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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Spatiotemporal flame propagations, combustion and solid particle emissions from lean and stoichiometric gasoline direct injection engine operation

Increased particle number and mass emissions in gasoline direct injection (GDI) engines should require to investigate in-cylinder flame and combustion characteristics associated with primary source of particle emissions. In this article, in-cylinder spatiotemporal flame luminosity is quantitatively characterized to features combustion process and solid particle emissions from a GDI engine operating in two lean and one stoichiometric modes. Low- and high-steady state operating points were used to compare combustion strategies on flame development and emission characteristics. A fiber-optic sensor composed of eight measurement channels detected the flame front and the direction of the flame propagation in the combustion chamber. Solid particle emissions in the exhaust were measured using an engine exhaust particle sizer and a micro soot sensor. Results of the experiments showed that two lean combustion modes by injection strategies resulted in distinct combustion and flame development. Lean combustion modes generated high diffusion flame by burning stratified rich-mixture. Although the lean cases resulted in strong diffusion flames, the lean-homogeneous produced similar particle size distributions with the stoichiometric mode with high ash particles. Piston pool fires on the piston surface in the lean-stratified mode induced a large accumulation mode with high particle mass concentrations.

42 ENGINEERING↗
DOE OSTI2023

Time-domain Brillouin scattering theory for probe light and acoustic beams propagating at an angle and acousto-optic interaction at material interfaces

A theory has been developed to interpret time-domain Brillouin scattering (TDBS) experiments involving coherent acoustic pulse (CAP) and light pulse beams propagating at an angle to each other. It predicts the influence of the directivity pattern of their acousto-optic interaction on TDBS signals when heterodyne detection of acoustically scattered light is in backward direction to incident light. The theory reveals relationships between the carrier frequency, amplitude and duration of acoustically induced ”wave packets” in light transient reflectivity signals, and factors such as CAP duration, widths of light and sound beams, and their interaction angle. It describes the transient dynamics of these wave packets when the light and CAP encounter material interfaces, and how the light scattering by the incident CAP transforms into scattering by the reflected and transmitted CAPs. The theory suggests that single-point TDBS experiments can determine not only depth positions of buried interfaces but also their local inclinations/orientations.

42 ENGINEERING↗
DOE OSTI2020

Intense Reactivity in Sulfur–Hydrogen Mixtures at High Pressure under X-ray Irradiation

Superconductivity near room temperature in the sulfur–hydrogen system arises from a sequence of reactions at high pressures, with X-ray diffraction experiments playing a central role in understanding these chemical–structural transformations and the corresponding S:H stoichiometry. Here we document X-ray irradiation acting as both a probe and as a driver of chemical reaction in this dense hydride system. We observe a reaction between molecular hydrogen (H 2 ) and elemental sulfur (S 8 ) under high pressure, induced directly by X-ray illumination, at photon energies of 12 keV using a free electron laser. The rapid synthesis of hydrogen sulfide (H 2 S) at 0.3 GPa was confirmed by optical observations, spectroscopic measurements, and microstructural changes detected by X-ray diffraction. These results document X-ray induced chemical synthesis of superconductor-forming dense hydrides, revealing an alternative production strategy and confirming the disruptive nature of X-ray exposure in studies on high-pressure hydrogen chalcogenides, from water to high-temperature superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Terahertz electric-field-driven dynamical multiferroicity in SrTiO 3

The emergence of collective order in matter is among the most fundamental and intriguing phenomena in physics. In recent years, the dynamical control and creation of novel ordered states of matter not accessible in thermodynamic equilibrium is receiving much attention. The theoretical concept of dynamical multiferroicity has been introduced to describe the emergence of magnetization due to time-dependent electric polarization in non-ferromagnetic materials. In simple terms, the coherent rotating motion of the ions in a crystal induces a magnetic moment along the axis of rotation. Here we provide experimental evidence of room-temperature magnetization in the archetypal paraelectric perovskite SrTiO 3 due to this mechanism. We resonantly drive the infrared-active soft phonon mode with an intense circularly polarized terahertz electric field and detect the time-resolved magneto-optical Kerr effect. A simple model, which includes two coupled nonlinear oscillators whose forces and couplings are derived with ab initio calculations using self-consistent phonon theory at a finite temperature, reproduces qualitatively our experimental observations. A quantitatively correct magnitude was obtained for the effect by also considering the phonon analogue of the reciprocal of the Einstein–de Haas effect, which is also called the Barnett effect, in which the total angular momentum from the phonon order is transferred to the electronic one. Our findings show a new path for the control of magnetism, for example, for ultrafast magnetic switches, by coherently controlling the lattice vibrations with light.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Carrier recombination parameters in diamond after surface boron implantation and annealing

In this study, an optical pump–probe technique was used to detect spatial distribution of carrier lifetimes across the thickness of a high-quality diamond device structure. Two samples with as-received and boron implanted surfaces were compared to assess the role of implantation and related processing on carrier recombination mechanisms. It was found that the two implanted surfaces show very different behaviors despite undergoing the same treatment. At one of the surfaces, carrier lifetimes remained relatively unchanged, indicating surface recombination rates in the 102–103 cm/s range. At the other surface, carrier lifetimes were almost a magnitude lower and correlated with the locally detected nitrogen vacancy defect that was attributed to the elevated concentration of residual nitrogen.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

MHz free electron laser x-ray diffraction and modeling of pulsed laser heated diamond anvil cell

A new diamond anvil cell experimental approach has been implemented at the European x-ray Free Electron Laser, combining pulsed laser heating with MHz x-ray diffraction. In this report we use this setup to determine liquidus temperatures under extreme conditions, based on the determination of time-resolved crystallization. The focus is on a Fe-Si-O ternary system, relevant for planetary cores. This time-resolved diagnostic is complemented by a finite-element model, reproducing temporal temperature profiles measured experimentally using streaked optical pyrometry. This model calculates the temperature and strain fields by including (i) pressure and temperature dependencies of material properties, and (ii) the heat-induced thermal stress, including feedback effect on material parameter variations. Making our model more realistic, these improvements are critical as they give 7000 K temperature differences compared to previous models. Laser intensities are determined by seeking minimal deviation between measured and modeled temperatures. Combining models and streak optical pyrometry data extends temperature determination below detection limit. The presented approach can be used to infer the liquidus temperature by the appearance of SiO 2 diffraction spots. In addition, temperatures obtained by the model agree with crystallization temperatures reported for Fe–Si alloys. Our model reproduces the planetary relevant experimental conditions, providing temperature, pressure, and volume conditions. Those predictions are then used to determine liquidus temperatures at experimental timescales where chemical migration is limited. This synergy of novel time-resolved experiments and finite-element modeling pushes further the interpretation capabilities in diamond anvil cell experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Probing cosmic velocities with the pairwise kinematic Sunyaev-Zel’dovich signal in DESI Bright Galaxy Sample DR1 and ACT DR6

We present a measurement of the pairwise kinematic Sunyaev-Zel’dovich (kSZ) signal using the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Sample (BGS) Data Release 1 (DR1) galaxy sample overlapping with the Atacama Cosmology Telescope (ACT) CMB temperature map. Our analysis makes use of 1.6 million galaxies with stellar masses log⁡ 𝑀 ⋆ /𝑀 ⊙ >10, and we explore measurements across a range of aperture sizes (2.1′ <𝜃 ap <3.5′) and stellar mass selections. This statistic directly probes the velocity field of the large-scale structure, a unique observable of cosmic dynamics and modified gravity. In particular, at low redshifts, this quantity is especially interesting, as deviations from General Relativity are expected to be largest. Notably, our result represents the highest-significance low-redshift (𝑧 ∼ 0.3) detection of the kSZ pairwise effect yet. In our most optimal configuration (𝜃 ap =3.3′, log⁡ 𝑀 ⋆ >11), we achieve a 5⁢𝜎 detection. Assuming that an estimate of the optical depth and galaxy bias of the sample exists via e.g., external observables, this measurement constrains the fundamental cosmological combination 𝐻 0 ⁡𝑓⁡𝜎$^2_8$. A key challenge is the degeneracy with the galaxy optical depth. We address this by combining CMB lensing, which allows us to infer the halo mass and galaxy population properties, with hydrodynamical simulation estimates of the mean optical depth, $\bar{𝜏}$ . We stress that this is a proof-of-concept analysis; with BGS DR2 data we expect to improve the statistical precision by roughly a factor of two, paving the way toward robust tests of modified gravity with kSZ-informed velocity-field measurements at low redshift.

Hadzhiyska, Boryana [Institute of Astronomy; Kavli↗
DOE OSTI2020

Broadband quadrature-squeezed vacuum and nonclassical photon number correlations from a nanophotonic device

We report demonstrations of both quadrature-squeezed vacuum and photon number difference squeezing generated in an integrated nanophotonic device. Squeezed light is generated via strongly driven spontaneous four-wave mixing below threshold in silicon nitride microring resonators. The generated light is characterized with both homodyne detection and direct measurements of photon statistics using photon number–resolving transition-edge sensors. We measure 1.0(1) decibels of broadband quadrature squeezing (~4 decibels inferred on-chip) and 1.5(3) decibels of photon number difference squeezing (~7 decibels inferred on-chip). Nearly single temporal mode operation is achieved, with measured raw unheralded second-order correlations g (2) as high as 1.95(1). Multiphoton events of over 10 photons are directly detected with rates exceeding any previous quantum optical demonstration using integrated nanophotonics. These results will have an enabling impact on scaling continuous variable quantum technology.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies

The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Chirped laser dispersion spectroscopy for spectroscopic chemical sensing with simultaneous range detection

Spectroscopic chemical detection requires knowledge or determination of an optical path for accurate quantification of path-integrated concentration of species. Continuous- wave-laser-based spectroscopic systems operating in an open integrated-path remote sensing configuration are usually not equipped for optical path determination. Here we demonstrate a measurement technique capable of simultaneous spectroscopic chemical quantification and range finding. Here, the range-finding functionality is implemented with chirped laser dispersion spectroscopy. The methodology is potentially useful for remote chemical sensing in a hard-target LIDAR configuration and for automatic calibration of gas cells with unknown or varying lengths.

03 NATURAL GAS↗
DOE OSTI2023

Molecules Functionalized with Cycling Centers for Quantum Information Science

The ability for a single molecule to produce enough laser-induced fluorescence to control and detect its quantum state is known as optical cycling. This property has been used to great effect in atoms, where the relatively small number of states promotes their ability to repeatedly fluoresce, for applications such as laser cooling, precision measurement, atomic clocks, and quantum information processing. The extension of this capability to molecular species, however, has been difficult, primarily due to their propensity to become vibrationally excited during illumination. This project brought together experts from atomic physics, physical chemistry, and computational quantum chemistry to develop means to create molecules capable of optical cycling. In particular, this program sought to produce an optical cycling center (OCC), a quantum functional group capable of chemical attachment to a wide variety of molecular hosts while retaining (and thereby furnishing to its host) the ability to optically cycle. Following our team’s theoretical identification of promising species, including alkaline-earth phenoxides, we then created these species and in many cases confirmed their optical cycle closure. We now not only know of the existence of a handful of large, molecular species that can be deployed for applications in quantum technologies and measurement, but have improved our knowledge of the molecular mechanisms leading to this discovery, which will enable generalization in the future. The extension of these ideas for informed chemical design of cycling molecules with bespoke properties is now being pursued by multiple groups.

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

Conceptual Machine Learning Strategy for Maintenance of Molten Salt Heat Exchanger

This report addresses the requirements of Milestone M3.2 Define maintenance scenarios and benchmarks. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

ORR Strain Sensor as Potential Detector for HEP

Slides describing the student's work during his internship as well as broader context for how it fits within the broader scientific program of developing optical ring resonator strain sensors for particle detection.

Perez Acosta, Angel↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

How High to Fly? Mapping Evapotranspiration from Remotely Piloted Aircrafts at Different Elevations

Recent advancements in remotely piloted aircrafts (RPAs) have made frequent, low-flying imagery collection more economical and feasible than ever before. The goal of this work was to create, compare, and quantify uncertainty associated with evapotranspiration (ET) maps generated from different conditions and image capture elevations. We collected optical and thermal data from a commercially irrigated potato (Solanum tuberosum) field in the Wisconsin Central Sands using a quadcopter RPA system and combined multispectral/thermal camera. We conducted eight mission sets (24 total missions) during the 2019 growing season. Each mission set included flights at 90, 60, and 30 m above ground level. Ground reference measurements of surface temperature and soil moisture were collected throughout the domain within 15 min of each RPA mission set. Evapotranspiration values were modeled from the flight data using the High-Resolution Mapping of Evapotranspiration (HRMET) model. We compared HRMET-derived ET estimates to an Eddy Covariance system within the flight domain. Additionally, we assessed uncertainty for each flight using a Monte Carlo approach. Results indicate that the primary source of uncertainty in ET estimates was the optical and thermal data. Despite some additional detectable features at low elevation, we conclude that the tradeoff in resources and computation does not currently justify low elevation flights for annual vegetable crop management in the Midwest USA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The X-shaped Radio Galaxy J0725+5835 is Associated with an AGN Pair

X-shaped radio galaxies (XRGs) are those that exhibit two pairs of unaligned radio lobes (main radio lobes and wings). One of the promising models for the peculiar morphology is jet reorientation. To clarify this, we conducted a 5 GHz observation with the European VLBI Network (EVN) of XRG J0725+5835, which resembles the archetypal binary active galactic nuclei (AGNs) 0402+379 in radio morphology, but it is larger in angular size. In our observation, two milliarcsecond-scale radio components with nonthermal radio emission are detected. Each of them coincides with an optical counterpart with similar photometric redshift and (optical and infrared) magnitude, corresponding to dual active nuclei. Furthermore, with the improved Very Large Array (VLA) images, we find a bridge between the two radio cores and a jet bending in the region surrounding the companion galaxy. This further supports the interplay between the main and companion galaxies. In addition, we also report the discovery of an arcsecond-scale jet in the companion. Given the projected separation of ~100 kpc between the main and companion galaxies, XRG J0725+5835 is likely associated with a dual jetted-AGN system. In both EVN and VLA observations, we find signatures that the jet is changing its direction, which is likely responsible for the X-shaped morphology. For the origin of jet reorientation, several scenarios are discussed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Advancing automated classification of atmospheric aerosols from Single-Particle Mass Spectrometry (SPMS) data remains challenging due to overlapping ion signatures, compositional diversity, and limited labeled data. This study evaluates supervised and semi-supervised learning frameworks to enhance aerosol identification by jointly leveraging labeled and unlabeled spectra. Four models were compared: a supervised Support Vector Machine (SVM), a self-training SVM, a stacked autoencoder classifier, and a stacked autoencoder trained using a temporal-ensembling Mean Teacher approach. All models achieved high and stable accuracies (90.0 %–91.1 %), surpassing previous results on the same dataset (87 %) and matching the performance of state-of-the-art deep learning methods. Despite small global metric differences (≤ 1 %), semi-supervised variants yielded up to 5 %–10 % improvements for compositionally rare particle types – such as soot (0.77 % of spectra, F1-score: 0.93–0.97) and hazelnut pollen (0.98 % of spectra, F1-score: 0.97–1.00) – equating to roughly ∼ 187 additional correctly classified spectra. These gains are scientifically significant, as such rare particles exert disproportionate influence on radiative absorption and ice nucleation processes; their improved detection reduces modeled uncertainties in aerosol absorption optical depth and mixed-phase cloud ice nucleation rates. The models' residual misclassifications (≈ 9 %) largely arise from true spectral overlap among chemically adjacent species (e.g., Na- vs. K-feldspar, coated vs. uncoated feldspars), reflecting physical compositional continuity rather than algorithmic error. Collectively, these findings demonstrate that leveraging unlabeled data to learn robust spectral representations and refine classification enhances both fidelity and interpretability, bridging data-driven analysis with aerosol–climate process understanding.

54 ENVIRONMENTAL SCIENCES↗