Applied Physics · Cross-listed from cond-mat.mtrl-sci
The completed High-Low method for interface state density analysis in MOS capacitors
Brian D. Rummel, Sarit Dhar and Robert J. Kaplar
Interface state densities, $D_{IT}$, in metal-
oxide-
semiconductor (MOS) capacitors are rarely reported in the accumulation energy range. It is recognized that the determination of $D_{IT}$ in accumulation is fundamentally obscured by small inaccuracies in the user-defined
oxide capacitance, $C_{OX}$. This source of error prevents the High-Low frequency technique from reporting accumulation $D_{IT}$, even for sufficiently fast high-frequency measurements. To resolve this, an electrostatic constraint that is uniquely satisfied by a physically consistent $C_{OX}$ is derived from the established theory, thereby completing the High-Low framework. The "completed" framework's theoretical validity is confirmed using simulated capacitance data for an n-SiC MOS structure, and the method's frequency limitations are demonstrated. This analytical advancement ensures a physically consistent extraction of $D_{IT}$ near the band edge, overcoming a fundamental limitation in MOS capacitor characterization.
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Optics
A Physics-Guided Neural Framework for Rheology Measurement from Dynamical Laser Speckles
Titanliang Wang, Thomas Goudoulas, Ehsan Fattahi et al. (+7 more)
Critical breakthroughs in the area of biomedicine and materials science increasingly depend on rapid, non-contact methods for viscoelastic characterization.
Laser Speckle Rheology (LSR) is positioned to meet this demand, effectively circumventing the speed and invasiveness bottlenecks inherent to traditional mechanical rheometer. However, its application in turbid fluids is severely constrained by multiple scattering, where standard physical inversions rely heavily on precise, sample-specific optical transport parameters that are difficult to measure in situ. To overcome this barrier, we propose a physics-guided deep learning framework that infers a Maxwell relaxation spectrum from the intensity autocorrelation g2(t) and speckle-intensity histogram statistics. The resulting spectrum is then propagated through a Maxwell forward model to predict G'and G” under physics-consistency constraints. Quantitatively, the framework achieves RMSElog as low as 0.009 against reference and generalizes to previously unseen scattering conditions, preserving physically plausible frequency dependence and G'- G” phase behavior. It reduces reliance on optical transport parameters that are hard to determine in situ and returns an interpretable generalized Maxwell relaxation spectrum, improving the practicality of LSR in turbid media.
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All authors Titanliang Wang, Thomas Goudoulas, Ehsan Fattahi, Dominik Geier, Yiyuan Yang, Ivan Ezhov, Yixiao Liu, Yi Li, Martin Booth, Thomas Becker
Computational Physics
NEP-CG and NEP-AACG: Efficient coarse-grained and multiscale all-atom-coarse-grained neuroevolution potentials
Zheyong Fan, Wenjun Zhang, Zhenhao Zhang et al. (+3 more)
Machine-learned coarse-grained (CG) models often suffer from noisy training data, limiting their accuracy and transferability. We propose a method to generate low-noise training data based on the potential of mean force by constraining CG beads during atomistic simulations and accumulating time-averaged forces. Implemented within the neuroevolution potential (NEP) framework, our approach achieves training accuracy comparable to atomistic models trained on density functional theory data. For
liquid water, the NEP-CG model accurately reproduces densities from 1 bar to 1 GPa, successfully extrapolating beyond the 0.5 GPa training limit, with a virial correction essential for the correct equation of state. For an anisotropic C$_{60}$ monolayer, distinguishing crystallographically distinct bead types reduces stress errors by an order of magnitude and captures directional thermal conductivity. We further introduce a multiscale NEP-AACG model integrating all-atom (AA) and CG degrees of freedom, demonstrated for gold nanowire fracture at an experimentally relevant strain rate. Computational speeds for NEP-CG models reach hundreds to thousands of ns/day using a single consumer-grade GPU. This work provides a robust framework for constructing accurate, transferable, and efficient CG models across diverse systems.
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All authors Zheyong Fan, Wenjun Zhang, Zhenhao Zhang, Ke Xu, Xuecheng Shao, Haikuan Dong
Geophysics
Velocity Model Building and Editing with Guided Denoising Diffusion Implicit Models
Francesco Brandolin and Tariq Alkhalifah
Velocity-model building is a fundamental component of seismic imaging, yet it remains a challenging inverse problem due to limited data coverage, nonlinearity, and the need to integrate heterogeneous information such as well logs. We introduce a unified framework for velocity-model editing and full velocity-model building that combines learned
diffusion priors with structurally preconditioned inverse formulations. A
diffusion model trained on high-resolution synthetic velocity examples provides a data-driven prior that is exploited through Denoising
Diffusion Implicit Model (DDIM) inversion and guided sampling. For localized editing, the
diffusion prior is coupled with a structurally preconditioned Tikhonov well-matching inversion, enabling controlled modification of selected regions while preserving global consistency. For full velocity-model building, we formulate a well-matching inverse problem augmented with imaging-based regularization and solve it using conventional least-squares, the proposed DDIM-guided method, and
Diffusion Posterior Sampling (DPS). Synthetic experiments demonstrate that
diffusion-based approaches recover sharper and more realistic velocity structures than classical inversion. Field-data applications on the Viking Graben
dataset confirm robustness under realistic acquisition conditions. An ablation study highlights the critical role of structural slope guidance in inversion performance. Overall, the proposed framework bridges inverse problems and generative modeling, offering a flexible approach for practical seismic imaging workflows.
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Instrumentation and Detectors · Cross-listed from cond-mat.supr-con
Scanning Tunneling Microscopy in high vectorial magnetic fields
Jaime Rumeu Ozores, Miguel Águeda Velasco, Edwin Herrera et al. (+10 more)
The Scanning Tunneling Microscope (STM) is a powerful instrument to study electronic density of states at surfaces down to atomic scale. Many interesting samples require studying variations as a function of the
magnetic field, which is most often applied perpendicular to the surface. Conventional STM designs make it challenging to perform measurements when the
magnetic field must be applied in other directions. Here we present a new STM setup installed on a rotatable platform. We have designed and built a new STM, which is small enough to allow for full rotation on a space with a diameter of 37 mm, well below the available space within many magnets. We show that the new rotatable STM setup preserves the performance of state-of-the-art STMs in terms of noise and accuracy. Our new approach significantly enhances control over the direction of the applied
magnetic field and opens exciting new possibilities to study
quantum materials.
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All authors Jaime Rumeu Ozores, Miguel Águeda Velasco, Edwin Herrera, Pablo García Talavera, Jose D. Bermúdez-Pérez, José A. Moreno, Paula Obladen, Rafael Álvarez Montoya, José Navarrete, Juan Ramón Marijuan, José A. Galvis, Isabel Guillamón, Hermann Suderow
Optics
Precise and Robust Domain Engineering Based on Faraday Cage Effect for Thin-film Lithium Niobate Photonics
Yanqun Wang, Furong Zhong, Lin Liu et al. (+4 more)
Thin-film lithium niobate (TFLN) waveguides are promising for efficient second-harmonic generation (SHG) owing to their strong optical confinement and large second-order nonlinearity. However, robust fabrication of high-efficiency devices remains challenging, as existing domain engineering methods lack precise and robust controls of polarity distributions. Here, we propose and demonstrate a domain engineering technique that utilizes nanoscale Faraday cages to shape the electric-field distributions during poling, physically defining polarity distribution by the geometry of the Faraday cages without real-time monitoring. As a proof of concept, the method is used to fabricate a spatially selectively poled TFLN waveguide, where all regions are poled except for a 400-nm-wide central. This waveguide achieves a normalized SHG efficiency of 6242 %W-1cm-2. Systematic investigation of the inverted domain growth dynamics confirms the enhanced precision and robustness of this approach over conventional time-controlled methods, paving the way for scalable nonlinear photonic
circuits.
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All authors Yanqun Wang, Furong Zhong, Lin Liu, Yuntao Zhu, Jinyi Wang, Zhongjin Lin, Xinlun Cai
Plasma Physics
Kinetic Equilibrium Prediction at TCV using RAPTOR and FBT
C. E. Contré, A. Merle, O. Sauter et al. (+13 more)
We present results from a new Kinetic-
Equilibrium Prediction (KEP) workflow and shot preparation for full TCV discharges, by coupling predict-first RAPTOR transport simulations with FBT inverse
equilibrium calculations. RAPTOR is a 1.5D transport code which has been extensively used for
plasma shot optimization and real-time modeling. We show that rapid pre-shot simulations can be performed directly using information from the pulse schedule across a wide range of
plasma shapes and scenarios, given an estimate of the confinement quality factor H98(y,2) and line-averaged density. The resulting p' and TT' profiles are then provided to the pre-shot
equilibrium computation performed by FBT - a static free-boundary solver routinely used at TCV - achieving convergence between the two codes in a few iterations. Finally, we show that this coupling, when integrated into the TCV shot preparation, improves the evaluation of the coil currents needed to match the target
plasma shape; in particular providing an accurate estimate of critical quantities such as the internal inductance $l_i$ and normalized pressure $\beta_N$, giving more realistic information to tokamak operators about the expected pulse behavior and enabling them to adjust the plan correspondingly.
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All authors C. E. Contré, A. Merle, O. Sauter, S. Van Mulders, R. Coosemans, G. Durr-Legoupil-Nicoud, F. Felici, O. Février, C. Heiss, B. Labit, A. Pau, Y. Poels, C. Venturini, B. Vincent, the TCV team, the EUROfusion Tokamak Exploitation Team
Plasma Physics
Feasibility of Negative Triangularity Equilibria in the SPARC Tokamak
Narin Yüksek and Theodore Golfinopoulos
We investigate the feasibility of negative triangularity (NT)
plasma configurations in the SPARC tokamak, a compact, high-field device (up to 12.2 T) designed for positive triangularity (PT) operation. Using the FreeGS free-boundary
equilibrium solver, we systematically explore the parameter space through 600+
equilibrium calculations spanning triangularity (-0.7-0.7) and elongation (1.0-2.1) while respecting SPARC's coil current limits and first-wall constraints and selecting a reduced-field 8 T operating point. We find that moderate NT double-null
plasmas (delta = -0.35, kappa = 1.68) are achievable at 8 T with
plasma currents of 2.1 MA. However, NT operation requires 43%
plasma volume reduction (from 20.0 to 11.4 m$^3$) compared to the baseline PT design due to vessel walls optimized for PT conformity, and connection lengths are reduced by 40% due to
geometric mismatch with the PT-optimized divertor. Central solenoid current requirements decrease by 54% in NT configurations, though specific shaping coils (PF3) require a factor of 5.5 higher currents. All
equilibria satisfy fundamental MHD stability criteria with comfortable margins. Thus, while performance is degraded relative to baseline design, these results demonstrate SPARC's potential as a high-field experimental bridge between contemporary NT experiments and proposed NT reactor concepts, capable of testing whether NT's operational advantages (ELM-free operation, favorable
impurity transport) persist under reactor-relevant conditions provided by SPARC.
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Instrumentation and Detectors
Valley-peak modulation in phase space: a law-invariant VPM and its theta-function structure
Aaron J. Hendrickson and David P. Haefner
Valley-peak modulation (VPM) was introduced as a metric for quantifying read noise in deep sub-electron read noise (DSERN) CMOS image sensors. In its original amplitude-domain definition VPM depends on both read noise and quanta exposure, yet Starkey and Fossum demonstrated exposure-independent approximations valid in the DSERN regime. Here we identify the invariant object those approximations probe, and find its invariance extends beyond exposure to the electron-number law itself. A phase mapping quotients the sensor model by the integer electron
lattice, yielding a wrapped-Gaussian density parameterized only by read noise and admitting
lattice-sum and Jacobi theta-function representations. The invariant is the theta ratio $R=\vartheta_4(q)/\vartheta_3(q)$ with nome $q$, of which any VPM is a contrast normalization; the existing approximations are low-order truncations of its
lattice sums, and the amplitude-domain metric converges to its phase-space counterpart at large exposure. A closed-form inverse for read noise in terms of VPM follows from
elliptic integrals. The identification yields a moment method of characterization: conversion gain and read noise are estimated jointly from the modulus of the empirical characteristic function of the raw gray values, without specifying the number law, and with leading-order precision depending on it only through its variance.
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Accelerator Physics
Midterm Status Report of the ILC Technology Network Activities
ILC Technology Network
The ILC Technology Network (ITN) was established in 2022 by the ILC International Development Team, a subcommittee of the International Committee for Future Accelerators, to advance engineering studies toward the realisation of the International Linear
Collider (ILC). While the ITN work packages focus on engineering activities for the ILC, their topics are also relevant to a broad range of accelerator applications in particle physics and beyond. These work packages are being carried out now by laboratories in Asia and Europe in close collaboration. This report summarises the current status of the ITN activities.
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Geophysics
Temperature dependence of broadband seismometer sensitivity
Tomofumi Shimoda, Wataru Kokuyama and Hideaki Nozato
Broadband seismometers are widely used in global observation networks deployed for geophysics research. Recently, the calibration of their sensitivity has become an important factor for ensuring observation accuracy. One of the limiting factors of calibration uncertainty is the temperature dependence of the sensitivity because seismometers operate in a wide range of temperatures. However, systems that accurately measure the temperature coefficient in seismometers have not been established. Herein, we develop such a system using a triaxial vibration exciter combined with a thermostatic chamber. Using this system, we calibrated several broadband seismometers (Trillium Compact, Trillium Horizon 360, and CMG-3T) at various temperatures, ranging from $-15 ^\circ$C to $+45 ^\circ$C. We found that the temperature coefficient was $(0.11\pm0.01)$ %/$^\circ$C, which can be attributed to the internal magnet of the seismometers. The variation of the phase delay corresponded to a change of less than 2 ms in output time delay. Additionally, we found that the relative frequency response below 1 Hz was stable against temperature variations. These values are useful for evaluating the measurement accuracy of seismometer observation networks.
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Applied Physics
Magnetic Omniconversion: Source-Independent Molding of Magnetostatic Fields
Jaume Cunill-Subiranas, Natanael Bort-Soldevila, Fabian Resare et al. (+3 more)
Magnetic fields are constrained by the geometry and location of their sources, limiting the ability to freely tailor their spatial distribution. We introduce a general framework to passively convert the
magnetic field generated by arbitrary sources into any prescribed desired field within a finite source-free region. Our method relies on field shaping using linear
magnetic materials, enabling source-independent
magnetic-field molding. We provide the general recipe, analytical and numerical demonstrations for some paradigmatic examples, and a proof-of-concept experiment that validates the idea and materials implementation. This approach enables novel possibilities in
magnetic shielding, targeted field delivery, advanced imaging technologies, and a broad range of field-control applications.
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All authors Jaume Cunill-Subiranas, Natanael Bort-Soldevila, Fabian Resare, Nuria Del-Valle, Witlef Wieczorek, Carles Navau
Computational Physics · Cross-listed from hep-ph
Multi-channel phase space with Feynman-diagram-gauge amplitudes
Kaoru Hagiwara, Junichi Kanzaki, Fabio Maltoni et al. (+2 more)
Multi-channel phase space with a single Feynman diagram enhancement is a powerful tool for high-energy physics event generation if a diagram with a singular propagator dominates the total scattering amplitude at the corresponding singular kinematical region, and when the interference among amplitudes is not larger than the square of each amplitude. These conditions are satisfied in the Feynman-diagram-gauge amplitudes for both unbroken (QED and QCD) and broken (EW) gauge theories. We illustrate the usefulness of this method in
lepton collider processes that are challenging to accurately simulate at very high energies, i.e., $ l\bar{l} \to \nu_l \bar{\nu}_l t\bar{t} H $, $ l \bar{\nu}_l t\bar{b} H $, and $ l \bar{l} t\bar{t} H $, in the SMEFT with a complex top-Yukawa coupling. The total cross sections of the latter two processes contain
lepton-mass singularities arising from $t$-channel
photon-exchange diagrams. To address this issue, we develop a phase-space parametrization that accurately generates the distributions of forward-emitted charged
leptons. We modify the \HELAS\ library to evaluate helicity amplitudes in the singular region so that vertices at very small invariant momentum square of order $m_e^2$ can be accurately evaluated even at multi-TeV energies.
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All authors Kaoru Hagiwara, Junichi Kanzaki, Fabio Maltoni, Kentarou Mawatari, Ya-Juan Zheng
Medical Physics
Lead-Time Simple Exponential Smoothing as a Configurable, Uncertainty-Aware Framework for Adaptive Radiotherapy Monitoring
Robert Boyd and Wolfgang A. Tomé
Lead-time simple exponential smoothing (LT-SES) is developed as a configurable, uncertainty-aware framework for adaptive radiotherapy monitoring, in which a prospectively selected smoothing parameter alpha determines how strongly new observations update the current level. Using the standard innovations formulation of SES, closed-form expressions were derived for the predictive mean and variance of the remaining-course aggregate, enabling prediction intervals and conditional threshold probabilities to be updated after each fraction. OAR mean-dose time series from 32 prostate and 23 HN treatment courses were evaluated at prespecified $\alpha$-values from 0.1 to 1.0 across remaining-course horizons of 5, 10, 15, and 20 fractions, with a running-mean
benchmark. Two parotid time series illustrated threshold probabilities, probability maps, and probability-weighted rank scores for $\alpha$ = 0.25, 0.50, and 1.0. Prediction intervals widened with forecast horizon and with increasing $\alpha$. Undercoverage was greatest for the running-mean
benchmark and the lowest positive $\alpha$-values, especially at longer horizons, whereas $\alpha \geq 0.4$ produced wider intervals with coverage generally above nominal. In the parotid examples, lower $\alpha$-values provided greater stability, while higher $\alpha$-values responded more rapidly to short-term variation and sustained level shifts. Probability weighting produced greater numerical separation than the conventional scalar comparator and identified the sustained-shift time series several fractions before the scalar ratio crossed unity. LT-SES contextualizes projected scalar threshold differences using time series-specific interfractional innovation scale and remaining-course uncertainty. Its probability-scale outputs and rank scores provide a relative, uncertainty-aware complement to conventional scalar dose-metric monitoring.
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Optics · Cross-listed from cond-mat.mtrl-sci
Networking Molecular Quantum Emitters on a Single Chain : From Single to Cooperative Emitters
Jean-Baptiste Marceau, Juliette Le Balle, Christel Poujol et al. (+4 more)
Engineering light-matter interactions between multiple free-space
quantum emitters is a central challenge for scalable
quantum photonic technologies. In particular, accessing regimes of coherent emitter-emitter interactions, where several emitters are coupled through a shared electromagnetic environment, is essential for coherent emission and
quantum functionalities. Such interactions require precise control over emitter separation and stabilization at sub-wavelength distances, a level of spatial organization that remains extremely difficult to achieve at the molecular scale in solid-state systems. Here we introduce Encoded
Quantum Chains (EQC), a one-dimensional architecture in which cooperative radiative behaviour is programmed through spatial encoding of identical molecular emitters. Organic emitters and inert spacer molecules are co-encapsulated inside dielectric boron nitride nanotubes (BNNTs), enabling statistical control of intermolecular spacing from nanometres to micrometres while enforcing dipole
alignment and one-dimensional confinement. Time-resolved fluorescence under ambient conditions reveals accelerated radiative decay, enhanced emission rates per emitter, and the emergence of non-mono-exponential dynamics as spacing falls below the optical wavelength, consistent with cooperative radiative states in one dimension. Bundling of EQCs enables coupling between emitters in neighbouring BNNTs, driving a dimensional crossover toward higher-dimensional delocalisation of the excitation. This modular building-block approach provides a scalable route to engineer light-matter interactions and many-body optical phenomena in confined molecular systems, opening new opportunities for distributed single-
photon sources, programmable
quantum emitters, and photonic architectures for
quantum technologies.
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All authors Jean-Baptiste Marceau, Juliette Le Balle, Christel Poujol, Frédéric Fossard, Annick Loiseau, Gaëlle Recher, Etienne Gaufrès
Fluid Dynamics
Structure-preserving Randomized Neural Networks for Incompressible Magnetohydrodynamics Equations
Yunlong Li, Fei Wang and Lingxiao Li
The incompressible magnetohydrodynamic (MHD) equations are fundamental in many scientific and engineering applications. However, their strong nonlinearity and dual divergence-free constraints make them highly challenging for conventional numerical solvers. To overcome these difficulties, we propose a Structure-Preserving Randomized Neural Network (SP-RaNN) that automatically and exactly satisfies the divergence-free conditions. Unlike deep neural network (DNN) approaches that rely on expensive nonlinear and nonconvex optimization, SP-RaNN reformulates the training process into a linear least-squares system, thereby eliminating nonconvex optimization. The method linearizes the governing equations through Picard or Newton iterations, discretizes them at collocation points within the domain and on the boundaries using finite-difference schemes, and solves the resulting linear system via a linear least-squares procedure. By design, SP-RaNN preserves the intrinsic mathematical structure of the equations within a unified space-time framework, ensuring both stability and accuracy. Numerical experiments on the Navier-Stokes, Maxwell, and MHD equations demonstrate that SP-RaNN achieves higher accuracy, faster convergence, and exact enforcement of divergence-free constraints compared with both traditional numerical methods and DNN-based approaches. This structure-preserving framework provides an efficient and reliable tool for solving complex PDE systems while rigorously maintaining their underlying physical laws.
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Accelerator Physics
Noninvasive ion fraction quantification of dual-species beams in synchrotrons
Elisabeth Renner, Matthias Kausel, David Ondreka et al. (+3 more)
The ion composition of dual-species beams in synchrotrons is typically inferred from invasive measurements performed after beam extraction. This paper introduces a complementary noninvasive method to determine the ion composition of such beams directly inside the synchrotron. The approach is applicable to low- and medium-energy ion synchrotrons and to small relative mass-to-charge ratio offsets, typically at the 1e-4 level. The method exploits dispersive orbit offsets between the two species and corresponding frequency corrections applied by the synchrotron RF radial regulation loop. This capability is of particular interest for ongoing research on online monitoring in carbon ion beam therapy using mixed 4He2+ and 12C6+ beams, which feature a relative mass-to-charge ratio offset of 0.065%. The proposed method is analytically derived and tested with particle tracking simulations using Xsuite. Its applicability under realistic experimental conditions is demonstrated at the MedAustron facility using mixed 4He2+ and 12C6+ beams. The results show good agreement with independent post-extraction measurements.
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All authors Elisabeth Renner, Matthias Kausel, David Ondreka, Hermann Fuchs, Katrin Holzfeind, Nana Okropiridze
Fluid Dynamics
Super-resolution of turbulent reacting flows on complex meshes using graph neural networks
Priyabrat Dash, Konduri Aditya, Christos E. Frouzakis et al. (+1 more)
State-of-the-art deep learning models have been extensively utilized to reconstruct small-scale structures from coarse-grained data in turbulent flows. However, their application has predominantly been restricted to structured uniform meshes, limiting their applicability to data associated with complex geometries that are typically simulated on structured non-uniform or unstructured meshes. Machine learning (ML) models based on graph neural networks (GNNs), known for their ability to process unstructured data, offer a promising alternative. In this study, we leverage the inherent flexibility of GNNs featuring message passing layers to develop a methodology for reconstructing unresolved small-scale structures from low-resolution data on complex meshes. The accuracy of the proposed approach is demonstrated using two cases: a reacting channel flow on a structured non-uniform mesh, and a reacting hydrogen fueled internal combustion (IC) engine featuring an unstructured mesh. Evaluation of results based on visual agreement, statistical metrics, and cumulative error reduction indicates the effectiveness of the method in accurately reconstructing fine-scale features. Overall, this study provides a pathway for integrating data-driven small-scale reconstruction and subgrid-scale modeling to enhance the accuracy of coarse-grained simulations on complex meshes.
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All authors Priyabrat Dash, Konduri Aditya, Christos E. Frouzakis, Mathis Bode
Fluid Dynamics
Extreme-value statistics of curl-of-vorticity precursor peaks in perturbed Taylor-Green vortex turbulence
Satori Tsuzuki
Precursor peaks in the wavenumber $k_{\mathrm{peak}}(t)$ maximizing the curl-of-vorticity spectrum have been observed to precede the dissipation peak in decaying
turbulence. Because small perturbations in the initial condition can shift peak times, the associated lead time should be characterized statistically. We perform a pseudospectral DNS ensemble of $N_s=1000$ perturbed Taylor–Green
vortex realizations at $N=256^3$ and $\nu=10^{-3}$. For each run we extract $k_{\mathrm{peak}}(t)$, several definitions of the precursor time $t_k$, the dissipation-peak time $t_\varepsilon$, and run-wise extrema including $K_{\max}=\max_t k_{\mathrm{peak}}(t)$ and $M_{\max}=\max_t\max_k \mathcal{C}(k,t)$, where $\mathcal{C}(k,t)$ is the isotropic curl-of-vorticity spectrum. The distribution of $\Delta t_{\varepsilon,k}=t_\varepsilon-t_k$ shows that the precursor typically leads, while rare lagging realizations occur and are strongly conditioned on $K_{\max}$. Using peaks-over-threshold extreme-value theory, we fit generalized Pareto models to the right tails of $X=-\Delta t_{\varepsilon,k}$ and $M_{\max}$; the negative shape estimates are consistent with effective bounded tails under the present finite-resolution sampling protocol and provide protocol-dependent endpoint estimates. Finally, $M_{\max}$ correlates strongly with $\varepsilon_{\max}$ and ensemble cross-correlations reveal a reproducible phase offset, consistent with an empirical association between high-
curvature activity and dissipation bursts.
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History and Philosophy of Physics
Evgeny P. Velikhov (1935-2024)
Günther Rüdiger
The master student at Lomonosov University in Moscow E.P. Velikhov formulated 1959 the theory of magnetorotational instability, which dominates current astrophysics. A meteoric career made him later the science,
nuclear and disarmament advisor to Gorbachev and Yeltsin. This article describes the interactions between Velikhov and the PROMISE team from Potsdam and Dresden-Rossendorf when it came to experimentally testing the theory in the laboratory in the 2000s. At an MHD conference in Catania, Sicily, he offered the public the use of small, transportable Russian
nuclear power plants anywhere in the world until the fusion machines currently under development had finally solved the energy problem.
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Fluid Dynamics
Data-Free Finite-Volume Convolutional Neural Networks for Steady Compressible Euler Flow around a Circular Cylinder
Ryosuke Yano
A data-free finite-volume convolutional neural network (FV-CNN) is formulated for steady Euler flow around a cylinder at eight Mach numbers, 2–15. Shared-face fluxes define conservative spatial balances, while automatic differentiation supplies parameter gradients. The derivative mismatch in the earlier automatic-differentiation-based convolutional neural network (AD-CNN) is derived. No computational fluid dynamics (CFD) or experimental field targets enter training; reference-independent selection of coefficients, seeds, and checkpoints is not established. Billig-based upstream fixing conditions shock-location agreement.For eight adopted fields, density and pressure relative $L_2$ discrepancies from finite-resolution CFD are $5.43$–$18.69\%$ and $6.25$–$19.70\%$. Closed-band defects are smaller than AD-CNN in 32/32 self-centered and 22–31/32 CFD-centered comparisons over seven half-widths, 0.06–0.12 cylinder diameters. AD-CNN retains smaller enthalpy root-mean-square (RMS) discrepancies in 7/8 cases, shock-position RMS at Mach 11 and 15, and temperature and transverse-velocity discrepancies at Mach 3.Ten new Mach-15 initializations and adopted seed 42 yield density discrepancies of $12.04$–$43.70\%$ (mean $24.34\%$, sample standard deviation $11.78$ percentage points); the new ten alone give $25.04\%$ and $12.17$ points. Dispersion exceeds the historical adopted-field gap of $3.40$ points; six of eleven outcomes are below AD-CNN. Transition-width ratios of AD-CNN to FV-CNN remain $4.07$–$4.66$, and all 44 self-centered seed–component balances at half-width 0.10 cylinder diameters are smaller for FV-CNN. Local sharpness and this balance metric persist, while initialization-robust accuracy superiority is not established.Component interventions remain one-seed conditional responses; Mach 2 uses a separate budget-limited protocol.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Nanoscale imaging reveals critical plating and stripping mechanisms in anode-free lithium and sodium solid-state batteries
J. Diaz-Sanchez, P. Hernandez-Martin, N. Kwiatek-Maroszek et al. (+12 more)
Achieving reversible anode-free solid-state batteries hinges on controlling alkali-metal plating and stripping at buried interfaces, yet the underlying nanoscale mechanisms remain unresolved. Here we introduce virtual-electrode low-energy electron microscopy (VE-LEEM), an imaging platform that enables nanoscale visualization of anode formation and dissolution by combining electron beam-induced plating with ultraviolet-driven stripping. By integrating VE LEEM with synchrotron-based photoemission electron microscopy and atomic force microscopy, we track the chemical and morphological
evolution of Li and Na anodes during cycling. We uncover a shared dynamic scaling regime governing anode growth, analogous to high mobility thin film deposition, but emerging through distinct morphological pathways dictated by metal-specific surface energetics. This universal scaling behaviour establishes a transferable quantitative framework for comparing anode-free plating across chemistries. In contrast, stripping proceeds through sequential grain-boundary unzipping and cluster decay mechanisms, demonstrating that dissolution is intrinsically asymmetric with respect to plating and leaves behind a persistent interfacial residual layer. These results overturn the common assumption of mirrored plating-stripping dynamics and identify interfacial and grain boundary energetics as fundamental constraints on reversibility. VE LEEM thus provides a general route to resolve buried electrochemical interfaces at the nanoscale and establishes an energetic framework to guide the design of durable, high energy anode free solid state batteries.
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All authors J. Diaz-Sanchez, P. Hernandez-Martin, N. Kwiatek-Maroszek, H.R. Bratlie, R. Anton, A. Lowack, A. Galindo, K. Kataoka, E. Vasco, K. Nikolowski, D. Rettenwander, E.G. Michel, M.A. Nino, M. Foerster, C. Polop
Applied Physics
3D aperture-engineered diffractive neural networks for super-resolution electromagnetic wave computing
Sheng Gao, Songtao Yang, Haiou Zhang et al. (+2 more)
The rapid progress in 6G communication and high-bandwidth radar has driven an unprecedented surge in the spatial density of signal sources, resulting in an increasingly congested electromagnetic (EM) environment. When resolving closely spaced signals and interference, existing architectures are strictly bounded by the inherent diffraction limits of two-dimensional (2D) physical apertures, hindering super-resolution sensing and multi-interference mitigation in complex scenarios. Here, we present a 3D aperture-engineered diffractive neural network (AE-DNN) that achieves super-resolution sensing and computing by extending the traditional 2D aperture into 3D. The 3D aperture engineering framework is realized by constructing deep cascaded metasurface layers so that the diffractive propagation from oblique incident fields can be layer-wise modulated and piecewise encoded for perceiving EM fields far exceeding physical aperture limits. The N-layer AE-DNN has the capability to achieve ~N times higher angular resolution than the 2D aperture diffraction limit. The multi-dimensional synthetic aperture (MSA) training is developed to achieve speed-of-light coherent synthesis of the 3D aperture and integrate neural network-based modeling of multi-dimensional metasurface modulation. By orthogonalizing array response vectors in the analog domain, AE-DNN performs parallel super-resolution angle estimation, source number estimation, and source separation for up to 10 independent coherent or incoherent sources. Experimental results across the 36-41 GHz band demonstrate that AE-DNN resolves and suppresses closely spaced multi-interference by ~20 dB, enhances communication capacity by 13.5X, and reduces latency by three orders of magnitude. AE-DNN heralds a paradigm shift in signal processing for advanced radar and 6G communications.
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All authors Sheng Gao, Songtao Yang, Haiou Zhang, Yuan Shen, Xing Lin
Applied Physics
Depletion to Enhancement Mode Transition and Strongly Suppressed Hysteresis in Surface Engineered Multilayer MoS2 FETs
Samiksha Bhatia, Ramesh Singh Bisht and Pramod Kumar
Two-dimensional (2D)
semiconductors such as molybdenum disulfide (MoS2) have recently attracted extensive research
attention due to their promising compatibility with silicon based electronics. However, several key challenges still limit their practical integration. Two of the critical issues are (1) the intrinsic depletion-mode (normally on) operation of MoS2 field-effect transistors (FETs), and (2) the large hysteresis commonly observed in the transfer characteristics of MoS2 FETs due to the inherent sulfur defects. Addressing them is essential for CMOS compatible 2D-transistor technologies. In this work, we report for the first time that surface modification of the exfoliated multilayer MoS2 FETs with PBTTT C14 (poly(2,5 bis(3 tetradecylthiophen-2-yl)thieno[3,2 b]thiophene)), a p type conjugated organic
polymer, converts the device from depletion mode to enhancement mode operation while simultaneously and strongly suppressing hysteresis. Specifically, the threshold voltage (Vth) shifts from -9.6 V to +5.9 V (total shift 15.5 V), and the hysteresis window decreases from 8.8 V to 1.3 V (85% reduction). This originates from interfacial charge transfer at the MoS2/PBTTT C14 interface, enabled by favourable band
alignment. To further validate this charge transfer driven mechanism, P3HT (poly(3 hexylthiophene 2,5 diyl)) with similar energy levels to PBTTT C14 was employed, and it also showed similar enhancement-mode behaviour and hysteresis suppression.
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Plasma Physics · Cross-listed from astro-ph.SR
Stability and wave dynamics in polytropic Eddington-inspired Born-Infeld gravitating solar plasmas
Souvik Das and Pralay Kumar Karmakar
We investigate the influence of nonlinear
gravity corrections, arising from the Eddington-inspired Born-Infeld (EiBI) theory on wave dynamics, stability, and energy transport processes in polytropic, viscous, and turbulent
solar plasmas. Analytical and numerical analyses of the Jeans-normalized quadratic dispersion relation demonstrate that both the EiBI
gravity parameter $(\chi)$ and the relative polytropic sound speed $(\beta)$ independently regulate oscillation frequencies, growth rates, phase velocities, perturbation energy partitioning, and outward acoustic energy flux. Positive $\chi$ systematically elevates oscillation frequencies, phase velocities, and outward energy flux level by $\sim$10% relative to the Newtonian predictions, while larger $\beta$ enhances them by up to 55%, thereby promoting wave propagation and efficient acoustic transport. Conversely, negative $\chi$ strengthens gravitational binding and increases damping rates by $\sim$40%, particularly for the g-modes. Energy partitioning analyses reveal that the EiBI corrections fundamentally restructure the kinetic-electrostatic-gravitational energy balance. While the Newtonian
gravity contributes negligibly ($<$4%), nonzero $\chi$ channels up to one-third of oscillation energy into gravitational modes. The modal surface flux calculations further confirm that only the p-modes drive outward energy transport (amplification for $\chi>0$, suppression for $\chi<0$). A direct comparative analysis with four years of SDO/HMI Doppler velocity observations demonstrate a robust theoretical agreement for $\chi=3\times10^7$ m$^5$kg$^{-1}$s$^{-2}$, providing the first empirical constraint on the
solar EiBI
gravity through helioseismology. The findings offer a rigorous framework for advancing our understanding about
solar plasma stability, helioseismic signatures, and ambient atmospheric energy transport processes.
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Plasma Physics
Nonlinear magnetohydrodynamic modeling of ideal ballooning modes in high-$\beta$ Wendelstein 7-X plasmas
Yao Zhou, K. Aleynikova, Chang Liu et al. (+1 more)
We present nonlinear magnetohydrodynamic (MHD) simulations of high-$\beta$ Wendelstein 7-X
plasmas using the stellarator extension of the M3D-$C^1$ code, building on the recent work that shows benign saturation of ideal ballooning modes above the designed $\beta$ limit in the standard configuration [Y. Zhou et al, Phys. Rev. Lett. 133, 135102 (2024)]. First, we examine the results' sensitivity to the parallel thermal conductivity. It is found that while an increased parallel conductivity reduces the linear growth rate, the saturated pressure profile is barely affected. Second, we consider the dependence on the profile shape. It is shown that an
equilibrium with a peaked pressure profile and lower $\beta$ is subject to more significant change than a broad profile with higher $\beta$ and a larger growth rate, suggesting that benign saturation, or nonlinear stability, is not guaranteed and not dictated by linear growth. Third, we study the influence of the
magnetic configuration, with the
equilibrium rotational transform varied by adjusting the planar coil current. With similar growth rates, similar magnitudes of profile change are found regardless of the presence of a low-order resonance, which implies that the saturation mechanism is not specific to a resonant or non-resonant mode. These results indicate that MHD stability should still be treated seriously in stellarator operation and design, for which nonlinear modeling using tools like M3D-$C^1$ can play an instrumental role.
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All authors Yao Zhou, K. Aleynikova, Chang Liu, N. M. Ferraro
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Stress-driven dynamic evolution of core-shell structured cavities with H and He in BCC-Fe under fusion conditions
Jin Wang, Fengping Luo, Yiheng Chen et al. (+11 more)
Understanding the dynamic behavior of microstructures formed under fusion conditions is critical for designing high-performance structural materials for fusion reactors. Under fusion conditions, cavities of core-shell structures are formed due to the interaction between irradiation-induced vacancies and H and He atoms produced via transmutation. In this study, thermodynamic analysis and molecular dynamics simulations are combined to investigate the atomic-scale mechanisms and dynamic response of core-shell cavities formed in BCC-Fe under applied stress/strain fields. The thermodynamic analysis provides both the foundational reference for cavity structures under fusion
neutron irradiation and the initial configurations for atomistic simulations. Building on this framework, atomic-scale simulations demonstrate that H and He play a decisive role in the stress-strain response and the
evolution of elastic-plastic deformation within the cavities. In core-shell configurations, H atoms serve a function analogous to that in He-filled cavities, synergistically interacting with He to induce cavity deformation under mechanical loading.
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All authors Jin Wang, Fengping Luo, Yiheng Chen, Denghuang Chen, Bowen Zhang, Yuxin Liu, Guangyu Wang, Yunbiao Zhao, Sheng Mao, Mohan Chen, Hong-Bo Zhou, Jianming Xue, Yugang Wang, Chenxu Wang