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NASA NTRSDate not supplied

AI Curation Methods for NASA Scientific Data

The NASA Open Science Data Repository (OSDR) serves as a central hub for sharing and accessing NASA's vast collection of scientific data, supporting researchers across diverse fields. To enhance the efficiency, accuracy, and accessibility of this data, we are leveraging advanced artificial intelligence (AI) techniques as part of the AI for Curation project. By integrating large language models (LLMs) into our data curation workflow, we aim to streamline the entire process—from data submission to user interaction. This initiative focuses on improving key areas, including data ingestion, curation, and user engagement with curated datasets, impacting multiple domains and a wide user base. First, we are developing tools that can automatically parse data in various formats, using LLMs to convert unstructured data into structured, standardized formats. This reduces the manual effort required for curation, allowing curators to focus on more critical scientific analyses. Additionally, AI and machine learning (ML) models are being implemented to automate data validation and verification, ensuring the highest standards of data quality and reliability. Finally, we are creating a conversational AI agent to interact with the curated scientific studies in OSDR, helping users easily navigate the repository and access relevant data. By enhancing data discoverability and accessibility, these advancements will foster new research opportunities and promote the principles of open science.

Walter Alvarado↗

NASA NTRS1998

Rapid Prototyping of Composite Structures

This progress report for the project Rapid Production of Composite Structures covers the period from July 14, 1997 to June 30, 1998. It will present a short overview of the project, followed by the results to date and plans for the future. The goal of this research is to provide a minimum 100x reduction in the time required to produce arbitrary, laminated products without the need for a separate mold or an autoclave. It will accomplish this by developing the science underlying the rapid production of composite structures, specifically those of carbon fiber-epoxy materials. This scientific understanding will be reduced to practice in a demonstration device that will produce a part on the order of 12" by 12" by 6". Work in the past year has focussed on developing an understanding of the materials issues and of the machine design issues. Our initial goal was to use UV cureable resins to accomplish full cure on the machine. Therefore, we have centered our materials work around whether or not UV cureable resins will work. Currently, the answer seems to be that they will not work, because UV light cannot penetrate the carbon fibers, and because no "shadow" curing seems to occur. As a result, non-UV cureable resins are being investigated. This has resulted in a change in the machine design focus. We are now looking into a "dip and place" machine design, whereby a prepreg layer would have one side coated with a curing agent, and then would be placed onto the previous layer. This would lead to cure at the interface, but not to the top of the layer. The formulation of the resins to accomplish this task at room or slightly elevated temperatures is being investigated, as is the machine design needed to apply the curing agent and then cure or partially cure the part. A final, out-of-autoclave, post-cure may be needed with this strategy, as final cure on the machine may not be possible, as it was for the initial UV cure strategy. The remainder of this report details the progress in the materials and machine design areas. Materials Development The material system must be designed to fulfill the following requirements: to reduce the time and labor requirements of typical cure cycles; to reduce the thermal stresses developed during conventional heat curing; and to develop a structure that the build sequence requires. In order to accomplish these goals, there have been parallel tracks of investigation. One area has tested photopolymerizable (ultraviolet (UV) curable) materials and combinations of these materials with standard heat curing resins. The second area has investigated resins that cure rapidly at room or low heat temperatures. The main goal of these investigations has been to identify a system that will rapidly set or cure at room temperature during a tape lay-up process and hold its structure during a post-cure cycle.

Colton, Jonathan S.↗

DOE OSTI2019

A novel role mediated by adenoviral E1A in suppressing cancer through modulating decorin

Oncolytic adenovirus is an emerging alternative to current therapeutics. The adenoviral E1A, the first protein expressed upon oncolytic adenoviral infection, has been identified as an antitumor agent, but the mechanisms of its tumor inhibition ability are unclear enough. Decorin is ubiquitous in the extracellular matrix (ECM), which regulates multiple functions through interaction with ECM. Here, we intended to explore the effects of adenoviral E1A on the tumor extracellular matrix during gene therapy. We demonstrated that reduced decorin expression was found in patients with lung cancer. The adenoviral E1A or a mutant adenoviral E1A with Rb-binding ability absent (E1A 30–60aa, 120–127aa deletion) could increase the expression of decorin and down-regulate VEGF, two members of tumor ECM, involved in both vasculogenesis and angiogenesis. E1A/mE1A-mediated suppressing the migration and invasion ability of tumor cells was depended on decorin. E1A interacted with decorin directly and induced the proteasomal degradation of VEGF. In addition, E1A or mE1A can inhibit tumor growth in a subcutaneous lung cancer xenograft model. It suggested that decorin might be a crucial mediator among ECM components for adenoviral E1A-mediated antitumor activities. These studies on adenovirus E1A provide a new mechanism for the emerging therapies of tumor gene therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2021

An investigation of hard-disk drive circularity accounting for socio-technical dynamics and data uncertainty

The installed data storage capacity in the U.S. will reach 2.2 Zettabytes by 2025, generating about 50 million units of end-of-life (EOL) hard-disk drives (HDDs) per year. Due to data security concerns, most EOL HDDs are currently shredded (even when still functioning), representing an economic loss. Moreover, raw material extraction linked to the increased demand for storage causes environmental impacts. Besides mitigating the threat posed by sudden restrictions of raw materials, the circular economy (CE) offers to maximize value retention in the economy and reduce the environmental impacts of human activities. Common CE strategies are reusing and recycling products. However, the reuse of hard disk drives is currently burdened by the lack of trust HDD end-users have toward other non-physical means of data removal than shredding. Here, an agent-based modeling (ABM) approach is proposed to explore how techno-economic and social factors affect end-users' decisions to adopt EOL management practices other than shredding. The proposed method also accounts for data uncertainty by applying a semi-quantitative approach. Results demonstrate how increased green procurement and more robust standards could spur end-users' trust toward data-wiping technologies. Even when accounting for uncertainty, HDDs' reuse brings better environmental and economic benefits than HDD shredding followed by material recovery. The semi-quantitative approach proposed in this study could be more universally applied in future ABM, especially given the often-stochastic nature of such models. The developed ABM is also the first to represent several HDD industry stakeholders and demonstrate how the HDD shredding lock-in situation could be resolved.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Solvation directed morphological control in metal oxide nanostructures

The development of structural hierarchy on various length scales during the crystallization process is ubiquitous in biological systems and minerals and is common in synthetic nanomaterials. The driving forces for the formation of complex architectures range from local interfacial interactions, that modify interfacial speciation, local supersaturation, and nucleation barriers, to macroscopic interparticle forces. Although it is enticing to interpret the formation of hierarchical architectures as the assembly of independently nucleated building blocks, crystallization pathways often follow monomer-by-monomer addition with structural complexity arising from interfacial chemical coupling and strongly correlated fluctuation dynamics in the electric double layers. Here, we show that the development of structural hierarchy through heterogeneous nucleation is driven by dipolar and solvation forces. Specifically, coupled simulations and experimental studies revealed that dipole build-up along the slow growth direction can trigger twinning and the development of branched architectures. Enthalpic solvation interactions were shown to either enhance or reduce the dipole moment of the nanoparticles and, thereby, control crystal morphology and architecture. The systematic studies of chemical coupling between different solvents and undercoordinated surface atoms of the growing nanocrystals revealed the mechanism of dimensionality control and the development of structural hierarchy without ligands or structure-directing agents.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Energy Efficiency: What Has Research Delivered in the Last 40 Years?

This article presents a critical assessment of 40 years of research that may be brought under the umbrella of energy efficiency, spanning different aggregations and domains—from individual producing and consuming agents to economy-wide effects to the role of innovation to the influence of policy. After 40 years of research, energy efficiency initiatives are generally perceived as highly effective. Innovation has contributed to lowering energy technology costs and increasing energy productivity. Energy efficiency programs in many cases have reduced energy use per unit of economic output and have been associated with net improvements in welfare, emission reductions, or both. Rebound effects at the macro level still warrant careful policy attention, as they may be nontrivial. Complexity of energy efficiency dynamics calls for further methodological and empirical advances, multidisciplinary approaches, and granular data at the service level for research in this field to be of greatest societal benefit.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Expanding Medicaid to Reduce Human Immunodeficiency Virus Transmission in Houston, Texas

Context: Medicaid expansion has been nationally shown to improve engagement in the human immunodeficiency virus (HIV) treatment and prevention continua, which are vital steps to stopping the HIV epidemic. New HIV infections in the United States are disproportionately concentrated among young Black men who have sex with men (YBMSM). Houston, TX, is the most populous city in the Southern United States with a racially/ethnically diverse population that is located in 1 of 11 US states that have not yet expanded Medicaid coverage as of 2021. Methods: An agent-based model that incorporated the sexual networks of YBMSM was used to simulate improved antiretroviral treatment and pre-exposure prophylaxis (PrEP) engagement through Medicaid expansion in Houston, TX. Analyses considered the HIV incidence (number of new infections and as a rate metric) among YBMSM over the next 10 years under Medicaid expansion as the primary outcome. Additional scenarios, involving viral suppression and PrEP uptake above the projected levels achieved under Medicaid expansion, were also simulated. Results: The baseline model projected an HIV incidence rate of 4.96 per 100 person years (py) and about 368 new annual HIV infections in the 10th year. Improved HIV treatment and prevention continua engagement under Medicaid expansion resulted in a 14.9% decline in the number of annual new HIV infections in the 10th year. Increasing viral suppression by an additional 15% and PrEP uptake by 30% resulted in a 44.0% decline in new HIV infections in the 10th year, and a 27.1% decline in cumulative infections across the 10 years of the simulated intervention. Findings: Simulation results indicate that Medicaid expansion has the potential to reduce HIV incidence among YBMSM in Houston. Achieving HIV elimination objectives, however, might require additional effective measures to increase antiretroviral treatment and PrEP uptake beyond the projected improvements under expanded Medicaid.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Metal-Mediated Directional Capping of Rod-Packing Metal–Organic Frameworks

We report two new rod-packing metal–organic frameworks (RPMOF) are constructed by regulating the in situ formation of the capping agent. In CPM-s7, carboxylate linkers extend 1D manganese-oxide chains in four additional directions, forming 3D RPMOF. The substitution of Mn 2+ with a stronger Lewis acidic Co 2+ , leads to an acceleration of the hydrolysis-prone sulfonate linker, resulting in presence of sulfate ions to reduce two out of the four carboxylate-extending directions, and thus forming a new 2D rod-packing CPM-s8. Density functional theory calculations and magnetization measurements reveal ferrimagnetic ordering of CPM-s8, signifying the potential of exploring 2D RPMOF for effective low-dimensional magnetic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗

DOE OSTI2024

Can Wholesale Electricity Markets Achieve Resource Adequacy and High Clean Energy Generation Targets in the Presence of Self-Interested Actors?

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗

DOE OSTI2020

Reduction and potential remediation of U(VI) by dithionite at an in-situ recovery mine: Insights gained by δ 238 U

In-situ recovery is a common method of mining U ore. This technique involves injecting an oxidant to oxidize U deposited as insoluble U(IV) to soluble U(VI) and a complexing agent to form mobile U(VI) compounds, which are extracted from mining waters. Unfortunately, U concentrations often remain elevated in groundwater following this mining process. One promising technique to decrease U concentrations to acceptable levels is in-situ remediation where a reductant is injected into the mined area to reduce U(VI) back to insoluble U(IV). Here we assess the efficacy of the chemical reductant dithionite to reduce U(VI) concentrations. While dithionite can reduce environmental Cr(VI), dithionite has previously never been tested for reducing U(VI) in the environment. We determine how effectively dithionite remediates U(VI) contamination using aqueous U concentration and δ 238 U data. U concentrations and δ 238 U decrease during the dithionite experiment, demonstrating that dithionite induces U(VI) reduction. As dithionite is removed by pumping, U concentrations and δ 238 U increase towards background levels, but U(VI) reduction continues for an extended period. In conclusion, measurements demonstrate that re-oxidation and remobilization does not appear to occur due to the reducing condition of groundwater.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Design and Fabrication of Polyisocyanurate Foams toward Significantly Enhanced Thermal Resistivity

Developing high-performance thermal insulation is vital for addressing the ongoing global demand for reduced energy costs. Polyisocyanurate (PIR) foams are commonly used in residential and commercial buildings and in various industrial applications owing to their relatively high thermal insulation properties and fire resistivity. Here, this study aims to further improve the thermal resistivity of PIR foams by (1) incorporating low thermal conductivity blowing agents; (2) tailoring the anisotropy of their cells; (3) tuning polymeric isocyanate quantities; and (4) incorporating a facer barrier, while using steps that easily integrate into current manufacturing processes for PIR foams. The resulting PIR foams exhibit a significant enhancement in thermal resistivity, achieving initial values as high as 8.3 h·ft 2 ·°F/Btu/in., commonly abbreviated as R-8.3/in., which is a 20% improvement compared with that of commercially used PIR foams that achieve approximately R-7/in. The detailed analysis of thermal conductivity measurements, mechanical testing, and morphological characterization elucidates the structure–property relationships. The developed high-performance PIR foams provide a critical pillar for next-generation high-performance insulation, offering promising thermal insulation for buildings and many other applications that have a significant effect on global energy costs.

Anisotropic pores↗

DOE OSTI2022

Efficient monolithic all-perovskite tandem solar modules with small cell-to-module derate

All-perovskite tandem solar modules are promising to reduce the cost of photovoltaic systems with their high efficiency and solution fabrication, but their sensitivity to air still imposes a great challenge. Here a hot gas-assisted blading method is developed to accelerate the perovskite solidification, forming compact and thick narrow bandgap (NBG) perovskite films. Adding a reduction agent into NBG films followed by a short period of air exposure and a post-fabrication storage surprisingly increases carrier recombination lifetime and enables laser scribing in ambient conditions without obvious loss of device performance. This combination suppresses tin and iodide oxidation and forms a thin SnO 2 layer on the NBG film surface. Monolithic all-perovskite tandem solar modules showed a champion efficiency of 21.6% with a 14.3 cm 2 aperture area, corresponding to an active area efficiency of 23.0%. Lastly, the very small cell-to-module derate of 6.5% demonstrates the advantage of a tandem monolithic structure for solar modules.

14 SOLAR ENERGY↗

DOE OSTI2024

Wholesale Electricity Markets and Resource Adequacy with High Clean Energy Generation Targets

capacity expansion↗

NASA NTRS1973

Effect of noble gases on an atmospheric greenhouse /Titan/.

Several models for the atmosphere of Titan have been investigated, taking into account various combinations of neon and argon. The investigation shows that the addition of large amounts of Ne and/or Ar will substantially reduce the hydrogen abundance required for a given greenhouse effect. The fact that a large amount of neon should be present if the atmosphere is a relic of the solar nebula is an especially attractive feature of the models, because it is hard to justify appropriate abundances of other enhancing agents.

Cess, R.↗

NASA NTRS2009

Treating Fibrous Insulation to Reduce Thermal Conductivity

A chemical treatment reduces the convective and radiative contributions to the effective thermal conductivity of porous fibrous thermal-insulation tile. The net effect of the treatment is to coat the surfaces of fibers with a mixture of transition-metal oxides (TMOs) without filling the pores. The TMO coats reduce the cross-sectional areas available for convection while absorbing and scattering thermal radiation in the pores, thereby rendering the tile largely opaque to thermal radiation. The treatment involves a sol-gel process: A solution containing a mixture of transition-metal-oxide-precursor salts plus a gelling agent (e.g., tetraethylorthosilicate) is partially cured, then, before it visibly gels, is used to impregnate the tile. The solution in the tile is gelled, then dried, and then the tile is fired to convert the precursor salts to the desired mixed TMO phases. The amounts of the various TMOs ultimately incorporated into the tile can be tailored via the concentrations of salts in the solution, and the impregnation depth can be tailored via the viscosity of the solution and/or the volume of the solution relative to that of the tile. The amounts of the TMOs determine the absorption and scattering spectra.

Zinn, Alfred↗

DOE OSTI2025

Zooming in on virtual commutes: Telecommuting impacts on mobility and sustainability

Motivated by a societal shift towards remote work and rapid advancement in information and communication technologies, this study examines the impact of telecommuting on urban mobility across the nine counties of the San Francisco Bay Area, California. Utilizing a behaviorally realistic integrated agent-based transportation model and recent data on telecommuting patterns, we simulate the impact of multiple telecommuting scenarios on transportation system outcomes. This provides a comprehensive picture of the impact of remote work on travel costs and accessibility of all travelers, factoring in changes in mode use and congestion. We analyze how telecommuting influences broader societal factors such as transportation energy consumption. Our findings indicate that increased telecommuting reduces overall person miles traveled and transportation energy consumption. Furthermore, telecommuting results in externality benefits by improving accessibility and reducing commute times for non-telecommuters.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗