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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Mixtures of CO 2 and poly(fluoroacrylate) based on monomers containing only six or four fluorinated carbons: Phase behavior and solution viscosity

In this study, homopolymerization of fluoroacrylate monomers containing a segment with eight fluorinated carbons, (CH 2 CHCOO(CH 2 ) 2 (CF 2 ) 7 CF 3 ), yield poly(fluoroacrylate) (PFA) with remarkably high solubility in CO 2 , but the corresponding ultimate PFA degradation product perfluorooctanoic acid (PFOA) is bio-accumulative. Based on cloud point loci in the 1–4 wt% PFA in CO 2 concentration range at temperatures of 25, 50, 75, 100 and 125°C, it is shown that C 6 F 13 -based and C 4 F 9 -based PFA, with relatively benign degradation products perfluorohexanoic acid (PFHxA) and perfluorobutanoic acid (PFBA), respectively, retain the same level of CO 2 -solubility as that of C 8 F 17 -based PFA. Further, both C 6 F 13 -based and C 4 F 9 -based PFA can increase the viscosity of single-phase CO 2 -PFA solutions (i.e. thicken CO 2 ) as much as C 8 F 17 -based PFA in the 25–125°C range. Therefore, the design of CO 2 -soluble PFA need not be constrained to monomers containing the C 8 F 17 moiety; fluoroacrylate monomers containing shorter fluoroalkyl segments with only four or six fluorocarbons can be utilized.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Scale-Up Studies for the Dehydration of C 4+ Alcohols into Drop-In Diesel Fuel

Herein, we demonstrate the production of liter quantities of drop-in diesel-range ethers from biomass-derived alcohols. We report scale-up resultsfor the dehydration of a mixture of C 4+ alcohols using powder and pellet zeolite Ycatalyst in continuous flow and batch reactors. The activity of zeolite Y decreaseswith the introduction of an alumina binder. Large alcohols, as well as branchedand secondary alcohols, increase the coke content over the pellet Y catalyst. Thepellet formulation had lower carbon balances and selectivity to C 10+ ethers,suggesting that the pellet formulation increases alcohol absorption and cokeproduction. Crushing the pellet Y catalyst to smaller particle sizes does notrecover the activity of zeolite Y in its powder form. Cold flow experiments showthat particle agglomeration contributes to pressure buildup in the continuous flowreactor. C 10+ ether blends can be produced in batch reactors at high alcohol conversion regimes. One liter of a diesel #2 blend wasproduced by scaling up this reaction. The final blend consists of a substantial portion of C 10+ ethers (63.7 wt %), followed by heavyunknown products (27.4 wt %). Furthermore, the final alcohol and butyl ether concentration values were 7.3 and 1.5 wt %, respectively. Theblendstock reported in this paper can satisfy diesel #2 ASTM standards for density, cloud point, flashpoint, and cetane number.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel–Ligand Docking

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Self-Assembled Thermoresponsive Molecular Brushes as Nanoreactors for Asymmetric Aldol Addition in Water

The manipulation and tunability of self-assembled block copolymers through external stimuli presents an attractive strategy to develop smart polymer-based nanoreactors as supports for non-orthogonal tandem catalysis. We report on thermoresponsive core-shell micelles based on poly[norbornene-poly(2-methyl-2-oxazoline-block-2-propyl-2-oxazoline)]-graft-poly[norbornene L-proline] (P[NB-P(MeOx-b-PropOx)]-graft-P[NB-L-proline]) as catalyst supports for L-proline. These molecular brushes exhibit large differences in lower critical solution temperature behavior and nanostructure size in water depending on the chain-lengths and proline connectivity. L-Proline-mediated aldol reactions expose the efficiency by which the molecular brushes self-assemble into micelles. Molecular brushes with an extended backbone show higher activity and selectivity, suggesting better core-shell segregation of the micelles and exclusion of water from the catalytic site. Catalytic efficiencies are not improved above the cloud point temperature, the catalytic be-havior is rather sensitive to conformational changes of the polymer chains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Self-Assembly Driven Microlithography via Near-Infrared Light Activation

Current vat photopolymerization (VP) relies on UV or visible light to start the rapid crosslinking of liquid photocurable resins into 3D-printed structures. Here, we develop a self-assembly-driven photopatterning approach to photothermally generate polymeric solids by combining thermoplasmonic nanoparticles and thermoresponsive polymers, in which near-infrared (NIR) light activates thermoplasmonic heating of nanoparticles, triggering the irreversible self-assembly of thermoresponsive polymers into insoluble mesoglobules. A small amount of thermal initiator and crosslinker leads to irreversible self-assembly of polymer nanocomposites. NIR light offers deeper penetration and reduced scattering compared to UV, enabling more uniform curing of thicker or filled materials and expanded process control for composites or opaque systems. Thermoplasmonic heat generation is achieved using surface-modified gold nanorods (AuNRs) with a longitudinal localized surface plasmon resonance peak in the NIR region. Key variables such as polymer composition, molecular weight, physical interactions at the nanoparticle–polymer interface, which can be tuned by surface functionalization, AuNR concentration, and pH can be used to tailor the assembly behavior of these systems, including photothermal effect, flocculation, and cloud point temperature, and the mechanical properties of the final structures. Collectively, these results highlight a platform for photothermally-driven microlithography of polymer solids with diverse, tunable macroscopic properties, enabled by low-energy NIR light-activated self-assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Enhanced ordering in length-polydisperse carbon nanotube solutions at high concentrations as revealed by small angle X-ray scattering

Carbon nanotubes (CNTs) are stiff, all-carbon macromolecules with diameters as small as one nanometer and few microns long. Solutions of CNTs in chlorosulfonic acid (CSA) follow the phase behavior of rigid rod polymers interacting via a repulsive potential and display a liquid crystalline phase at sufficiently high concentration. Here, we show that small-angle X-ray scattering and polarized light microscopy data can be combined to characterize quantitatively the morphology of liquid crystalline phases formed in CNT solutions at concentrations from 3 to 6.5% by volume. We find that upon increasing their concentration, CNTs self-assemble into a liquid crystalline phase with a pleated texture and with a large inter-particle spacing that could be indicative of a transition to higher-order liquid crystalline phases. We explain how thermal undulations of CNTs can enhance their electrostatic repulsion and increase their effective diameter by an order of magnitude. By calculating the critical concentration, where the mean amplitude of undulation of an unconstrained rod becomes comparable to the rod spacing, we find that thermal undulations start to affect steric forces at concentrations as low as the isotropic cloud point in CNT solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Deep neural network uncertainty quantification for LArTPC reconstruction

We evaluate uncertainty quantification (UQ) methods for deep learning applied to liquid argon time projection chamber (LArTPC) physics analysis tasks. As deep learning applications enter widespread usage among physics data analysis, neural networks with reliable estimates of prediction uncertainty and robust performance against overconfidence and out-of-distribution (OOD) samples are critical for their full deployment in analyzing experimental data. While numerous UQ methods have been tested on simple datasets, performance evaluations for more complex tasks and datasets are scarce. Here we assess the application of selected deep learning UQ methods on the task of particle classification using the PiLArNet monte carlo 3D LArTPC point cloud dataset. We observe that UQ methods not only allow for better rejection of prediction mistakes and OOD detection, but also generally achieve higher overall accuracy across different task settings. We assess the precision of uncertainty quantification using different evaluation metrics, such as distributional separation of prediction entropy across correctly and incorrectly identified samples, receiver operating characteristic curves (ROCs), and expected calibration error from observed empirical accuracy. We conclude that ensembling methods can obtain well calibrated classification probabilities and generally perform better than other existing methods in deep learning UQ literature.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. Here, to inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

47 OTHER INSTRUMENTATION↗
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↗
DOE OSTI2024

Full phase space resonant anomaly detection

Physics beyond the Standard Model that is resonant in one or more dimensions has been a longstanding focus of countless searches at colliders and beyond. Recently, many new strategies for have been developed, where sideband information can be used in conjunction with modern machine learning, in order to generate synthetic datasets representing the Standard Model background. Until now, this approach was only able to accommodate a relatively small number of dimensions, limiting the breadth of the search sensitivity. Using recent innovations in point cloud generative models, we show that this strategy can also be applied to the full phase space, using all relevant particles for the anomaly detection. As a proof of principle, we show that the signal from the R&D dataset from the LHC Olympics is findable with this method, opening up the door to future studies that explore the interplay between depth and breadth in the representation of the data for anomaly detection. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Multi-Resolution UAV Path Replanning for Inspection of Tailings Dams

Autonomous inspection of large and complex structures with a commercial unmanned aerial vehicle (UAV) is a challenging problem that has been addressed in recent years. In this paper, we address the global motion planning problem of creating autonomous inspection missions for UAVs considering photogrammetry constraints. We focus on the inspection of large tailings dams, which are dam structures used to store waste byproducts of mining. Our method uses a prior sparse point cloud of the dam to generate a voxel grid, where paths satisfying photogrammetry constraints are tested for collisions. We then apply the A* algorithm as a local planner to avoid obstacles within the global mission. Moreover, we address the problem of changing routes online by using octree-based multi-resolution grids for efficient and fast pathfinding. Our results, obtained using tridimensional maps of an actual coal mine tailings dam, show that using octrees for multi-resolution motion planning is faster than using a fixed voxel grid in online missions while inspecting large structures.

42 ENGINEERING↗
DOE OSTI2023

AlignOT: An Optimal Transport Based Algorithm for Fast 3D Alignment With Applications to Cryogenic Electron Microscopy Density Maps

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. Here, we quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOT can outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.

3D alignment↗
DOE OSTI2023

Comparing Mapper Graphs of Artificial Neuron Activations

The mapper graph is a popular tool from topological data analysis that provides a graphical summary of point cloud data. It has been used to study data from cancer research, sports analytics, neurosciences, and machine learning. In particular, mapper graphs have been used recently to visualize the topology of high-dimensional artificial neural activations from convolutional neural networks and large language models. However, a key question that arises from using mapper graphs across applications is how to compare mapper graphs to study their structural differences. In this paper, we introduce a distance between mapper graphs using tools from optimal transport. We demonstrate the utility of such a distance by studying the topological changes of neural activations across convolutional layers in deep learning, as well as by capturing the loss of structural information for multiscale mapper.

mapper graphs, computational topology, machine lea↗
DOE OSTI2024

Manifold Learning: What, How, and Why

Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to find the low-dimensional structure of data. Dimension reduction for large, high-dimensional data is not merely a way to reduce the data; the new representations and descriptors obtained by ML reveal the geometric shape of high-dimensional point clouds and allow one to visualize, denoise, and interpret them. This review presents the underlying principles of ML, its representative methods, and their statistical foundations, all from a practicing statistician's perspective. It describes the trade-offs and what theory tells us about the parameter and algorithmic choices we make in order to obtain reliable conclusions.

Mathematics↗
DOE OSTI2021

Aerial 3D Building Reconstruction from Drone Imagery (A3DBR) v1

This toolkit is composed of several modules for extracting buildings geometrical and thermal characteristics from RGB and thermal imagery captured using a drone. - Building 3D reconstruction module: leverage a photogrammetry software to construct a 3D point cloud from RGB drone imagery, which is then used in conjunction with image processing and geometric methods to extract building footprint and building height (i.e., 3D model of the building). - Windows to wall ratio estimation module: leverage deep learning semantic segmentation modeling to detect windows on 2D drone RGB images. The detected windows are then projected onto the extracted building 3D model (using building 3D reconstruction module) and their area is computed to obtain window to wall ratio estimation. - Thermal anomalies detection module: leverage image processing and machine learning algorithm to detect on 2D drone thermal images potential thermal anomalies within building's facades and roofs.

Granderson, Jessica↗
DOE OSTI2021

molecule-tda v0.1.0

A general framework to extract topological information (via topological data analysis) from some type of point cloud data, to be used in downstream machine learning tasks.

Krishnapriyan, Aditi↗