Exploring the potential of transition-metal-based hollow micro- and nanoparticles in supercapacitor electrodes
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Materials Science↗
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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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Radio AGN selection in LoTSS DR2
ABSTRACT The wide-area component of the LOFAR Two-Metre Sky Survey (LoTSS) is currently the largest radio survey ever carried out, and a large fraction of the 4.5 million radio sources it contains have been optically identified with galaxies or quasars with spectroscopic or photometric redshifts. Identification of radio-luminous active galactic nucleus (AGN) from this LoTSS source catalogue is not only important from the point of view of understanding the accretion history of the universe, but also enables a wide range of other science. However, at present the vast majority of the optical identifications lack spectroscopic information or well-sampled spectral energy distributions. We show that colour and absolute magnitude information from the Wide-Field Infrared Survey Explorer (WISE) allows for the robust and efficient selection of radio AGN candidates, generating a radio AGN candidate sample of around 600 000 objects with flux density $> 1.1$ mJy, spanning 144-MHz luminosities between $10^{21}$ and $10^{29}$ W Hz$^{-1}$. We use the catalogue to constrain the total sky density of radio-luminous AGN and the evolution of their luminosity function between $z=0$ and $z\approx 1$, and show that the typical mass of their host galaxies, around $10^{11} {\rm M}_\odot$, is essentially independent of radio luminosity above around $L_{144} \approx 10^{24}$ W Hz$^{-1}$. Combining with Very Large Array Sky Survey (VLASS) data, we show that the core prominences, radio spectral indices and variability of extended sources from the sample are qualitatively consistent with the expectations from unified models. A catalogue of the radio AGN candidates is released with this paper.
Hardcastle, M. J. (ORCID:0000000342231117)↗
A Summary of Advances in Document Summarization from 2023-2024
In computer science, Document Summarization is the task of condensing some quantity of text and related content through automated means. In this document, we review recent literature in text summarization. “Hybrid” extractive-abstractive approaches continue to be explored. Some of the latest efforts have also sought to enable users to adjust summaries with queries or other structure and begun to test reinforcement-learning style agentic LLM-based solutions.
97 MATHEMATICS AND COMPUTING↗
Understanding Twinning and Deformation in High Entropy Alloys
On the one hand, multi-principal element alloys (MPEAs) have created a paradigm shift in alloy design due to large compositional space, whereas on the other, they have presented enormous computational challenges for theory-based materials design, especially density functional theory (DFT), which is inherently computationally expensive even for traditional dilute alloys. In this project, we developed a machine learning framework, namely PREDICT ( PR edict properties from E xisting D atabase I n C omplex alloys T erritory), that opens a pathway to predict elastic constants in large compositional space with little computational expense. The framework only relies on the DFT database of binary alloys and predicts Voigt–Reuss–Hill Young’s modulus, shear modulus, bulk modulus, elastic constants, and Poisson’s ratio in MPEAs. We show that the key descriptors of elastic constants are the A–B bond length and cohesive energy. The framework can predict elastic constants in hypothetical compositions as long as the constituent elements are present in the database, thereby enabling property exploration in multi-compositional systems. We illustrate predictions in a FCC Ni-Cu-Au-Pd-Pt system.
36 MATERIALS SCIENCE↗
Advancing Geothermal Research: Fiscal Year 2025 Accomplishments Report
This is a summary of geothermal work done at the National Renewable Energy Laboratory (NREL) in Fiscal Year 2025. This year brought increased attention to the geothermal industry and NREL's geothermal research portfolio. With more than 70 active projects, NREL research spanned the areas of resource exploration and characterization; conventional and next-generation geothermal technologies; subsurface thermal energy storage; heating and cooling; co-production of geothermal with critical minerals and oil and gas; modeling and analysis leveraging expertise in data science and machine learning; and more.
15 GEOTHERMAL ENERGY↗
Advancing Geothermal Research: Fiscal Year 2025 Accomplishments Report
This is a summary of geothermal work done at the National Laboratory of the Rockies in Fiscal Year 2025. This year brought increased attention to the geothermal industry and NLR's geothermal research portfolio. With more than 70 active projects, NLR research spanned the areas of resource exploration and characterization; conventional and next-generation geothermal technologies; subsurface thermal energy storage; heating and cooling; co-production of geothermal with critical minerals and oil and gas; modeling and analysis leveraging expertise in data science and machine learning; and more.
15 GEOTHERMAL ENERGY↗
Defining the Mechanism of Proton Pumping in Heme-Copper Oxidases
During this award, progress was made in two distinct areas of science, one was within the context of bioinorganic chemistry and another in the context of biomimetic, small molecule chemistry. The overarching goals of this work was to understand the fundamental mechanistic drivers for biological reactions specifically, proton transfer reactions. In the biochemical sphere, this goal was investigated through investigation of the bacterial proton pump, ubiquinol oxidase (UbO). Further work in the bioinorganic space was completed in collaboration with the Bandarian group on a novel natural peptide product. While in the biomimetic space, this work was explored through a computational collaborative study with the Riordan group relating to proton coupled electron transfers in iron complexes as relating to the heme active site in proton pumping enzymes.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Effects of salinity and nutrients on metabolism and growth of Ulva lactuca: implications for bioremediation in San Diego Bay and Coastal Watershed
Our project sought to explore the ability of seaweed (Ulva lactuca) farms to clean polluted waterways of excess nitrogen and phosphorus through bioremediation. We addressed this goal in three primary ways. First, we worked with an undergraduate student from the Environmental Sciences Department at SDSU (Emily Bews) to examine how Ulva would perform under elevated nutrients and decreased salinity conditions, such as would be expected on the seaweed farms during periods of high rainwater runoff. This would show whether Ulva could indeed be grown on farms during these periods. We conducted laboratory experiments at SDSU’s CMIL (marine laboratory) using orthogonal combinations of two salinities and three nutrient loadings, and measured several aspects of Ulva physiology, namely growth, photosynthetic rates, chlorophyll fluorescence, and stable isotope analyses of Ulva’s tissues, and tissue uptake of phosphorus and nitrogen. Our results clearly show Ulva is an ideal candidate for using on farms during periods of heavy rains and takes up excess nutrients. The results of this were published in Marine Pollution Bulletin, with undergraduate Bews as lead author (Bews, E., L. Booher, T. Polizzi, C. Long, J-H Kim, MS Edwards. 2021. Effects of salinity and nutrients on metabolism and growth of Ulva lactuca: implications for bioremediation of coastal watersheds. Marine Pollution Bulletin 166: 121299.).
59 BASIC BIOLOGICAL SCIENCES↗
Critical role of pore size on perfluorooctanoic acid adsorption behaviors in carbonaceous sorbents
This study uses contrast matched small angle neutron scattering and simulations to explore how increased pore size in carbon sorbents influences perfluorooctanoic acid adsorption and aggregation, facilitating semi-cylinder micelle formation.
Robertson, Mark [School of Polymer Science and Eng↗
Spin fluctuations, absence of magnetic order, and crystal electric field studies in the Yb 3+ -based triangular lattice antiferromagnet Rb 3 Yb(VO 4 ) 2
Here, we report a comprehensive experimental investigation of the structural, thermodynamic, static, and dynamic properties of a triangular lattice antiferromagnet Rb 3 Yb(VO 4 ) 2 . Through the analysis of magnetic susceptibility, magnetization, and specific heat, complemented by crystal electric field (CEF) calculations, we confirm the Kramers' doublet with effective spin 𝐽 eff = 1/2 ground state. Magnetic susceptibility and isothermal magnetization analysis reveal a weak antiferromagnetic interaction among the 𝐽 eff = 1/2 spins, characterized by a small Curie-Weiss temperature (𝜃$^{\textrm{LT}}_{\textrm{CW}}$ ≃−0.26 K) or a reduced exchange coupling (𝐽/𝑘 B ≃ 0.18 K). The 51 V NMR spectra and spin-lattice relaxation rate (1/𝑇 1 ) show no evidence of magnetic long-range-order down to 1.6 K but reflect strong influence of CEF excitations in the intermediate temperatures. At low temperatures, 1/𝑇 1 (𝑇) shows pronounced frequency dependence and 1/𝑇 1 vs field at different temperatures follows the scaling behavior, highlighting the role of paramagnetic fluctuations. The CEF calculations using the point charge approximation divulge a large energy gap ( ∼18.61 meV) between the lowest and second lowest energy doublets, further establishing Kramers' doublet as the ground state. Our calculations also reproduce the experimental magnetization and specific heat data and indicate an in-plane magnetic anisotropy. These findings position Rb 3 Yb(VO 4 ) 2 as an ideal candidate to explore intrinsic quantum fluctuations and possible quantum spin-liquid physics in a Yb 3+ -based triangular lattice antiferromagnet.
Sebastian, Sebin J. [Indian Institute of Science E↗
Spin-phonon coupling in AFM transition-metal mono-oxide
Time-of-flight INS measurements were performed on single crystal NiO with the Wide Angular Range Chopper Spectrometer (ARCS) at the Spallation Neutron Source. Experiments were performed on NiO single crystal mounted in an aluminum can and cooled using a closed-cycle helium refrigerator. Measurements were conducted at T = 100 K and 650 K, with the [HHL] scattering plane aligned horizontally. A Fermi chopper with slit spacing of 1.52mm, spinning at 300 Hz, was used to select an incident neutron energy of 100 meV. All datasets were normalized to a vanadium standard to correct for detector efficiency and solid angle coverage. The data sets include the .nxs files, the generated .hdf5 files (for use with Phonon Explorer), and Python scripts used to create them.
36 MATERIALS SCIENCE↗
Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model
Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.
54 ENVIRONMENTAL SCIENCES↗
Emergency Management of Tomorrow: Emerging Technologies and Concepts
This article for the Domestic Preparedness Journal introduces the Emergency Management of Tomorrow Research Program and its role exploring artificial intelligence for emergency management and emergency operation centers in the future.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
A Visual Understanding of Circular Dichroism Spectroscopy
Mapping chemical and structural properties to electronic and magnetic responses is critical to many applications such as quantum information science, where the precise storage and transmission of unique information is paramount. Specifically, constructing molecules and materials that provide strong polarized responses at tunable frequencies and with large anisotropies is key to optical processing of quantum information. Chiral molecules provide chiroptical response to circularly polarized light, making them attractive for quantum information science and other applications related to sensing, polarized photodetectors, and spintronics. Predicting a molecular design, a priori, with large anisotropies to circularly polarized light is challenging due to the complex interplay between electric and magnetic components of the optical response. In this work, we explore a visual representation of the electronic chiroptical response by decomposing the rotary strength into its constituent components. Here, we make use of the intuitive electronic oscillator framework to develop classical intuition regarding the rotary strength and its constituents. We explore three model chemical systems that exhibit local and global chirality. Our analysis reveals that local chirality necessarily exhibits competition between the local chiral center and chirality induced in other fragments of the molecule, resulting in both unexpected nonmonotonic trends and sign flips in chemically adjacent geometries. Furthermore, we can visually distinguish between local and global chirality via examination of the transition chiral tensor. Interestingly, we make strong connections to ferromagnetic and antiferromagnetic spin systems in that chiroptically inactive transitions exhibit antiferromagnetic-like alternating orbital patterns while active transitions show domain formation in an ferromagnetic-like alignment that produces a net chiroptical response.
36 MATERIALS SCIENCE↗
Uncertain Pathways to a Future Safe Climate
Abstract Global climate change is often thought of as a steady and approximately predictable physical response to increasing forcings, which then requires commensurate adaptation. But adaptation has practical, cultural and biological limits, and climate change may pose unanticipated global hazards, sudden changes or other surprises–as may societal adaptation and mitigation responses. These poorly known factors could substantially affect the urgency of mitigation as well as adaptation decisions. We outline a strategy for better accommodating these challenges by making climate science more integrative, in order to identify and quantify known and novel physical risks including those arising from interactions with ecosystems and society. We need to do this even–or especially–when they are highly uncertain, and to explore risks and opportunities associated with mitigation and adaptation responses by engaging across disciplines. We argue that upcoming climate assessments need to be more risk‐aware, and suggest ways of achieving this. These strategies improve the chances of anticipating potential surprises and identifying and communicating “safe landing” pathways that meet UN Sustainable Development Goals and guide humanity toward a better future.
Sherwood, S. C.↗
Xcel Energy’s electric carbon emissions reduction trajectories in the context of 1.5°C and 2°C warming pathways
In 2015 the Paris Agreement established the goals of limiting global average warming to well below 2°C and pursuing efforts to limit warming to below 1.5°C. A large and growing number of scenarios have been developed by the climate research community that explore global energy and emissions pathways that would achieve those goals. We draw on the most recent database of such scenarios to update a previous analysis of Xcel Energy’s emissions reduction goals in light of evolving climate science. We assess the outlook for the role of the US electricity sector in current economy-wide and global emissions pathways and compare it to Xcel Energy’s near-term resource plans to 2030. We find that global scenarios that achieve the 1.5°C goal span a range of US/North America electricity sector emissions reductions by 2030 of about 65-85%. Xcel Energy’s emissions reductions to date have exceeded those of the US electricity sector as a whole, and its projected trajectory to 2030 under current approved resource plans falls within this range. Scenarios achieving the 2°C goal have a wider range of reductions (about 40-85%). In scenarios achieving either goal, electricity sector emissions fall faster than economy-wide emissions, a robust feature of mitigation scenarios, which typically rely on low carbon electricity to achieve climate targets.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
FAIRLinked: Data FAIRification Tools for Materials Data Science
FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of data from CSV into JSONLDs and vice versa in a way that captures the semantics of the data using MDS-Onto. Lastly, QBWorkflow is a serialization and deserialization workflow that incorporates RDF Data Cube vocabulary, useful for working with multidimensional datasets. By offering these packages, FAIRLinked lowers the barrier of creating FAIR, machine-actionable data for researchers in the materials science community.
FAIR↗