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At least 1,063 records · Page 59

Nuclear Data Evaluations in the Resolved Resonance Region and Proposed Updates to R­matrix Modeling in the SAMMY Code [Slides]

This detailed presentation starts off with an evaluation workflow scheme along with observables definition. Provided next are typical cases of explicit experimental effects and the basic quantities for reproducibility. Lastly the presentation mentions the Inverse channel, Complex radius, and the level­ dependent boundary condition. A visual workflow scheme is provided.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

On the statistical theory of self-gravitating collisionless dark matter flow: Scale and redshift variation of velocity and density distributions

The statistics of velocity and density fields are crucial for cosmic structure formation and evolution. Here, this paper extends our previous work on the two-point second-order statistics for the velocity field [Phys. Fluids 35, 077105 (2023)] to one-point probability distributions for both density and velocity fields. The scale and redshift variation of density and velocity distributions are studied by a halo-based non-projection approach. First, all particles are divided into halo and out-of-halo particles so that the redshift variation can be studied via generalized kurtosis of distributions for halo and out-of-halo particles, respectively. Second, without projecting particle fields onto a structured grid, the scale variation is analyzed by identifying all particle pairs on different scales $r$. We demonstrate that: (i) Delaunay tessellation can be used to reconstruct the density field. The density correlation, spectrum, and dispersion functions were obtained, modeled, and compared with the N-body simulation; (ii) the velocity distributions are symmetric on both small and large scales and are non-symmetric with a negative skewness on intermediate scales due to the inverse energy cascade on small scales with a constant rate $\varepsilon_u$; (iii) On small scales, the even order moments of pairwise velocity $\Delta u_L$ follow a two-thirds law $\propto{(-\varepsilon_ur)}^{2/3}$, while the odd order moments follow a linear scaling $\langle(\Delta u_L)^{2n+1}\rangle=(2n+1)\langle(\Delta u_L)^{2n}\rangle\langle\Delta u_L\rangle\propto{r}$; (iv) The scale variation of the velocity distributions was studied for longitudinal velocities $u_L$ or $u_L^{'}$, pairwise velocity (velocity difference) $\Delta u_L$=$u_L^{'}$-$u_L$ and velocity sum $\Sigma u_L$=$u^{'}_L$+$u_L$. Fully developed velocity fields are never Gaussian on any scale, despite that they can initially be Gaussian; (v) On small scales, $u_L$ and $\Sigma u_L$ can be modeled by a $X$ distribution to maximize the entropy of the system. The distribution of $\Delta u_L$ can be different; (vi) On large scales, $\Delta u_L$ and $\Sigma u_L$ can be modeled by a logistic or a $X$ distribution, while $u_L$ has a different distribution; (vii) the redshift variation of the velocity distributions follows the evolution of the $X$ distribution involving a shape parameter $\alpha(z)$ decreasing with time.

79 ASTRONOMY AND ASTROPHYSICS↗

Diabatic quantum annealing for the frustrated ring model

Abstract Quantum annealing (QA) is a continuous-time heuristic quantum algorithm for solving or approximately solving classical optimization problems. The algorithm uses a schedule to interpolate between a driver Hamiltonian with an easy-to-prepare ground state and a problem Hamiltonian whose ground state encodes solutions to an optimization problem. The standard implementation relies on the evolution being adiabatic: keeping the system in the instantaneous ground state with high probability and requiring a time scale inversely related to the minimum energy gap between the instantaneous ground and excited states. However, adiabatic evolution can lead to evolution times that scale exponentially with the system size, even for computationally simple problems. Here, we study whether non-adiabatic evolutions with optimized annealing schedules can bypass this exponential slowdown for one such class of problems called the frustrated ring model. For sufficiently optimized annealing schedules and system sizes of up to 39 qubits, we provide numerical evidence that we can avoid the exponential slowdown. Our work highlights the potential of highly-controllable QA to circumvent bottlenecks associated with the standard implementation of QA.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Mechanical Actuation via Homeomorphic Transformations of Topological Solitons within Polymer Coatings

Abstract Topological solitons are currently under investigation for their exotic properties, especially in nonlinear physics, optics, and material sciences. However, challenges of robust generation and limited stability over time have hindered their practical uses. To address this issue, an approach is developed to form structured arrays of solitons in films of polymerizable liquid crystals. Their complex molecular architecture is preserved by in situ photopolymerization forming a stable liquid crystal network. Most excitingly, their properties are advanced to include responsiveness functions. When thermally actuated, these topological solitons mediate the reconfiguration of surface topographies. Complex shape changes occur depending on the intrinsic complex spatial distribution of the director, which may even lead to full shape inversion and topographical changes as high as ≈40% of the initial thickness. Conversely, the shape changes provide information on the initial director profile, which is consistent with the mathematical model. The soliton‐containing polymer coatings are applicable in multiple domains, ranging from tunable optics to haptics, and from shape‐coupled sensing systems to temperature‐coupled heat management.

36 MATERIALS SCIENCE↗

Autonomous Output‐Oriented Aerosol Jet Printing Enabled by Hybrid Machine Learning

Additive manufacturing (AM) is rapidly revolutionizing modern manufacturing with recent progress in advanced printing methods and improved properties of printed materials. However, traditional AM methods are limited by their input‐oriented nature, which demands tedious trial‐and‐error tuning of printing parameters to achieve desired output properties. Here, in this work, an output‐oriented artificial intelligence‐integrated AM (AIAM) method is reported that enables an user to specify desired output properties while the printer autonomously discovers the optimal input printing parameters by integrating hybrid machine learning models and in situ measurements. Based on a predictive mapping between the input printing parameters and the output properties of interests established with <20 experiments designed by active learning, inverse design tasks are performed to intelligently generate the printing parameter settings that lead to desired outcomes using reinforcement learning. This method is demonstrated by autonomous aerosol jet printing (AJP) of conductive polymer films and achieving user‐defined electrical resistances with an ultralow error of 3.7%. The AIAM method, with its output‐oriented nature, holds the potential to significantly improve the autonomy, predictability, efficiency, and accessibility of the AM processes, which will unlock new possibilities in the autonomous and intelligent printing of a broad range of functional materials and devices.

36 MATERIALS SCIENCE↗

Switching kinetics in ferroelectric zinc magnesium oxide thin films

The switching kinetics of RF-magnetron reactively sputtered ~200 nm thick Zn 1-x Mg x O (ZMO) ferroelectric thin films with x = 0.41 and x = 0.27 prepared on Pt/Ti/SiO 2 /Si substrates were studied at applied fields near the coercive fields, ranging from 3.4 to 5.1 MV cm -1 , and at temperatures ranging from room temperature to 100 °C. Polarization reversal in ZMO followss the Kolmogorov-Avrami-Ishibashi kinetics model for nucleation and growth at applied fields near the coercive field, and obeys an individual column switching (ICS) model at higher applied fields required to switch most of the spontaneous polarization. Switching in the high-applied field regime can be described using either the Gaussian or inverse gamma distribution functions depending on the mole fraction of magnesium in the film. The switching current transients of the high-Mg content ZMO film (x = 0.41) are always bi-modal, whereas the low Mg composition film (x = 0.27) is described by the Gaussian distribution initially following wake-up, and becomes bimodal with continued cycling. Rayleigh-like behavior of the dielectric constant revealed a simultaneous increase of the irreversible and decrease of the reversible contributions to the dielectric constant, which was ascribed to an increase in the density of mobile domain walls with cycling and resulted in faster switching.

30 DIRECT ENERGY CONVERSION↗

Inverse Faraday effect in Mott insulators

The inverse Faraday effect (IFE), where a static magnetization is induced by circularly polarized light, offers a promising route to ultrafast control of spin states. Here we study the IFE in Mott insulators using the Floquet theory. We find two distinct IFE behavior governed by the inversion symmetry. In the Mott insulators with inversion symmetry, we find that the effective magnetic field induced by the IFE couples ferromagnetically to the neighboring spins. While for the Mott insulators without inversion symmetry, the effective magnetic field due to IFE couples antiferromagnetically to the neighboring spins. We apply the theory to the spin-orbit coupled single- and multiorbital Hubbard model that is relevant for the Kitaev quantum spin liquid material and demonstrate that the magnetic interactions can be tuned by light.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Semiclassical shell-structure micro-macroscopic approach for the level density

Level density ρ(E,A) is derived for a one-component nucleon system with a given energy E and particle number A within the mean-field semiclassical periodic-orbit theory beyond the saddle-point method of the Fermi gas model. We obtain ρ∝I ν (S)/S ν , with I ν (S) being the modified Bessel function of the entropy S. Within the micro-macro-canonical approximation (MMA), for a small thermal excitation energy U, with respect to rotational excitations E rot , one obtains ν = 3/2 for ρ(E,A). In the case of excitation energy U larger than E rot but smaller than the neutron separation energy, one finds a larger value of ν = 5/2. A role of the fixed spin variables for rotating nuclei is discussed. The MMA level density ρ reaches the well-known grand-canonical ensemble limit (Fermi gas asymptote) for large S related to large excitation energies, and also reaches the finite micro-canonical limit for small combinatorial entropy S at low excitation energies (the constant “temperature” model). Fitting the ρ(E,A) of the MMA to the experimental data for low excitation energies, taking into account shell and, qualitatively, pairing effects, one obtains for the inverse level density parameter K a value which differs essentially from that parameter derived from data on neutron resonances.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Imaging of a fluid injection process using geophysical data — A didactic example

In many subsurface industrial applications, fluids are injected into or withdrawn from a geologic formation. It is of practical interest to quantify precisely where, when, and by how much the injected fluid alters the state of the subsurface. Routine geophysical monitoring of such processes attempts to image the way that geophysical properties, such as seismic velocities or electrical conductivity, change through time and space and to then make qualitative inferences as to where the injected fluid has migrated. The more rigorous formulation of the time-lapse geophysical inverse problem forecasts how the subsurface evolves during the course of a fluid-injection application. Using time-lapse geophysical signals as the data to be matched, the model unknowns to be estimated are the multiphysics forward-modeling parameters controlling the fluid-injection process. Properly reproducing the geophysical signature of the flow process, subsequent simulations can predict the fluid migration and alteration in the subsurface. The dynamic nature of fluid-injection processes renders imaging problems more complex than conventional geophysical imaging for static targets. This work intents to clarify the related hydrogeophysical parameter estimation concepts.

58 GEOSCIENCES↗

PFLOTRAN-SIP: A PFLOTRAN Module for Simulating Spectral-Induced Polarization of Electrical Impedance Data

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10–100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivities at near-zero frequency. These bulk electrical conductivities were modified while using the Cole–Cole model to account for frequency dependence. Using the estimated frequency-dependent bulk conductivities, E4D simulated the real and complex electrical potential signals for selected frequencies for SIP. These frequency-dependent bulk conductivities contain information that is relevant to geochemical changes in the system. This study demonstrated that the PFLOTRAN-SIP framework is able to detect the presence of a tracer in the subsurface. SIP offers a significant benefit over ERT in the form of greater information content. It provided multiple datasets at different frequencies that better constrained the tracer distribution in the subsurface. Consequently, this framework allows for practitioners of environmental hydrogeophysics and biogeophysics to monitor the subsurface with improved resolution.

54 ENVIRONMENTAL SCIENCES↗

Identifying synoptic controls on boundary layer thermodynamic and cloud properties in a regional forecast model

Although most of our understanding of boundary layer cloudiness is based on idealized, subtropical, barotropic marine environments, boundary layer clouds exist across a range of conditions. In this study, we use the Naval Research Laboratory's Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) and an automated cold-front-relative analysis framework to explore the boundary layer structure associated with low clouds across a transect through the cold front of a midlatitude synoptic cyclone. The model credibly captures boundary layer structure in line with conceptual models of midlatitude cyclones and ground-based observations at Graciosa Island in the Azores. The warm sector is conditionally unstable, with clouds that are too shallow and with too little liquid water, compared to cloud property retrievals from satellite and surface-based instruments. The cold-frontal region exhibits convection associated with weak stability and ascent. Northwest of the cold front, the boundary layer is well mixed, deeper, and capped by a strong inversion maintained by large-scale subsidence. Simulated clouds in frontal and post-frontal regions are mostly too thick, with too much liquid water and too little cloud base drizzle. The post-frontal clouds are associated with grid-scale updrafts, which appear to be the model's attempt to represent mesoscale organization of cellular convection typically observed in the cold sector of midlatitude cyclones. The deep, well-mixed post-frontal boundary layers and cloudiness are maintained by strong surface fluxes, as in cold air outbreaks. Our analysis framework serves as a unique approach to model verification, and our results offer insights into differences in boundary layer cloud behavior between subtropical and synoptic cold-sector regimes.

Eissner, Jordan M. [Univ. of Kansas, Lawrence, KS ↗

Estimation of constituent properties of concrete materials with an artificial neural network based method

Multi-scale models are developed for heterogeneous concrete materials to estimate their macroscopic mechanical properties in terms of micro-structural data. One crucial challenge of those models is the identification of local properties of constituent phases. In this paper, we present an efficient method based on Artificial Neural Networks (ANN). Typical concrete materials are taken as example. A macroscopic analytical strength criterion is established from three steps of nonlinear homogenization procedure. The macroscopic strength of materials is determined as a function of the frictional coefficient and cohesion of solid cement particles at nanometer scale, intra-particle pores, inter-particle pores and aggregates (inclusions). The objective is to identify the nanoscopic frictional coefficient and cohesion of cement particle from measured macroscopic values of uniaxial compression and tensile strengths. For this purpose, a numerical method based on the ANN is developed. With the analytical macroscopic strength criterion, sensitivity studies are first realized to identify the most important micro-structural parameters influencing the macroscopic strength of concrete. A simplified analytical macroscopic strength criterion is then proposed. A large dataset is further constructed through the inversion of the analytical strength criterion by using the aggregates volume fraction, porosity, macroscopic uniaxial tensile and compressive strengths as input variables and the frictional coefficient and cohesion of cement particles as output unknowns. An ANN model containing four hidden layers and 100 neurons in each layer is constructed and trained by using this dataset. Various types of validation of the ANN model are performed. It is found that the proposed ANN based model can effectively predict the frictional coefficient and cohesion of porous cement paste at the microscopic scale with a very good accuracy.

36 MATERIALS SCIENCE↗

Simulations of divertor plasmas with inverse sheaths

The effect of strong electron emission from material surfaces has been proposed to form an “inverse sheath”: a region with a positive potential relative to the near-wall plasma which prevents the flow of ions to the wall [M. D. Campanell, “Negative plasma potential relative to electronemitting surfaces,” Phys. Rev. E. 88, 033103 (2013); M. D. Campanell and M. V. Umansky, “Strongly emitting surfaces unable to float below plasma potential,” Phys. Rev. Lett. 116, 1–5 (2016); M. D. Campanell and G. R. Johnson, “Thermionic cooling of the target plasma to a sub-ev temperature,” Phys. Rev. Lett. 122, 1–5 (2019)]. We assess the viability of this regime in a tokamak device using the 2D edge plasma transport code UEDGE [T. Rognlien et al ., “A fully implicit, time dependent 2-D fluid code for modeling tokamak edge plasmas,” J. Nucl. Mater. 196–198, 347–351 (1992)]. Since the UEDGE code does not consider the sheath region directly, we apply boundary conditions at the divertor targets which emulate the physics of both “standard” and “inverse” sheath regimes [R. Masline et al ., “Influence of the inverse sheath on divertor plasma performance in tokamak edge plasma simulations,” Contrib. Plasma Phys. 60, e201900097 (2020)]. Using these boundary conditions, we perform scoping studies to assess plasma parameters near the target by varying the density at the core-edge interface. We observe a smooth transition in the resultant profiles of plasma parameters for the standard sheath, and a bifurcation across the simulation set for plasmas with an inverse sheath. The cause of this bifurcation is assessed by performing the parameter scan both with and without impurity radiation; we observe that the bifurcation persists in both cases, indicating that this bifurcation is caused by plasma recombination.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Acoustic Tomography of the Atmosphere: A Large-Eddy Simulation Sensitivity Study

Accurate measurement of atmospheric turbulent fluctuations is critical for understanding environmental dynamics and improving models in applications such as wind energy. Advanced remote sensing technologies are essential for capturing instantaneous velocity and temperature fluctuations. Acoustic tomography (AT) offers a promising approach that utilizes sound travel times between an array of transducers to reconstruct turbulence fields. This study presents a systematic evaluation of the time-dependent stochastic inversion (TDSI) algorithm for AT using synthetic travel-time measurements derived from large-eddy simulation (LES) fields under both neutral and convective atmospheric boundary-layer conditions. Unlike prior work that relied on field observations or idealized fields, the LES framework provides a ground-truth atmospheric state, enabling quantitative assessment of TDSI retrieval reliability, sensitivity to travel-time measurement noise, and dependence on covariance model parameters and temporal data integration. A detailed sensitivity analysis was conducted to determine the best-fit model parameters, identify the tolerance thresholds for parameter mismatch, and establish a maximum spatial resolution. The TDSI algorithm successfully reconstructed large-scale velocity and temperature fluctuations with root mean square errors ( RMSE s) below 0.35 m/s and 0.12 K, respectively. Spectral analysis established a maximum spatial resolution of approximately 1.4 m, and reconstructions remained robust for travel-time measurement uncertainties up to 0.002 s. These findings provide critical insights into the operational limits of TDSI and inform future applications of AT for atmospheric turbulence characterization and system design.

17 WIND ENERGY↗

Experimental demonstration of transient CHI start-up using a floating biased electrode configuration

Transient coaxial helicity injection (CHI) current start-up in a spherical tokamak (ST) has been demonstrated for the first time using a reactor-relevant floating single biased (FSB) electrode configuration on QUEST. Unlike previous applications of CHI on HIT-II and on NSTX in which the entire ST vessel was split in two using two toroidal ceramic rings as vacuum breaks, in the FSB electrode configuration on QUEST, only the lower outer divertor plate is electrically insulated from the rest of the vessel. This is the CHI injector region. A CHI discharge is generated by driving current, using an external power supply, along magnetic field lines that connect the inner and outer divertor plates in the CHI injector region. The CHI plasma evolution and closed flux formation on QUEST are very similar to those on NSTX and have generated toroidal currents of 150 kA with 100 kA of current flowing on closed flux surfaces. An alternate, force-free model approach has been used to derive the CHI scaling relations. The resulting CHI scaling relations from the model are consistent with the conventional CHI scaling relations, which state that the CHI generated toroidal current is proportional to the CHI injector flux and the CHI injector current is inversely proportional to the toroidal field. These results from QUEST bode well for the application of CHI in a new generation of compact high field STs and low aspect ratio tokamaks in which the space for the central solenoid is very restricted.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Time-reversal invariant topological moiré flat band: A platform for the fractional quantum spin Hall effect

Motivated by recent observation of the quantum spin Hall effect in monolayer germanene and twisted bilayer transition-metal-dichalcogenides (TMDs), we study the topological phases of moir twisted bilayers with time-reversal symmetry and spin sz conservation. By using a continuum model description which can be applied to both germanene and TMD bilayers, we show that at small twist angles the emergent moir flat bands can be topologically nontrivial due to inversion symmetry breaking. Each of these flat bands admits a lowest-Landau-level description for each spin projection in the chiral limit and at magic twist angle. Furthermore, this allows for the construction of a many-body Laughlin state with time-reversal symmetry which can be stabilized by a short-range pseudopotential, and therefore serves as an ideal platform for realizing the so-far elusive fractional quantum spin Hall effect with emergent spin-1/2 U(1) symmetry.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Measurement of the Ne 18 ( α , p ) Na 21 reaction with the ANASEN active-target detector system at E c . m . = 2.5 – 4 MeV

The 18 Ne(α,p) 21 Na reaction plays a significant role in Type-I X-ray bursts. It is a major path in the breakout from the hot-CNO cycles to the synthesis of heavier elements in the αp- and rp-processes. An experiment to determine the cross section of this reaction was performed with the ANASEN active-target detector system, determining the cross section at energies between 2.5 and 4 MeV in the center-of-mass frame. The measured cross sections for reactions populating the ground state in 21 Na are consistent with results obtained from the time-inverse reaction, but significantly lower than the previously published experimental data of direct measurements. The total cross sections are also compared with those derived from indirect methods and statistical-model calculations. Furthermore, this experiment establishes a new experimental data set on the excitation function of the 18 Ne(α,p) 21 Na reaction, revealing the significance of the excited states' contributions to the total reaction cross section and allowing us to separate the contribution of the (α,2p) reaction. The impact of the measured cross section on thermal reaction rates is discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Mesophyll conductance and cell wall composition: insights from a meta‐analysis across species

Mesophyll conductance (g m ) plays a critical role in plant photosynthesis by regulating the diffusion of CO 2 from substomatal cavities to the site of carbon fixation within the chloroplasts. Despite its importance, our understanding of the factors influencing g m , particularly the role of cell wall properties, remains incomplete. In this issue of New Phytologist , Roig-Oliver et al. ( 2025 ; pp. 2384–2391) address this knowledge gap through a comprehensive meta-analysis across multiple species from the major clades of land plants. By analyzing trends in g m and two other key physiological parameters – the ratio of pectin to cellulose and hemicellulose in the cell wall by weight (P/(C + H)), and cell wall thickness (T cw ) – their findings reveal strong correlations of gm with the combination of P/(C + H) and T cw , where g m is directly proportional to P/(C + H) and inversely proportional to T cw . This work not only underscores the influence of cell wall composition on CO 2 diffusion but also provides a valuable framework for further experimental and modeling studies. By shedding light on the dynamic properties of cell walls, shaped by both composition and environmental regulation, this study opens new frontiers for understanding and improving photosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗