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At least 775 records · Page 43

DECOVALEX-2019: An international collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems

The DECOVALEX Project is an international research collaboration for advancing the understanding and modeling of coupled thermo-hydro-mechanical-chemical (THMC) processes in geological systems. DECOVALEX stands for “DEvelopment of COupled Models and VALidation against EXperiments”. The creation of this international initiative, now running for almost 30 years, was motivated by the recognition that prediction of these coupled effects is an essential part of the performance and safety assessment of geologic disposal systems for radioactive waste and spent nuclear fuel, and also for a range of other sub-surface engineering activities. DECOVALEX emphasizes joint analysis and comparative modeling of state-of-the-art field and laboratory experiments, across a range of host rock options and repository designs. Participating research teams are from radioactive waste management organizations, national research institutes, regulatory agencies, universities, and consulting groups, providing a wide range of perspectives and solutions to these complex problems. The most recent phase of the DECOVALEX Project, here referred to as DECOVALEX-2019, started in 2016 and ended in 2019. Modeling teams from 13 international partner organizations participated in the comparative evaluation of seven modeling tasks involving complex field and/or laboratory experiments. Furthermore, this Virtual Special Issue on DECOVALEX-2019 provides an in-depth overview of these collaborative research efforts and how these have advanced the state-of-the-art of understanding and modeling coupled THMC processes.

58 GEOSCIENCES↗

Austenitic parent grain reconstruction in martensitic steel using deep learning

In this work we develop a deep convolutional architecture to estimate the prior austenite structure from observed martensite electron backscatter diffraction micrographs. A novel data augmentation strategy randomizes the global reference coordinate system which makes it possible to train our model from only four micrographs. The model is much faster than algorithmic approaches and generalizes well when applied to micrographs of a different material. Empirical evidence suggests the efficacy of the model depends on the scale of the microstructure and receptive field of the vision model. Furthermore, this work demonstrates that modern computer vision approaches are well suited for capturing complex spatial-orientation patterns present in orientation imaging micrographs.

36 MATERIALS SCIENCE↗

Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics

Abstract A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the library’s ability to be useful outside the lab by detecting malathion in the environment. The engineered host cell, a living malathion sensor, can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

59 BASIC BIOLOGICAL SCIENCES↗

Efficient sparse state preparation via quantum walks

Continuous-time quantum walks (CTQWs) on dynamic graphs, referred to as dynamic CTQWs, are a recently introduced universal model of computation that offers a new paradigm in which to envision quantum algorithms. In this work, we develop an algorithm that converts single-edge and self-loop dynamic CTQWs to the gate model of computation. We use this mapping to introduce an efficient sparse quantum state preparation framework based on dynamic CTQWs. Our approach utilizes combinatorics techniques such as minimal hitting sets, minimum spanning trees, and shortest Hamiltonian paths to reduce the number of controlled gates required to prepare sparse states. We show that our framework encompasses the current state of the art ancilla-free sparse state preparation method by reformulating this method as a CTQW. This CTQW-based framework offers an alternative to the uniformly controlled rotation method used by Qiskit by requiring fewer CX gates when the target state has a polynomial number of non-zero amplitudes.

dynamic continuous time quantum walks↗

Magneto mitochondrial dysfunction mediated cancer cell death using intracellular magnetic nano-transducers

Mitochondria are crucial regulators of the intrinsic pathway of cancer cell death. The high sensitivity of cancer cells to mitochondrial dysfunction offers opportunities for emerging targets in cancer therapy. Herein, magnetic nano-transducers, which convert external magnetic fields into physical stress, are designed to induce mitochondrial dysfunction to remotely kill cancer cells. Spindle-shaped iron oxide nanoparticles were synthesized to maximize cellular internalization and magnetic transduction. The magneto-mechanical transduction of nano-transducers in mitochondria enhances cancer cell apoptosis by promoting a mitochondrial quality control mechanism, referred to as mitophagy. Furthermore, in the liver cancer animal model, nano-transducers are infused into the local liver tumor via the hepatic artery. After treatment with a magnetic field, in vivo mitophagy-mediated cancer cell death was also confirmed by mitophagy markers, mitochondrial DNA damage assay, and TUNEL staining of tissues. This study is expected to contribute to the development of nanoparticle-mediated mitochondria-targeting cancer therapy and biological tools, such as magneto-genetics.

60 APPLIED LIFE SCIENCES↗

On the Cauchy problem for Boltzmann equation modeling a polyatomic gas

In the present article, we consider the Boltzmann equation that models a polyatomic gas by introducing one additional continuous variable, referred to as microscopic internal energy. We establish existence and uniqueness theory in the space homogeneous setting for the full non-linear case, under an extended Grad-type assumption on transition probability rates, which comprises hard potentials for both the relative speed and internal energy with the rate in the interval 0,2, multiplied by an integrable angular part and integrable partition functions. The Cauchy problem is resolved by means of an abstract ordinary differential equation (ODE) theory in Banach spaces for the initial data with finite and strictly positive gas mass and energy, finite momentum, and additionally finite K* polynomial moment, with K* depending on the rate of the transition probability and the structure of a polyatomic molecule or its internal degrees of freedom. Moreover, we prove that polynomially and exponentially weighted Banach space norms associated with the solution are both generated and propagated uniformly in time.

Physics↗

Apparent Increasing Lithium Diffusion Coefficient with Applied Current in Graphite

In this study, we assert that the apparent lithium diffusion coefficient in graphite active particles in the negative electrodes of lithium-ion cells increases appreciably with the intercalation rate. This assertion is based on an electrochemical model analysis of a wide range of electrochemical micro-reference electrode full cell studies on a coated natural graphite and other literature results. Although the mechanism for this increase is a subject of further investigation, the results of our study suggest that the lithium transport in the graphite does not limit the maximum attainable charging rate in typical lithium-ion cells for electric vehicles.

25 ENERGY STORAGE↗

pvlib python: 2023 project update

pvlib python is a community-developed, open-source software toolbox for simulating the performance of solar photovoltaic (PV) energy components and systems. It provides reference implementations of over 100 empirical and physics-based models from the peer-reviewed scientific literature, including solar position algorithms, irradiance models, thermal models, and PV electrical models. In addition to individual low-level model implementations, pvlib python provides high-level workflows that chain these models together like building blocks to form complete “weather-to-power” photovoltaic system models. It also provides functions to fetch and import a wide variety of weather datasets useful for PV modeling. pvlib python has been developed since 2013 and follows modern best practices for open-source python software, with comprehensive automated testing, standards-based packaging, and semantic versioning. Its source code is developed openly on GitHub and releases are distributed via the Python Package Index (PyPI) and the conda-forge repository. pvlib python’s source code is made freely available under the permissive BSD-3 license. Here we (the project’s core developers) present an update on pvlib python, describing capability and community development since our 2018 publication (Holmgren, Hansen, & Mikofski, 2018).

14 SOLAR ENERGY↗

High Flux Isotope Reactor Low-Enriched Uranium Low Density Silicide Fuel Design Parameters

High Flux Isotope Reactor (HFIR) highly enriched uranium (HEU) to low-enriched uranium (LEU) conversion activities are ongoing as part of the Department of Energy (DOE) National Nuclear Security Administration (NNSA)’s nuclear nonproliferation mission. Design activities studying the conversion of HFIR from HEU to LEU fuel explored different fuel design features and shapes with a low density uranium-silicide dispersion (U 3 Si 2 -Al) fuel, which has a uranium density of 4.8 gU/cm 3 . The goal of these studies is to generate several HFIR LEU fuel designs of varying fuel fabrication complexity that meet the current HEU performance metrics and safety requirements. The documented designs will serve as references for fuel fabrication and qualification activities. Recent advancements in modeling and simulation tools enable quick prototyping of fuel designs. Shift, a Monte Carlo neutron transport and depletion tool optimized for high-performance computing (HPC) architectures, is used for efficient fuel cycle and performance metrics calculations. The HFIR Steady State Heat Transfer Code (HSSHTC) is used to vet the thermal safety margin. Also, a new automation tool that connects all fuel design analysis steps, named Python HFIR Analysis and Measurement Engine (PHAME), has been developed to expedite the design study in an efficient and reproducible manner. Leveraging these tools, several candidate fuel designs were selected for varying fabrication complexity. This report provides design feature details for four selected HFIR LEU low density U 3 Si 2 -Al fuel designs and their corresponding performance and safety metrics. Nominal, best-estimate design parameters and irradiation conditions, including fission rate densities, power densities, heat fluxes, and cumulative fission densities are provided for candidate fuel designs relevant to framing irradiation experiments to support fuel qualification efforts. Simulations show that the low density U 3 Si 2 -Al, with design features to enhance safety, can meet HEU core performance metrics and safety requirements if the reactor power is increased from 85 MW (HEU) to 95 MW (LEU) and if the active fuel length is increased from 50.80 cm (HEU) to 55.88 cm (LEU).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

SiPA | Simple Partnership Agreement for accelerating early-stage collaborations between startups and large corporations

The technologies required to advance society will come from research from a broad range of scientific disciplines including materials science, chemistry, physics, and biology where the innovation relies on a scientific breakthrough rather than a software or business model innovation. This science-based technology focus can be referred to as “hardtech,” although there is much crossover with the concept of “cleantech.” These new technology developments will have vast reach across most industrial sectors - energy, materials, manufacturing, electronics, life sciences, etc. - with great economic consequences. The majority of these scientific breakthroughs will be produced and developed by those companies best suited for high-risk, early-stage development work: startups.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Technical and Economic Assessment and Gap Analysis of Advanced Nuclear Reactor Integration with a Reference Oil Refinery

Efforts to identify the most-economic methods to decarbonize several sectors of the U.S. economy are underway. Industrial processes such as crude-oil refining rely heavily on energy-dense and easily stored and transported fossil fuels for powering their operations. Refineries use large amounts of energy, primarily derived from fossil sources to separate crude-oil components, break down heavier hydrocarbons into lighter compounds, remove impurities, reform hydrocarbon molecules, and generate steam and electricity for pumps and compressors and other various auxiliary systems. Crude-oil refining operations such as distillation, cracking, desulfurization, reforming, utilities systems and some offsite facilities collectively account for most of the energy consumption. Other operations such as hydrocracking or hydrotreating also require hydrogen for developing hydrogenation reactions which involve substantial heating to keep the reactors at high-temperature and pressure levels. All heat and energy demands are typically provided by natural gas (NG), oil, or other fuels, which makes refinery industry one of the most-difficult sectors to decarbonize. Nuclear power is a viable and energy-dense source of clean electricity, heat, and hydrogen to provide the large, sustainable energy supply that the refining industry demands. The U.S. Department of Energy’s (DOE’s) Integrated Energy Systems (IES) program is working to perform research and development, design, economic siting, and risk analysis. This state-of-the-art work will enable the first on-site demonstrations and commercial deployments of advanced small modular nuclear reactors (SMNRs) integrated with industries such as chemical production, refining, iron and steel making, and more. IES seeks to demonstrate the ability of advanced nuclear reactors to meet the heat and power demands of these industries while reducing carbon emissions in a sustainable and cost-competitive way. The primary objective of this research effort is to analyze industrial-scale SMNR integration intended to decarbonize refining facilities. The foreseen outcome is the provision of reliable, cost-competitive, and sustainable clean energy, alongside a reduction of carbon emissions. Specifically, the focus of this work lies on meeting the reference facilities’ heat and electricity demands with nuclear power while also supplying clean hydrogen via integrated high-temperature steam electrolysis (HTSE). This report presents a comprehensive technical and economic assessment of the integration of advanced nuclear reactors into a reference refinery, leveraging financial incentives from the Inflation Reduction Act (IRA). The evaluation aims to explore the potential economic benefits and challenges associated with incorporating advanced nuclear reactors into refinery operations, particularly in terms of energy efficiency, economic implications and environmental impact. By examining both the technical feasibility and economic viability, this analysis seeks to identify existing gaps and propose solutions for successful nuclear integration implementation. The findings are intended to provide valuable insights for stakeholders considering the adoption of advanced nuclear reactors in the refining sector. A refinery reference-plant was developed, using an open-source refinery model, Petroleum Refinery Lifecycle Inventory Model (PRELIM) and expert assessment, as a base case for comparison with various nuclear integration options. The capacity of 100 kbd/day (KBD) of heavy crude-oil feed was selected to represent a general coking-type refinery with deep conversion capabilities (incorporating heavy-oil upgrading with FCC, coking, and associated hydrotreating process units), using a heavy crude-oil feed, which represents about 70% of U.S. refineries configurations. A summary of all cases considered in this study is shown in Table 1.

13 HYDRO ENERGY↗

SULI Intern Final Report: Computationally Investigating Hydrogen Thermo-Diffusion in Yttrium Hydride Using Multiscale Methods

The renaissance of nuclear energy has arrived, heralding an age of abundant inexpensive clean energy, and renewed space exploration. In nuclear-powered spacecraft and microreactors, safety and size are of utmost importance. Yttrium Hydride (YHx) is being researched for its utility as a neutron moderator in nuclear reactors; the hydrogen in YHx slows down neutrons, enabling a continuous nuclear reaction in the reactor. This has the benefit of allowing reactors to be more safe, compact, and efficient. The goal of this effort is to computationally predict the coefficient of temperature-dependent hydrogen diffusion within YHx, the Soret coefficient. This parameter is essential for determining the safe operating modes of YHx moderators. Zirconium Hydride (ZrHx) is used in the Training, Research, Isotopes, General Atomics (TRIGA) reactor, is the reference material for these calculations. In this work, nanoscale atomic modeling in the Vienna Ab initio Simulation Package (VASP) is combined with the mesoscale finite element phase-field module in the Multiphysics Object-Oriented Simulation Environment (MOOSE); this culminates in a new multiscale computational method to simulate Soret diffusion of hydrogen in YHx. This data is useful for predicting experimental outcomes. This workflow involves convergence testing followed by static, Nudged Elastic Band (NEB), Quasi-Harmonic Approximation (QHA), and Molecular Dynamics (MD) calculations - linked with phase field simulation. NEB simulates hydrogen migration, while QHA and MD predict temperature-dependent properties. The static calculations align with literature, and preliminary NEB and QHA calculations yield accurate results. Once the atomic calculations are complete, we will incorporate Electron Backscatter Diffraction (EBSD) images and VASP-generated parameters into the phase field module to simulate intra- and intergranular transport of hydrogen in ZrHx and YHx. Future research will extend our approach to fuel-moderator materials systems such as Uranium-Yttrium Hydride (U-YHx). This work contributes to the development of advanced nuclear energy solutions for space travel.

36 - MATERIALS SCIENCE↗

Cluster-dynamics-based parameterization for sulfuric acid–dimethylamine nucleation: comparison and selection through box and three-dimensional modeling

Clustering of gaseous sulfuric acid (SA) enhanced by dimethylamine (DMA) is a major mechanism for new particle formation (NPF) in polluted atmospheres. However, uncertainty remains regarding the SA–DMA nucleation parameterization that reasonably represents cluster dynamics and is applicable across various atmospheric conditions. This uncertainty hinders accurate three-dimensional (3-D) modeling of NPF and the subsequent assessment of its environmental and climatic impacts. Here we extensively compare different cluster-dynamics-based parameterizations for SA–DMA nucleation and identify the most reliable one through a combination of box model simulations, 3-D modeling, and in situ observations. Results show that the parameterization derived from Atmospheric Cluster Dynamic Code (ACDC) simulations, incorporating the latest theoretical insights (DLPNO-CCSD(T)/aug-cc-pVTZ//ωB97X-D/6-311++G(3df,3pd) level of theory) and adequate representation of cluster dynamics, exhibits dependable performance in 3-D NPF simulation for both winter and summer conditions in Beijing and shows promise for application in diverse atmospheric conditions. Another ACDC-derived parameterization, replacing the level of theory with RI-CC2/aug-cc-pV(T+d)Z//M06-2X/6–311++G(3df,3pd), also performs well in NPF modeling at relatively low temperatures around 280 K but exhibits limitations at higher temperatures due to inappropriate representation of SA–DMA cluster thermodynamics. Additionally, a previously reported parameterization incorporating simplifications is applicable for simulating NPF in polluted atmospheres but tends to overestimate particle formation rates under conditions of elevated temperature (>∼300 K) and low-condensation sink (<∼3×10 -3 s −1 ). Our findings highlight the applicability of the new ACDC-derived parameterization, which couples the latest SA–DMA nucleation theory and holistic cluster dynamics, in 3-D NPF modeling. The ACDC-derived parameterization framework provides a valuable reference for developing parameterizations for other nucleation systems.

Shen, Jiewen↗

Dark Energy Survey Supernova 5YR Data Release

This data set was generated by the Dark Energy Survey (DES) team, and was used for the cosmological analysis in the DES key supernova cosmology paper (arXiv:2401.02929). When using this data please cite both the data release paper (Sanchez et al. 2024, ApJ, arXiv:2406.05046) and the DES key paper (Dark Energy Survey Collaboration 2024, ApJL, arXiv:2401.02929). At DES-SN5YR Github Repo (https://github.com/des-science/DES-SN5YR), we provide a comprehensive list of references for the different data products (SALT3 light-curve model, calibration, DCR corrections, SN classification, SN distances and uncertainties covariance matrix). If you have questions, post an issue on the DES-SN5YR Github repository and/or get in touch with the contact people indicated on the Github repository.

Contractor, Fermilab [Fermilab]↗

ERA5-Land Data for LASSO-CACTI Overview Paper

The European Centre for Medium-Range Weather Forecasts (ECMWF) generated a soil reanalysis dataset for the land component of the fifth generation of European ReAnalysis (ERA5), referred to as ERA5-Land. This is a model-generated dataset, with the original version available for the period 1950 to present. The version archived in this DOE ARM product is a subset of the data is for the period of the CACTI field campaign plus several preceding months, specifically from August 1, 2018 through March 22, 2019 with hourly intervals. The ARM copy is also a sub-region of the original global product; the ARM copy is for -60 to -5 °N by -105 to -30 °W. Only variables necessary to drive the WRF-Hydro model are included, which are the 2-m temperature and specific humidity, 10-m wind components, surface pressure, rain rate, and downward surface short and longwave radiation. These data have been obtained from the Copernicus Data Store.

10m wind u-component↗

ResStock Measure Documentation: Residential Two-Stage Geothermal Heat Pump (4.0 COP, 20.5 EER) With Envelope Improvements and Advanced Air Sealing

The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson, et al. 2022). This document focuses on a single end-use savings shape measure: Residential Two-Stage Geothermal Heat Pump (GHP) (4.0 COP, 20.5 EER) With Envelope Improvements. This measure combines a two-stage GHP with envelope improvements as a single package. As this package is a combination of two other measures, this document focused on documenting the results associated with this combination of technologies, with individual measure documents for two-stage GHPs and envelope improvements providing the information on the details of these measures. When the two technologies are combined, envelope improvements can modestly reduce energy consumption by a further 10%-15%, but also reduce the required size of the ground heat exchanger and heat pump by approximately 33% on average across all sites. The cost of installing envelope improvements in these homes is likely to be more than paid for by the reduction in equipment and drilling costs in these buildings for the majority of the stock.

15 GEOTHERMAL ENERGY↗

ResStock Measure Documentation: Residential Single-Stage Geothermal Heat Pump (3.8 COP, 18.6 EER)

The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson et al. 2022). This documentation focuses on a single end-use savings shape measure: Residential Single-Stage Geothermal Heat Pump (GHP).?Single-stage GHPs are able to reduce energy consumption by 31% for the entire stock. Additional results provided below detail how savings changes for sections of the housing stock with different base heating fuel and in different climate zones, as well as the savings potential by state for both heating and cooling. Utility bills and electric panel impacts are also shown and discussed.

15 GEOTHERMAL ENERGY↗

ResStock Measure Documentation: Residential Variable-Speed Geothermal Heat Pump (4.4 COP, 30.9 EER)

The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock (TM) is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson et al. 2022). This documentation focuses on a single end-use savings shape measure: Residential Variable-Speed Geothermal Heat Pump (GHP). This document provides the relevant new modeling information for variable-speed systems not previously covered in either the single-stage or two-stage documents. Variable-speed GHPs represent the most efficient option available for this technology: They provide the most savings, with up to 46% for the applicable portion of the housing stock, compared to 31% for less efficient single-stage GHPs. Additional results shown here detail how the savings change for sections of the housing stock with different base heating fuels and in different climate zones, and they show the savings potential by state for both heating and cooling. Utility bills and electric panel impacts are also shown and discussed.

15 GEOTHERMAL ENERGY↗