocyclizations stem from the issue of controlling competition between Woodward‐Hoffmann allowed and forbidden pathways. Our approach moves beyond the traditional view of activation barriers and reveals that second‐order saddle points are crucial in dictating the competition between disrotatory and conrotatory pathways. These insights suggest new opportunities to manipulate the competition between these pathways through geometric constraints, fundamentally altering the connectivity of the potential energy surface. Our study also emphasizes the necessity of multi‐reference methods and the need to conduct higher‐dimensional explorations for competing pathways beyond photoswitch design.

Chemistry↗
DOE OSTI2024

The S-PLUS Fornax Project (S+FP): A first 12-band glimpse of the Fornax galaxy cluster

ABSTRACT The Fornax galaxy cluster is the richest nearby (D ∼ 20 Mpc) galaxy association in the southern sky. As such, it provides a wealth of opportunities to elucidate on the processes where environment holds a key role in transforming galaxies. Although it has been the focus of many studies, Fornax has never been explored with contiguous homogeneous wide-field imaging in 12 photometric narrow and broad bands like those provided by the Southern Photometric Local Universe Survey (S-PLUS). In this paper, we present the S-PLUS Fornax Project (S+FP) that aims to comprehensively analyse the galaxy content of the Fornax cluster using S-PLUS. Our data set consists of 106 S-PLUS wide-field frames (FoV∼1.4 × 1.4 deg2) observed in five Sloan Digital Sky Survey-like ugriz broad bands and seven narrow bands covering specific spectroscopic features like [O ii], Ca ii H+K, Hδ, G band, Mg b triplet, Hα, and the Ca ii triplet. Based on S-PLUS specific automated photometry, aimed at correctly detecting Fornax galaxies and globular clusters in S-PLUS images, our data set provides the community with catalogues containing homogeneous 12-band photometry for ∼3 × 106 resolved and unresolved objects within a region extending over ∼208 deg2 (∼5 Rvir in RA) around Fornax’ central galaxy, NGC 1399. We further explore the eagle and IllustrisTNG cosmological simulations to identify 45 Fornax-like clusters and generate mock images on all 12 S-PLUS bands of these structures down to galaxies with M⋆ ≥ 108 M⊙. The S+FP data set we put forward in this first paper of a series will enable a variety of studies some of which are briefly presented.

Astronomy & Astrophysics↗
DOE OSTI2026

Resources for E-Mobility Program Implementation [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Successful Energy Project Implementation" will explore common challenges in implementing energy projects and learn strategies to turn project plans into reality. Each participant will focus on a local energy project or priority as a cornerstone for their learning throughout the series.This presentation overviews the basics of developing electric mobility projects with emphasis on leveraging the right tools to advance technical scoping and program design.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

The cluster decomposition of the configurational energy of multicomponent alloys

Abstract The cluster expansion method (CEM) is a widely used lattice-based technique in the study of multicomponent alloys. Despite its prevalent use, a clear understanding of expansion terms is lacking. We present a modern mathematical formalism of the CEM and introduce thecluster decomposition—a unique and basis-independent decomposition for functions of the atomic configuration in a crystal. We identify the cluster decomposition as an invariant ANOVA decomposition; and demonstrate how functional analysis of variance and sensitivity analysis can be used to interpret interactions among species. Furthermore, we show how the mathematical structure of the cluster decomposition enables numerical evaluation that scales with the number of clusters and is independent of the number of species. Overall, our work enables rigorous interpretations of interactions among species, provides opportunities to explore parameter estimation beyond linear regression, introduces a numerical efficient implementation, and enables analysis of cluster expansions based on established mathematical and statistical principles.

Chemistry↗
DOE OSTI2025

Role of Matter Interactions in Superradiant Phenomena

The superradiant phenomenon, usually described by the Dicke model, is a hallmark of strong light-matter interaction. Here, we explore how matter-matter interactions influence this phenomenon by performing ground-state simulations of Dicke-like models with both isotropic and anisotropic interactions. We find that Ising-type interactions produce two qualitatively distinct phase boundaries, one of which gives rise to an antiferromagnetic-normal phase connected to the superradiant regime via a first-order phase transition. Under anisotropic couplings, we uncover a strongly correlated phase where in-plane spin order coexists with superradiance, exhibiting sublinear scaling of the photon occupation per site and power-law decay of spin correlations. Furthermore, superradiance is strengthened by tuning either isotropic or anisotropic interactions, highlighting the role of intrinsic many-body correlations in shaping light-matter quantum phases.

Dicke model↗
DOE OSTI2026

Few is different: deciphering many-body dynamics in mesoscopic quantum gases

Emergent macroscopic descriptions of matter, such as hydrodynamics, are central to our description of complex physical systems across a wide spectrum of energy scales. The conventional understanding of these many-body phenomena has recently been shaken by a number of experimental findings. Collective behavior of matter has been observed in mesoscopic systems, such as high-energy hadron–hadron collisions, or ultracold gases with only a few strongly interacting fermions. In such systems, the separation of scales between macroscopic and microscopic dynamics (at the heart of any effective theory) is inapplicable. To address the conceptual challenges that arise from these observations and explore the universality of emergent descriptions of matter, the EMMI Rapid Reaction Task Force was assembled. This document summarizes the RRTF discussions on recent theoretical and experimental advances in this rapidly developing field. Leveraging technological breakthroughs in the control of quantum systems, we can now quantitatively explore what it means for a system to exhibit behavior beyond the sum of its individual parts. In particular, the report highlights how the (in)applicability of hydrodynamics and other effective theories can be probed across three principal frontiers: the size frontier, the equilibrium frontier, and the interaction frontier.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Variational Quantum Circuits to Prepare Low Energy Symmetry States

We explore how to build quantum circuits that compute the lowest energy state corresponding to a given Hamiltonian within a symmetry subspace by explicitly encoding it into the circuit. We create an explicit unitary and a variationally trained unitary that maps any vector output by ansatz A(α → ) from a defined subspace to a vector in the symmetry space. The parameters are trained varitionally to minimize the energy, thus keeping the output within the labelled symmetry value. The method was tested for a spin XXZ Hamiltonian using rotation and reflection symmetry and H 2 Hamiltonian within S z = 0 subspace using S 2 symmetry. We have found the variationally trained unitary gives good results with very low depth circuits and can thus be used to prepare symmetry states within near term quantum computers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗