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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Developing Efficient Benzene Additives for 19.43% Efficiency of Organic Solar Cells by Crossbreeding Effect of Fluorination and Bromination

Employing volatile solid additives have emerged as a promising method to optimize the morphology and improve the performance of organic solar cells (OSCs). However, principles governing the efficient design of solid additives remain elusive. In this work, the programmed fluorination and/or bromination on benzene core to develop efficient additives for OSCs is reported. The programmed fluorination and/or bromination endow the five halogen benzene derivatives, 1,3,5-trifluorobenzene, hexafluorobenzene, 1,3,5-tribromo-2,4,6-trifluorobenzene (TFTB), 1,3,5-tribromobenzene, and hexabromobenzene, with different melting and boiling points, volatility, as well as interactions with the host blend. Studies indicate that the additives with extremely high and low volatility are almost powerless and even detrimental to the morphology evolution. Among them, the combination of fluorine and bromine atoms on TFTB not only enables the more appropriate m.p./b.p. and volatility, but also exerts stronger molecular interactions with the host blend, giving rise to higher ordered molecular packing and more favorable morphology. Importantly, TFTB exhibits good universality to optimize the performances of OSCs with high power conversion efficiencies (PCEs; over 18%) in a group of binary blend systems, and an impressive PCE of 19.43% in the ternary PBTz-F:PM6:L8-BO system.

36 MATERIALS SCIENCE↗
DOE OSTI2023

LISA Galactic binaries in the Roman Galactic Bulge Time-Domain Survey

ABSTRACT Short-period Galactic white dwarf binaries detectable by Laser Interferometer Space Antenna are the only guaranteed persistent sources for multimessenger gravitational-wave astronomy. Large-scale surveys in the 2020s present an opportunity to conduct preparatory science campaigns to maximize the science yield from future multimessenger targets. The Nancy Grace Roman Space Telescope Galactic Bulge Time-Domain Survey will (in its Reference Survey design) image seven fields in the Galactic Bulge approximately 40 000 times each. Although the Reference Survey cadence is optimized for detecting exoplanets via microlensing, it is also capable of detecting short-period white dwarf binaries. In this paper, we present forecasts for the number of detached short-period binaries the Roman Galactic Bulge Time-Domain Survey will discover and the implications for the design of electromagnetic surveys. Although population models are highly uncertain, we find a high probability that the baseline survey will detect of the order of ∼5 detached white dwarf binaries. The Reference Survey would also have a ${\gtrsim} 20\,{\rm per\,cent}$ chance of detecting several known benchmark white dwarf binaries at the distance of the Galactic Bulge.

Astronomy & Astrophysics↗
DOE OSTI2025

Dual aggregation steering in bulk-heterojunction via solvent engineering toward efficient and stable binary organic solar cells

In high-performance organic solar cells (OSCs), efficient charge transport hinges on a well-optimized morphology of the photoactive layer, which depends critically on controlled aggregation and favorable interactions between donor and acceptor materials. In this work, we introduce a cascade solvent system comprising high-boiling-point ethylbenzene (EB) and low-boiling-point chloroform (CF) to finely tune the aggregation behavior of the D18 donor and L8-BO acceptor. The incorporation of EB not only promotes the H-aggregation of D18 and the J-aggregation of L8-BO but also facilitates the formation of ideal nanoscale phase separation, thereby suppressing bimolecular recombination. As a result, devices processed with the EB/CF solvent blend achieve a best power conversion efficiency (PCE) of 19.6 % and enhanced operational stability, outperforming those fabricated with pure CF (17.1 %). In conclusion, this study offers a reliable and effective strategy for optimizing donor and acceptor aggregation, providing a viable pathway toward higher-performance OSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Binary vector copy number engineering improves Agrobacterium -mediated transformation

The copy number of a plasmid is linked to its functionality, yet there have been few attempts to optimize higher-copy-number mutants for use across diverse origins of replication in different hosts. We use a high-throughput growth-coupled selection assay and a directed evolution approach to rapidly identify origin of replication mutations that influence copy number and screen for mutants that improve Agrobacterium-mediated transformation (AMT) efficiency. By introducing these mutations into binary vectors within the plasmid backbone used for AMT, we observe improved transient transformation of Nicotiana benthamiana in four diverse tested origins (pVS1, RK2, pSa and BBR1). For the best-performing origin, pVS1, we isolate higher-copy-number variants that increase stable transformation efficiencies by 60–100% in Arabidopsis thaliana and 390% in the oleaginous yeast Rhodosporidium toruloides. Our work provides an easily deployable framework to generate plasmid copy number variants that will enable greater precision in prokaryotic genetic engineering, in addition to improving AMT efficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Alloying Effects on the Transport Properties of Refractory High-entropy Alloys

Additive Manufacturing (AM) has opened new frontiers for the design of refractory high-entropy alloys (HEAs) for high-temperature applications. The thermal conductivity of the AM feedstock is among the most important thermo-physical properties that control the melting and solidification process. Despite its significance, there remains a notable gap in both computational and experimental research concerning the thermal conductivity of HEAs. Here, we use density functional theory (DFT) to systematically investigate the alloying effects on the transport properties of Ti-Cr-Mo-W-V-Nb-Ta RHEAs, including electrical and thermal conductivities and Seebeck coefficient. The relaxation time of charge carriers is a key underlying parameter determining thermal conductivity that is exceedingly challenging to predict from first principles alone, and we thus follow the approach by Mukherjee, Satsangi, and Singh [Chem Mater 32, 6507 (2022)] to optimize the relaxation time for RHEAs. Here we validated thermal conductivity predictions on elemental solids, binary and ternary alloys, and RHEAs and compared them against thermodynamic (CALPHAD) predictions and our experiments with good correlations. To understand observed trends in thermal conductivity, we assessed the phase stability, electronic structure, phonon, and intrinsic- and tensile strength of down-selected RHEAs. Our electronic structure and phonon results connect well with the observed compositional trends for thermal transport in RHEAs. Our DFT assessment and CALPHAD predictions provide a unique design guide for RHEAs with tailored thermal conductivity, a critical consideration for AM and thermal-management applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Designing an Optimal Kilonova Search Using DECam for Gravitational-wave Events

We address the problem of optimally identifying all kilonovae detected via gravitational-wave emission in the upcoming LIGO/Virgo/KAGRA observing run, O4, which is expected to be sensitive to a factor of ~7 more binary neutron star (BNS) alerts than previously. Electromagnetic follow-up of all but the brightest of these new events will require >1 m telescopes, for which limited time is available. We present an optimized observing strategy for the DECam during O4. We base our study on simulations of gravitational-wave events expected for O4 and wide-prior kilonova simulations. We derive the detectabilities of events for realistic observing conditions. We optimize our strategy for confirming a kilonova while minimizing telescope time. For a wide range of kilonova parameters, corresponding to a fainter kilonova compared to GW170817/AT 2017gfo, we find that, with this optimal strategy, the discovery probability for electromagnetic counterparts with the DECam is ~80% at the nominal BNS gravitational-wave detection limit for O4 (190 Mpc), which corresponds to an ~30% improvement compared to the strategy adopted during the previous observing run. For more distant events (~330 Mpc), we reach an ~60% probability of detection, a factor of ~2 increase. For a brighter kilonova model dominated by the blue component that reproduces the observations of GW170817/AT 2017gfo, we find that we can reach ~90% probability of detection out to 330 Mpc, representing an increase of ~20%, while also reducing the total telescope time required to follow up events by ~20%.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Binary Fe/Mn-Based Nanocomposites as Li-Free Cathode Materials for Li Batteries Assembled in Charged State

Li-ion batteries play important roles in this mobile society. The ever-increasing demand for energy storage, particularly from electric vehicles, requires next-generation Li batteries with higher energy density and better safety. In contrast to existing Li-ion batteries using lithiated cathodes and Li-free anodes, we explore Li-free cathodes coupled with lithium/ lithiated anodes in order to construct new Li batteries assembled in a charged state. Here, two highly promising Li-free cathode materials of 1-D FeOF nanorods and 2-D monolayer MnO 2 nanosheets are integrated to make FeOF@MnO 2 nanocomposites. FeOF nanorods are sandwiched by monolayer MnO 2 nanosheets where FeOF nanoparticles could prevent the restacking of the monolayer MnO 2 nanosheets and the presence of monolayer MnO 2 nanosheets could enhance the electrical integration of the FeOF nanorods. Synergistic effects of the binary Fe/Mn-based cathodes can lead to both high voltage and high capacity, compared to individual components. Electrochemical evaluation reveals that binary Fe/Mn-based Li-free cathodes demonstrate promising performances and are worthy of further investigation and optimization.

25 ENERGY STORAGE↗
DOE OSTI2025

Maskman

SAND2025-04369O Maskman is a user-friendly tool designed to create hex masks, which are essential for optimizing application performance in high-performance computing environments. By converting a list of integers into binary and then hex masks, Maskman simplifies the process of setting application affinity. This ensures that software runs efficiently on specific nodes within a computing cluster. Ideal for researchers and developers, Maskman streamlines the preparation of inputs for HPC schedulers, enhancing resource management and improving overall system performance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Pase, Douglas [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2022

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments

This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.

R. Kulkarni, Shruti↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2024

Quantifying Epistemic Uncertainty in Binary Classification via Accuracy Gain

ABSTRACT Recently, a surge of interest has been given to quantifying epistemic uncertainty (EU), the reducible portion of uncertainty due to lack of data. We propose a novel EU estimator in the binary classification setting, as the posterior expected value of the empirical gain in accuracy between the current prediction and the optimal prediction. In order to validate the performance of our EU estimator, we introduce an experimental procedure where we take an existing dataset, remove a set of points, and compare the estimated EU with the observed change in accuracy. Through real and simulated data experiments, we demonstrate the effectiveness of our proposed EU estimator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids: Preprint

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusing on external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programming (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILPPS) optimization through McCormick's relaxation to reduce computational complexity. A predictive window window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrating the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources while shifting the external power transfers to specified time durations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

A high transmission tender X-ray monochromator employing a matched pair of multilayer grating and mirror

It is now well established that multilayer coated gratings may offer high diffraction efficiencies over the tender X-ray range, from about 1 keV to 5 keV, covering the gap between single layer coated grating monochromators and crystal monochromators. Nevertheless, few beamlines in the world are using such gratings in their monochromator. The successful implementation of a multilayer grating monochromator requires producing a matched pair of a multilayer grating and a multilayer mirror, and this matching is not straightforward as it must account for different incidence angles and refraction effects on each element. Here we review the realization of the multilayer grating monochromator of the SIRIUS beamline which has been successfully in service for several years. We show how, by alternating computer simulation with our diffraction code and measurements, we could optimize the monochromator transmission on a very wide energy range. After the grating was coated, it was found that the angle of optimal efficiency versus photon energy was significantly different from what was predicted by a simple conformal model of binary layers. Layer interdiffusion and profile smoothing during the deposition process must be added to the multilayer model to reproduce the measured data. The critical adjustment of the mirror multilayer period is achieved by the lateral translation of the mirror, which was given a small transverse period gradient. The monochromator is thus providing high transmission efficiency in the 1 to 5 keV energy range, more than 30% over 2.5 keV and up to 46% at 4.6 keV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

From Structure to Function: Zn/Mn-Modified Maghemite as an Advanced Nanoplatform for Magnetic Hyperthermia and Radionuclide Therapy

The development of nanoplatforms capable of efficient heat generation and stable radionuclide delivery is essential for effective bimodal cancer therapy. Here, in this study, binary (Fe–M) and ternary (Fe–M–M′) metal oxide nanoparticles were synthesized via a polyol method optimized to produce flower-like γ-Fe 2 O 3 (maghemite) structures, with M and M′ representing Zn and/or Mn. Comprehensive structural and magnetic characterization was conducted to explain the relationship between composition, defect structure, and hyperthermic performance. The analyses revealed that cation substitution induced an Fe-site vacancy, primarily at octahedral positions, leading to local structural distortions, as confirmed by powder X-ray diffraction and pair distribution function analysis. The optimized composition, with Zn/Mn/Fe = 0.040:0.182:1, exhibited the highest concentration of vacancies and structural disorder. These vacancies altered the bonding environment, enhancing magnetic interactions at tetrahedral sites while weakening those at the octahedral positions. The resulting multicore nanoflowers (20–63 nm; core size 13–18 nm) displayed strong heating performance, with intrinsic loss power ranging from 0.34 to 5.77 nHm 2 kg –1 . The optimized sample achieved a temperature increase of 30 °C within 2 min and a specific absorption rate of 369 W g –1 . This composition was further coated with citrate (CA) and successfully radiolabeled with 177 Lu, achieving a radiolabeling yield of 92.7% and excellent stability, thus forming a robust nanoplatform for combined magnetic hyperthermia and radionuclide therapy. Biological evaluation of the optimized S5 composition revealed selective cytotoxicity toward HeLa and LS174 cells, while toxicity was significantly lower to A549, A375, and normal MRC-5 cells. Citrate coating of S5 nanoparticles (S5@CA) drastically reduced their cytotoxicity across all tested cell lines (IC 50 > 200 μg mL –1 ), confirming their enhanced biocompatibility for therapeutic applications. In HeLa cells subjected to magnetic hyperthermia, the viability decreased to approximately 84% after 30 min and 61% after 60 min of treatment, demonstrating the sustained hyperthermic effect at a controlled working temperature of 48 °C. These results underscore the effectiveness of cation substitution and vacancy engineering in tailoring the functional properties of maghemite-based nanomaterials for advanced multimodal cancer therapies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗