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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Redesigned Hybrid Nylons with Optical Clarity and Chemical Recyclability

Aliphatic polyamides, or nylons, are typically highly crystalline and thermally robust polymers used in high-performance applications. Nylon 6, a high-ceiling-temperature (HCT) polyamide from e-caprolactam, lacks expedient chemical recyclability, while low-ceiling temperature (LCT) nylon 4 from pyrrolidone exhibits complete chemical recyclability, but it is thermally unstable and not melt-processable. Here, we introduce a hybrid nylon, nylon 4/6, based on a bicyclic lactam composed of both HCT ..epsilon..-caprolactam and LCT pyrrolidone motifs in a hybridized offspring structure. Hybrid nylon 4/6 overcomes trade-offs in (de)polymerizability and performance properties of the parent nylons, exhibiting both excellent polymerization and facile depolymerization characteristics. This stereoregular polyamide forms nanocrystalline domains, allowing optical clarity and high thermal stability, however, without displaying a melting transition before decomposition. Of a series of statistical copolymers comprising nylon 4/6 and nylon 4, a 50/50 copolymer achieves the greatest synergy in both reactivity and polymer properties of each homopolymer, offering an amorphous nylon with favorable properties, including optical clarity, a high glass transition temperature, melt processability, and full chemical recyclability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Installing Controlled Stereo-Defects Yields Semicrystalline and Biodegradable Poly(3-Hydroxybutyrate) with High Toughness and Optical Clarity

Stereo-defects present in stereo-regular polymers often diminish thermal and mechanical properties, and hence suppressing or eliminating them is a major aspirational goal for achieving polymers with optimal or enhanced properties. Here, we accomplish the opposite by introducing controlled stereo-defects to semicrystalline biodegradable poly(3-hydroxybutyrate) (P3HB), which offers an attractive biodegradable alternative to semicrystalline isotactic polypropylene but is brittle and opaque. We enhance the specific properties and mechanical performance of P3HB by drastically toughening it and also rendering it with the desired optical clarity while maintaining its biodegradability and crystallinity. This toughening strategy of stereo-microstructural engineering without changing the chemical compositions also departs from the conventional approach of toughening P3HB through copolymerization that increases chemical complexity, suppresses crystal-lization in the resulting copolymers, and is thus undesirable in the context of polymer recycling and performance. More specifically, syndio-rich P3HB (sr-P3HB), readily synthesized from the eight-membered meso-dimethyl diolide, has a unique set of stereo-microstructures comprising enriched syndiotactic [rr] and no isotactic [mm] triads but abundant stereo-defects randomly distributed along the chain. This sr-P3HB material is characterized by high toughness (UT = 96 MJ/m 3 ) as a result of its high elongation at break (>400%) and tensile strength (34 MPa), crystallinity (T m = 114 degrees C), optical clarity (due to its submicron spherulites), and good barrier properties, while it still biodegrades in freshwater and soil.

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

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗
DOE OSTI2025

Clarity through the neutrino fog: constraining new forces in dark matter detectors

The PandaX-4T and XENONnT experiments present indications of Coherent Elastic Neutrino Nucleus Scattering (CEνNS) from 8 B solar neutrinos at 2.6σ and 2.7σ, respectively. This constitutes the first observation of the neutrino “floor” or “fog”, an irreducible background that future dark matter searches in terrestrial detectors will have to contend with. Here, we first discuss the contributions from neutrino–electron scattering and from the Migdal effect in the region of interest of these experiments, and we argue that they are non-negligible. Second, we make use of the recent PandaX-4T and XENONnT data to derive novel constraints on light scalar and vector mediators coupling to neutrinos and quarks. We demonstrate that these experiments already provide world-leading laboratory constraints on new light mediators in some regions of parameter space.

neutrino interactions↗
DOE OSTI2025

Cultivating clarity: understanding the impact of land cost assumptions on biofuel viability

The transition to sustainable energy has increased interest in biofuel production to reduce greenhouse gas emissions, decrease reliance on imported oil, and ensure energy resilience. Here, this study examines the often-overlooked impact of land cost assumptions on the economic viability of biofuel production. Using a discounted cash flow techno-economic framework, we evaluated three land cost scenarios—no land costs, land rental costs, and land purchase costs—across six bioenergy feedstocks (corn, soybeans, switchgrass, miscanthus, poplar, and microalgae) and three biofuel products (corn ethanol, soybean biodiesel, and sustainable aviation fuel) at the county level for the contiguous United States. The analysis reveals substantial variation in minimum fuel selling prices due to these scenarios. High-yield crops like algae showed low sensitivity to land costs, while low-yield crops such as soybeans were highly sensitive. Geographical differences were significant, with minimum fuel selling price increases most pronounced in high-value land regions like the Corn Belt. Case studies further illustrate the influence of local productivity and land costs on economic outcomes across the United States. These findings emphasize the importance of maintaining consistent land cost assumptions in biofuel economic assessments. By quantifying the interplay between land value, crop productivity, and economic feasibility, this study provides essential insights for policymakers and stakeholders to advance sustainable energy solutions.

09 BIOMASS FUELS↗
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
DOE OSTI2023

Using small angle x-ray scattering to examine the aggregation mechanism in silica nanoparticle-based ambigels for improved optical clarity

Silica-based aerogels are a promising low-cost solution for improving the insulation efficiency of single-pane windows and reducing the energy consumption required for space heating and cooling. Two key material properties required are high porosity and small pore sizes, which lead to low thermal conductivity and high optical transparency, respectively. However, porosity and pore size are generally directly linked, where high porosity materials also have large pore sizes. This is unfavorable as large pores scatter light, resulting in reduced transmittance in the visible regime. In this work, we utilized preformed silica colloids to explore methods for reducing pore size while maintaining high porosity. Further, the use of preformed colloids allows us to isolate the effect of solution conditions on porous gel network formation by eliminating simultaneous nanoparticle growth and aggregation found when using typical sol–gel molecular-based silica precursors. Specifically, we used in situ synchrotron-based small-angle x-ray scattering during gel formation to better understand how pH, concentration, and colloid size affect particle aggregation and pore structure. Ex situ characterization of dried gels demonstrates that peak pore widths can be reduced from 15 to 13 nm, accompanied by a narrowing of the overall pore size distribution, while maintaining porosities of 70%–80%. Optical transparency is found to increase with decreasing pore sizes while low thermal conductivities ranging from 95 +/− 13 mW/m K are maintained. Mechanical performance was found to depend primarily on effective density and did not show a significant dependence on solution conditions. Overall, our results provide insights into methods to preserve high porosity in nanoparticle-based aerogels while improving optical transparency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automatic Receptacle Controls: Evaluating the clarity of receptacle markings

Plug loads and the electric loads of devices plugged into receptacles in commercial buildings play a significant and growing role in commercial buildings. Because plug loads are portable and are often placed in the building by the occupants, they are a challenging load to manage. This report summarizes an analysis of commercially available automatic receptacle controls (ARCs). As a part of this study, generic versions of ARCs were presented to a general user audience via an online survey, wherein survey respondents indicated which markings more clearly indicated that the receptacle was controlled. A total of 210 responses were collected and included in the analysis presented in this report. The results showed that receptacles with high-contrast markings were consistently clearer and more evident to the users than low-contrast markings. The survey responses also revealed that receptacles with a border drawn around each controlled receptacle were clearer and distinct to the users compared to other markings. These results led to the conclusion that for energy saving technology to be effective, consistent and easy to understand markings are necessary.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments [Slides]

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. The ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

14 SOLAR ENERGY↗
DOE OSTI2024

From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.

artificial intelligence↗
DOE OSTI2020

Hedonic Price Estimates of Lake Water Quality: Valued Attribute, Instrumental Variables, and Ecological-Economic Benefits

We estimate the effect of lake water quality on residential housing prices using cross-sectional data on water clarity from 113 lakes across the United States. An instrumental-variables approach is developed to address potential endogeneity bias in the water-clarity variable. Three lake-based physical variables serve as instrumental variables: total nitrogen concentration in lake water, total phosphorus concentration in lake water, and water temperature near the lake surface. The econometric results include three methodological findings: the instruments are valid and strong; the ordinary-least-squares estimate of the coefficient on the water-clarity variable is unbiased; and water clarity is the attribute of water quality valued in the housing market. The estimated water-clarity effect shows that a one-tenth of a meter change in water clarity leads to a one-percent change in housing price, or an elasticity of 0.20 at mean clarity. Coupling this effect with estimates of an ecological production function, a lake-specific benefit index is developed that shows the effect on housing values of bringing lakes into compliance with the US Environmental Protection Agency's regional recommendations for phosphorous concentration in lake waters.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Lithium-ion battery physics and statistics-based state of health model

A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.

Crawford, Aladsair J.↗
DOE OSTI2022

Use of Physics to Improve Solar Forecast: Part II, Machine Learning and Model Interpretability

Machine learning (ML) models have been applied to forecast solar energy; however, they often lack clarity of interpretability and underlying physics. This work addresses such challenges by developing a hierarchy of ML models that gradually introduce predictors to improve the forecast accuracy based on a physics-based framework. Three ML models (ARIMA, LSTM, and XGBoost) are examined and compared with four physics-informed persistence models reported in Part I and the simple persistence model to assess the improvement of different models. The 7-year measurements at the U.S. Department of Energy's Atmospheric Radiation Measurement's Southern Great Plains Central Facility site are used for forecasts and evaluations. The results reveal that the step-by-step introduction of predictors leads to different improvements for models at different hierarchical levels. Comparison of the ML models with persistence models shows that LSTM and XGBoost outperform all the persistence models, with LSTM having the overall best performance; however, ARIMA underperforms the four physics-informed persistence models. This study demonstrates the importance and utility of incorporating physics into ML models in improving forecast accuracy by introducing a hierarchy of physics-based predictors, distinguishing predictor contributions, and enhancing the ML interpretability. The combined use of Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) significantly improves the forecast accuracy compared to using individual irradiances alone because the pair contains more information on cloud-radiation interactions.

interpretability↗
DOE OSTI2026

An AI-driven framework for evaluating local and state authorities’ permitting processes

The demand for new energy infrastructure is increasing across the United States, but heterogenous permitting processes and embedded requirements across different local jurisdictions can cause project delays, increase “soft costs,” and hinder developer expansion. This study analyzes the variability in local permitting requirements across the U.S. and develops a quantitative approach to describe their clarity and effectiveness in enabling infrastructure project development. By using an Energy Language Model (ELM), a large language model (LLM) for energy technologies, we systematically gathered permitting information from nearly 300 state-, county-, and city-level documents, creating a structured dataset of requirements and procedures on an unprecedented scale and speed. Our analysis revealed that local (city and county) permitting requirement documents are underrepresented compared to state-level guidance documents, which can impede timely and cost-effective installation of new electric infrastructure. Our validation process showed that the final database has an accuracy of approximately 95%. We, further, created a new quantitative method to score permitting requirements for clarity and efficiency, with electric vehicle supply equipment as an initial use case. The average local permitting document scored a 1.8 out of 5, which we interpret as meaning that half of the requirements developers face when installing electric infrastructure are ambiguous, increasing both cost and time. We also created a “Generalized Permit Process”, highlighting common procedural steps and identifying specific opportunities for municipalities to improve their documentation. This research establishes a systematic and scalable framework for evaluating the complexities of local infrastructure permitting processes by combining LLM-powered data collection and quantitative scoring. The framework enables policymakers and developers to identify and mitigate procedural bottlenecks, with the expectation that these improvements can accelerate application review and approval, reduce project costs, and expedite connection to utility distribution grids. As a foundational approach for streamlining local project development processes, this study’s methods are intended to be extended to a wide range of energy applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Improved Manufacturability and Throughput of Ultra- Transparent, Super-Insulating Aerogels

AeroShield Materials produces a novel silica aerogel material with exemplary thermal performance and unprecedented optical clarity, offering the potential for super-insulating fenestration and insulated glass to reduce thermal losses in the built environment by billions of dollars every year. One of the most significant challenges facing AeroShield today is the total amount of time that is required to produce large monolithic aerogel samples. This overall process can require as much as 144 hours total, which can significantly hinder scale-up to an economically viable product. The purpose of this Phase 1 research was to continue development of our novel aerogel manufacturing process to reduce material processing time by up to 10x, greatly improving product throughput and reducing cost. AeroShield’s manufacturing process can be divided into 4 major stages, each with their own distinct set of parameters and time requirements. Under this Phase 1 award, AeroShield was able to identify and optimize a number of these parameters, including molding materials, solvent rinse conditions, critical point drying time, and annealing conditions. AeroShield also performed thorough analyses on how these optimized parameters affected important final characteristics of the gels, including optical clarity, thermal conductivity, and dimensional stability. This campaign culminated in the production of laboratory scale aerogel samples using significantly lower process times of both 36 and 18 total hours, which represent Phase 1 Target and Stretch goals. In order to achieve widespread market adoption, monolithic sheets of the aerogel material must be made to industry-standard sizes (8’ x 12’) at a cost that provides 5-7 year or less breakeven energy savings for consumers (<$2 sq/ft). The work performed under this Phase 1 award shows that time and materials required to make aerogel samples, which make up a significant portion of their overall cost, can be greatly reduced without sacrificing quality. AeroShield plans to use these optimized processes to produce larger, product-relevant sized aerogels in order to achieve target final material costs.

Wilke, Kyle↗
DOE OSTI2025

On the Error Covariance Correction Step of an ESKF Attitude Update

The attitude states of an error-state Kalman filter (ESKF) behave differently than most other states in the system due to their multiplicative (rather than additive) nature. One way in which they differ is an error covariance correction step after an ESKF error reset, which is not required for, for example, position and velocity states. This covariance correction step is not intuitive, and it has only been recently derived for coordinate transform matrices. The author of this memo, however, found the provided derivation in [1] confusing due to a lack of clarity surrounding the invoked reference frames, and clarity is required as there are at least 4 different ways to parameterize small-angle attitude errors in an ESKF. Furthermore, while reproducing the work, the author of this memo found a more straightforward derivation that provides additional insight into the correction step. This memo offers a derivation of the attitude error covariance correction step of an ESKF, which pays specific attention to the coordinate reference frames.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

From Obsolete to Optimal (ATR Demineralized Water System)

My project was migrating a Human-Machine Interface (HMI) and Programmable Logic Controller (PLC) program from legacy software to a modern platform, enhancing operational efficiency and eliminating unsupported, obsolete equipment. Initially, I used a migration tool to transfer the programs to the new software, then manually updated variables to align with the new PLC. I optimized the PLC code by leveraging new scaling features in the I/O cards, removing over 45 obsolete rungs, and enhancing clarity with functional tag aliases. Next, I modernized the HMI's visuals with an updated color scheme and 3D buttons. I developed multiple interface versions iteratively refining them based on operator feedback. I also designed and created a dedicated "Rounds" screen incorporating insights from a previous intern’s project to streamline daily rounds. To improve clarity, consistency, and efficiency I redesigned the entire interface. Adding visual indicators to enhance operational awareness: red is used for values outside normal specifications, green for normal operating modes, yellow for manual mode, and grey for offline units. Additionally, I added a dynamic visual representation of tank levels, creating PLC logic to show the level in inches as well as inches and feet. I then incorporated a navigation sidebar to facilitate efficient screen transitions. Risk of human error was mitigated by defining narrow input ranges for tank level shutoff and password protecting setpoint changes. I also fixed issues with datatypes that caused inaccuracies in the original code. Overall, this comprehensive upgrade significantly enhanced the HMI's functionality, user experience, and operational reliability.

47 - OTHER INSTRUMENTATION↗