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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Mars Molniya Orbit Atmospheric Resource Mining

This NIAC (NASA Advanced Innovative Concepts) work will focus on Mars and will build on previous efforts at analyzing atmospheric mining at Earth and the outer solar system. Spacecraft systems concepts will be evaluated and traded, to assess feasibility. However the study will primarily examine the architecture and associated missions to explore the closure, constraints and critical parameters through sensitivity studies. The Mars atmosphere consists of 95.5 percent CO2 gas which can be converted to methane fuel (CH4) and Oxidizer (O2) for chemical rocket propulsion, if hydrogen is transported from electrolyzed water on the Mars surface or from Earth. By using a highly elliptical Mars Molniya style orbit, the CO2 atmosphere can be scooped, ram-compressed and stored while the spacecraft dips into the Mars atmosphere at periapsis. Successive orbits result in additional scooping of CO2 gas, which also serves to aerobrake the spacecraft, resulting in a decaying Molniya orbit.

mining↗
NASA NTRS2018

Predicting the Operational Acceptance of Airborne Flight Reroute Requests Using Data Mining

For tools that generate more efficient flight routes or reroute advisories, it is important to ensure compatibility of automation and autonomy decisions with human objectives so as to ensure acceptability by the human operators. In this paper, the authors developed a proof of concept predictor of operational acceptability for route changes during a flight. Such a capability could have applications in automation tools that identify more efficient routes around airspace impacted by weather or congestion and that better meet airline preferences. The predictor is based on applying data mining techniques, including logistic regression, a decision tree, a support vector machine, a random forest and Adaptive Boost, to historical flight plan amendment data reported during operations and field experiments. Cross validation was used for model development, while nested cross validation was used to validate the models. The model found to have the best performance in predicting air traffic controller acceptance or rejection of a route change, using the available data from Fort Worth Air Traffic Control Center and its adjacent Centers, was the random forest, with an F-score of 0.77. This result indicates that the operational acceptance of reroute requests does indeed have some level of predictability, and that, with suitable data, models can be trained to predict the operational acceptability of reroute requests. Such models may ultimately be used to inform route selection by decision support tools, contributing to the development of increasingly autonomous systems that are capable of routing aircraft with less human input than is currently the case.

Operational Acceptability↗
NASA NTRS1999

Mapping Environmental Contaminants at Ray Mine, AZ

Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data was collected over Ray Mine as part of a demonstration project for the Environmental Projection Agency (EPA) through the Advanced Measurement Initiative (AMI).

AVIRIS↗
DOE OSTI2025

Machine Learning for Well Log Analysis in Uranium Mining

This project explores the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate well log analysis for uranium mining. Geophysical log data—spontaneous potential, resistivity, and gamma ray—were used to classify lithology, correlate well logs and identify roll front zonation patterns, which are critical for locating uranium ore bodies. Supervised ML algorithms such as eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Random Forest were trained to classify lithology with high accuracy. Gradient Boosting Machines (GBM), XGBoost, Random Forest, and Neural Networks were also used for role front zone identification. Moreover, a Fast Dynamic Time Warping (FastDTW) algorithm was employed for well log correlation. Additionally, sample lag was addressed using dynamic programming. Results demonstrate the potential of AI and ML to streamline well log analysis and enhance uranium exploration workflows.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

Developing advanced catalysts for acidic oxygen evolution reaction (OER) is crucial for sustainable hydrogen production. This study presents a multistage machine learning (ML) approach to streamline the discovery and optimization of complex multimetallic catalysts. Our method integrates data mining, active learning, and domain adaptation throughout the materials discovery process. Unlike traditional trial-and-error methods, this approach systematically narrows the exploration space using domain knowledge with minimized reliance on subjective intuition. Then, the active learning module efficiently refines element composition and synthesis conditions through iterative experimental feedback. The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst. Our workflow also enhances theoretical simulations with domain adaptation strategy, providing deeper mechanistic insights aligned with experimental findings. By leveraging diverse data sources and multiple ML strategies, we demonstrate an efficient pathway for electrocatalyst discovery and optimization. This comprehensive, data-driven approach represents a paradigm shift and potentially benchmark in electrocatalysts research.

Science & Technology - Other Topics↗
DOE OSTI2025

An RNA ligase partner for the prokaryotic protein-only RNase P: insights into the functional diversity of RNase P from genome mining

RNase P can use either an RNA- or a protein-based active site to catalyze 5'-maturation of transfer RNAs (tRNAs). This distinctive attribute in the biocatalytic repertoire raises questions about the underlying evolutionary driving forces, especially if each variant somehow affords a selective advantage under certain conditions. Upon mining all publicly available prokaryotic genomes and examining gene co-occurrence, we discovered that an RNA ligase with circularization activity was significantly overrepresented in genomes that contain the protein form of RNase P. This unexpected linkage inspires testable ideas to understand the bases for scenarios that might favor RNase P variants of different architectures/make-up.

HARP↗
DOE OSTI2025

An Exploratory Data Mining Investigation for Constructing a Publicly Sourced Dataset of Foreign Hypersonic Tests

This document details a data mining exercise that resulted in an exploratory dataset of publicly reported foreign (non-US) hypersonic vehicle test events. Using a combination of targeted English language searches and country-specific queries, the study aggregates information from digital news media, official press releases, and social media posts. The resulting list of events captures the publicly available accounts of foreign hypersonic tests, although it does not represent an exhaustive record. Limitations such as inconsistent reporting, translation challenges, and the inherently provisional nature of open-source data are acknowledged. This dataset serves as an initial reference point for further inquiries into high-speed atmospheric phenomena and may facilitate future efforts to correlate these events with geophysical measurements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Recovery and Refining of Rare Earth Elements from Lignite Mine Wastes

The University of North Dakota (UND), in collaboration with a comprehensive team of technical, business and host-site partners, built on prior technology development to complete a front-end engineering and design (FEED) and business planning study to recover and refine rare earth elements (REE) and critical minerals (CM) from North Dakota (ND) lignite mine wastes. The end of project goal was to have an investment quality project and a committed team ready to commercialize the proposed technologies in a future construction and operations phase.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Energy-Efficient Selective Removal of Metal Ions from Mining Influenced Waters (MIW) Using H-Bonded Organic-Inorganic Framework (HOIFS) (CRADA Final Report)

The work developed a hydrogen-bonded organic–inorganic framework (HOIF), specifically zinc imidazole salicylaldoxime supramolecule (ZIOS), for selective Cu removal and recovery from acidic mining-impacted waters (AMD), with emphasis on scalable synthesis, membrane integration, durability in real AMD (RAMD), mechanistic understanding, and recovery/regeneration pathways. This research breaks new ground in selective resource recovery from AMD waters – no other sorbents can operate reliably in this pH range. This project is unique in what it has delivered and is of general use to the public for projects relating to critical materials recovery.

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

"Mining" for Critical Minerals: Critical Minerals from Fossil Energy Waste Byproducts

NETL researcher Mengling Stuckman is an invited speaker for the panel discussion session of "Mining for Critical Minerals" at the Marcellus Shale Coalition event, "Shale 2.0: Learn the Facts about Upstream, Midstream and Downstream". Recent studies from DOE have shown that produced water and drill cuttings from the development of unconventional shale in Appalachia has a significant source of valuable critical minerals and will support bolstering America's supply chain security, putting Pennsylvania in a unique position to capitalize. The panel discussion facilitates obtaining an overview of pipeline capacity needs, downstream users for natural gas and effectively educating and engaging the public relevant to the Marcellus Shale community. The event also offers opportunities for local oil and gas industries, water and waste management companies to work with NETL and participate in FECM’s Critical Mineral program. Industrial feedbacks and participation as outcomes of this invited talk will accelerate the technology and knowledge transfer for the DOE’s Critical Mineral program and for DOE’s mission to unleash American Energy and lead in energy innovation.

critical minerals↗
DOE OSTI2025

Mining Thermophile Photosynthesis Genes: A Synthetic Operon Expressing Chloroflexota Species Reaction Center Genes in Rhodobacter sphaeroides

Photosynthesis is the foundation of the vast majority of life systems, and is therefore the most important bioenergetic process on earth. The greatest diversity of photosynthetic systems is found in microorganisms. However, our understanding of the biophysical and biochemical processes that transduce light into chemical energy is derived from a relatively small subset of proteins from microbes that are amenable to cultivation, in contrast to the huge number of predicted proteins that catalyze the initial photochemical reactions deposited in databases, such as from metagenomics. We describe the use of a Rhodobacter sphaeroides laboratory strain for the expression of heterologous photosynthesis genes to demonstrate the feasibility of mining this resource, focusing on hot spring Chloroflexota gene sequences. Using a synthetic operon of genes, we produced a photochemically active complex of reaction center proteins in our biological system. We also present bioinformatic analyses of anoxygenic type II reaction center sequences from metagenomic samples collected from hot (42–90 °C) springs available through the JGI IMG database, to generate a resource of diverse sequences that are potentially adapted to photosynthesis at such temperatures. These data provide a view into the natural diversity of anoxygenic photosynthesis, through a lens focused on high-temperature environments. The approach we took to express such genes can be applied for potential biotechnology purposes as well as for studies of fundamental catalytic properties of these heretofore inaccessible protein complexes.

Chloroflexota↗