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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Importance of an Ensemble Approach for Modeling Aerosol‐Convection Interactions

Aerosol-convection interactions modulate cloud microphysics, thermodynamics, and updraft intensity, contributing to climate-scale aerosol-radiative forcing. However, quantifying aerosol indirect effects in mixed-phase deep convection remains challenging due to uncertainties in parameterized physics and initial conditions driving nonlinear evolution of convective processes. This study investigates the convective updraft sensitivity to random initial temperature perturbations using an idealized ensemble modeling framework informed by in situ thermodynamic and aerosol observations from the DOE TRACER field campaign. We analyze the impact of small-scale initial perturbations on updraft velocity and supersaturation, and determine the ensemble size required to minimize stochastic internal variability. Results show that minor thermodynamic perturbations can produce updraft variability comparable to aerosol-induced changes reported in prior work. An ensemble of 10 members sufficiently reduces variability, enabling robust investigation of aerosol-related updraft invigoration and informing the design of future ensemble-based aerosol-convection interaction studies in environments with significant spatiotemporal mesoscale thermodynamic and aerosol heterogeneity.

aerosol-convection interactions↗
DOE OSTI2020

Record thermopower found in an IrMn-based spintronic stack

The Seebeck effect converts thermal gradients into electricity. As an approach to power technologies in the current Internet-of-Things era, on-chip energy harvesting is highly attractive, and to be effective, demands thin film materials with large Seebeck coefficients. In spintronics, the antiferromagnetic metal IrMn has been used as the pinning layer in magnetic tunnel junctions that form building blocks for magnetic random access memories and magnetic sensors. Spin pumping experiments revealed that IrMn Néel temperature is thickness-dependent and approaches room temperature when the layer is thin. Here, we report that the Seebeck coefficient is maximum at the Néel temperature of IrMn of 0.6 to 4.0 nm in thickness in IrMn-based half magnetic tunnel junctions. We obtain a record Seebeck coefficient 390 (±10) μV K -1 at room temperature. Our results demonstrate that IrMn-based magnetic devices could harvest the heat dissipation for magnetic sensors, thus contributing to the Power-of-Things paradigm.

42 ENGINEERING↗
DOE OSTI2022

A measurement of stellar surface gravity hidden in radial velocity differences of comoving stars

The gravitational redshift induced by stellar surface gravity is notoriously difficult to measure for non-degenerate stars, since its amplitude is small in comparison with the typical Doppler shift induced by stellar radial velocity. In this study, we make use of the large observational data set of the Gaia mission to achieve a significant reduction of noise caused by these random stellar motions. By measuring the differences in velocities between the components of the pairs of comoving stars and wide binaries, in this work we are able to statistically measure the combined effects of gravitational redshift and convective blueshifting of spectral lines, and nullify the effect of the peculiar motions of the stars. For the subset of stars considered in this study, we find a positive correlation between the observed differences in Gaia radial velocities and the differences in surface gravity and convective blueshift inferred from effective temperature and luminosity measurements. The results rule out a null signal at the 5σ level for our full data set. Additionally, we study the subdominant effects of binary motion, and possible systematic errors in radial velocity measurements within Gaia. Results from the technique presented in this study are expected to improve significantly with data from the next Gaia data release. Such improvements could be used to constrain the mass–luminosity relation and stellar models that predict the magnitude of convective blueshift.

(stars:) binaries: general↗
DOE OSTI2021

Benchmarking SARS CoV-2 Infection in the Workplace to Support Continuity of Operations

Objective: The COVID-19 pandemic jeopardizes continuity of operations of workplaces and the health and safety of workers. Exemplar workplace-related SARS-CoV-2 benchmarks are described and illustrated with empirical data. Methods: Benchmarks were collected over a 9-month period on a large workplace (N = 5500+). These ranged from quantitative indices associated with RT-qPCR targeted testing and random surveillance screening, surveillance for new variants of SARS-CoV-2, intensive contact tracing, case management, return to work procedures, to monitoring of antibody seropositive status. Results: Data and analyses substantiated effectiveness of interventions. This was evidenced in suppressed infection rates, rapid case identification and isolation, acceptance of the program by employees, documentation of presumptive immunity, and working relationships with senior management. Conclusions: These SARS-CoV-2 exemplar benchmarks provided an evidence-base for practice and contributed strategically to organizational decisions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Weak decays in superheavy nuclei

Superheavy nuclei represent the extreme atoms and nuclides known at the limit of mass and charge. The observed superheavy nuclei are all proton-rich; they decay primarily by emitting 𝛼 particles and by fission with a possible small electron capture (EC) branch. Here, due to the huge atomic numbers and associated relativistic effects, EC decays of superheavy systems are expected to differ from what is known in lighter nuclei. In this letter, using the quantified relativistic nuclear density functional theory and the quasiparticle random-phase approximation with the interaction optimized to experimental EC/𝛽 ± -decay half-lives, and Gamow-Teller resonance energies, we study the EC/𝛽 ± -decays in 𝑍=101–118 nuclei. Both allowed (1 + ) and first-forbidden (0 − ,1 − and 2 − ) transitions are considered. We show that the first-forbidden 1 − transitions dominate the decay rates in almost all studied nuclei. For proton-rich nuclei, EC dominates over 𝛽 + decay. Based on calculations with two relativistic energy density functionals, we identify 45 candidate nuclei in which a competition between weak decays and 𝛼 decay and spontaneous fission is expected.

A ≥ 220↗
DOE OSTI2024

A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening

It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Signatures of aerosol-cloud interactions in GiOcean: a coupled global reanalysis with two-moment cloud microphysics

Aerosols influence the Earth's radiative balance through direct interactions with radiation and by affecting cloud properties. Anthropogenic aerosols have led to cooling during the industrial era through aerosol–cloud interactions (ACI), including aerosol effects on cloud microphysical properties and the subsequent adjustments. However, large uncertainties remain in Earth system models (ESMs) regarding the magnitude of this cooling. In part, ESMs substantially disagree on cloud properties, thermodynamics, the hydrological cycle, and general circulation. Reanalysis provides a useful avenue for exploring the impact of ACI on clouds and radiation because its atmosphere is forced to match realistic conditions through the assimilation of observations. Here, we explore the impact of ACI on clouds in the GiOcean reanalysis – the first to incorporate aerosol-cloud interactions. We contrast variables important for ACI between GiOcean and satellite observations and develop 2-dimensional lookup tables of ACI for both using a source-sink budget perspective to attribute the changes in cloud droplet number (Nd) and liquid water path (LWP) to aerosol and meteorology. A compositing analysis using lookup tables shows that GiOcean captures key aspects of aerosol–cloud–precipitation interactions, including (1) activation of aerosol into cloud droplets, (2) effective precipitation scavenging of Nd, (3) suppression of precipitation by high Nd in regions with heavy aerosol emissions. In contrast, satellite observations do not exhibit clear patterns for processes (2) and (3). Random Forest analysis shows that interannual variability in Nd and LWP over the Northern Hemisphere ocean in GiOcean is primarily driven by precipitation, consistent with satellite observations.

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

Total-Ionizing-Dose Effects on Long-term Data Retention Characteristics of Commercial 3-D NAND Memories

This article evaluates the data retention characteristics of irradiated multi-level cell (MLC) 3-D NAND flash memories. We irradiate the memory chips by a Co-60 gamma-ray source for up to 50 krad(Si) and then write a random data pattern on the irradiated chip to find its retention characteristics. The experimental results show that the data retention property of the irradiated chips is significantly degraded compared to the un-irradiated ones. We evaluate two independent strategies to improve the data retention characteristics of the irradiated chips. Furthermore, the first method involves high-temperature annealing of the irradiated chip while the second method suggests pre-programming the memory modules before deploying them in radiation-prone environments.

3-D NAND flash↗
DOE OSTI2020

Severe Dirac Mass Gap Suppression in Sb 2 Te 3 -Based Quantum Anomalous Hall Materials

The quantum anomalous Hall (QAH) effect appears in ferromagnetic topological insulators (FMTIs) when a Dirac mass gap opens in the spectrum of the topological surface states (SSs). Unaccountably, although the mean mass gap can exceed 28 meV (or ~320 K), the QAH effect is frequently only detectable at temperatures below 1 K. Using atomic-resolution Landau level spectroscopic imaging, we compare the electronic structure of the archetypal FMTI Cr 0.08 (Bi 0.1 Sb 0.9 ) 1.92 Te 3 to that of its nonmagnetic parent (Bi 0.1 Sb 0.9 ) 2 Te 3 , to explore the cause. In (Bi 0.1 Sb 0.9 ) 2 Te 3 , we find spatially random variations of the Dirac energy. Statistically equivalent Dirac energy variations are detected in Cr 0.08 (Bi 0.1 Sb 0.9 ) 1.92 Te 3 with concurrent but uncorrelated Dirac mass gap disorder. Additionally, these two classes of SS electronic disorder conspire to drastically suppress the minimum mass gap to below 100 μeV for nanoscale regions separated by <1 μm. This fundamentally limits the fully quantized anomalous Hall effect in Sb 2 Te 3 -based FMTI materials to very low temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Effects of quasiparticle-vibration coupling on Gamow-Teller strength and 𝛽 decay with the Skyrme proton-neutron finite-amplitude method

We adapt the proton-neutron finite-amplitude method, which in its original form is an efficient implementation of the Skyrme quasiparticle random phase approximation, to include the coupling of quasiparticles to like-particle phonons. The approach allows us to add beyond-quasiparticle random-phase approximation correlations to computations of Gamow-Teller strength and 𝛽-decay rates in deformed nuclei for the first time. We test the approach in several deformed isotopes for which measured strength distributions are available. Furthermore, the additional correlations dramatically improve agreement with the data, and will lead to improved global 𝛽-decay rates.

Beta decay↗
DOE OSTI2023

Nanoscale fluctuation of stacking fault energy strengthens multi-principal element alloys

Chemical randomness and the associated energy fluctuation are essential features of multi-principal element alloys (MPEAs). Due to these features, nanoscale stacking fault energy (SFE) fluctuation is a natural and independent contribution to strengthening MPEAs. However, existing models for conventional alloys (i.e., alloys with one principal element) cannot be applied to MPEAs. The extreme values of SFEs required by such models are unknown for MPEAs, which need to calculate the nanoscale volume relevant to the SFE fluctuation. In the present work, we developed an analytic model to evaluate the strengthening effect through the SFE fluctuation, profuse in MPEAs. The model has no adjustable parameters, and all parameters can be determined from experiments and ab initio calculations. Further, this model explains available experimental observations and provides insightful guidance for designing new MPEAs based on the SFE fluctuation. It generally applies to MPEAs in random states and with chemical short-range order.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An Optimized Fibril Network Morphology Enables High‐Efficiency and Ambient‐Stable Polymer Solar Cells

Abstract Morphological stability is crucially important for the long‐term stability of polymer solar cells (PSCs). Many high‐efficiency PSCs suffer from metastable morphology, resulting in severe device degradation. Here, a series of copolymers is developed by manipulating the content of chlorinated benzodithiophene‐4,8‐dione (T1‐Cl) via a random copolymerization approach. It is found that all the copolymers can self‐assemble into a fibril nanostructure in films. By altering the T1‐Cl content, the polymer crystallinity and fibril width can be effectively controlled. When blended with several nonfullerene acceptors, such as TTPTT‐4F, O‐INIC3, EH‐INIC3, and Y6, the optimized fibril interpenetrating morphology can not only favor charge transport, but also inhibit the unfavorable molecular diffusion and aggregation in active layers, leading to excellent morphological stability. The work demonstrates the importance of optimization of fibril network morphology in realizing high‐efficiency and ambient‐stable PSCs, and also provides new insights into the effect of chemical structure on the fibril network morphology and photovoltaic performance of PSCs.

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

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Further, milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

42 ENGINEERING↗
DOE OSTI2023

Using machine learning to predict the correlation of spectra using SDSS magnitudes as an improvement to the Locus Algorithm

The Locus Algorithm is a new technique to improve the quality of differential photometry by optimising the choices of reference stars. At the heart of this algorithm is a routine to assess how good each potential reference star is by comparing its SDSS magnitude values to those of the target star. In this way, the difference in wavelength-dependent effects of the Earth’s atmospheric scattering between target and reference can be minimised. This paper sets out a new way to estimate the quality of each reference star using machine learning. A random subset of stars from SDSS with spectra was chosen. For each one, a suitable reference star, also with a spectrum, was chosen. The correlation between the two spectra in the SDSS r band (between 550 nm and 700 nm) was taken to be the gold-standard measure of how well they match up for differential photometry. The five SDSS magnitude values for each of these stars were used as predictors. A number of supervised machine learning models were constructed on a training set of the stars and were each evaluated on a testing set. The model using Support Vector Regression had the best performance of these models. It was then tested on a final, hold-out, validation set of stars to get an unbiased measure of its performance. With an R 2 of 0.62, the SVR model presents enhanced performance for the Locus Algorithm technique.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Effects of Ionic Group Distribution on the Structure and Dynamics of Amorphous Polymer Melts

Ionizable groups tethered to a polymer backbone often associate to form ionic clusters, whose features are determined by the balance of the polymer backbone and electrostatics. These assemblies impact the dynamics of the macromolecules and their ability to transport ions. Here, using fully atomistic molecular dynamics (MD) simulations, we investigate the effects of the distribution of ionizable groups along the polymer backbone on cluster characteristics and the resulting impacts on the structure and dynamics of amorphous polymers. Particularly, we probe polystyrene sulfonates (PSS) with random, precise, and block configurations of the SO 3 – sulfonate groups along the backbone with Na + as the counterion. We find that the distribution of the ionic groups affects the shape and distribution of the clusters as well as the internal packing of the ionizable groups in the cluster and the number of unique chains that participate in each cluster, affecting the structure and the dynamics of the polymers. Furthermore, the signature of ionic clusters, observed in the static structure factor S(q) for all three distributions, is significantly more pronounced for the precise and blocky polymers compared to the random one. Remarkably, we find that the local mobility of the polymer segments is not only affected by the number and size of the clusters but also by the number of polymer chains associated with clusters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Magnetic flux noise in superconducting qubits and the gap states continuum

In the present study we investigate the selected local aspects of the metal-induced gap states (MIGSs) at the disordered metal–insulator interface, that were previously proposed to produce magnetic moments responsible for the magnetic flux noise in some of the superconducting qubit modalities. Our analysis attempts to supplement the available studies and provide new theoretical contribution toward their validation. In particular, we explicitly discuss the behavior of the MIGSs in the momentum space as a function of the onsite energy deviation, that mimics random potential disorder at the interface in the local approximation. It is found, that when the difference between the characteristic electronic potentials in the insulator increases, the corresponding MIGSs become more localized. This effect is associated with the increasing degree of the potential disorder that was earlier observed to produce highly localized MIGSs in the superconducting qubits. At the same time, the presented findings show that the disorder-induced localization of the MIGSs can be related directly to the decay characteristics of these states as well as to the bulk electronic properties of the insulator. As a result, our study reinforces plausibility of the previous corresponding investigations on the origin of the flux noise, but also allows to draw future directions toward their better verification.

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

Development and Validation of Algorithms That Analyze Communicating Thermostat Data to Identify Enclosure Retrofit Opportunities

Annual energy savings of up to $\$ 4$ to $\$ 5$ billion could be achieved nationwide through basic insulation and heating system retrofits of existing homes. However, current utility energy efficiency programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and online advertising that lack specificity about home-specific retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments (HEAs) that are inconvenient to homeowners, expensive, and of variable accuracy. We developed computational algorithms that automatically analyze communicating thermostat (CT) heating data that could be used to increase the customer uptake of insulation and air sealing energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. The algorithms are based on an extended second-order grey-box model that characterizes a building’s thermal response using lumped elements, coupled with an empirical model of infiltration that accounts for both wind and stack effects. The basic parameters of the model correspond to actual physical parameters of the home, i.e., the home’s overall R-value of and the building envelope ACH50. Unlike the conventional approach, which estimates model parameters based on the best fit to the observed time-dependent room temperature, our approach derives correlations between the daily heating system runtime and temperature difference (indoor-outdoor) that are more robust to data quality issues in real-world applications. We also used HEA data for algorithm development and validation. With the help of our utility partners, Eversource and National Grid, we obtained data sets for hundreds of Massachusetts homes. For each home, these data sets included three sets of information anonymized by the utility: (1) CT data (HVAC runtime, room temperature, and, for some vendors, outdoor temperature and wind speed) collected by the CT vendor (one of three) over a heating season, (2) HEA report performed by the HEA vendor (same vendor for all homes), (3) Monthly utility gas bills coincident with the CT data (3 to 24 per home, depending on availability). For some homes, we also obtained blower-door test results. Initially, we applied the algorithms developed to homes with a single CT and then extended them to homes with two CTs by using an equivalent home approach. Finally, we developed algorithms for prediction of energy savings and a methodology of comparing our predictions with those generated by HEAs. The main technical results indicate that we can reliably identify homes with insulation and/or air sealing retrofit opportunities and provide accurate savings predictions. Our hypothesis is that the algorithms could be applied to utility energy efficiency programs to identify homes that could realize significant energy savings from insulation and/or air sealing retrofits. This information could then be used to reach out to those homes with highly customized outreach, thereby delivering increased program energy savings and cost-effectiveness. This would: Significantly increase the uptake rate of on-site HEAs, and Significantly increase the fraction of HEAs resulting in ECM implementation. To test these hypotheses, we designed and conducted a randomized controlled trial (RCT). The RCT results suggest that personal messaging leads to a two- to five-fold increase in the HEA uptake rate.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Origin of Mechanical Enhancement in Polymer Nanoparticle (NP) Composites with Ultrahigh NP Loading

Polymer nanoparticle composites (PNCs) with ultrahigh loading of nanoparticles (NPs) (>50%) have been shown to exhibit markedly improved strength, stiffness, and toughness simultaneously compared to the neat systems of their components. Recent experimental studies on the effect of polymer fill fraction in these highly loaded PNCs reveal that even at low polymer fill fractions, hardness and modulus increase significantly. In this work, we aim to understand the origin of these performance enhancements by examining the dynamics of both polymer and NPs under tensile deformation. We perform molecular dynamics simulations of coarse-grained, glassy polymer in random-close-packed NP packings with a varying polymer fill fraction. We characterize the mechanical properties of the PNC systems, compare the NP rearrangement behavior, and study the polymer segmental and chain-level dynamics during deformation below the polymer glass transition. Finally, our simulation results confirm the experimentally observed increase in modulus at low polymer fill fractions, and we provide evidence that the source of mechanical enhancement is the polymer bridging effect.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗