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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Synthesis and growth of solution-processed chiral perovskites

In materials science, chiral perovskites stand out due to their exceptional optoelectronic properties and the versatility in their structure and composition, positioning them as crucial in the advances of technologies in spintronics and chiroptical systems. This review underlines the critical role of synthesizing and growing these materials, a process integral to leveraging their complex interplay between structural chirality and distinctive optoelectronic properties, including chiral-induced spin selectivity and chiroptical activity. The paper offers a comprehensive summary and discussion of the methods used in the synthesis and growth of chiral perovskites, delving into extensive growth techniques, fundamental mechanisms, and strategic approaches for the engineering of low-dimensional perovskites, alongside the creation of novel chiral ligands. The necessity of developing new synthetic approaches and maintaining precise control during the growth of chiral perovskites is emphasized, aiming to enhance their structural chirality and boost their efficiency in spin and chiroptical selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancing electrocatalytic performance of RuO 2 -based catalysts: mechanistic insights, strategic approaches, and recent advances

Abstract Electrochemical water splitting presents the ultimate potential of hydrogen and oxygen production; however, regulating the rate and efficiency of water splitting is highly dependent on the accessibility of extremely efficient electrode materials for slow performance kinetics and large overpotential of both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Ruthenium oxide (RuO 2 ) based materials display high performance for OER and HER because of their capacity to bind oxygen, eminent catalytic activity, low cost compared to other precious metals, and stability in a wide pH range. However, there is still much space to promote the OER and HER activity and stability of RuO 2 to fulfill the necessity for practical applications in water splitting. Different researchers applied multiple approaches that boosted the catalytic performance of RuO 2 -based electrocatalysts toward overall water splitting. Herein, this review provides a comprehensive overview of recent advancements in RuO 2 -based materials in the field of water electrolysis for the generation of alternative energies. It gives a general description of water splitting in acidic and alkaline settings, including reaction mechanisms as well as common evaluation elements for the catalytic function of the materials. Most of the reviews reported based on RuO 2 materials are only focused on OER performance, but this review highlighted comprehensive ideas on different strategies like morphology design, electronic structure, electrolytes, and compositions for optimizing both electrocatalytic HER and OER functioning of RuO 2 -based electrocatalysts.

KC, Binod Raj (ORCID:0009000885806906)↗
DOE OSTI2025

Overcoming sparse datasets with multi-task learning as applied to high entropy alloys

Abstract The design of novel High Entropy Alloys for use in high-temperature applications is an area of active interest due to their potential to provide exceptional properties compared to conventional alloys. Since the increased popularity of machine learning, an important cog in the design process has been training surrogate models on alloy properties. However, these Single-Task models are trained on individual mechanical properties and do not take advantage of the relatedness between properties. Multi-Task models can capture the interdependencies between tasks, leading to potentially more accurate predictions for all tasks. In this paper, we investigate if Multi-Task models can show improvement over Single-Task models when used for predicting the mechanical properties of these alloys. To ensure fair evaluation between the models, we apply L 0 regularization and skip connections to the models, which allows them to adjust the number of model parameters and depth for optimal performance. We find that the Multi-Task models can leverage task relationships to perform better than Single-Task models, especially for high amounts of missing data in the tasks. Furthermore, adding simple auxiliary targets can boost Multi-Task performance even further despite not being effective as input descriptors to single-task models themselves. We anticipate that the proposed strategies can achieve more accurate predictions and consequently enable better design capabilities for such data-constrained domains without incurring much additional computational cost.

Debnath, Arindam (ORCID:0000000194274499)↗
DOE OSTI2025

Aspen Open Jets: unlocking LHC data for foundation models in particle physics

Foundation models are deep learning models pre-trained on large amounts of data which are capable of generalizing to multiple datasets and/or downstream tasks. This work demonstrates how data collected by the CMS experiment at the Large Hadron Collider can be useful in pre-training foundation models for HEP. Specifically, we introduce the AspenOpenJets (AOJs) dataset, consisting of approximately 178 M high p T jets derived from CMS 2016 Open Data. We show how pre-training the OmniJet-α foundation model on AOJs improves performance on generative tasks with significant domain shift: generating boosted top and QCD jets from the simulated JetClass dataset. In addition to demonstrating the power of pre-training of a jet-based foundation model on actual proton–proton collision data, we provide the ML-ready derived AOJs dataset for further public use.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Optimized matching conditions for self-guided laser wakefield accelerators

We revisit the matching conditions for self-guided laser pulse propagation in plasma and refine their formulation to maximize the energy of electrons produced via laser wakefield acceleration. Bayesian optimization, combined with particle-in-cell simulations carried out in a quasi-three-dimensional geometry and a Lorentz-boosted frame, is employed. The optimization identifies the maximum electron energy that a self-guided laser wakefield accelerator, driven by a laser of a given energy, can produce, together with the corresponding acceleration distance. Our results further demonstrate that electrons with energies close to the maximum value can be obtained across a relatively wide range of input parameters and without the need for their precise tuning. This provides substantial flexibility for experimental implementation and significantly relaxes the operational constraints associated with self-guided laser wakefield accelerators.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Machine-learning techniques for model-independent searches in dijet final states

Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles without depending on a specific theory model. The effectiveness of these approaches in enhancing sensitivity to various simulated signal samples is studied and compared using data collected in proton–proton collisions at a center-of-mass energy of 13 TeV. In an example analysis, the capabilities of anomaly detection methods are further demonstrated by identifying large-radius jets consistent with Lorentz-boosted hadronically decaying top quarks in a model-agnostic framework.

CMS↗
DOE OSTI2023

K-RET: knowledgeable biomedical relation extraction system

Abstract Motivation Relation extraction (RE) is a crucial process to deal with the amount of text published daily, e.g. to find missing associations in a database. RE is a text mining task for which the state-of-the-art approaches use bidirectional encoders, namely, BERT. However, state-of-the-art performance may be limited by the lack of efficient external knowledge injection approaches, with a larger impact in the biomedical area given the widespread usage and high quality of biomedical ontologies. This knowledge can propel these systems forward by aiding them in predicting more explainable biomedical associations. With this in mind, we developed K-RET, a novel, knowledgeable biomedical RE system that, for the first time, injects knowledge by handling different types of associations, multiple sources and where to apply it, and multi-token entities. Results We tested K-RET on three independent and open-access corpora (DDI, BC5CDR, and PGR) using four biomedical ontologies handling different entities. K-RET improved state-of-the-art results by 2.68% on average, with the DDI Corpus yielding the most significant boost in performance, from 79.30% to 87.19% in F-measure, representing a P-value of 2.91×10−12. Availability and implementation https://github.com/lasigeBioTM/K-RET.

Sousa, Diana F. (ORCID:0000000305979273)↗
DOE OSTI2024

3D Optical Coherence Tomography image processing in BISCAP: characterization of biofilm structure and properties

Abstract Motivation BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP’s first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Results Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. Availability and implementation BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d.

Narciso, Diogo A. C. (ORCID:0000000227767477)↗
DOE OSTI2023

New cyclophilin D inhibitor rescues mitochondrial and cognitive function in Alzheimer’s disease

Abstract Mitochondrial dysfunction is an early pathological feature of Alzheimer disease and plays a crucial role in the development and progression of Alzheimer’s disease. Strategies to rescue mitochondrial function and cognition remain to be explored. Cyclophilin D (CypD), the peptidylprolyl isomerase F (PPIase), is a key component in opening the mitochondrial membrane permeability transition pore, leading to mitochondrial dysfunction and cell death. Blocking membrane permeability transition pore opening by inhibiting CypD activity is a promising therapeutic approach for Alzheimer’s disease. However, there is currently no effective CypD inhibitor for Alzheimer’s disease, with previous candidates demonstrating high toxicity, poor ability to cross the blood–brain barrier, compromised biocompatibility and low selectivity. Here, we report a new class of non-toxic and biocompatible CypD inhibitor, ebselen, using a conventional PPIase assay to screen a library of ∼2000 FDA-approved drugs with crystallographic analysis of the CypD-ebselen crystal structure (PDB code: 8EJX). More importantly, we assessed the effects of genetic and pharmacological blockade of CypD on Alzheimer’s disease mitochondrial and glycolytic bioenergetics in Alzheimer’s disease-derived mitochondrial cybrid cells, an ex vivo human sporadic Alzheimer’s disease mitochondrial model, and on synaptic function, inflammatory response and learning and memory in Alzheimer’s disease mouse models. Inhibition of CypD by ebselen protects against sporadic Alzheimer’s disease- and amyloid-β-induced mitochondrial and glycolytic perturbation, synaptic and cognitive dysfunction, together with suppressing neuroinflammation in the brain of Alzheimer’s disease mouse models, which is linked to CypD-related membrane permeability transition pore formation. Thus, CypD inhibitors have the potential to slow the progression of neurodegenerative diseases, including Alzheimer’s disease, by boosting mitochondrial bioenergetics and improving synaptic and cognitive function.

Neurosciences & Neurology↗
DOE OSTI2024

Scarcity of fixed carbon transfer in a model microbial phototroph–heterotroph interaction

Although the green alga Chlamydomonas reinhardtii has long served as a reference organism, few studies have interrogated its role as a primary producer in microbial interactions. Here, we quantitatively investigated C. reinhardtii’s capacity to support a heterotrophic microbe using the established coculture system with Mesorhizobium japonicum, a vitamin B 12 -producing α-proteobacterium. Using stable isotope probing and nanoscale secondary ion mass spectrometry (nanoSIMS), we tracked the flow of photosynthetic fixed carbon and consequent bacterial biomass synthesis under continuous and diurnal light with single-cell resolution. We found that more 13 C fixed by the alga was taken up by bacterial cells under continuous light, invalidating the hypothesis that the alga’s fermentative degradation of starch reserves during the night would boost M. japonicum heterotrophy. 15 NH 4 assimilation rates and changes in cell size revealed that M. japonicum cells reduced new biomass synthesis in coculture with the alga but continued to divide—a hallmark of nutrient limitation often referred to as reductive division. Despite this sign of starvation, the bacterium still synthesized vitamin B 12 and supported the growth of a B 12 -dependent C. reinhardtii mutant. Finally, we showed that bacterial proliferation could be supported solely by the algal lysis that occurred in coculture, highlighting the role of necromass in carbon cycling. Collectively, these results reveal the scarcity of fixed carbon in this microbial trophic relationship (particularly under environmentally relevant light regimes), demonstrate B 12 exchange even during bacterial starvation, and underscore the importance of quantitative approaches for assessing metabolic coupling in algal–bacterial interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Phosphorolytic degradation of leaf starch via plastidic α-glucan phosphorylase leads to optimized plant growth and water use efficiency over the diel phases of Crassulacean acid metabolism

In plants with Crassulacean acid metabolism (CAM), it has been proposed that the requirement for nocturnal provision of phosphoenolpyruvate as a substrate for CO 2 uptake has resulted in a re-routing of chloroplastic starch degradation from the amylolytic route to the phosphorolytic route. To test this hypothesis, we generated and characterized four independent RNAi lines of the obligate CAM species Kalanchoë fedtschenkoi with a >10-fold reduction in transcript abundance of plastidic α-glucan phosphorylase (PHS1). The rPHS1 lines showed diminished nocturnal starch degradation, reduced dark CO 2 uptake, a reduction in diel water use efficiency (WUE), and an overall reduction in growth. A re-routing of starch degradation via the hydrolytic/amylolytic pathway was indicated by hyperaccumulation of maltose in all rPHS1 lines. Further examination indicated that whilst operation of the core circadian clock was not compromised, plasticity in modulating net dark CO 2 uptake in response to changing photoperiods was curtailed. The data show that phosphorolytic starch degradation is critical for efficient operation of the CAM cycle and for optimizing WUE. This finding has clear relevance for ongoing efforts to engineer CAM into non-CAM species as a means of boosting crop WUE for a warmer, drier future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A systematic study of proto-neutron star convection in three-dimensional core-collapse supernova simulations

ABSTRACT This paper presents the first systematic study of proto-neutron star (PNS) convection in three dimensions (3D) based on our latest numerical fornax models of core-collapse supernova (CCSN). We confirm that PNS convection commonly occurs, and then quantify the basic physical characteristics of the convection. By virtue of the large number of long-term models, the diversity of PNS convective behaviour emerges. We find that the vigour of PNS convection is not correlated with CCSN dynamics at large radii, but rather with the mass of PNS − heavier masses are associated with stronger PNS convection. We find that PNS convection boosts the luminosities of νμ, ντ, $\bar{\nu }_{\mu }$, and $\bar{\nu }_{\tau }$ neutrinos, while the impact on other species is complex due to a competition of factors. Finally, we assess the consequent impact on CCSN dynamics and the potential for PNS convection to generate pulsar magnetic fields.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Reliable photometric membership (RPM) of galaxies in clusters – I. A machine learning method and its performance in the local universe

ABSTRACT We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive a membership classification. After testing several machine learning techniques (such as stochastic gradient boosting, model averaged neural network and k-nearest neighbours), we found the support vector machine algorithm to perform better when applied to our data. Our training and validation data are from the Sloan Digital Sky Survey main sample. Hence, to be complete to $M_r^* + 3$, we limit our work to 30 clusters with $z$phot-cl ≤ 0.045. Masses (M200) are larger than $\sim 0.6\times 10^{14} \, \mathrm{M}_{\odot }$ (most above $3\times 10^{14} \, \mathrm{M}_{\odot }$). Our results are derived taking in account all galaxies in the line of sight of each cluster, with no photometric redshift cuts or background corrections. Our method is non-parametric, making no assumptions on the number density or luminosity profiles of galaxies in clusters. Our approach delivers extremely accurate results (completeness, C $\sim 92{\rm{ per\ cent}}$ and purity, P $\sim 87{\rm{ per\ cent}}$) within R200, so that we named our code reliable photometric membership. We discuss possible dependencies on magnitude, colour, and cluster mass. Finally, we present some applications of our method, stressing its impact to galaxy evolution and cosmological studies based on future large-scale surveys, such as eROSITA, EUCLID, and LSST.

Lopes, Paulo A. A.↗
DOE OSTI2020

Interacting galaxies in the IllustrisTNG simulations - I: Triggered star formation in a cosmological context

ABSTRACT We use the IllustrisTNG cosmological hydrodynamical simulations to investigate how the specific star formation rates (sSFRs) of massive galaxies (M* > 1010 M⊙) depend on the distance to their closest companions. We estimate sSFR enhancements by comparing with control samples that are matched in redshift, stellar mass, local density, and isolation, and we restrict our analysis to pairs with stellar mass ratios of 0.1 to 10. At small separations (∼15 kpc), the mean sSFR is enhanced by a factor of 2.0 ± 0.1 in the flagship (110.7 Mpc)3 simulation (TNG100-1). Statistically significant enhancements extend out to 3D separations of 280 kpc in the (302.6 Mpc)3 simulation (TNG300-1). We find similar trends in the EAGLE and Illustris simulations, although their sSFR enhancements are lower than those in TNG100-1 by about a factor of two. Enhancements in IllustrisTNG galaxies are seen throughout the redshift range explored (0 ≤ $z$ < 1), with the strength of the enhancements decreasing with increasing redshift for galaxies with close companions. In order to more closely compare with observational results, we separately consider 2D projected distances between galaxies in IllustrisTNG. We detect significant sSFR enhancements out to projected separations of 260 kpc in TNG300-1, with projection effects diluting the size of the enhancements by about 20 per cent below 50 kpc. We find similar sSFR enhancements in TNG100-1 and Sloan Digital Sky Survey galaxies, with enhancements extending out to projected separations of about 150 kpc for star-forming galaxies at $z$ < 0.2. Finally, by summing over all separations, we estimate that the presence of closest companions boosts the average sSFR of massive galaxies in TNG100-1 by 14.5 per cent.

Patton, David R.↗
DOE OSTI2021

Core formation in high-z massive haloes: heating by post-compaction satellites and response to AGN outflows

ABSTRACT Observed rotation curves in star-forming galaxies indicate a puzzling dearth of dark matter in extended flat cores within haloes of mass ≥1012M⊙ at z ∼ 2. This is not reproduced by current cosmological simulations, and supernova-driven outflows are not effective in such massive haloes. We address a hybrid scenario where post-compaction merging satellites heat up the dark-matter cusps by dynamical friction, allowing active galactic nucleus (AGN)-driven outflows to generate cores. Using analytic and semi-analytic models (SatGen), we estimate the dynamical friction heating as a function of satellite compactness for a cosmological sequence of mergers. Cosmological simulations (VELA) demonstrate that satellites of initial virial masses >1011.3M⊙, which undergo wet compactions, become sufficiently compact for significant heating. Constituting a major fraction of the accretion on to haloes ≥1012M⊙, these satellites heat up the cusps in half a virial time at z ∼ 2. Using a model for outflow-driven core formation (CuspCore), we demonstrate that the heated dark-matter cusps develop extended cores in response to removal of half the gas mass, while the more compact stellar systems remain intact. The mergers keep the dark matter hot, while the gas supply, fresh and recycled, is sufficient for the AGN outflows. AGNs indeed become effective in haloes ≥1012M⊙, where the black hole growth is no longer suppressed by supernovae and its compaction-driven rapid growth is maintained by a hot circumgalactic medium. For simulations to reproduce the dynamical friction effects, they should resolve the compaction of the massive satellites and avoid artificial tidal disruption. AGN feedback could be boosted by clumpy black hole accretion and clumpy response to AGN.

Dekel, Avishai↗
DOE OSTI2021

Unveiling the internal structure of the Hercules supercluster

ABSTRACT We have investigated the structure of the Hercules supercluster (SCL160) based on data originally extracted from the Sloan Digital Sky Survey (SDSS-DR7). We have traced the mass distribution in the field through the numerical density, weighted by the r′ luminosity of the galaxies, and classified objects based on their spatial position and redshift. This has allowed us to address not only the kinematics of the supercluster as a whole, but also the internal kinematics of each cluster, which had not been explored further before. We have confirmed that the Hercules supercluster is composed of the galaxy clusters A2147, A2151, and A2152. A2151 consists of five subclusters, A2147 two, and A2152 at least two. They form the heart of the Hercules supercluster. We also have found two other gravitationally bound clusters, therefore increasing the known members of the supercluster. We have estimated a total mass of 2.1 ± 0.2 × 1015 M⊙ for the Hercules supercluster. To determine the dynamical masses in this work, we have resorted to the M200−σ scaling relation and the caustic technique. Comparing both methods with simulated data of bimodal merging clusters, we found that the caustic, as well as the σ-based masses, is biased through the merger age, showing a boost just after pericentric passage. This is not in line with the principle of the caustic method, which affirms it does not depend on the cluster dynamical state.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Insights into the origin of halo mass profiles from machine learning

ABSTRACT The mass distribution of dark matter haloes is the result of the hierarchical growth of initial density perturbations through mass accretion and mergers. We use an interpretable machine-learning framework to provide physical insights into the origin of the spherically-averaged mass profile of dark matter haloes. We train a gradient-boosted-trees algorithm to predict the final mass profiles of cluster-sized haloes, and measure the importance of the different inputs provided to the algorithm. We find two primary scales in the initial conditions (ICs) that impact the final mass profile: the density at approximately the scale of the haloes’ Lagrangian patch RL ($R\sim 0.7\, R_L$) and that in the large-scale environment (R ∼ 1.7 RL). The model also identifies three primary time-scales in the halo assembly history that affect the final profile: (i) the formation time of the virialized, collapsed material inside the halo, (ii) the dynamical time, which captures the dynamically unrelaxed, infalling component of the halo over its first orbit, (iii) a third, most recent time-scale, which captures the impact on the outer profile of recent massive merger events. While the inner profile retains memory of the ICs, this information alone is insufficient to yield accurate predictions for the outer profile. As we add information about the haloes’ mass accretion history, we find a significant improvement in the predicted profiles at all radii. Our machine-learning framework provides novel insights into the role of the ICs and the mass assembly history in determining the final mass profile of cluster-sized haloes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The metallicity’s fundamental dependence on both local and global galactic quantities

ABSTRACT We study the scaling relations between gas-phase metallicity, stellar mass surface density (Σ*), star formation rate surface density (ΣSFR), and molecular gas surface density ($\Sigma _{{\rm H}_2}$) in local star-forming galaxies on scales of a kpc. We employ optical integral field spectroscopy from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, and ALMA data for a subset of MaNGA galaxies. We use partial correlation coefficients and Random Forest regression to determine the relative importance of local and global galactic properties in setting the gas-phase metallicity. We find that the local metallicity depends primarily on Σ* (the resolved mass–metallicity relation, rMZR), and has a secondary anticorrelation with ΣSFR (i.e. a spatially resolved version of the ‘Fundamental Metallicity Relation’, rFMR). We find that $\Sigma _{{\rm H}_2}$ is less important than ΣSFR in determining the local metallicity. This result indicates that gas accretion, resulting in local metallicity dilution and local boosting of star formation, is unlikely to be the primary origin of the rFMR. The local metallicity depends also on the global properties of galaxies. We find a strong dependence on the total stellar mass (M*) and a weaker (inverse) dependence on the total SFR. The global metallicity scaling relations, therefore, do not simply stem out of their resolved counterparts; global properties and processes, such as the global gravitational potential well, galaxy-scale winds and global redistribution/mixing of metals, likely contribute to the local metallicity, in addition to local production and retention.

79 ASTRONOMY AND ASTROPHYSICS↗