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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
NASA NTRSDate not supplied

Microscopy Methods for Life Detection on Ocean Worlds

On Earth, light microscopy is commonly used in microbiology to identify organisms and observe their interactions with the environment; this makes it an attractive technique for in situ life detection methods on ocean worlds. As a standalone technique, brightfield microscopy, while able to provide important contextual information, has limited usefulness as a life detection technique because it is often challenging to differentiate between abiotic and biotic particles based solely on their size and shape, which may introduce risks of false positive or false negative interpretations. However, these risks can be reduced by combining brightfield microscopy with fluorescence microscopy to provide a method that correlate sample chemistry with sample morphology. In this work, we have used the Luminescence Imager for Exploration (LIfE), a brightfield and epifluorescence microscope with an integrated sample processing system (matured under the Concepts for Ocean worlds Life Detection Technology and Instrument Concepts of Europa Exploration programs) to develop methods that increase the fidelity of in situ microscopy life detection measurements through two main approaches. First, native fluorescence is excited in molecules that contain aromatic moieties such as proteins (using deep UV excitation), and energy carrying molecules and endogenous chromophores (using visible-light excitation), to correlate the location of these species with cell-like structural features (brightfield imaging). Second, fluorescent stains are used to selectively image cells and cell fragments by targeting proteins, lipids, and nucleic acids. We discuss the results of tests, obtained using ocean world analog samples, that have examined trades associated with implementing these methods autonomously in planetary missions, including the intrinsic properties of candidate fluorescence dyes and long-term storage and radiation stability.

Pavel E. Z. Klier↗
NASA NTRSDate not supplied

Microscopy Methods for Life Detection on Ocean Worlds

On Earth, light microscopy is commonly used in microbiology to identify organisms and observe their interactions with the environment; this makes it an attractive technique for in situ life detection methods on ocean worlds. As a standalone technique, brightfield microscopy, while able to provide important contextual information, has limited usefulness as a life detection technique because it is often challenging to differentiate between abiotic and biotic particles based solely on their size and shape, which may introduce risks of false positive or false negative interpretations. However, these risks can be reduced by combining brightfield microscopy with fluorescence microscopy to provide a method that correlate sample chemistry with sample morphology. In this work, we have used the Luminescence Imager for Exploration (LIfE), a brightfield and epifluorescence microscope with an integrated sample processing system (matured under the Concepts for Ocean worlds Life Detection Technology and Instrument Concepts of Europa Exploration programs) to develop methods that increase the fidelity of in situ microscopy life detection measurements through two main approaches. First, native fluorescence is excited in molecules that contain aromatic moieties such as proteins (using deep UV excitation), and energy carrying molecules and endogenous chromophores (using visible-light excitation), to correlate the location of these species with cell-like structural features (brightfield imaging). Second, fluorescent stains are used to selectively image cells and cell fragments by targeting proteins, lipids, and nucleic acids. We discuss the results of tests, obtained using ocean world analog samples, that have examined trades associated with implementing these methods autonomously in planetary missions, including the intrinsic properties of candidate fluorescence dyes and long-term storage and radiation stability.

Pavel E Z Klier↗
DOE OSTI2025

Deep inference of simulated strong lenses in ground-based surveys

The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical power of such large samples will require further development of automated lens modeling techniques. We show that deep learning and simulation-based inference (SBI) methods produce informative and reliable estimates of parameter posteriors for strong lensing systems in ground-based surveys. We present the examination and comparison of two approaches to lens parameter estimation for strong galaxy-galaxy lenses — Neural Posterior Estimation (NPE) and Bayesian Neural Networks (BNNs). We perform inference on 1-, 5-, and 12-parameter lens models for ground-based imaging data that mimics the Dark Energy Survey (DES). We find that NPE outperforms BNNs, producing posterior distributions that are more accurate, precise, and well-calibrated for most parameters. For the 12-parameter NPE model, the calibration is consistently within <10% of optimal calibration for all parameters, while the BNN is rarely within 20% of optimal calibration for any of the parameters. Similarly, residuals for most of the parameters are smaller (by up to an order of magnitude) with the NPE model than the BNN model. This work takes important steps in the systematic comparison of methods for different levels of model complexity.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

An obscured quasar census with the 4MOST IR AGN survey: design, predicted properties, and scientific goals

ABSTRACT We present the 4MOST (4-metre Multi-Object Spectroscopic Telescope) infrared (IR) AGN survey, the first large-scale optical spectroscopic survey characterizing mid-infrared (MIR) selected obscured active galactic nuclei (AGNs). The survey targets $\approx 212\,000$ obscured IR AGN candidates over $\approx 10\,000 \rm \: deg^2$ down to a magnitude limit of $r_{\rm AB}=22.8 \, \rm mag$ and will be $\approx 100 \times$ larger than any existing obscured IR AGN spectroscopic sample. We select the targets using an MIR colour criterion applied to the unWISE catalogue from the WISE (Wide-field Infrared Survey Explorer) all-sky survey, and then apply a $r-W2\ge 5.9 \rm \: mag$ cut; we demonstrate that this selection will mostly identify sources obscured by $N_{\rm H}>10^{22} \rm \: cm^{-2}$. The survey complements the 4MOST X-ray survey, which will follow up $\sim 1\,\rm M$ eROSITA (extended ROentgen Survey with an Imaging Telescope Array)-selected (typically unobscured) AGN. We perform simulations to predict the quality of the spectra that we will obtain and validate our MIR–optical colour-selection method using X-ray spectral constraints and UV-to-far-IR spectral energy distribution (SED) modelling in four well-observed deep-sky fields. We find that: (1) $\approx 80-87{{\ \rm per\ cent}}$ of the WISE-selected targets are AGN down to $r_{\rm AB}=22.1-22.8 \: \rm mag$ of which $\approx 70{{\ \rm per\ cent}}$ are obscured by $N_{\rm H}>10^{22} \: \rm cm^{-2}$, and (2) $\approx 80{{\ \rm per\ cent}}$ of the 4MOST IR AGN sample will remain undetected by the deepest eROSITA observations due to extreme absorption. Our SED-fitting results show that the 4MOST IR AGN survey will primarily identify obscured AGN and quasars ($\approx 55{{\ \rm per\ cent}}$ of the sample is expected to have $L_{\rm AGN,IR}>10^{45} \rm \: erg \: s^{-1}$) residing in massive galaxies ($M_{\star }\approx 10^{10}-10^{12} \rm \: M_{\odot }$) at $z\approx 0.5-3.5$ with $\approx 33{{\ \rm per\ cent}}$ expected to be hosted by starburst galaxies.

Andonie, Carolina (ORCID:0000000255804298)↗
DOE OSTI2024

Machine Learning‐Assisted Microearthquake Location Workflow for Monitoring the Newberry Enhanced Geothermal System

Abstract Enhanced geothermal systems (EGS) offer a sustainable energy source but face challenges in accurately locating microearthquakes induced during reservoir stimulation. Locating these microearthquakes provides reliable feedback on the stimulation progress. Current deep learning methods for locating earthquakes require extensive data sets for training, which is problematic as detected microearthquakes are often limited. To address the scarcity of training data, we propose a practical workflow using probabilistic multilayer perceptron (PMLP) which predicts microearthquake locations from cross‐correlation time lags in waveforms. Utilizing a 3D velocity model of Newberry site derived from ambient noise interferometry, we generate numerous synthetic microearthquakes and 3D acoustic waveforms for PMLP training. Accurate synthetic tests prompt us to apply the trained network to the 2012 and 2014 stimulation field waveforms. To enhance the accuracy of source localization, we carefully handpick the P‐arrival times. Predictions on the 2012 stimulation data set show major microseismic activity at depths of 0.5–1.2 km, correlating with a known casing leakage scenario. In the 2014 data set, the majority of predictions concentrate at 2.0–2.9 km depths, consistent with results obtained from conventional physics‐based inversion, and align with the presence of natural fractures from 2.0 to 2.7 km. We validate our findings by comparing the synthetic and field picks, demonstrating a satisfactory match for the first arrivals. By combining the benefits of quick inference speeds and accurate location predictions, we demonstrate the feasibility of using realistic synthetic data set to locate microseismicity for EGS monitoring.

15 GEOTHERMAL ENERGY↗
NASA NTRS2001

Method of Making a Nickel Fiber Electrode for a Nickel Based Battery System

The general purpose of the invention is to develop a high specific energy nickel electrode for a nickel based battery system. The invention discloses a method of producing a lightweight nickel electrode which can be cycled to deep depths of discharge (i.e., 40% or greater of electrode capacity). These deep depths of discharge can be accomplished by depositing the required amount of nickel hydroxide active material into a lightweight nickel fiber substrate.

Doris L Britton↗
DOE OSTI2025

An analysis of physics limited dispatch of nuclear renewable integrated energy systems using deep reinforcement learning and dynamic modeling

Previous approaches to dispatching nuclear integrated energy systems (NIES) have focused on the profitability and flexibility of these systems to operate on energy grids with highly variable pricing. However, due to the complexity involved in modeling and designing these systems, there has been less emphasis on ensuring that these dispatch strategies are physically achievable. It is imperative to develop methods that allow the system to remain within the desired NIES operating conditions and perform this based on realistic limited forecasted information. This research employs next generation artificial intelligence, namely deep reinforcement learning (DRL), and a dynamic system model written in Modelica to find a safe and profitable dispatch strategy for a solar nuclear hybrid design. The DRL agent is shown to find a novel dispatch strategy that manages both power ramping and power levels while respecting operational limits. This DRL-based dispatch is compared to other dispatching strategies including an optimal design solution from mixed integer linear programming (MILP). It is found that incorporating the physics of such a tightly coupled NIES limits the profitability of the MILP-based dispatch strategy. As a result, the MILP solution overestimates the design’s generated revenue. In contrast, DRL significantly reduces the number of breaches of safe operational conditions during energy arbitrage while maintaining profitability. Furthermore, this work paves the way for a more detailed assessment of NIES profitability and could be used to aid operator decisions on future NIES projects.

14 - SOLAR ENERGY↗
DOE OSTI2026

The effect of hydroxyl spacing in diols on the solvation structure, dynamics, and transport properties of choline chloride-based deep eutectic solvents

Deep eutectic solvents (DESs) are a class of liquids that offer great potential in alleviating some of the challenges present in today's long-term energy storage methods because they have physical properties that are favorable for storable electrolyte solutions. In this work, a series of glycols (ethylene glycol, 1,3-propanediol, 1,4-butanediol, and 1,5-pentanediol) were studied as potential hydrogen bond donors (HBD) with a common choline chloride (ChCl) as the hydrogen bond acceptor (HBA). The solvation dynamics of the prepared systems were studied by measuring the solvent reorganization response using femtosecond transient absorption spectroscopy (fs-TA). Conductivity, viscosity, density, ET(30) polarity, and dynamics of the prepared DESs were analyzed, with a particular interest in determining the effect of HBD chain length on these parameters. Here, classical molecular dynamics simulations were employed to investigate how the local liquid structure, solvent dynamics, and bulk solvent properties vary with changes in glycol chain length.

Conductivity↗
NASA NTRS1993

Calculations on the competition between association and reaction for C3H(+) + H2

The ab initio results presently obtained for the potential energy surface of competing associative and reactive ion-molecule processes of the reactants H2 and C3H(+) show that the linear ion is able to directly access the deep potential well of the propargyl ion H2CCCH(+). Using the minimum energy potential pathway and properties of the stationary point structures determined via the ab initio methods, calculation results are obtained for both the association-rate coefficient for C3H3(+) production as a function of density, and the branching ratio between the propargyl and cyclic structures of the ion.

Maluendes, Sergio A.↗
NASA NTRS1993

Electronic characterization of defects in narrow gap semiconductors

The study of point defects in semiconductors has a long and honorable history. In particular, the detailed understanding of shallow defects in common semiconductors traces back to the classic work of Kohn and Luttinger. However, the study of defects in narrow gap semiconductors represents a much less clear story. Here, both shallow defects (caused by long range potentials) and deep defects (from short range potentials) are far from being completely understood. In this study, all results are calculational and our focus is on the chemical trend of deep levels in narrow gap semiconductors. We study substitutional (including antisite), interstitial and ideal vacancy defects. For substitutional and interstitial impurities, the efects of relaxation are included. For materials like Hg(1-x)Cd(x)Te, we study how the deep levels vary with x, of particular interest is what substitutional and interstitial atoms yield energy levels in the gap i.e. actually produce deep ionized levels. Also, since the main technique utilized is Green's functions, we include some summary of that method.

Patterson, James D.↗
DOE OSTI2025

Improved heavy-ion PID using scintillation light detector with neural network analysis: a Monte Carlo simulation study

The photon collection efficiency of gaseous scintillator detectors varies according to the position of the impinging charged particles in the medium that generates scintillation light. Thus, when impinging particles are distributed over a large area, the intrinsic photon-number resolution of the system is affected by a large variation. This work presents and discusses a method for adjusting the total number of detected photons to account for variation in the photon collection efficiency as a function of the position of the light source within the scintillating medium. The method was developed and validated by processing data from systematic simulation studies based on GEANT4 that model the response of the Energy Loss Optical Scintillation System (ELOSS) detector. The position of the charged particle is calculated using a deep neural network algorithm. This is accomplished by analyzing the distribution of scintillation light recorded by the array of photosensors. The estimated particle position is then used to calculate the correction factor and adjust the amount of captured light to account for variations in the photon collection efficiency. The neural network algorithm provides excellent tracking capabilities, achieving sub-millimeter position resolution and an angular resolution of 12 mrad, approaching the performance of traditional tracking detectors (e.g., drift chambers). The present method can be generalized to any optical scintillation system where the photon collection efficiency depends on the position of the impinging particle.

Heavy-ion detectors↗
NASA NTRS1980

Wavelength-modulated photocapacitance spectroscopy

Derivative deep-level spectroscopy was achieved with wavelength-modulated photocapacitance employing MOS structures and Schottky barriers. The energy position and photoionization characteristics of deep levels of melt-grown GaAs and the Cr level in high-resistivity GaAs were determined. The advantages of this method over existing methods for deep-level spectroscopy are discussed.

Kamieniecki, E.↗
NASA NTRS2022

New Spectroscopic Confirmations of Lyα Emitters at Z ∼ 7 from the LAGER Survey

We report spectroscopic confirmations of 15 Lyα galaxies at z ∼ 7, implying a spectroscopic confirmation rate of ∼80% on candidates selected from the Lyα Galaxies in the Epoch of Reionization (LAGER), which is the largest (24 deg2) survey aimed at finding Lyα emitters (LAEs) at z ∼ 7 and uses deep narrowband imaging from the Dark Energy Camera at CTIO. LAEs at high redshifts are sensitive probes of cosmic reionization, and narrowband imaging is a robust and effective method for selecting a large number of LAEs. In this work, we present results from the spectroscopic follow-up of LAE candidates in two LAGER fields, COSMOS and WIDE-12, using observations from Keck/LRIS. We report the successful detection of Lyα emission in 15 candidates. Three of these in COSMOS have matching confirmations from a previous spectroscopic follow-up and are part of the overdense region, LAGER-z7OD1. Two other candidates that were not detected with LRIS have prior spectroscopic confirmations from Magellan. Including these, we obtain a spectroscopic confirmation success rate of ∼80% for LAGER LAE candidates. Thorough checks were performed to reject the possibility of these detections being foreground emission resulting with a probability of, at most, one contaminant. We do not detect any other UV nebular lines in our LRIS spectra, apart from Lyα. We estimate a 2σ upper limit for the ratio of N v/Lyα, fNV/fLyα ≲ 0.27. Including confirmations from this work, a total of 33 LAE sources from LAGER are now spectroscopically confirmed. LAGER has more than doubled the sample of spectroscopically confirmed LAE sources at z ∼ 7.

Santosh Harish↗
DOE OSTI2026

Deep Learning enabled spectral energy conversion for in situ exposure measurements

A detector-specific deep learning (DL) approach is presented for spectra-to-exposure conversion using large-format sodium iodide (NaI(Tl)) detectors deployed for in situ environmental radiation measurements in emergency response scenarios. Accurate determination of exposure from NaI spectra is challenging due to poor energy resolution, partial energy absorption, and the strong sensitivity of traditionally deployed analytical conversion methods to calibrated source geometry and pre-deployment assumptions. Here, to address these limitations, a multi-layer perceptron model was trained on a hybrid in situ /Monte Carlo dataset constructed to span a broad range of photon energies, spatial extents, and realistic deployment variability, representative of general in situ emergency response conditions. The DL model was evaluated against commonly fielded analytical approaches under matched simulation conditions, including a single-factor method, a G-function method, and a modeled pressurized ion chamber (PIC) baseline. This study was intentionally computational in scope to enable controlled, like-for-like comparisons between conversion techniques while minimizing confounding real-world variability. Comparison to the modeled PIC provides contextual benchmarking and is not intended as a field inter-comparison with deployed instruments. Across the evaluated 20 keV to 3 MeV energy range, the DL approach consistently exhibited higher accuracy and reduced variance relative to the analytical methods against a deterministically calculated exposure. This may indicate improved robustness to spectral complexity without reliance on source-, geometric-, or spectral region-specific optimization. While results do not represent real-world validation, the presented work demonstrates that deep learning may effectively learn the nonlinear detector response-to-exposure relationship for asymmetric NaI(Tl) detectors and offers a promising pathway for improving in situ exposure estimation using spectroscopic systems already integrated into initial real-time emergency response operations.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
NASA NTRSDate not supplied

Parameterization of Vertical Cloud Distribution from C3M and MERRA Data Using ML Method

Clouds play a key role in regulating the hydrological cycle and the Earth's radiative energy budget. However, global climate models (GCMs) with a horizontal grid spacing on the order of 100 km have limitations in representing sub-grid cloud dynamics with spatial scales on the order of 1 km, leading to potential uncertainties in cloud radiative feedback on the global scale. In our research, we will leverage the capabilities of Deep Machine Learning (DML) methods to construct parameterizations of sub-grid volumetric cloud fraction (VCF), which is the frequency of occurrence on a grid volume accumulated in the horizontal and vertical directions. Our investigation delves into the intricate relationship between VCF obtained from the NASA CALIPSO-CloudSat-CERES-MODIS (CCCM) satellite observation data and 3-D MERRA-2 reanalysis meteorological profiling data (e.g., wind, relative humidity, temperature). Through a comprehensive one-year data training utilizing the Sequence to Sequence DML method, we have successfully disentangled the complicated cloud formation dynamics across diverse meteorological conditions through a day-to-day analysis framework. Preliminary findings reveal promising statistical agreements in geographical and vertical distributions and seasonal variations of volumetric cloud fraction between ML prediction and satellite measurements. These results underscore the aptitude of our DML model to discern underlying cloud physical processes and accurately represent sub-grid cloud formation dynamics. Additionally, we have also employed trained neural network to analyze uncertainties arising from errors in meteorological data, further enhancing the robustness of our VCF parameterization.

Shan Zeng↗
NASA NTRS1985

Calculation of intensity of high energy muon groups observed deep underground

The intensity of narrow muon groups observed in Kolar Gold Field (KGF) at the depth of 3375 m.w.e. was calculated in terms of quark-gluon strings model for high energy hadron - air nuclei interactions by the method of direct modeling of nuclear cascade in the air and muon propagation in the ground for normal primary cosmic ray composition. The calculated intensity has been found to be approx. 10 to the 4 times less than one observed experimentally.

Vavilov, Y. N.↗
NASA NTRS2011

Z-Pinch Fusion Propulsion

Fusion-based nuclear propulsion has the potential to enable fast interplanetary transportation. Shorter trips are better for humans in the harmful radiation environment of deep space. Nuclear propulsion and power plants can enable high Ispand payload mass fractions because they require less fuel mass. Fusion energy research has characterized the Z-Pinch dense plasma focus method. (1) Lightning is form of pinched plasma electrical discharge phenomena. (2) Wire array Z-Pinch experiments are commonly studied and nuclear power plant configurations have been proposed. (3) Used in the field of Nuclear Weapons Effects (NWE) testing in the defense industry, nuclear weapon x-rays are simulated through Z-Pinch phenomena.

Miernik, Janie↗