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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Particle track record in Apollo 15 deep core from 54 to 80 cm depths.

Particle track measurements have been made in nearly 500 individual grains from 13 levels in the 54-80 cm depth range of the Apollo 15 deep core. They reveal a wide range of track densities at all depths and some systematic variations within layers, indicating that both predepositional mixing and subsequent layering are present and that separate sublayers exist within larger regions where no sublayers are visible. Minimum track densities are inferred to be useful measures of maximum residence times for undisturbed layers. Using the observed minimum track densities, we conclude that the average deposition rate in this section of soil column was greater than 0.4 cm/million years.

Fleischer, R. L.↗
NASA NTRS1977

Olivine separates from Murchison and Cold Bokkeveld - Particle tracks and noble gases

Olivine separates from Murchison and Cold Bokkeveld were analyzed for particle tracks and noble gases. The matrix remaining after olivine separation was also analyzed for noble gases. The olivines from both meteorites have comparable fractions of solar-flare-irradiated grains, but the highest track densities in Murchison are an order of magnitude greater than those in Cold Bokkeveld. Solar Ne content in Murchison olivines follows this trend, being at least an order of magnitude higher than that in Cold Bokkeveld. Track gradients in Cold Bokkeveld olivines are flatter than those in Murchison or recently exposed lunar crystals. Relative to the matrix, olivine separates in both meteorites have small enrichments at the heavy and light Xe isotopes and smaller Ar-36/Ar-38 ratios. These noble-gas effects may be related to a chromite impurity in the olivine separates.

Macdougall, J. D.↗
DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗
NASA NTRS1972

Solar particle tracks in glass from Surveyor 3

Samples of glass from the Surveyor 3 TV camera filter were examined for particle tracks by optical and scanning electron microscopy. The corrected density value is 1.7 million + or - 0.1 million tracks/sq cm, and the track density vs depth curve is determined. Comparisons with other estimated and calculated data are discussed, and lack of agreement between data sets is considered. It is felt that considerable erosion occurs, and that erosion also occurs by a flaking of small thicknesses of material, possibly caused by solar wind irradiation.

Crozaz, G.↗
DOE OSTI2025

In-pixel integration of signal processing and AI/ML based data filtering for particle tracking detectors

We present the first physical realization of in-pixel signal processing with integrated AI-based data filtering for particle tracking detectors. Building on prior work that demonstrated a physics-motivated edge-AI algorithm suitable for ASIC implementation, this work marks a significant milestone toward intelligent silicon trackers. Our prototype readout chip performs real-time data reduction at the sensor level while meeting stringent requirements on power, area, and latency. The chip is taped-out in 28nm TSMC CMOS bulk process, which has been shown to have sufficient radiation hardness for particle experiments. This development represents a key step toward enabling fully on-detector edge AI, with broad implications for data throughput and discovery potential in high-rate, high-radiation environments such as the High-Luminosity LHC.

Parpillon, Benjamin [Fermilab; Illinois U., Chicag↗
DOE OSTI2026

Particle Tracking Methods for Battery Precipitation Reactions

Precipitation and deposition reactions at solid–liquid interfaces play a key role in a number of battery chemistries, including Li-ion, so-called “anode free” batteries, zinc-based battery chemistries, and lithium–sulfur, among others. Although models with heterogeneous nucleation and growth phenomena are present in the literature, papers have not to date provided much detail on the numerical algorithms used to track the temporal evolution of the particle size distribution of deposits on electrode surfaces. In this paper we examine several approaches to discretize and track the particle size distribution, demonstrating that common approaches lead to anomalous flattening of the particle size distribution. We conclude by presenting an algorithm that preserves the appropriate particle size distribution during particle growth.

Algorithms↗
DOE OSTI2026

On-chip probabilistic inference for charged-particle tracking at the sensor edge

Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection efficiency by deciding which information to save for further analysis. Particle tracking detectors at the Large Hadron Collider exemplify this challenge: pixelated silicon sensors generate rich spatiotemporal ionization patterns, yet most of this information is discarded due to data-rate limitations. Concurrently, advancements in co-design tools provide rapid turn-around for incorporating machine learning into application-specific integrated circuits, motivating designs for particle detectors with new integrated technologies. We demonstrate that neural networks embedded in the front-end electronics can infer charged-particle kinematic parameters from a single silicon layer. We regress hit positions and incident angles with calibrated uncertainties, while satisfying stringent constraints on numerical precision, latency, and silicon area. Our results establish a path toward probabilistic inference directly at the edge, opening new opportunities for intelligent sensing in high-rate scientific instruments.

Das, Arghya Ranjan [Purdue U.] (ORCID:000000018451↗
DOE OSTI2024

Predicting Missing Regions in Charged Particle Tracks Using a Sparse 3D Convolutional Neural Network

The 2x2 Demonstrator is a prototype of ND-LAr, the liquid argon time-projection chamber of the Deep Underground Neutrino Experiment’s Near Detector complex. Both the 2x2 Demonstrator and ND-LAr are modular detectors that will have pixelated charge readouts and inactive regions wherein there is no sensitivity to charge deposition and light signals that arise from charged particle interactions with liquid argon. In the 2x2, these inactive regions are located in between the active detector modules, which introduces the challenge of inferring what charge signals ought to look like in these regions. This study explores the use of a Sparse 3D Convolutional Neural Network (ConvNet) to infer missing regions in charged particle tracks. Hits corresponding to energy depositions are voxelized into a three-dimensional grid for each track. Voxels that fall into predefined inactive regions are removed to simulate the lack of detector output. The model is trained to infer the topology of the missing track voxels, with the ultimate goal of inferring the missing charge or energy values in these voxels as well. Results indicate that this approach shows promise in prediction of missing track regions with some accuracy.

Utaegbulam, Hilary↗
NASA NTRS1972

On charged particle tracks in cellulose nitrate and Lexan

Investigations were performed aimed at developing plastic nuclear track detectors into quantitative tools for recording and measuring multicharged, heavy particles. Accurate track etch rate measurements as a function of LET were performed for cellulose nitrate and Lexan plastic detectors. This was done using a variety of incident charged particle types and energies. The effect of aging of latent tracks in Lexan in different gaseous atmospheres was investigated. Range distributions of high energy N-14 particle bevatron beams in nuclear emulsion were measured. Investigation of charge resolution and Bragg peak measurements were carried out using plastic nuclear track detectors.

Benton, E. V.↗
NASA NTRS1972

Chemistry and particle track studies of Apollo 14 glasses.

The abundance and the composition of Apollo 14 glasses have been studied. Glass particles were analyzed for Si, Ti, Al, Fe, Mn, Mg, Na, and K by electron microprobe analysis. The refractive indices of 26 particles were determined by the oil immersion method. Track analyses have been carried out in order to determine the uranium content and the radiation history of glass particles. The proper identification of galactic and solar flare nuclei tracks makes it possible to estimated residence times of the glass particles in the top layer of the lunar soil.

Glass, B. P.↗
NASA NTRS1975

Microdosimetric structure of HZE particle tracks in tissue

Heavy nuclei of the primary galactic radiation in space can have the same linear energy transfer yet greatly different lateral distribution patterns of the energy in the microstructure of tissue. Track structure thus presents itself as a new dosimetric parameter for HZE particles which is at present incompletely understood in its radiobiological significance. The theory of track structure distinguishes two regions: core and penumbra. The core is a narrow region with a radius far below 1 micron in tissue where energy deposition occurs mainly through excitations and collective oscillations of electrons. Energy density in the core accounts for slightly more than half the total LET. The penumbra surrounding the core extends laterally several to many microns depending on the energy of the primary. Energy density in the penumbra decreases steeply with the square of increasing radius. The relationships are illustrated with nuclear emulsion micrographs and plots of energy density profiles. The implications of the findings for a dosimetric system for HZE particles are discussed.

Schaefer, H. J.↗
DOE OSTI2025

Sub-10 nm upconversion nanocrystals for long-term single-particle tracking

Lanthanide-doped upconversion nanoparticles are attractive single-molecule imaging probes due to their high photostability and anti-Stokes luminescence. However, achieving both small particle size and strong brightness has remained a major challenge, as reducing size often leads to dimmer emission. Herein, we fabricate a sub-10 nm cascade actively protected upconversion nanoparticles, which shows a 33-fold enhanced upconversion efficiency at the single-particle level compared to larger ~19 nm conventional nanoparticles. Theoretical modeling and time-resolved measurements show that emission loss mainly comes from energy leakage of Er 3+ ions to surface defects. By introducing a NaYbF 4 layer as photon-harvesting and protective intermediate layer, we minimize this energy loss and significantly boost brightness. A monolayer of inert NaLuF 4 can effectively suppress the surface quenching to Yb 3+ . Using these ultra-small bright probes, we successfully tracked single epidermal growth factor receptor molecules on live cells for up to one hour, revealing dynamic switching between different diffusion modes.

Qiu, Xiaochen [Fudan Univ., Shanghai (China); Shen↗
DOE OSTI2025

A GPU ‐Accelerated 3D Unstructured Mesh Based Particle Tracking Code for Multi‐Species Impurity Transport Simulation in Fusion Tokamaks

ABSTRACT This paper presents the multi‐species global impurity transport capability developed in a GPU‐accelerated fully 3D unstructured mesh‐based code, GITRm, to simultaneously track multiple impurity species and handle interactions of these impurities with mixed‐material surfaces. Different computational approaches to model particle‐surface interaction or surface response have been developed and compared. Sheath electric field is taken into account by employing a fast distance‐to‐boundary calculation, which is carried out in parallel on distributed or partitioned meshes on multiple GPUs without the need for any inter‐process communication during the simulation. Several example cases, including two for the DIII‐D tokamak, that is, one with the SAS‐V divertor and the other with the collector probes, are used to demonstrate the utility of the current multi‐species capability. For the DIII‐D probe case, the capability of GITRm to resolve the spatial distribution of particles in localized regions, such as diagnostic probes, within non‐axisymmetric tokamak geometries is demonstrated. These simulations involve up to 320 million particles and utilize up to 48 GPUs.

Nath, Dhyanjyoti D. [Scientific Computation Resear↗