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Metamagnetism and kinetic arrest in a long-range ferromagnetically ordered multicaloric double perovskite Y 2 CoMnO 6
A systematic magnetic study of the double perovskite oxide Y 2 CoMnO 6 (YCMO) has been performed. A monoclinic P21/n phase of YCMO was synthesized using a sol-gel method. Neutron diffraction (ND) measurements evidence the onset of long-range ferromagnetic (FM) ordering at TC ~ 76 K, which persists down to 5 K. The presence of 25% antisite disorder, estimated from the ND data, leads to the appearance of short-range antiferromagnetic (AFM) interactions. The existence of thermal hysteresis due to competing interactions between FM and AFM phases is observed in the M vs. T measurements. The pinning of magnetic domain walls at the Co/Mn antiphase boundaries results in a metamagnetic-like behavior. The field dependence of thermomagnetic irreversibility and the nature of virgin curves indicate the occurrence of a kinetic arrest phenomenon, which is further verified via cooling and heating of the system in an unequal fields (CHUF) protocol. In full agreement with the ND data, a detailed analysis of critical exponents near the paramagnetic (PM)-FM phase transition also establishes YCMO as a long-range interacting mean-field system. A close examination of the temperature- and field-dependent magnetic entropy change yields an in-depth understanding of coexisting magnetically ordered and disordered phases in YCMO, leading to a comprehensive magnetic phase diagram of this multifunctional double perovskite system.
Sub-critical assembly die-away analysis with organic scintillators
Determining the reactivity of systems containing fissile material is essential for verifying safety, and possession and enrichment levels. The goal of this experiment was to characterize a subcritical nuclear fuel assembly irradiated by a pulsed neutron generator through differential die-away analysis. Similar characterizations are commonly performed with thermal neutron detectors, but these detectors are bulky and cumbersome, the materials are not widely available, and timing can be clouded by the required thermalization time. In this work, an organic scintillator capable of detecting both neutrons and photons was used to measure the die-away profiles of both particles. These time profiles were then used to estimate the reactivity of the assembly. Here, the calculated reactivity estimates compare favorably to simulated estimates, and therefore provide a useful tool for fissile material characterization.
The water use of data center workloads: A review and assessment of key determinants
The global importance of data center water use is increasing with the rapid growth of digitalization and artificial intelligence. This study analyzes the factors influencing workload-level water use, measured in liters consumed per workload, to guide water-saving strategies in data centers. Our findings reveal workload-level water use variations exceeding 10,000-fold, driven by over 1000-fold differences in water consumption per kilowatt hour of server electricity consumed and approximately 10-fold differences in server workload efficiency. Key determinants are ranked as server efficiency, electrical grid water consumption factors, server utilization, cooling system type, infrastructure efficiency, climate zone, inactive server percentage, and server refresh cycle. Notably, there is no single recipe for minimizing water use; instead, optimal outcomes depend on tailored combinations of these factors. This analysis addresses critical knowledge gaps by identifying the determinants of data center water use and exploring their achievable minima under diverse site-specific constraints.
Design improvements for a recirculating reactor: Enhanced temperature measurement and sample-isolated reactivity in steady-state kinetic studies
Building upon a previous recirculating reactor design [S.A. Tenney, K. Xie, J.R. Monnier, A. Rodriguez, R.P. Galhenage, S. Audrey, D.A. Chen, Rev. Sci. Instrum. 84, 104101 (2013)], we present significant improvements that address key limitations in steady-state kinetic measurements for heterogeneous catalysis. The enhanced reactor design features direct sample heating with a focused IR lamp and temperature measurement in direct contact with the sample, enabling more accurate temperature control and improved kinetic analysis. A critical advancement is the isolation of sample reactivity from reactor wall contributions, ensuring that only the sample contributes to measured reaction rates. This was a limitation in earlier designs where the entire reactor contributed to the observed reactivity. The system incorporates a bypass flow cell for direct comparison with powder catalysts under identical conditions using a standard plug-flow reactor configuration. We demonstrate these capabilities through CO oxidation experiments on Pt(111) single crystals and graphene-passivated Pt(111), highlighting the system's ability to differentiate catalytic activity in model systems and directly compare them with high surface area powder catalysts. This reactor is particularly suited for thin films and low surface area catalysts that are not effectively evaluated in traditional flow reactors, especially for samples with low numbers of active sites or slow reaction rates.
Towards the spatial resolution of metalloprotein charge states by detailed modeling of XFEL crystallographic diffraction
Oxidation states of individual metal atoms within a metalloprotein can be assigned by examining X-ray absorption edges, which shift to higher energy for progressively more positive valence numbers. Indeed, X-ray crystallography is well suited for such a measurement, owing to its ability to spatially resolve the scattering contributions of individual metal atoms that have distinct electronic environments contributing to protein function. However, as the magnitude of the shift is quite small, about +2 eV per valence state for iron, it has only been possible to measure the effect when performed with monochromated X-ray sources at synchrotron facilities with energy resolutions in the range 2–3 × 10 −4 (Δ E / E ). This paper tests whether X-ray free-electron laser (XFEL) pulses, which have a broader bandpass (Δ E / E = 3 × 10 −3 ) when used without a monochromator, might also be useful for such studies. The program nanoBragg is used to simulate serial femtosecond crystallography (SFX) diffraction images with sufficient granularity to model the XFEL spectrum, the crystal mosaicity and the wavelength-dependent anomalous scattering factors contributed by two differently charged iron centers in the 110-amino-acid protein, ferredoxin. Bayesian methods are then used to deduce, from the simulated data, the most likely X-ray absorption curves for each metal atom in the protein, which agree well with the curves chosen for the simulation. The data analysis relies critically on the ability to measure the incident spectrum for each pulse, and also on the nanoBragg simulator to predict the size, shape and intensity profile of Bragg spots based on an underlying physical model that includes the absorption curves, which are then modified to produce the best agreement with the simulated data. This inference methodology potentially enables the use of SFX diffraction for the study of metalloenzyme mechanisms and, in general, offers a more detailed approach to Bragg spot data reduction.
Performance Evaluation of Heterogeneous GPU Programming Frameworks for Hemodynamic Simulations
Preparing for the deployment of large scientific and engineering codes on upcoming exascale systems with GPU-dense nodes is made challenging by the unprecedented diversity of device architectures and heterogeneous programming models. In this work, we evaluate the process of porting a massively parallel, fluid dynamics code written in CUDA to SYCL, HIP, and Kokkos with a range of backends, using a combination of automated tools and manual tuning. We use a proxy application along with a custom performance model to inform the results and identify additional optimization strategies. At scale performance of the programming model implementations are evaluated on pre-production GPU node architectures for Frontier and Aurora, as well as on current NVIDIA device-based systems Summit and Polaris. Real-world workloads representing 3D blood flow calculations in complex vasculature are assessed. Our analysis highlights critical trade-offs between code performance, portability, and development time.
Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices
Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.
Analysis of the dependence of critical electric field on semiconductor bandgap
Abstract Understanding of semiconductor breakdown under high electric fields is an important aspect of materials’ properties, particularly for the design of power devices. For decades, a power-law has been used to describe the dependence of material-specific critical electrical field ( $${\mathcal{E}}_{\text{crit}}$$ E crit ) at which the material breaks down and bandgap ( E g ) . The relationship is often used to gauge tradeoffs of emerging materials whose properties haven’t yet been determined. Unfortunately, the reported dependencies of $${\mathcal{E}}_{\text{crit}}$$ E crit on E g cover a surprisingly wide range in the literature. Moreover, $${\mathcal{E}}_{\text{crit}}$$ E crit is a function of material doping. Further, discrepancies arise in $${\mathcal{E}}_{\text{crit}}$$ E crit values owing to differences between punch-through and non-punch-through device structures. We report a new normalization procedure that enables comparison of critical electric field values across materials, doping, and different device types. An extensive examination of numerous references reveals that the dependence $${\mathcal{E}}_{\text{crit}}$$ E crit ∝ E g 1.83 best fits the most reliable and newest data for both direct and indirect semiconductors. Graphical abstract
A Big Problem for Small Earthquakes: Benchmarking Routine Magnitudes and Conversion Relationships with Coda Envelope-Derived Mw in Southern Kansas and Northern Oklahoma
Earthquake magnitudes are widely relied upon measures of earthquake size. Although moment magnitude (M w ) has become the established standard for moderate and large earthquakes, difficulty in reliably measuring seismic moments for small (generally M w <4) earthquakes has meant that magnitudes for these events remain plagued by a patchwork of inconsistent measurement scales. Because of this, magnitudes of small earthquakes and statistics derived from them can be biased. Furthermore, because small earthquakes are much more numerous than large ones, many applications, such as seismic hazard modeling, depend critically on analysis of events characterized by magnitudes other than M w . Therefore, to assess this problem, we apply coda envelope analysis to reliably determine moment magnitudes for a case study of small earthquakes from northern Oklahoma and southern Kansas. Not surprisingly, we find significant differences among M L , m bLg , and M w for M ~2–4 earthquakes examined here. More troublingly, we find that relations designed to convert other magnitudes to M w , which are relied upon for important applications such as seismic hazard analysis, often increase rather than decrease this bias for our dataset. In our case study, we find that converted magnitudes can result in a systematic bias sometimes exceeding 0.5 magnitude units, a difference that typically corresponds to a factor of ~3 in seismicity rate. Moreover, we find a correspondingly large bias in Gutenberg–Richter b-values, controlled primarily by inaccurate magnitude scaling in the conversion relationships. Although this study focuses on a relatively small geographic area, we can expect that similar issues exist with varying severity in other regions. Therefore, magnitudes of small earthquakes and their associated statistics, including seismicity rates and b-values, should be treated with caution.
Overview of SRNS NNSA Projects in the Eyes of Criticality Safet
Overview of NNSA Projects (Criticality Safety) – Savannah River Plutonium Processing Facility – Surplus Plutonium Disposition Project – Tritium Finishing Facility – Progress of Criticality Safety Analysis – Processes requiring CAAS
Benchmark Gap Assessment for the Manufacturing of High-Assay Low-Enriched Uranium Fuels
This document develops basic critical conditions for spheres—moderated and unmoderated, as well as reflected and unreflected—in consideration of nuclear criticality safety of a potential fuel production facility producing high-assay low-enriched uranium (HALEU) fuel of several different types like tristructural-isotropic (TRISO), uranium metal and alloys, oxide and non-metallic forms. In addition to spherical arrangements, TRISO particle manufacturing process–specific equipment is modeled as it would be for the criticality safety analysis. The objective is to develop representative systems that can then be used for comparison with existing benchmarks. SCALE/TSUNAMI is used to assess the similarity index between these systems to assess validation gaps for possible fuel production applications of proposed advanced reactors. Several different fuel types were evaluated, including TRISO, uranium metal, uranium molybdenum, uranium zirconium, uranium dioxide, uranium nitride, uranium hydride, U-ZrH, and uranium chloride. This selection of fuel types covers a breadth of proposed reactor types, as well as intermediate steps in the production and fabrication of the fuel
Preparation and Qualification of Preproduction SSR2 Jacketed Cavities for PIP-II
The qualification of 325 MHz Single Spoke Resonators type 2 (SSR2) jacketed cavities to meet technical requirements represents a significant milestone in the development of the SSR2 cryomodules for the PIP-II Project at Fermilab. This poster reports the procedures and lessons learned in processing and preparing these cavities for horizontal cold testing prior to integration into a cavity string assembly, with a focus on addressing the field emission issues observed during the cold testing. A comprehensive root cause analysis identified critical fabrication, processing, and handling factors impacting field emission onset. New techniques were successfully developed and implemented to achieve field emission-free SSR2 cavities, and efforts were made to correlate radiation levels measured at the test stand with expected levels in the LINAC tunnel. Additionally, the evolution of field emission through assembly steps was thoroughly investigated, leading to a reassessment of design choices and enhancing our understanding of their effects on cavity performance.
An entropy-based debiasing approach to quantifying experimental coverage for novel applications of interest in the nuclear community
This manuscript proposes a novel information-theoretic approach to the quantification of experimental relevance, i.e., coverage, to achieve optimal data assimilation results for nuclear engineering applications. Specifically, this work posits the need for a new metric, called coverage (q C ) of an application’s quantity of interest, i.e., eigenvalue or power peaking for an advanced reactor concept, defined herein as the theoretically maximum achievable reduction in the quantity’s uncertainty given measurements from a pool of experiments in a manner that is independent of the data assimilation procedure employed. Currently, reduction in a quantity’s uncertainty is strongly biased by the underlying assumptions of the assimilation procedure to account for the under-determined nature of such problems and the similarity criterion employed to identify relevant experiments. To address this challenge, this work has developed a coverage metric, q C , based on mutual information, which establishes a new conceptual framework for assessing coverage, one that is independent of the model parameters and responses degree of variations in both the experimental and application domains, i.e., linear vs non-linear, and their prior uncertainty distributions, i.e., Gaussian vs. non-Gaussian. The q C is an entropic measure capable of addressing coverage for general nonlinear problems with non-Gaussian uncertainties and inclusive of the measurement uncertainties from multiple experiments. Numerical experiments from manufactured analytical problems as well as a set of benchmarks from the ICSBEP handbook are employed to demonstrate its theoretical and practical performance as compared to the c k -based experiment selection methodology, commonly employed in the neutronic community. The manuscript then employs other well-known adaptations to existing data assimilation methodologies for nonlinear and non-Gaussian problems capable of achieving the coverage posited by q C .
Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting
Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.
Commercial Energy Code Compliance – Just the Facts, Ma’am
What percent of newly constructed commercial buildings comply with the energy code? How much energy and cost could be saved if the compliance increased? Which code requirements have both low compliance rates and high savings potential? These are the questions U.S. Department of Energy (DOE) is trying to answer through its Commercial Energy Code Field Study. Previous commercial studies have been very limited and did not result in a widely accepted and tested methodology. DOE’s goal is to create a standardized methodology that can produce actionable results at a reasonable study cost, that can be used by state and local governments and utilities and provides valuable information to policy makers. This paper builds on the methodology presented at the 2016 Summer Study and provides a first glimpse of the results as seen in the field. The field study team has implemented the pilot methodology, compiling a data set of over 200 office and retail buildings in climate zones 2A and 5A. The approach is based on identifying lost savings on a total energy cost basis rather than simply counting the quantity of measures that do not meet code. This paper will review the findings of that analysis, discuss critical aspects of the methodology, such as sampling and recruitment strategies, and identify areas where the greatest return—bang for the buck—exists in improving compliance with codes in commercial buildings. In addition, the team will make specific recommendations for jurisdictions looking to use the methodology to maximize energy code savings achieved in the field.
Sustainable Aviation
New technologies are changing the future of aviation, providing actionable pathways for lowering greenhouse gas emissions in a sector that is among the most difficult to decarbonize. The National Renewable Energy Laboratory (NREL) is developing public and private partnerships to accelerate this evolution with critical research, analysis, modeling, and validation expertise in energy carriers, aircraft components, and airport infrastructure. Our solutions will not only permanently lower the carbon intensity of flight, but fundamentally improve the carbon intensity, mobility, and resiliency of the entire aviation ecosystem.