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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Using MARCUS, MICRE, and COMBLE data to improve understanding and modeling of cloud, aerosol, and boundary layer processes at high-latitudes

Because it is believed general circulation (GCM) and numerical weather prediction (NWP) models underestimate shortwave radiation over the Southern Ocean (SO) due to inadequate representations of boundary layer (BL) and cloud processes, it is critical to improve our understanding of key aerosol, cloud, precipitation, and BL processes. Over the north Atlantic Ocean (NA), cold-air outbreaks are common, yet few studies of the aerosol and environmental controls of the associated convective BL clouds exist as needed to develop and evaluate GCM representations. Although processes cannot be observed, cloud and aerosol properties can be measured in-situ or remotely retrieved, which when combined with numerical simulations enable process level understanding required to improve model representations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

CalTestBed - Twelve Benefit Co - Understanding Degradation of Water Management Properties in Gas Diffusion Layers (CRADA Final Report)

The research between Lawrence Berkeley National Lab and Twelve Benefit Co. enhanced Twelve’s understanding of how chemical changes to the gas diffusion layer lead to failure of their CO 2 electrolyzers. During the CRADA period, LBNL team tested Twelve’s gas diffusion layer samples (GDL), some with microporous layer (MPL) and some without MPL using the water-air capillary pressure setup. LBNL team also tested microporous layer samples using the fuel cell test stands and compared those results with commercial GDL and MPL. Understanding the hydrophobicity of the material, along with electrochemical performance is the key to determining Twelve’s ability to scale their platform. Further improvements to Twelve’s CO 2 electrolyzer can enhance the net zero emissions with their technology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report)

The major goals of this work are twofold: (1) to enable transformative basic understanding of structure-property-performance relationships governing oxygen transport in oxygen-active materials and (2) facilitate the discovery and rational design of new oxygen-active materials which transport oxygen efficiently at low temperature. Transformative understanding and materials design will be accomplished by synergistically combining materials data mining, machine learning, high-throughput computation and targeted experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Improved Fundamental Understanding of Aluminum Chemistry and Interactions of Aluminate Anion with Co-Anions: ORNL Project Progress Report

The U.S. Department of Energy (DOE)’s Hanford Site in Washington State houses 177 underground storage tanks containing millions of gallons of nuclear and chemical waste with high aluminum content. Aluminum (Al) salt reactions with strong bases produce aluminum hydroxides, like boehmite (γ-AlOOH), which are main components of insoluble nuclear waste sludge. Understanding the morphology, crystallinity, and dissolution behavior of boehmite under waste tank conditions is necessary for improving waste management and mitigation strategies. The ORNL team uses in situ multimodal analysis strategy to study Al chemistry of simulated tank waste using microfluidic reactors and advanced chemical imaging and mass spectrometry (MS) techniques. Because the SALVI device is vacuum compatible and transferrable among different platforms, we can use it in scanning electron microscopy (SEM), vacuum ultraviolet single photon mass spectrometry (VUV SPI-MS), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) to study the chemical speciation and colloid stability in liquids in this project. Additionally, ex situ transmission electron microscopy (TEM) and x-ray diffraction (XRD) spectroscopy can be used to verify particle phase to further the understanding of the Al-bearing phases. This report gives a summary of the technical progress of the ORNL tasks. A plan for year 2 performance is recommended in the summary.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Collaborative R&D with REEL Solar Inc (REEL) to Understand and Overcome Performance Limitations in CdTe Solar Cells: Cooperative Research and Development (Final Report)

This CRADA will focus on processing, advanced characterization, and testing of photovoltaic materials and devices to understand and improve REEL CdTe solar technology. This will include examining process variations and different buffer, absorber, and contact layers from REEL and NLR to maximize performance. The unique and diverse advanced characterization tools at NLR, such as time-resolved photoluminescence, capacitance-voltage measurements, electron beam scattered diffraction, cathodoluminescence, electron microscopy, TOF-SIMS, and other measurements will be applied to characterize REEL processing to improve understanding and guide experimental directions. Accelerated stability and potential induced degradation tests will be used to analyze metastability, short-and-long term degradation, and improve bankability. A second and major thrust this period will be joint development of Si/CdTe tandem solar cells to overcome industry wide terrestrial solar efficiency limits with the two lowest cost and manufacturable solar materials today. This will include developing novel transparent back contacts that can be incorporated into tandem structures and other novel solar applications, detailed analysis of designs and configurations for CdTe/Si tandem modules, and prototyping REEL CdTe Technology with Si bottom cells in tandem structures.

14 SOLAR ENERGY↗
DOE OSTI2026

Interactions between molecular-scale biogeochemical processes and hyporheic exchange for understanding coupled Fe-S-C cycling in iron-rich riparian wetlands (Final Technical Report)

Riparian wetlands are dynamic interfaces that exert strong control over water quality, contaminant mobility, and greenhouse gas emissions. These systems are characterized by hyporheic exchange between oxic surface water and anoxic groundwater, which generates steep redox gradients and promotes spatially and temporally variable microbial activity. Despite growing recognition of tightly coupled iron (Fe), sulfur (S), and carbon (C) cycling in these environments, the mechanisms governing these interactions—particularly under low-sulfate freshwater conditions—remain poorly constrained. This project developed a mechanistic understanding of how hydrologic variability and microbial processes interact to control Fe–S–C cycling in iron-rich riparian wetlands. Using a multi-scale and multi-method approach integrating field observations, geochemical and spectroscopic analyses, metagenomics, and reactive transport modeling, we demonstrate that “cryptic” sulfur cycling—rapid sulfur transformations involving intermediate-valence species—plays a dominant and previously underrecognized role in freshwater wetlands. These processes persist despite low sulfate concentrations and significantly influence iron reduction, carbon mineralization, and methane dynamics. The results show that cryptic sulfur cycling enhances dissolved Fe 2+ production, regulates methane concentrations, and is strongly controlled by climate-driven hydrologic fluxes. By linking hydroclimate, subsurface flow, and biogeochemical reactions, this work provides a predictive framework for understanding how wetland systems respond to environmental change, with direct implications for water quality and carbon cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Research Development and Partnership Pilot (RDPP): Developing plans and partnerships for incorporating tree reproduction to understand Earth system change

The goals for this Research Development and Partnership Pilot (RDPP) proposal were to i) learn about Department of Energy (DOE) research and use the PI’s expertise on patterns and environmental drivers of tree reproduction as a basis to make connections with individuals and research groups at DOE National Laboratories, and ii) to develop plans and form partnerships that will both enhance the PI’s research. The objectives of the proj ect were for the PI to i) participate in the Department of Energy's Office of Science program in Biological and Environmental Research training and outreach activities, ii) conduct directed fact-finding on Earth and Environmental Systems Sciences Division (EESSD) research projects and new partnerships with individuals and groups at National Laboratories, ii) conduct meetings with potential collaborators at National Laboratories to discuss EESSD-relevant research ideas, and iv) to develop a plan for future research efforts. During the period of the award, the objectives of the proposal were met, as the PI attended Environmental System Sciences meetings, American Geophysical Union meetings, SPRUCE experiment meetings, and visited the Oak Ridge National Lab. These activities led to increased understanding of the research being conducted by DOE scientists and the PI had meetings with potential collaborators to develop future research plans that leverage expertise at DOE and the research interests of the PI to increase understanding of the role of tree reproduction in future carbon allocation and in tree regeneration models in boreal ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Understanding Defect Activation and Kinetics in Next Generation CdTe Absorbers

This project investigated the role of defects and dopants in cadmium telluride (CdTe) solar cells to improve efficiency and long-term stability. Advanced X-ray microscopy and spectroscopy techniques were used to map composition, identify defect structures, and measure charge collection at the nanoscale. These experimental results were combined with computational modeling to understand how dopants such as copper and arsenic interact with other elements during processing. The study found that many dopants become electrically inactive due to the formation of complexes with chlorine and oxygen, limiting carrier concentration and device performance. It also showed that selenium, used to enhance efficiency, can migrate within the material during both manufacturing and operation, altering local structure and potentially affecting stability. The project developed new diagnostic and analysis tools for studying photovoltaic materials and provided a mechanistic understanding of key performance limitations. These insights support the development of more efficient, stable, and cost-effective CdTe solar technologies for large-scale energy deployment.

14 SOLAR ENERGY↗
DOE OSTI2026

Understanding the connections between grain growth and flux expulsion in low RRR niobium SRF cavities

The SRF community has shown that high temperature annealing can improve the flux expulsion of niobium cavities during cooldown. The required temperature will vary between cavities and different batches of material, typically around 800 C and up to 1000 C. However, for niobium with a low residual resistance ratio (RRR), even 1000 C is not enough to improve its poor flux expulsion. The purpose of this study is to observe the grain growth behavior of low RRR niobium coupons subjected to high temperature annealing to identify the mechanism for improving flux expulsion in low RRR cavities. We anneal the low RRR material up to 1200 C to understand the limits of flux expulsion performance. We observe that low RRR material experiences less grain growth than high RRR when annealed at the same temperature. We search for the limitations to grain growth in low RRR material and develop a diagnostic based on grain structure to determine the appropriate recipe for good flux expulsion. The results of this study have the potential to unlock a new understanding on SRF materials and enable the next generation of high Q/high gradient surface treatments.

Howard, K. [Chicago U.]↗
DOE OSTI2025

Data and Code for Understanding Generative AI Content with Embedding Models

This repository contains code for the experiments in the paper "Understanding Generative AI Content with Embedding Models". Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representations based on domain expertise, deep neural networks (DNNs) now offer a radically different approach. DNNs implicitly engineer features by transforming their input data into hidden feature vectors called embeddings. For embedding vectors produced by foundation models -- which are trained to be useful across many contexts -- we demonstrate that simple and well-studied dimensionality-reduction techniques such as Principal Component Analysis uncover inherent heterogeneity in input data concordant with human-understandable explanations. Of the many applications for this framework, we find empirical evidence that there is intrinsic separability between real samples and those generated by artificial intelligence (AI).

Vargas, Max [Pacific Northwest National Laboratory↗
DOE OSTI2024

Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development

The opioid crisis in the United States is a significant public health issue, with a nearly threefold increase in opioid-related fatalities between 1999 and 2014. In response to this crisis, society has made numerous efforts to mitigate its impact. Recent advancements in understanding the structural intricacies of the κ opioid receptor (KOR) have improved our knowledge of how opioids interact with their receptors, triggering downstream signaling pathways that lead to pain relief. This review concentrates on the KOR, offering crucial structural insights into the binding mechanisms of both agonists and antagonists to the receptor. Through comparative analysis of the atomic details of the binding site, distinct interactions specific to agonists and antagonists have been identified. These insights not only enhance our understanding of ligand binding mechanisms but also shed light on potential pathways for developing new opioid analgesics with an improved risk-benefit profile.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Making a Monkey out of Human Immunodeficiency Virus/Simian Immunodeficiency Virus Pathogenesis: Immune Cell Depletion Experiments as a Tool to Understand the Immune Correlates of Protection and Pathogenicity in HIV Infection

Understanding the underlying mechanisms of HIV pathogenesis is critical for designing successful HIV vaccines and cure strategies. However, achieving this goal is complicated by the virus’s direct interactions with immune cells, the induction of persistent reservoirs in the immune system cells, and multiple strategies developed by the virus for immune evasion. Meanwhile, HIV and SIV infections induce a pandysfunction of the immune cell populations, making it difficult to untangle the various concurrent mechanisms of HIV pathogenesis. Over the years, one of the most successful approaches for dissecting the immune correlates of protection in HIV/SIV infection has been the in vivo depletion of various immune cell populations and assessment of the impact of these depletions on the outcome of infection in non-human primate models. Here, we present a detailed analysis of the strategies and results of manipulating SIV pathogenesis through in vivo depletions of key immune cells populations. Although each of these methods has its limitations, they have all contributed to our understanding of key pathogenic pathways in HIV/SIV infection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Understanding EV Charging Pain Points Through Deep Learning Analysis

Current and potential electric vehicle (EV) owners express concerns about the charging infrastructure, mentioning non-functional chargers, prolonged charging times, inconvenient charger locations, long wait times, and high costs as major barriers. Addressing these issues often requires analyzing actual vehicle charging data, which is typically proprietary and inconsistent due to diverse standards and protocols. To understand and improve the EV charging experience, customer reviews are typically used to identify common customer pain points (CPPs). However, there is not a comprehensive method to map customer reviews to a standardized set of CPPs. In collaboration with the National Charging Experience (ChargeX) Consortium, this study bridges these gaps by proposing a Systematic Categorization and Analysis of Large-scale EV-charging Reviews (SCALER) framework. SCALER is an integrated, deep learning framework that segments, actively labels, analyzes, and classifies EV charging customer reviews into six CPP categories. To test its effectiveness, we used SCALER to analyze over 72,000 reviews from customers charging various EV models on different networks across the United States. SCALER achieves a classification accuracy of 92.5%, with an F1 score exceeding 85.7%. By demonstrating real-world applications of SCALER, we enhance the industry’s ability to understand and address CPPs to improve the EV charging experience.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2024

Advancing Understanding of Geothermal Representation in the Power Sector to Accelerate Deployment

Driven mostly by decarbonization goals, geothermal interest in the US power sector has grown considerably, gradually evolving from being considered a niche technology, to being recognized as a viable source of clean, baseload, grid-balancing power, and renewable electric power generation. Furthermore, recent technical advances that could greatly accelerate deployment in the near future, and US federal incentives for low-carbon generation technologies, including geothermal, can enable opportunities for integrating geothermal into utilities resource planning portfolios. However, in general, utilities do not have in-house expertise to evaluate geothermal technologies and its potential role in helping decarbonize the grid as well as help them achieve their individual decarbonization goals. To date, geothermal is rarely included in Capacity Expansion Models (CEM) which utilities use in their planning activities and resource/technologies prioritization. To this end, EPRI and NREL are working together in a DOE-GTO funded research project to improve geothermal understanding (opportunities, value, risks) among the power industry to help accelerate geothermal deployment. In the present paper we describe the approach and preliminary findings of this work, focused on two topics: 1) Expand the degree of understanding on the value, opportunity, and risk of geothermal technologies among utilities and related companies/groups, specifically around geothermal for power generation. 2) Improve representation of geothermal power technologies in capacity expansion models (CEM).

capacity expansion models↗
DOE OSTI2025

Across the Scales of the Nucleus: Understanding Short Range Correlations from Medium Modification to Probe Independence

The atomic nucleus presents an intricate system due to the non-linear forces described by Quantum Chromodynamics (QCD) that govern its structure. The range of scales involved is remarkable; the most massive nuclei weigh approximately five orders of magnitude more than the quarks that compose them. The nucleus can be analyzed at various levels, from quarks to hadrons to the nucleus as a whole. Short-Range Correlations (SRCs) within the nucleus play a significant role that spans these diverse scales. At the most fundamental level, SRCs influence the interaction between nucleons. The nucleon-nucleon (NN) interaction, arising from QCD, is crucial in determining nuclear properties. SRCs serve as valuable probes for measuring this NN interaction, as the nucleons within SRCs become effectively decoupled from the rest of the nucleus. Multiple experimental techniques, including electron scattering, have been employed to investigate the NN interaction through SRCs. However, our first project demonstrates that inclusive measurements alone are inadequate to constrain this interaction fully. Moving to the scale of the nucleus, SRCs contribute to the high-momentum tail of the nuclear spectral function. While the low-momentum region is characterized by nucleons exhibiting bulk properties, nucleons begin to pair into SRCs at higher momenta. Our research aims to bridge the understanding between the mean-field portion of the nucleus and its high-momentum SRC components. Additionally, SRCs affect the quark structure of protons, as evidenced by the EMC effect, which indicates that quarks behave differently when protons are embedded within a nucleus—an effect referred to as medium modification. This thesis explores the correlation between SRCs and medium modification across various experimental setups. Finally, we seek to establish an interpretation of the nuclear ground state. Accomplishing this requires demonstrating that our SRC observables are independent of the probe’s scale and scheme. The concluding project of this thesis illustrates how we utilize triple coincidence quasi-elastic scattering across a range of (Q2) values to develop a model-dependent framework for understanding SRC distributions within the nucleus’s ground-state wavefunction.

Denniston, A. W. Denniston [University of Tel Aviv↗
DOE OSTI2025

Understanding the Role of Hydroxyl Functionalization in Linear Poly(Ethylenimine) for Oxidation‐Resistant Direct Air Capture of CO 2

Aminopolymer-based adsorbents are a prominent class of materials being used for direct air capture of CO 2 at the industrial scale. However, improving their working lifetime, specifically by increasing their resilience to oxidative degradation, remains an ongoing challenge. Toward this end, functionalization of aminopolymers with non-amine functionalities such as hydroxyls has emerged in recent years as a promising strategy toward improving adsorbent lifetime. Although there is a growing body of work demonstrating the effectiveness of this approach and investigating the origin of this improved stability, studies to date have primarily focused on branched aminopolymer systems such as branched poly(ethylenimine). In this work, hydroxyl-functionalized linear poly(ethylenimine) is used to continue to probe the underlying protective mechanism of this strategy. A combination of thermogravimetric analysis, NMR relaxometry, differential scanning calorimetry, and computational simulations is used to better understand the relationship between the extent of chemical functionalization, physical properties, and adsorbent performance.

amine-based adsorbent↗
DOE OSTI2026

Understanding Recombination Pathways of CdSeTe/CdTe Devices Using Temperature Dependence of Fill Factor

Understanding the temperature dependence of photovoltaic parameters is critical for identifying performance limiting mechanisms in CdTe-based solar cells. In this study, we investigate the temperature dependence of fill factor (FF(T)) in CdSeTe/CdTe solar cells using a combination of experimental current density-voltage (JV) measurements and numerical simulations. The measured FF(T) exhibits a nonlinear response with temperature, peaking at moderate temperature values (10–30°C). Through numerical modeling, we reveal that this peaking behavior in FF(T) arises when the dominant recombination mechanism transitions from bulk to interface with increasing temperature. This transition is primarily driven by low hole mobility (0.6–1.2 cm2/V·s), while electron mobility, interface recombination velocities, and carrier lifetime also contribute to the effect. Our findings demonstrate that FF(T) analysis is a promising diagnostic tool for distinguishing recombination pathways and guiding the optimization of thin-film CdTe solar cells under real-world conditions.

CdTe photovoltaics↗