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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 235 records · Page 13

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DOE OSTI2026

Scaling open-weight large language models for hydropower regulatory information extraction: A systematic analysis

Information extraction from regulatory and technical documents using large language models (LLMs) involves practical trade-offs between extraction quality and computational cost. We evaluate eight open-weight LLMs spanning 0.6B–70B parameters on hydropower licensing documents and report deployment-oriented evidence under a unified extraction schema and evaluation protocol. Across the model set, we observe clear scale-dependent trends in both baseline extraction quality and the effectiveness of reflective reasoning (self-checking) under our fixed-prompt, no-augmentation setting. Mid-scale models often provide a favorable balance of accuracy and efficiency, whereas the smallest models show limited or inconsistent gains from the reasoning variants tested. Larger models achieve the highest overall F1 scores but incur substantially greater compute and infrastructure requirements. We further find that reliability failure modes can distort conventional metrics in this domain: in particular, high recall can coincide with systematic extraction errors when models fabricate values for fields that are absent from the source text, underscoring the importance of conservative null handling and evidence-grounded evaluation. Overall, our study provides a reproducible resource–performance comparison for open-weight LLM-based extraction in hydropower regulatory documentation and offers practical guidance for model selection under different deployment constraints.

Evaluation protocol↗

DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗

DOE OSTI2022

A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties

Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool.

09 BIOMASS FUELS↗

DOE OSTI2023

Methane foam performance in oil-wet unconsolidated porous media: A systematic experimental investigation at reservoir conditions

Methane foam performance was systematically evaluated using high-pressure and high-temperature experiments performed on oil-wet proppant packs. The sensitivities of foam performance to various key factors, including surfactant concentration, foam quality, total injection rate, salinity, operating pressure, and initial oil saturation were examined. The results showed that even though the foamability and foam strength in oil-wet proppant packs were adversely affected by wettability and presence of oil, the steady-state pressure drops across the proppant packs were insensitive to the quantity of oil initially present. The presence of oil above the critical oil saturation impeded the onset of foam generation. This suggested that the foamability of surfactants depends on the synergetic effects of surfactant’s ionic nature and foam parameters, such as foam quality and total injection rate, on their ability to reduce the oil saturation to a threshold value below which favorable bubble generation sites could be created. It was observed that, for low injection rate, anionic surfactant was ineffective at low salinity conditions, while amphoteric agent performed well at all salinities. Additionally, an increase in salinity resulted in enhanced pressure drop across the proppant packs for both anionic and amphoteric surfactants. Further, the results also established that too high operating pressures are somewhat detrimental to the foam strength and stability, irrespective of the ionic nature of foaming agents and the wettability of the porous medium. For the anionic surfactant, the increase in the total flow rate was found to mitigate the adverse effects of low salt concentration and operating pressure. Results from quality-sensitivity foam tests identified two regions for the foam behavior, named low-quality and high-quality regimes, separated by the transition foam quality. Finally, we discuss the implications of the results for the design and effective implementation of enhanced oil recovery (EOR) schemes that deploy hydrocarbon-based foams.

02 PETROLEUM↗

DOE OSTI2021

Executive Summary of the American Radium Society Appropriate Use Criteria for Operable Esophageal and Gastroesophageal Junction Adenocarcinoma: Systematic Review and Guidelines

Limited guidance exists regarding the relative effectiveness of treatment options for nonmetastatic, operable patients with adenocarcinoma of the esophagus or gastroesophageal junction (GEJ). In this systematic review, the American Radium Society (ARS) gastrointestinal expert panel convened to develop Appropriate Use Criteria (AUC) evaluating how neoadjuvant and/or adjuvant treatment regimens compared with each other, surgery alone, or definitive chemoradiation in terms of response to therapy, quality of life, and oncologic outcomes.