Modeling and observation of detonation front formation with microwave interferometry during the deflagration-to-detonation transition
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Chemistry - Chemical explosives↗
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First β -delayed neutron spectroscopy of 24 O
The β decay of 24 O was investigated at NSCL/MSU using a combination of neutron and γ-ray spectroscopy. For the first time, the β-delayed neutron energy spectrum of 24 O was measured, revealing three intensely populated, isolated neutron-unbound states in 24 F. This allowed for the extraction of the decay strength in 24 F up to 6.2 MeV. A comprehensive comparison of the experimental results with various nuclear theories, ranging from the empirical shell model to the most advanced ab initio calculations, was conducted. While most theoretical predictions align with the experimental data for low-lying states, discrepancies arise at higher excitation energies. Finally, in the transition from 24 O to 24 F, shell model calculations using the empirical USDB interaction predicted the structure of both nuclei without invoking the need for a stronger proton-neutron tensor force, which was postulated for the neighboring isotone 25 F.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Analysis of differential scanning calorimetry data for aged plutonium
Differential scanning calorimetry data for samples of a 52 year old plutonium alloy with 3.3 at. % Ga that were heated beyond the melting point is analyzed using transition state theory to find activation energies for the δ to ε and ε to liquid phase transitions. A Bayesian statistical method involving a Gaussian process model is used to find mean values and confidence intervals for the activation energies. The activation energy for the δ to ε phase transition increases by 3.3 ± 3.8% per decade, relative to the case when all age related plutonium lattice point defects have been removed through annealing. The corresponding increase in activation energy for the ε to liquid transition is shown to be 7.1 ± 1.8% per decade. It is postulated that the change in activation energy with age for both phase transitions is caused, in part, by the accumulation of the same type of lattice point defects associated with the observed increase in elastic bulk modulus over time.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
High-Pressure Electrides: A Quantum Chemical Perspective
It has long been assumed that all matter will adopt simple close-packed lattices and become metallic under pressure, in accordance with the Thomas–Fermi–Dirac (TFD) model. However, this model struggles to explain pressure-driven complex structural transitions that have been observed in elements, including sodium, challenging our conventional understanding of compressed matter. Moreover, in stark contrast to the TFD model, first-principles calculations suggest that various elements and compounds become electrides under pressure. Electrides, characterized by concentrations of charge density at interstitial regions, can be thought of as ionic compounds where electrons behave as the anions. Though ambient-pressure molecular electrides have been extensively studied via experiments and computations, high-pressure electrides (HPEs) are not well-understood. The identification and characterization of HPEs have been, to date, based purely on theory, including topological analysis of the electron density and the electron localization function. Here, we review these theoretical analysis tools and suggest guidelines that can be used to classify systems as electrides. Moreover, we describe models used to rationalize the electronic structure of HPEs, drawing parallels with ambient-pressure molecular systems, and encourage the development of experimental techniques that provide evidence for the theoretically calculated charge localization.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Machine Learning a Simple Interpretable Short-Range Potential for Silica
A wide array of models, spanning from computationally expensive ab initio methods to a spectrum of force-field approaches, have been developed and employed to probe silica polymorphs and understand growth processes and atomic-level dynamical transitions in silica. However, the quest for a model capable of making accurate predictions with high computational efficiency for various silica polymorphs is still ongoing. Recent developments in short-range machine-learned models, such as GAP and NNPScan, have shown promise in providing reasonable descriptions of silica, but their computational cost remains high compared to force fields such as BKS which are based on simple interpretable functional forms. Here, in this study, we build on the recent success of our reinforcement learning (RL) workflow to derive a new set of optimal parameters for a promising short-range BKS-based model proposed by Soules. We use RL to navigate the eight-dimensional parameter space of the Soules potential using an experimental training data set that includes both local and global structural features from approximately 21 experimentally realized silica polymorphs, including high density phases and porous zeolites. We compare the performance of our machine-learned ML-Soules model with other high quality models including our recent machine-learned parametrization of BKS (ML-BKS), a machine-learned potential (GAP), as well as predictions of ab initio calculations with the highly fidelity SCAN functional. The ML-Soules accurately captures the relative energetic ordering of various polymorphs as well as their structural features at a significantly reduced computational expense. The ML-Soules model also reasonably captures the structure, density, and elastic constants of quartz, as well as metastable silica polymorphs. We further discuss the limitations of the Soules functional form and propose potential enhancements, including the incorporation of additional three-body terms and/or the utilization of different short-ranged functional forms to achieve greater accuracy for both global and local features in the modeling of silica while retaining low computational cost.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Hybrid Modeling of Three-Phase Grid-Supporting Inverters for Dynamic Studies
Grid technologies connected by power electronic converter (PEC) interfaces continually implement grid support functions mandated by grid codes and standards. The transition to converter-based generation demands precise PEC models to assess system dynamics, which have been previously overlooked in conventional power systems. This study proposes a hybrid method for analyzing grid-connected three-phase PEC dynamics with the IEEE standard 1547-2018 Volt-VAr mode that combines physics and data-driven techniques. The physics model reflects the PEC’s internal behavior, whereas the data-driven modeling technique evaluates the grid-supporting capabilities of the smart PEC. The system identification approach is used to generate dynamic PEC models based on changing grid voltage and measured current injected into the grid by the PEC. In the Volt-VAr support mode, a detailed topological model including switches is utilized to compare the goodness-of-fit of the extracted hybrid dynamic model. The results demonstrate that the hybrid PEC model in the Volt-VAr mode accurately matches the dynamics with the topological model.
Subedi, Sunil↗
Yrast structures of 98 Nb and 99 Mo
Neutron-rich nuclei in the A ≈ 100 mass region are interesting due to a rapid shape transition, especially pronounced in the Zr isotopes, and more recently observed in Nb isotopes. 98 Nb, with only one proton and one neutron outside the subshell closure nucleus of 96 Zr, is amenable to shell model calculations. To further examine the rapid shape transition the yrast structure of 98 Nb was established in this work. This was the only yrast structure missing from all immediate neighbors to 96 Zr. The yrast structure of 98 Nb was studied in the fission of the compound systems formed in three heavy-ion induced reactions, 24 Mg (134.5 MeV) + 173 Yb, 23 Na (129 MeV) + 176 Yb, and 18 O (91 MeV) + 208 Pb. Prompt γ-ray spectroscopy was accomplished using the Gammasphere array. Excitation energies up to 3 MeV were observed for the first time in 98 Nb. The yrast structure above the previously known (5) + isomer was established. In the process of studying 98 Nb the yrast structure of positive-parity states in 99 Mo was extended to 3.7 MeV excitation energy, the previously-known 99 Nb level scheme was enriched, and two new levels were added in the level scheme of 97 Zr. The coupling of the odd proton occupying the g 9/2 orbital to the yrast states in the core nucleus of 97 Zr can account for all observed states in 98 Nb. The yrast structure for the positive-parity states of 99 Mo is compared to the deformed ground-state bands of the 101 Ru isotone and of 98,100 Mo.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Velocity Dependence of Rate‐And‐State Friction in Granular Fault Gouge and Implications for Slow‐Slip Events
Abstract The standard rate‐and‐state friction (RSF) has extensively captured frictional behaviors, but it fails to explain the velocity dependence of frictional stability transition and widespread slow‐slip events (SSEs) in experiments and nature adequately. An alternative microphysical Chen‐Niemeijer‐Spiers (CNS) model can well describe the velocity dependence of frictional behaviors of granular gouges. Using the original CNS model, standard RSF parameters can be quantified microphysically. However, some micro‐parameters are not easy to estimate quantitatively, making it difficult to extrapolate to natural and experimental conditions. Here, we simplify the microphysically‐derived RSF parameters including direct effect a , evolution effect b , and critical slip distance D c , as well as equivalent values ( a eq , b eq , and D eq ). The simplified friction parameters directly illustrate their velocity dependence, namely the essentially constant a , a eq , and D c , negatively velocity‐dependent b and b eq , as well as varying D eq for different laws. They are roughly consistent with experimental results in various fault gouges. A modified CNS model is further derived from the original CNS model, establishing a direct link between the standard RSF and CNS models. The modified CNS model exhibits virtually identical frictional behaviors to the original CNS, but differs from the standard RSF at large velocity perturbations. Moreover, the linearized stability analysis indicates that the critical stiffness for the modified CNS model is velocity‐dependent. Compared with the standard RSF, the modified CNS model not only explains the velocity dependence of frictional stability transition, but also exhibits a more gradual transition for SSEs with a broader range of stiffness ratios.
Geochemistry & Geophysics↗
ARM shortwave spectrometers to study the clear-cloud transition zone and mixing processes
The proposed research is a collaborative effort between NASA/Goddard Space Flight Center and the Hebrew University of Jerusalem. While the NASA team focused on analyzing ground-based hyper-spectral radiance observations to understand cloud edge properties and their connection to mixing processes, the Hebrew University team tackled the problem through cloud modeling activities. By approximating the shortwave spectra in the cloud-clear transition zone as a linear combination of purely clear and purely cloudy spectra we can characterize the variations of cloud optical thickness and cloud droplet effective radius in the transition zone. When applying this method to the measurements of a ground-based shortwave spectroradiometer at the ARM’s SGP site, representing continental conditions, and MAGIC field campaign between Log Angeles, California and Honolulu, Hawaii, representing maritime scenarios, we found that cloud optical depth consistently decreases in both cases, but droplet size decreases much more substantially for the continental regime, suggesting different mixing processes for the continental and maritime conditions. The investigation and measurements of radiation clouds were coupled with a unique cloud modeling. A novel spectral bin microphysics was developed and implemented to the System of Atmospheric Modeling (SAM). In order to resolve cloud transition zones with high spatial gradients of microphysical variables a unique high resolution (10 m) was used in simulations. The model calculates droplet size distributions in each grid point. The model output was transferred to the NASA/GSFC team for utilization in radiative calculations and testing of both radiative algorithm and model representation.
54 ENVIRONMENTAL SCIENCES↗
Liquid-liquid phase transition of hydrogen and its critical point: Analysis from ab initio simulation and a machine-learned potential
We simulate high-pressure hydrogen in its liquid phase close to molecular dissociation using a machine-learned interatomic potential. The model is trained with density functional theory (DFT) forces and energies, with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional. We show that an accurate NequIP model, an E(3)-equivariant neural network potential, accurately reproduces the phase transition present in PBE. Moreover, the computational efficiency of this model allows for substantially longer molecular dynamics trajectories, enabling us to perform a finite-size scaling (FSS) analysis to distinguish between a crossover and a true first-order phase transition. Here, we locate the critical point of this transition, the liquid-liquid phase transition (LLPT), at 1200-1300 K and 155-160 GPa, a temperature lower than most previous estimates and close to the melting transition.
08 HYDROGEN↗
A novel ignition model for low velocity impact of heterogeneous explosives based on interacting hot spots
While numerous studies have focused on the ignition of explosives occurring in high velocity impact and the associated shock-to-detonation transition, there has been growing interest in developing computational models focused on low-velocity impact regimes. A predictive low-velocity impact ignition model will be important for analyzing high explosive safety and potential accident scenarios. This work introduces a novel ignition model based on the concept of thermally interacting hot spots to simulate low velocity impacted heterogeneous explosives where observed ignition times are on the order of milliseconds. The model asserts that relevant hot spots are micron-sized, the typical separation between neighboring hot spots is on the order of a hundred microns, and that neighbors interact thermally through heat conduction across the interstitial region between them. To achieve tractable numerical solutions, hot spots are assumed to form a periodic array as opposed to the highly irregular positioning in an actual explosive. This idealization allows a single two hotspot system to characterize the ignition process. Consequently, the model is referred to as the two hot spot Frank-Kamenetskii ignition model. In the present study, hot spots are modeled as constant heat sources terms, but this can be extended to include grain-scale phenomena like frictional heating of micron-sized growing cracks that are confined under high pressure. Because the micron-sized features are below the scale that can be efficiently resolved at a systems level, an efficient subscale scheme based on the Method of Weighted Residuals (MWR) is used to efficiently solve the equations. In conclusion, we carry out numerical examples and analytic predictions illustrating the accuracy and the functioning of the model.
97 MATHEMATICS AND COMPUTING↗
Baryon number violation: from nuclear matrix elements to BSM physics
Processes that violate baryon number, most notably proton decay and $n\bar{n}$ transitions, are promising probes of physics beyond the Standard Model (BSM) needed to understand the lack of antimatter in the Universe. To interpret current and forthcoming experimental limits, theory input from nuclear matrix elements to UV complete models enters. Thus, an interplay of experiment, effective field theory, lattice QCD, and BSM model building is required to develop strategies to accurately extract information from current and future data and maximize the impact and sensitivity of next-generation experiments. Here, we briefly summarize the main results and discussions from the workshop ‘INT-25-91W: Baryon Number Violation: From Nuclear Matrix Elements to BSM Physics,’ held at the Institute for Nuclear Theory, University of Washington, Seattle, WA, 13–17 January 2025.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Changes in soil water content and lateral flow exert large effects on soil thermal dynamics across Alaskan landscapes
Both lateral surface and subsurface water flow affect soil moisture dynamics, yet most land surface models only solve subsurface water movement vertically. Here, we use a 3D ecosystem model that considers both land surface and subsurface hydrologic processes to simulate soil moisture, which is then used to drive a 1-D vertical soil thermal model to simulate the soil moisture effects on soil thermal dynamics in central Alaska. Our coupled model improves soil temperature (ST) estimates by 43.5% in comparison with observational data. Soil moisture has little effect on ST during the wet season (-1.5%) and a substantial influence during the dry season (60%). Spatially, water lateral flow has significant impacts on both soil moisture and ST, causing model estimates for thawed areas in the transition season to increase by ~10% in the study area. Our results highlight the importance of considering dynamical soil moisture, as well as lateral flow effects, on soil thermal dynamics in permafrost regions.
54 ENVIRONMENTAL SCIENCES↗
Magneto-optical Evidence of the Tilting Effect in Coupled Weyl Bands
Theories have revealed the universality of the band tilting effect in topological Weyl semimetals (WSMs) and its implications for the material physical properties. However, the experimental identification of tilted Weyl bands remains less explored. Here, we report the magneto-optical evidence of the tilting effect in WSM niobium phosphide. Specifically, we observe Landau level transitions with rich features that are well reproduced within a model of coupled tilted Weyl points. Our analysis indicates that the tilting effect relaxes the selection rules and leads to transitions that would otherwise be forbidden in the non-tilt case. Additionally, we observe unconventional interband transitions with flat and negative magnetic field dispersions, highlighting the importance of coupling between Weyl points. Our results not only emphasize the significance of the tilting effect in the optical responses of WSMs but also demonstrate magneto-optics as an effective tool for probing the tilting effect in electronic band structures.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
NSA Site Science: Use of ARM Observations from Northern Alaska to Evaluate and Improve Prediction Capabilities
The Arctic is warming at a rate nearly double that of the rest of the planet, leading to profound changes in atmospheric, oceanic, and ice processes. The U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility has played a significant role in Arctic research, operating observatories in Alaska's North Slope for over 25 years. These observatories provide a rich, and wide-reaching dataset that offers insight into atmospheric processes in northern Alaska. This report details the results of a nine-year research project (2015–2024) supported by the DOE Atmospheric Systems Research (ASR) program, that leverages data from ARM’s deployment of observing facilities at Utqiaġvik (known as the North Slope of Alaska, or NSA, site) and Oliktok Point, Alaska. The project was conducted in two phases: - Phase 1 (2015–2019): Focused on understanding key atmospheric processes at Oliktok Point, including cloud formation, high-latitude precipitation, aerosol-cloud interactions, and cloud properties. - Phase 2 (2019–2024): Extended the research to the broader North Slope region, using data from both Oliktok Point and Utqiaġvik. Topics explored included surface energy budgets, atmospheric stability, ice nucleation processes, and microphysics in Arctic clouds. The project resulted in numerous research products, including 50 peer-reviewed publications and dissertations, 169 presentations, and 10 data products. These products cover a variety of topics, including: - Cloud Macro- and Microphysical Properties: Arctic clouds play a crucial role in energy transfer, and accurate representation in models is critical. The study explored cloud transitions, ice crystal shapes, and dual-wavelength radar data to understand ice crystal habits and size distributions. - Aerosol Properties and Processes: The team examined aerosol sources in the Arctic, including industrial emissions and natural sources. Observations showed significant spatial gradients in aerosol concentrations due to human activities and wildfire smoke. The influence of aerosols on cloud formation and the surface energy budget was also assessed. - Aerosol-Cloud Interactions: Research revealed that aerosols might suppress cloud ice production, affecting cloud radiative forcing and precipitation. The impact of local industrial emissions on cloud properties was also investigated. - Contextualizing the North Slope of Alaska in the context of the broader Arctic: To understand broader trends, the project evaluated large-scale circulation patterns and the influence of weather systems on the Arctic. Studies indicated that large-scale processes play a significant role in temperature patterns and the timing of snowmelt. - Advancing ARM Observational and Modeling Capabilities: The project developed new radar data products and advanced measurement techniques, including clutter mitigation and drizzle detection. Uncrewed aerial systems (UAS) and tethered balloon systems (TBS) were deployed to gather detailed atmospheric data. Additionally, the project supported 10 early career scientists, providing training and mentorship to undergraduate interns, graduate students, postdoctoral researchers, and early career researchers. These efforts contributed to the advancement of ARM research capabilities and fostered a new generation of scientists skilled in Arctic atmospheric research. Ultimately, this ASR-supported project has provided valuable insights into Arctic atmospheric processes and their broader climate implications. Recommendations for future work include continuing support for long-term observing at Arctic locations to foster additional research, further exploration of aerosol-cloud interactions and the potential impacts of enhanced industrialization of the Arctic, and expanded use of uncrewed systems to gather data in this remote and harsh environment. Additionally, the data products developed by this work, and the data products developed through the ARM infrastructure, leave a treasure-trove of additional information that should be explored for many years to come to gain additional insight into physical processes in the Arctic atmosphere that drive the rapid changes occurring in at high latitudes and their global impact.
58 GEOSCIENCES↗
Structured interactions drive abrupt transitions in the spatial organization of microbial communities
Bacteria possess diverse mechanisms to regulate their motility in response to environmental and physiological signals, enabling them to navigate complex habitats and adapt their behavior. Some of these mechanisms are species specific and enable cells to modulate their movement based on the ecological identity of neighboring species. Here, we introduce a model in which bacteria interact via local signals that either enhance or suppress the motility of neighboring cells depending on species type. Through large-scale simulations and a coarse-grained stochastic model, we demonstrate the emergence of a sharp transition driven by nucleation processes: increasing the density of motility-suppressing interactions drives the system from a fully mixed, motile phase to a state characterized by large, stationary bacterial clusters. Remarkably, in systems with a large number of interacting species, this transition can be triggered solely by altering the structure of the motility-regulation interaction matrix while maintaining species and interaction densities constant. In particular, we find that heterogeneous and modular interactions promote the transition more readily than homogeneous random ones. These findings add a dimension to the theory of motility-induced phase separation and contribute to the ongoing effort to understand microbial interactions, suggesting that structured, nonrandom ones may be key to reproducing commonly observed spatial patterns in microbial communities.
bacterial communities↗
Gaussian-process generative model for the QCD equation of state
We develop a generative model for the nuclear matter equation of state at zero net baryon density using the Gaussian process regression method. We impose first-principles theoretical constraints from lattice quantum chromodynamics and hadron resonance gas at high- and low-temperature regions, respectively. By allowing the trained Gaussian process regression model to vary freely near the phase transition region, we generate random smooth crossover equations of state with different speeds of sound that do not rely on specific parametrizations. Here, we explore a collection of experimental observable dependencies on the generated equations of state, which paves the groundwork for future Bayesian inference studies to use experimental measurements from relativistic heavy-ion collisions to constrain the nuclear matter equation of state.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A rigorous framework for an improved Messinger/Myers model of ice accretion under conditions of variable property and unsteady aircraft icing
We analyse the Messinger/Myers model by critically evaluating simplifying assumptions through a rigorous formulation of the rime ice accretion process. We explore the effects of both constant and variable ice density and thermal conductivity, along with the effects of sublimation from the ice surface. The effects of key factors such as droplet impact rate, ambient temperature relative to the freezing temperature and the temperature difference between the ambient air and the airfoil surface are examined. Under these varying conditions, the present rigorous formulation is used to assess the significance of unsteady effects, variable ice properties and sublimation. We observe that the Myers model performs remarkably well in certain icing situations and analyse the reasons for this strong performance. We also show that partially relaxing the model’s assumptions can lead to poorer performance. The Myers model can lead to overprediction of ice surface temperature and correspondingly underprediction of transition time under conditions of relatively weak sublimation and surface cooling. A modified Myers model is presented, which can be used to recover near-perfect results under widely varying icing conditions of relevance. This article is part of the theme issue ‘Heat and mass transfer in frost and ice’.
Science & Technology - Other Topics↗