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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Towards Precision Photometric Type ia Supernova Cosmology With Machine Learning

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can produce the required precision to unambiguously test concordance cosmology which could represent a monumental shift in our understanding of dark energy and its role in cosmic history. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Specifically, spectroscopic classification will not be possible for the vast majority of discovered objects, and only ~25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning method for photometric classification of SNe, Supernova Classification with a COnvolutional Neural Network (SCONE). Photometric classification enables SNe with no spectroscopic information to be confidently categorized, and is a critical component of current and future analysis pipelines. SCONE achieves >99% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and has been integrated into DES, LSST, and Roman analysis pipelines. We also demonstrate the efficacy of SCONE on early-time photometric classification, which will be vital for optimal allocation of spectroscopic resources. We show that SCONE can distinguish between 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose a machine learning alternative that uses SN photometry alone to extract redshift information. Most SNe Ia inherit redshift information from their host galaxy, but the process of matching SNe to the correct host galaxy can be challenging. We systematically analyze the impact of incorrect redshifts from host galaxy mismatch on 5 years of DES SN data, and conclude that improved host matching or redshift estimation methods can reduce our systematic errors by ~10%. In response to this finding, we present a SN photometry-only method for estimating redshifts independent of host galaxy information, Photo-zSNthesis. We show that Photo-zSNthesis redshift estimates are accurate to within 2% across the full redshift range of LSST, a first in the literature. Finally, we focus on the robustness of machine learning (ML) algorithms for real-world and scientific applications. ML models generalize poorly beyond their training set and often experience severe performance degradation when deployed on new data. We demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Probing crystallographic orientation-specific carrier lifetimes in epitaxial Ge/AlAs and InGaAs/InP heterostructures

Current silicon (Si) fin transistors rely on (100) and (110) crystallographically oriented surfaces, and the proposed alternate channel transistor technology comprises materials with higher mobility than Si. Crystallographically oriented epitaxial germanium (Ge) and indium–gallium arsenide (InGaAs) have the potential to replace Si in ultra-low power transistor applications. The higher carrier lifetime is an indication of superior material quality, which relates to the leakage current of a fin transistor. To gain insights into the carrier recombination dynamics in these crystallographically oriented epitaxial Ge and InGaAs layers, the contactless microwave photoconductive decay (μ-PCD) technique at an excitation wavelength of 1500 nm was employed to probe the orientation-specific carrier lifetimes. Highly effective carrier lifetimes >200 ns for (100)Ge/AlAs and (110)Ge/AlAs, and ~80 ns for (111)Ge/AlAs heterostructures, were extracted at room temperature. The measured carrier lifetime has a strong dependence on the surface orientation, which could be related to orientation-specific bulk trap states present within the bandgap of Ge. The (111)Ge orientation has 3 times lower carrier lifetime compared to the (100)Ge and (110)Ge surface orientations. On the other hand, the carrier lifetimes of 125 μs and 10 ns were determined from (100)InGaAs/InP and (110)InGaAs/InP heterostructures, respectively. The reduction in carrier lifetimes in both (111)Ge and (110)InGaAs was due to high electrical conductivity or higher bulk trap states present within the bandgap as well as the facet-dependent growth of the (110)InGaAs layer on InP. A surface passivating layer is indispensable for these orientation-specific epitaxial layers to improve the carrier lifetime. Therefore, the higher carrier lifetimes from technologically interesting (100)Ge and (110)Ge surfaces would offer a path for the development of Ge-based ultra-low power electronics, and optoelectronic devices based on the (100)InGaAs layer.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States

Abstract The U.S. Energy Information Administration (EIA) conducts a regular survey (form EIA-923) to collect annual and monthly net generation for more than ten thousand U.S. power plants. Approximately 90% of the ~1,500 hydroelectric plants included in this data release are surveyed at annual resolution only and thus lack actual observations of monthly generation. For each of these plants, EIA imputes monthly generation values using the combined monthly generating pattern of other hydropower plants within the corresponding census division. The imputation method neglects local hydrology and reservoir operations, rendering the monthly data unsuitable for various research applications. Here we present an alternative approach to disaggregate each unobserved plant’s reported annual generation using proxies of monthly generation—namely historical monthly reservoir releases and average river discharge rates recorded downstream of each dam. Evaluation of the new dataset demonstrates substantial and robust improvement over the current imputation method, particularly if reservoir release data are available. The new dataset—named RectifHyd—provides an alternative to EIA-923 for U.S. scale, plant-level, monthly hydropower net generation (2001–2020). RectifHyd may be used to support power system studies or analyze within-year hydropower generation behavior at various spatial scales.

13 HYDRO ENERGY↗
DOE OSTI2022

Open-source Tools for Solving Grid Optimization Problems: ARPA-e Benchmark Algorithm Overview [Slides]

This document contains the official formulation that will be used for evaluation in Challenge 2 of the Grid Optimization (GO) Competition. Minor changes may occur within the formulation. Entrants will be notified when a new version is released. Changes are not expected to be of a significance that would cause a change in approach for the Entrants. This formulation builds upon the Challenge 1 formulation published in ARPA-E DE-FOA-0001952. Entrants will be judged based on the current official Challenge 2 formulation posted on the GO Competition website (this document, which is subject to change), not the formulation posted in DE-FOA-0001952. Entrants are permitted and encouraged to use any alternative problem formulation and modeling convention within their own software (such as convex relaxation, decoupled power flow formulations, current-voltage formulations, etc.) in an attempt to produce an exact or approximate solution to this particular mathematical program. However, the judging of all submitted approaches must conform to the official formulation presented here.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Status of conceptual horizontal splitter design for FFA@CEBAF energy upgrade

Jefferson Lab’s Continuous Electron Beam Accelerator Facility (CEBAF) is currently investigating the feasibility of upgrading its maximum operating energy using Fixed-Field Alternating-gradient (FFA) recirculating arcs to increase the total number of recirculations the beam through the pair of LINACs. These FFA arcs will be composed of permanent magnets, with small Panofsky-style multipole correctors. Horizontal splitters are needed to control the beam parameters through these FFA arcs. The geometrical and physical constraints, as well as the beam matching requirements are very restrictive, complicating the design. This work will show the current status of the most mature design, which includes matching solutions, as well as options for extraction of the beam.

Accelerator Physics↗
DOE OSTI2025

Current status of conceptual horizontal splitter design for FFA@CEBAF energy Upgrade

Jefferson Lab’s Continuous Electron Beam Accelerator Facility (CEBAF) is currently investigating the feasibility of upgrading its maximum operating energy using Fixed-Field Alternating-gradient (FFA) recirculating arcs to increase the total number of recirculations of the beam through the pair of LINACs. These FFA arcs will be composed of permanent magnets, with small Panofsky-style multipole correctors. In order to control the beam parameters through these FFA arcs, horizontal splitters must be used. The geometrical and physical constraints, as well as the beam matching requirements are very restrictive, complicating the design. This work will show the current status of the most mature design, which includes matching solutions, as well as options for extraction of the beam.

Accelerator Physics↗
DOE OSTI2023

Design Study of a Superconducting Quadrupole Magnet System Sustainable Under Dark Current Heating in ILC Main Linac

The International Linear Collider (ILC) main linac comprises a series of 12 m-long cryomodules. The cryomodule contains eight 9-cell superconducting (SC) RF (SRF) cavities and an SC quadrupole magnet combined with dipole correctors to focus and steer electron and positron beams. The magnets are installed between the SRF cavity string and at the longitudinal center of a common cryomodule/cryostat. These magnets are conductively cooled by pure aluminum channels thermally connected to a 2 K two-phase helium pipe for cooling the SRF cavities. A recent study shows that field-emitted electrons, so-called “dark current” initiated in the SRF cavities, are transmitted through the SRF cavity string and reach the SC magnet. The energy is inevitably absorbed in the SC coil due to the magnetic field, resulting in risks of a quench caused by the coil heating. We are investigating alternate magnet designs by using Nb $_{3}$ Sn or $\text{MgB}_{2}$ SC and by adding the dark current absorber surrounding the beam-pipe to realize sustainable magnet operation under the dark current heating. We report the design study of the magnet system and interfaces to the cryomodule accommodating it with the SRF cavities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Anodic electrocatalytic conversion of carboxylic acids on thin films of RuO 2 , IrO 2 , and Pt

The electrocatalytic upgrading of carboxylic acids, abundant biomass-derived molecules, remains a challenging task. In this paper, we report an electrocatalytic decarboxylation (ECDX) approach for the conversion of carboxylic acids into paraffins, olefins, and alcohols via (non-)Kolbe electrolysis on thin films (TFs). The ECDX rate, product selectivity, and current efficiency were potential and electrode dependent. For example, the ECDX activity of RuO 2 -TF was similar to that of Pt foil, but the selectivity to Kolbe products was lower on the former. RuO 2 -TF showed about five times higher rates for the oxygen evolution reaction than Pt foil, which resulted in lower current efficiency. The ECDX and O 2 evolution activities of IrO 2 -TF were potential dependent, but this electrode was selective to non-Kolbe products with low current efficiency (<10 %). This work highlights the performance of thin films as an alternative to bulk metals as anodes for oxidative upgrading of carboxylic acids.

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

Data-Driven Strategies for Accelerated Materials Design

The ongoing revolution of the natural sciences by the advent of machine learning and artificial intelligence sparked significant interest in the material science community in recent years. The intrinsically high dimensionality of the space of realizable materials makes traditional approaches ineffective for large-scale explorations. Modern data science and machine learning tools developed for increasingly complicated problems are an attractive alternative. An imminent climate catastrophe calls for a clean energy transformation by overhauling current technologies within only several years of possible action available. Tackling this crisis requires the development of new materials at an unprecedented pace and scale. For example, organic photovoltaics have the potential to replace existing silicon-based materials to a large extent and open up new fields of application. In recent years, organic light-emitting diodes have emerged as state-of-the-art technology for digital screens and portable devices and are enabling new applications with flexible displays. Reticular frameworks allow the atom-precise synthesis of nanomaterials and promise to revolutionize the field by the potential to realize multifunctional nanoparticles with applications from gas storage, gas separation, and electrochemical energy storage to nanomedicine. In the recent decade, significant advances in all these fields have been facilitated by the comprehensive application of simulation and machine learning for property prediction, property optimization, and chemical space exploration enabled by considerable advances in computing power and algorithmic efficiency. In this Account, we review the most recent contributions of our group in this thriving field of machine learning for material science. We start with a summary of the most important material classes our group has been involved in, focusing on small molecules as organic electronic materials and crystalline materials. Specifically, we highlight the data-driven approaches we employed to speed up discovery and derive material design strategies. Subsequently, our focus lies on the data-driven methodologies our group has developed and employed, elaborating on high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning. We discuss the general ideas, their working principles, and their use cases with examples of successful implementations in data-driven material discovery and design efforts. Furthermore, we elaborate on potential pitfalls and remaining challenges of these methods. Finally, we provide a brief outlook for the field as we foresee increasing adaptation and implementation of large scale data-driven approaches in material discovery and design campaigns.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerated Discovery of Cost-Effective Photoabsorber Materials for Near-Infrared (λ = 1600 nm) Photodetector Applications

Current infrared sensing devices are based on costly materials with relatively few viable alternatives known. To identify promising candidate materials for infrared photodetection, we have developed a high-throughput screening methodology based on high-accuracy r 2 SCAN and HSE calculations in density functional theory. Using this method, we identify ten already synthesized materials between the inverse perovskite family, the barium silver pnictide family, the alkaline pnictide family, and ZnSnAs 2 as top candidates. Among these, ZnSnAs 2 emerges as the most promising candidate due to its experimentally verified band gap of 0.74 eV at 0 K and its cost-effective synthesis through Bridgman growth. BaAgP also shows potential with an HSE-calculated band gap of 0.64 eV, although further experimental validation is required. Lastly, we discover an additional material, Ca 3 BiP, which has not been previously synthesized, but exhibits a promising optical spectra and a band gap of 0.56 eV. The method applied in this work is sufficiently general to screen wider bandgap materials in high-throughput and now extended to narrow-band gap materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Lignin deconstruction by anaerobic fungi

Abstract Lignocellulose forms plant cell walls, and its three constituent polymers, cellulose, hemicellulose and lignin, represent the largest renewable organic carbon pool in the terrestrial biosphere. Insights into biological lignocellulose deconstruction inform understandings of global carbon sequestration dynamics and provide inspiration for biotechnologies seeking to address the current climate crisis by producing renewable chemicals from plant biomass. Organisms in diverse environments disassemble lignocellulose, and carbohydrate degradation processes are well defined, but biological lignin deconstruction is described only in aerobic systems. It is currently unclear whether anaerobic lignin deconstruction is impossible because of biochemical constraints or, alternatively, has not yet been measured. We applied whole cell-wall nuclear magnetic resonance, gel-permeation chromatography and transcriptome sequencing to interrogate the apparent paradox that anaerobic fungi (Neocallimastigomycetes), well-documented lignocellulose degradation specialists, are unable to modify lignin. We find that Neocallimastigomycetes anaerobically break chemical bonds in grass and hardwood lignins, and we further associate upregulated gene products with the observed lignocellulose deconstruction. These findings alter perceptions of lignin deconstruction by anaerobes and provide opportunities to advance decarbonization biotechnologies that depend on depolymerizing lignocellulose.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Bioplastic design using multitask deep neural networks

Abstract Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The growth and nanothermite reaction of 2Al/3NiO multilayer thin films

Nanothermite NiO–Al is a promising material system for low gas emission heat sources; still, its reactive properties are highly dependent on material processing conditions. In the current study, sputter deposition is used to fabricate highly controlled nanolaminates comprised of alternating NiO and Al layers. Films having an overall stoichiometry of 2Al to 3NiO were produced with different bilayer thicknesses to investigate how ignition and self-sustained, high temperature reactions vary with changes to nanometer-scale periodicity and preheat conditions. Ignition studies were carried out with both hot plate and laser irradiation and compared to slow heating studies in hot-stage x-ray diffraction. Ignition behavior has bilayer thickness and heating rate dependencies. The 2Al/3NiO with λ ≤ 300 nm ignited via solid/solid diffusion mixing (activation energy, E a = 49 ± 3 kJ/mole). Multilayers having λ ≥ 500 nm required a more favorable mixing kinetics of solid/liquid dissolution into molten Al (E a = 30 ± 4 kJ/mole). This solid/liquid dissolution E a is a factor of 5 lower than that of the previously reported powder compacts due to the elimination of a passivating Al oxide layer present on the powder. The reactant mixing mechanism between 300 and 500 nm bilayer thicknesses was dependent on the ignition source's heating rate. The self-propagating reaction velocities of 2Al/3NiO multilayers varied from 0.4 to 2.5 m/s. Pre-heating nanolaminates to temperatures below the onset reaction temperatures associated with forming intermediate nickel aluminides at multilayer interfaces led to increased propagation velocities, whereas pre-heating samples above the onset temperatures inhibited subsequent attempts at laser ignition.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Preparing sparse solvers for exascale computing

Sparse solvers provide essential functionality for a wide variety of scientific applications. Highly parallel sparse solvers are essential for continuing advances in high-fidelity, multi-physics and multi-scale simulations, especially as we target exascale platforms. This paper describes the challenges, strategies and progress of the US Department of Energy Exascale Computing project towards providing sparse solvers for exascale computing platforms. We address the demands of systems with thousands of high-performance node devices where exposing concurrency, hiding latency and creating alternative algorithms become essential. The efforts described here are works in progress, highlighting current success and upcoming challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Bifurcations of the magnetic axis and the alternating-hyperbolic sawtooth

Here we present a sawtooth model that explains observations where the central safety factor, q 0 , stays well below one, which is irreconcilable with current models that predict a reset to q 0 = 1 after the crash. We identify the structure of the field around the magnetic axis with elements of the Lie group $\mathrm{SL}(2,\ \mathbb{R})$ and find a transition to an alternating-hyperbolic geometry when q 0 = 2/3. This transition is driven by an ideal MHD instability and leads to a chaotic magnetic field near the axis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Harnessing Machine Learning for Agnostic Biodetection

The United States’ current list-based approach to biodefense is limited because it considers only known biological agents. Alternatively, developing and adopting a system based on agent-agnostic signatures would enable detection and characterization of both known and novel agents, thereby engendering greater adaptability in the face of an evolving threat landscape. Machine learning (ML) could aid in such a transition, as it can recognize and encode highly complex patterns from multiple input data modalities and has already demonstrated success in many healthcare and defense applications. Functionalizing ML for environmental biodetection requires understanding current technical capabilities. In this article, we provide a systematic review of existing ML platforms and discuss anticipated development efforts needed to achieve effective ML-enabled, agnostic biodetection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Process for Converting Corn Bran to Furfural without Mineral Acids

Furfural is a bio-derived industrial chemical that has been identified as a platform chemical for the production of renewable fuels and chemicals. Currently, domestic feedstock and processing costs are high, so United States-based furfural production is not competative on the global market. Furthermore, furfural production costs must be much less than the current market price if it is to be competative platform for bio fuels and chemicals. An alternative low-cost feedstock for furfural is corn bran from a corn ethanol process. We have proposed a new process for producing furfural from corn bran that consumes no mineral acids. The high hemicellulose content of corn bran reduces capital and operating costs. Eliminating mineral acids reduces chemical consumption and makes the residual solids suitable for consumption as animal feed. The combination of a low cost source of hemicellulose and high valued residue could make the proposed process more economical than current processes based on corncobs.

09 BIOMASS FUELS↗
DOE OSTI2024

Emerging magnetic materials for electric vehicle drive motors [Slides]

Increasing demand for electric vehicles (EVs) is increasing demand for the permanent magnets that drive their motors, as approximately 80% of modern EV drivetrains rely on high-performance permanent magnets to convert electricity into torque. In turn, these high-performance permanent magnets rely on rare earth elements for their magnetic properties. These elements are "critical" (i.e., at risk of limiting the growth of renewable energy technologies such as EVs), which motivates an exploration for alternative materials. In this article, we overview the relevant fundamentals of permanent magnets, describe commercialized and emerging materials, and add perspective on future areas of research. Currently, the leading magnetic material for EV motors is Nd 2 Fe 14 B, with samarium-cobalt compounds (SmCo 5 and Sm 2 Co 17 ) providing the only high-performing commercialized alternative. Emerging materials that address criticality concerns include Sm 2 Fe 17 N 3 , Fe 16 N 2 , and the L10 structure of FeNi, which use lower cost elements that produce similar magnetic properties. However, these temperature-sensitive materials are incompatible with current metallurgical processing techniques. We provide perspective on how advances in low-temperature synthesis and processing science could unlock new classes of high-performing magnetic materials for a paradigm shift beyond rare earth-based magnets. In doing so, we explore the question: What magnetic materials will drive future EVs?

42 ENGINEERING↗