Optics · Cross-listed from cond-mat.mtrl-sci
Crystallization Behavior of ZBLAN Glass Under Combined Thermal and Vibrational Effects: Part I – Experimental Investigation
Ayush Subedi, Anthony Torres, Jeff Ganley et al. (+1 more)
ZBLAN glass is a promising material for infrared optical fibers due to its wide transmission window and low theoretical attenuation. However, its strong tendency to crystallize during processing limits optical performance. While microgravity environments have been shown to suppress crystallization, the role of mechanical vibration under normal
gravity conditions remains poorly understood. This study systematically investigates the influence of vibration on the crystallization behavior of ZBLAN using a controlled heating and vibration apparatus. Samples were subjected to varied thermal and vibrational conditions, and their crystallization onset and morphological
evolution were examined through optical microscopy, scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and atomic force microscopy (AFM). Results show that vibration reduces the crystallization onset temperature, indicating enhanced atomic mobility and nucleation kinetics. Progressive morphological transitions from needle-like to bow-tie and feather-like
crystals were observed with increasing temperature and vibration intensity. Surface roughness analysis corroborates these findings, revealing a significant increase in nanoscale roughness in crystallized regions. Although brief exposure duration and partial thermal decoupling introduced variability among samples, the overall results confirm that vibration acts as a direct facilitator of nucleation rather than a purely thermal effect. This work provides new insight into vibration-induced crystallization in fluoride glasses and establishes the experimental foundation for follow-up modeling and apparatus optimization studies under terrestrial and microgravity conditions.
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All authors Ayush Subedi, Anthony Torres, Jeff Ganley, Ujjwal Dhakal
Computational Physics · Cross-listed from cs.LG
LieSolver: PDE-Constrained Learning for IBVPs via Lie Symmetries
René P. Klausen, Ivan Timofeev, Jonas Naujoks et al. (+4 more)
Initial-boundary value problems (IBVPs) provide the essential framework for modelling a wide range of phenomena in physics and engineering. We introduce a novel method for efficiently solving IBVPs using Lie
symmetries to enforce the associated partial differential equation (PDE) exactly by construction. By leveraging
symmetry transformations, our model embeds the underlying physical laws and learns the solution solely from initial and boundary data. Consequently, the boundary loss directly quantifies domain-wide error, enabling rigorous error estimation for well-posed IBVPs. We implement LieSolver and demonstrate its application to linear homogeneous PDEs, showing that it outperforms physics-informed neural networks (PINNs) in both speed and accuracy while yielding compact models. Overall, our approach significantly enhances the efficiency and reliability of predictions for PDE-constrained problems.
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All authors René P. Klausen, Ivan Timofeev, Jonas Naujoks, Johannes Frank, Thomas Wiegand, Sebastian Lapuschkin, Wojciech Samek
Fluid Dynamics
Confined floating active carpets generate coherent vortical flows that enhance transport
Felipe A. Barros, Italo Salas, Enkeleida Lushi et al. (+1 more)
Slicks are thin viscous films that can be found at the air–water interface of water bodies such as lakes, rivers and oceans. These micro-layers are enriched in surfactants, organic matter, and microorganisms, and exhibit steep physical and chemical gradients across only tens to hundreds of micrometers. In such geometrically confined environments, the hydrodynamics and transport of nutrients, pollutants, and microorganisms are constrained, yet they collectively sustain key biogenic processes. It remains however largely unexplored how the hydrodynamic flows and transport are affected by the vertical extent of slicks relative to the size of microbial colonies. Here, we study this question by combining analytical and numerical approaches to model a microbial colony as an active carpet: a two-dimensional distribution of micro-swimmers exerting dipolar forces. We show that there exists a ratio between the carpet size and the confinement height that is optimal for the enhancement of particle transport toward the colony edges through advective flows that recirculate in 3D
vortex-ring-like patterns with a characteristic length comparable to the confinement height. Our results demonstrate that finite, coherent
vortex-ring-like structures can arise solely from the geometrical confinement ratio of slick thickness to microbial colony size. These findings shed light on the interplay between collective activity and out-of-
equilibrium transport, and on how microbial communities form, spread, and persist in geometrically constrained environments such as surface slicks.
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All authors Felipe A. Barros, Italo Salas, Enkeleida Lushi, Francisca Guzman-Lastra
Accelerator Physics · Cross-listed from cond-mat.mtrl-sci
Intrinsic emittance properties of an Fe-doped Beta-Ga2O3(010) photocathode: Ultracold electron emission at 300K and the polaron self-energy
Louis A. Angeloni, Ir-Jene Shan, J.H. Leach et al. (+1 more)
Measurements of the spectral emission properties of an iron-doped a beta-Ga2O3(010) photocathode at 300 K reveal the presence of an ultracold contribution to the total electron beam emission with a 6 meV mean transverse energy (MTE) in the 3.5-4.4 eV
photon energy range (282-354 nm). This extreme sub-thermal photoemission signal is consistent with direct emission of electrons photoexcited from the Fe dopant states into the low effective mass and positive electron affinity primary conduction band, and it is superimposed on a stronger signal with a larger MTE associated with an (optical)
phonon-mediated momentum resonant Franck-Condon (FC) emission process from a thermally populated and negative electron affinity upper conduction band. For
photon energies above 4.5 eV, a transition from a long to a short transport regime is forced by an absorption depth reduction to below 100 nm and both MTE signals exhibit spectral trends consistent with
phonon-mediated FC emission if the polaron formation self-energy is included in the temperature of the initial thermalized photoexcited electron distribution.
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All authors Louis A. Angeloni, Ir-Jene Shan, J.H. Leach, W. Andreas Schroeder
Fluid Dynamics
An efficient implementation of the bidirectional buffer: towards laminar and turbulent open-boundary flows
Feng Wang and Xiangyu Hu
To effectively handle flows characterized by strong backflow and multiple open boundaries within particle-based frameworks, this study introduces three enhancements to improve the consistency, independence, and accuracy of the buffer-based open boundary condition in SPHinXsys. First, to improve the buffer consistency, the continuum hypothesis is introduced to prevent the excessive particle addition induced by strong backflow. Secondly, the independence of the bidirectional buffer is enhanced through region-constrained and independent labeling schemes, which effectively eliminate buffer interference and erroneous particle deletion in complex open-boundary flows. Thirdly, the original zeroth-order consistent pressure boundary condition is upgraded to first-order consistency by introducing a mirror boundary treatment for the correction
matrix. The implementation is based on the rigorously validated weakly compressible smoothed particle hydrodynamics coupled with Reynolds-averaged Navier-Stokes (WCSPH-RANS) method, and both laminar and turbulent flow simulations are performed. Four test cases, including straight and U-shaped channel flows, a plane jet, and the flow in a 3D self-rotational micro-mixer, are conducted to comprehensively validate the proposed improvements. Among these cases, the turbulent plane jet is successfully simulated at a moderate resolution within a very compact computational domain involving strong backflow, a condition that is usually challenging for mesh-based methods. The three improvements require only minor modifications to the code framework, yet they yield significant performance gains.
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Computational Physics · Cross-listed from cs.LG
Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics
Alessandro Lucchetti, Francesco Cadini, Marco Giglio et al. (+1 more)
Graph Neural Networks (GNNs) have recently been explored as surrogate models for numerical simulations. While their applications in computational fluid dynamics have been investigated, little
attention has been given to structural problems, especially for dynamic cases. To address this gap, we introduce the Graph Network-based Structural Simulator (GNSS), a GNN framework for surrogate modeling of dynamic structural problems. GNSS follows the encode-process-decode paradigm typical of GNN-based machine learning models, and its design makes it particularly suited for dynamic simulations thanks to three key features: (i) expressing node kinematics in node-fixed local frames, which avoids catastrophic cancellation in finite-difference velocities; (ii) employing a sign-aware regression loss, which reduces phase errors in long rollouts; and (iii) using a wavelength-informed connectivity radius, which optimizes graph construction. We evaluate GNSS on a case study involving a beam excited by a 50kHz Hanning-modulated pulse. The results show that GNSS accurately reproduces the physics of the problem over hundreds of timesteps and generalizes to unseen loading conditions, where existing GNNs fail to converge or deliver meaningful predictions. Compared with explicit finite element baselines, GNSS achieves substantial inference speedups while preserving spatial and temporal fidelity. These findings demonstrate that locality-preserving GNNs with physics-consistent update rules are a competitive alternative for dynamic, wave-dominated structural simulations.
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All authors Alessandro Lucchetti, Francesco Cadini, Marco Giglio, Luca Lomazzi
Fluid Dynamics · Cross-listed from cs.AI
Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
Federica Tonti and Ricardo Vinuesa
Unmanned Aerial Vehicles (
UAVs) are increasingly populating urban areas for delivery and surveillance purposes. In this work, we develop an optimal
navigation strategy based on Deep Reinforcement Learning. The environment is represented by a three-dimensional high-fidelity simulation of an urban flow, characterized by
turbulence and recirculation zones. The
algorithm presented here is a flow-aware Proximal Policy Optimization (PPO) combined with a Gated
Transformer eXtra Large (GTrXL) architecture, giving the
agent richer information about the turbulent flow field in which it navigates. The results are compared with a PPO+GTrXL without the secondary prediction tasks, a PPO combined with Long Short Term
Memory (LSTM)
cells and a traditional
navigation algorithm. The obtained results show a significant increase in the success rate (SR) and a lower crash rate (CR) compared to a PPO+LSTM, PPO+GTrXL and the classical Zermelo's
navigation algorithm, paving the way to a completely reimagined
UAV landscape in complex urban environments.
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Chemical Physics · Cross-listed from quant-ph
Quantum simulation of actinide chemistry: towards scalable algorithms on trapped ion quantum computers
Kesha Sorathia, Cono Di Paola, Gabriel Greene-Diniz et al. (+9 more)
Due to the wide range of technical applications of actinide elements, a thorough understanding of their electronic structure could complement technological improvements in many different areas.
Quantum computing could greatly aid in this understanding, as it can potentially provide exponential speedups over classical approaches, thereby offering insights into the complex electronic structure of actinide compounds. As a first foray into
quantum computational chemistry of actinides, this paper compares the method of
quantum computed moments (QCM) as a noisy intermediate-scale
quantum algorithm with a single-ancilla version of
quantum phase estimation (QPE), a
quantum algorithm expected to run on fault-tolerant
quantum computers. We employ these
algorithms to study the reaction energetics of plutonium
oxides and hydrides. In order to enable
quantum hardware experiments, we use several techniques to reduce resource requirements: screening individual
Hamiltonian Pauli terms to reduce the measurement requirements of QCM and variational compilation to reduce the depth of QPE
circuits. Finally, we derive electronic structure descriptions from a series of representative chemical models and compute the energetics from
quantum experiments on Quantinuum's H-series ion trap devices using up to 19
qubits. We find our experiments to be in excellent agreement with results from classical electronic structure calculations and state vector simulations.
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All authors Kesha Sorathia, Cono Di Paola, Gabriel Greene-Diniz, Carlo A. Gaggioli, David Zsolt Manrique, Joe Gibbs, Sean Harding, Thomas M. Soini, Neil Gaspar, Robert Harker, Mark Storr, David Munoz Ramo
Optics
Spatiotemporal control of laser intensity using differentiable programming
Kyle G Miller, Tomas E Gutierrez, Archis S Joglekar et al. (+3 more)
Optical techniques for spatiotemporal control can produce
laser pulses with custom amplitude, phase, or polarization structure. In nonlinear optics and
plasma physics, the use of structured pulses typically follows a forward design approach, in which the efficacy of a known structure is analyzed for a particular application. Inverse approaches, in contrast, enable the discovery of new structures with the potential for superior performance. Here, an implementation of the unidirectional pulse propagation equation that supports automatic differentiation is combined with gradient-based optimization to design structured pulses with features that are advantageous for a range of nonlinear optical and
plasma-based applications: (1) a longitudinally uniform intensity over an extended region, (2) a superluminal intensity peak that travels many Rayleigh ranges with constant duration, spot size, and amplitude, and (3) a
laser pulse that ionizes a gas to form a uniform column of
plasma. In the final case, optimizing the full spatiotemporal structure improves the performance by a factor of 15 compared to optimizing only spatial or only temporal structure, highlighting the advantage of spatiotemporal control.
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All authors Kyle G Miller, Tomas E Gutierrez, Archis S Joglekar, Amanda Elliott, Dustin H Froula, John P Palastro
Computational Physics
Optical excitations in nanographenes from the Bethe-Salpeter equation and time-dependent density functional theory: absorption spectra and spatial descriptors
Maximilian Graml and Jan Wilhelm
The GW plus Bethe-Salpeter equation (GW-BSE) formalism is a well-established approach for calculating excitation energies and optical spectra of molecules, nanostructures, and crystalline materials. We implement GW-BSE in the CP2K code and validate the implementation for a standard organic molecular test set, obtaining excellent agreement with reference data, with a mean absolute error in excitation energies below 3 meV. We then study optical spectra of nanographenes of increasing length, showing excellent agreement with experiment. We further compute the size of the excitation of the lowest optically active excitation which converges to about 7.6 $\r{A}$ with increasing length. Comparison with time-dependent density functional theory using functionals of varying exact-exchange fraction shows that none reproduce both the size of the excitation and optical spectra of GW-BSE, underscoring the need for many-body methods for accurate description of electronic excitations in nanostructures.
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Computational Physics
Bayesian MINFLUX localization microscopy
Steffen Schultze and Helmut Grubmüller
MINFLUX microscopy allows for localization of fluorophores with nanometer precision using targeted scanning with an illumination profile with a minimum. However, current scanning patterns and the overall procedure are based on heuristics, and may therefore be suboptimal. Here we present a rigorous
Bayesian that offers maximal resolutions from either minimal detected
photons or minimal exposures. We estimate using simulated localization runs that this approach should reduce the number of
photons required for 1 nm resolution by a factor of about four.
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Physics and Society
Citizen science dataset on residents' urban heat perception in outdoor public spaces of climate-vulnerable neighborhoods
Ferran Larroya, Isabelle Bonhoure, Femke Min et al. (+1 more)
We present a
dataset generated to investigate urban heat and thermal perception across five neighborhoods in the Barcelona metropolitan area. In collaboration with 14 non-academic partner organizations, we conducted a series of citizen science campaigns involving 439 residents as co-researchers engaged throughout all stages of the research process. Participants, residents of areas classified as highly or very highly
climate-vulnerable, identified 210 public outdoor sites relevant to their daily lives. These locations were subsequently characterized using a range of spatial and environmental indicators pertinent to urban heat island effects, urban health, and
climate resilience. Over the course of 48 thermal walks, participants carried portable, low-cost sensors that continuously recorded air temperature, relative humidity, and geolocation, resulting in 296,286 processed microclimatic data points. At pre-defined sites, individuals completed standardized surveys to report their Thermal Sensation Votes and Thermal Comfort Votes, yielding 5,169 self-reported entries. Sociodemographic data were also collected to further contextualize participants' responses. The resulting
dataset integrates objective environmental measurements with subjective perceptions of heat, enabling point-by-point analysis of thermal experience within the urban fabric. It offers a novel, multi-dimensional resource to support research on heat, thermal inequality, and the experiential dimensions of
climate vulnerability, and is intended to inform evidence-based decision-making in urban planning, public health, and
climate adaptation.
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All authors Ferran Larroya, Isabelle Bonhoure, Femke Min, Josep Perelló
Plasma Physics · Cross-listed from astro-ph.SR
Siqi Zhao, Huirong Yan, Terry Z. Liu et al. (+1 more)
Turbulence is a ubiquitous process that transfers energy across many spatial and temporal scales, thereby influencing particle transport and heating. Recent progress has improved our understanding of the anisotropy of
turbulence with respect to the mean
magnetic field; however, its exact form and implications for
magnetic topology and energy transfer remain unclear. In this study, we investigate the nature of
magnetic anisotropy in compressible magnetohydrodynamic (MHD)
turbulence within low-$\beta$
solar wind using measurements from the Cluster spacecraft. By decomposing small-amplitude fluctuations into Alfvén and compressible modes, we reveal that
magnetic anisotropy is largely mode dependent: Alfvenic fluctuations are broadly distributed in propagation angle, whereas compressible fluctuations are concentrated near the quasi-parallel (slab) direction, a feature closely linked to collisionless damping of compressible modes. For $\beta\rightarrow0$, compressible modes become dominant within the slab component at smaller scales. These findings advance our understanding of
magnetic anisotropy in
solar wind
turbulence and offer a new perspective on the three-dimensional
turbulence cascade, with broad implications for particle transport, acceleration, and
magnetic reconnection.
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All authors Siqi Zhao, Huirong Yan, Terry Z. Liu, Chuanpeng Hou
Atmospheric and Oceanic Physics
Predictability of Storms in an Idealized Climate Revealed by Machine Learning
Wuqiushi Yao, Or Hadas and Yohai Kaspi
The midlatitude
climate and weather are shaped by storms, yet the factors governing their predictability remain insufficiently understood. Here, we use a Convolutional Neural Network (CNN) to predict and quantify uncertainty in the intensity growth and trajectory of over 200,000 storms simulated in a 200-year aquaplanet GCM. This idealized framework provides a controlled
climate background for isolating factors that govern predictability. Results show that storm intensity is less predictable than trajectory. Strong baroclinicity accelerates storm intensification and reduces its predictability, consistent with theory. Crucially, enhanced jet meanders further degrade forecast skill, revealing a synoptic source of uncertainty. Using sensitivity maps from explainable AI, we find that the error growth rate is nearly doubled by the more meandering structure. These findings highlight the potential of machine learning for advancing understanding of predictability and its governing mechanisms.
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Chemical Physics
A Transferable Model of Molecular Exchange-Repulsion Interaction from Anisotropic Valence Density Overlap
Dahvyd Wing and Alexandre Tkatchenko
Pauli exchange-repulsion is the dominant short-range intermolecular interaction and it is an essential component of molecular force fields. Current approaches to modeling Pauli repulsion in molecular force fields often rely on over 20 atom types to achieve chemical accuracy, illustrating the challenge in finding models which have broadly transferable parameters, which hampers the development of force fields with
quantum-chemical accuracy that are transferable across many chemical systems. We present the anisotropic valence density overlap (AVDO) model for exchange-repulsion. The model produces sub-kcal/mol accuracy for dimers of organic molecules and contains two universal parameters, which we demonstrate are transferable for molecules composed of H, C, N, O, F, P, S, Cl, and Br. The model is tested on 1,872 unique molecular pairs selected from a set of 135 molecules, and samples dissociation curves and configurations from condensed-phase molecular dynamics trajectories. Given recent progress in machine learning of the electronic density, this model offers a promising path toward high-accuracy, next-generation machine-learned force fields.
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Fluid Dynamics
Heterogeneous Wettability Alters Methane Migration and Leakage in Shallow Aquifers
Sabber Khandoozi, Siddharth Gautam, Craig Dietsch et al. (+3 more)
Capillary heterogeneity is increasingly recognized as a first-order control on gas plume migration and trapping in aquifers and storage formations. We show that spatial variability in the water-methane contact angle, determined by mineralogy and salinity, alters capillary entry pressures and migration pathways. Using molecular dynamics simulations, we estimate contact angles on quartz and kaolinite under fresh and saline conditions and incorporate these results into continuum-scale multiphase flow simulations via a contact-angle-informed Leverett J function, mapping wettability directly onto continuum-scale flow properties. Accounting for contact angle heterogeneity affects methane behavior: mobile and residually trapped methane in aquifers decrease by up to 10 percent, while leakage to the atmosphere increases by as much as 20 percent. The magnitude of this effect depends on permeability contrast, leakage rate, salinity, and facies proportions. By coupling molecular-scale wettability to continuum-scale flow and transport, this cross-scale framework provides a physically grounded basis for groundwater protection and risk assessment and yields more reliable emissions estimates. The approach can be generalized to other subsurface gas transport problems, including hydrogen and carbon dioxide storage, as well as natural releases such as methane from permafrost thaw.
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All authors Sabber Khandoozi, Siddharth Gautam, Craig Dietsch, Muhammad Sahimi, David Cole, Mohamad Reza Soltanian
Fluid Dynamics
MoTIF: A Mode-Structured Tensor Framework for Multi-Parametric Approximation, Super-Resolution and Forecasting of Unsteady Systems
Guillermo Barragán, Ashton Hetherington, Arindam Sengupta et al. (+3 more)
We introduce MoTIF, a mode-structured tensor framework for multi-parametric approximation, super-resolution, and temporal
forecasting of high-dimensional unsteady systems. The methodology leverages High-Order Singular Value Decomposition (HOSVD) to obtain a structured multilinear representation of multi-dimensional
datasets, separating physical parameters, spatial coordinates, and temporal
evolution into distinct modal components. This decomposition enables the application of dedicated approximation operators to each mode. Gaussian Process Regression is employed to interpolate and extrapolate parametric and spatial modal
matrices, enabling database completion and resolution enhancement, while recurrent neural networks are applied to the temporal mode to forecast system
evolution. This decoupled operator-learning strategy preserves the intrinsic tensor structure while providing a flexible non-intrusive reduced-order modelling framework. The proposed methodology is validated on a database of unsteady laminar flow simulations with varying Reynolds numbers and angles of attack. Accurate reconstruction of unseen flow configurations and temporal prediction are achieved, with relative root mean square errors consistently below 2% compared to high-fidelity simulations. The framework provides a scalable and mathematically structured alternative to conventional surrogate modelling approaches for high-dimensional parametric dynamical systems.
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All authors Guillermo Barragán, Ashton Hetherington, Arindam Sengupta, Rodrigo Abadía-Heredia, Jesús Garicano-Mena, Soledad Le Clainche
Atomic Physics · Cross-listed from quant-ph
Engineering Atom-Photon Hybridization with Density-Modulated Atomic Ensembles in Coupled Cavities
Carlos E. Máximo, Romain Bachelard and Tobias Donner
Radiation-matter hybridization allows atoms to serve as mediators of effective interactions between light modes and, conversely, to interact among themselves via light. Here we exploit the spatial structure of atomic ensembles to control the coupling between modes of distinct cavities, thereby reshaping the resulting atom-
photon spectra. We show that extended homogeneous clouds suppress mode-mode couplings through destructive interference, whereas grated clouds can preserve them under specific Bragg conditions. This leads to mode-mode spectral subsplittings, where collectivity arises not only from the atom number but also from the ability to tune modes of different cavities independently. Our results establish spatially engineered atomic ensembles as a pathway to selective
photon transfer between modes and precise control of many-body complexity.
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Fluid Dynamics
Rush-to-equilibrium concept for minimizing reactive nitrogen emissions in ammonia combustion
Hernando Maldonado Colmán and Michael E. Mueller
Ammonia (NH3) is a zero-carbon fuel that has been receiving increasing
attention for power generation and even transportation. Compared to H2, NH3's volumetric energy density is higher, is not as explosive, and has well established transport and storage technologies. Yet, NH3 has poor flammability and flame stability characteristics and more reactive nitrogen (RN: NOx, N2O) emissions than hydrocarbon fuels, at least with traditional combustion processes. Partially cracking NH3 (into a NH3-H2-N2 mixture, AHN) addresses its flammability and stability issues. RN emissions remain a challenge, and mechanisms of their emissions are fundamentally different in NH3 and hydrocarbon combustion. While rich-quench-lean NH3 combustion strategies have shown promise, the largest contributions to RN emissions are the unrelaxed emissions in the fuel-rich stage due to overshoot of thermodynamic
equilibrium within the reaction zone of premixed flames coupled with finite residence times available for relaxation to
equilibrium. This work introduces a rush-to-
equilibrium concept for AHN combustion, which aims to reduce the unrelaxed RN emissions in finite residence times by accelerating the approach to
equilibrium. In the concept, a flow particle is subjected to a decaying mixing rate as it transits the premixed flame. This mitigates the mixing effects that prevents the particle approach to
equilibrium, and promotes the chemistry effects to push the particle toward
equilibrium, all while considering finite residence times. Evaluated with a state-of-the-art combustion model at gas turbine conditions, the concept shows the potential to reduce RN emissions by an order of magnitude, and that works irrespective of cracking extent, pressure, temperature, etc. A brief discussion of possible practical implementation reveals reasonable
geometric and flow parameters characteristic of modern gas turbine combustors.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting
Víctor Medina, Giovanny A. Cuervo-Londoño and Javier Sánchez
The accurate prediction of oceanographic variables is crucial for understanding
climate change, managing marine resources, and optimizing maritime activities. Traditional ocean
forecasting relies on numerical models; however, these approaches face limitations in terms of computational cost and scalability. In this study, we adapt Aurora, a foundational deep learning model originally designed for atmospheric
forecasting, to predict sea surface temperature (SST) in the Canary Upwelling System. By fine-tuning this model with high-resolution oceanographic reanalysis data, we demonstrate its ability to capture complex spatiotemporal patterns while reducing computational demands. Our methodology involves a staged fine-tuning process, incorporating latitude-weighted error metrics and optimizing hyperparameters for efficient learning. The experimental results show that the model achieves a low RMSE of 0.119K, maintaining high anomaly correlation coefficients (ACC $\approx 0.997$). The model successfully reproduces large-scale SST structures but faces challenges in capturing finer details in coastal regions. This work contributes to the field of data-driven ocean
forecasting by demonstrating the feasibility of using deep learning models pre-trained in different domains for oceanic applications. Future improvements include integrating additional oceanographic variables, increasing spatial resolution, and exploring physics-informed neural networks to enhance interpretability and understanding. These advancements can improve
climate modeling and ocean prediction accuracy, supporting decision-making in environmental and economic sectors.
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Geophysics
Learning Soil Physics from Partial Knowledge and Data: Partitioning Capillary and Adsorbed Soil Water
Sarem Norouzi, Per Moldrup, Ben Moseley et al. (+9 more)
Soil physics models have long relied on simplifying assumptions to represent complex processes, yet such assumptions can strongly bias model predictions. Here, we propose a paradigm-shifting differentiable hybrid modeling (DHM) framework that instead of simplifying the unknown, learns it from data. As a proof of concept, we apply the hybrid approach to the challenge of partitioning the soil water retention curve (SWRC) into capillary and adsorbed water components, a problem where traditional assumptions have led to divergent results. The hybrid framework derives this partitioning directly from data while remaining guided by a few parsimonious and universally accepted physical constraints. Using basic soil physical properties as inputs, the hybrid model couples an analytical formula for the dry end of the SWRC with data-driven physics-informed neural networks that learn the wet end, the transition between the two ends, and key soil-specific parameters. The model was trained on a SWRC
dataset from 482 undisturbed soil samples from Central Europe, spanning a broad range of soil texture classes and organic carbon contents. The hybrid model successfully learned both the overall shape and the capillary and adsorbed components of the SWRC. Notably, the model revealed physically meaningful pore-scale features without relying on explicit geometrical assumptions about soil pore shape or its distribution. Moreover, the model revealed a distinctly nonlinear transition between capillary and adsorbed domains, challenging the linear assumptions invoked in previous studies. The methodology introduced here provides a blueprint for learning other soil processes where high-quality
datasets are available but mechanistic understanding is incomplete.
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All authors Sarem Norouzi, Per Moldrup, Ben Moseley, David Robinson, Dani Or, Tobias L. Hohenbrink, Budiman Minasny, Morteza Sadeghi, Emmanuel Arthur, Markus Tuller, Mogens H. Greve, Lis W. de Jonge
Chemical Physics
On the Connection of High-Resolution NMR Spectrum Mirror Symmetry With Spin System Properties
Dmitry A. Cheshkov and Dmitry O. Sinitsyn
A correlation between the
symmetry of NMR spectra, including higher-order spectra, and the properties of the
spin system has been established. It is shown that for a spectrum to be symmetric about the mid-resonance frequency ({\nu}0), two conditions must be satisfied: the resonant frequencies of the
spins must be symmetrically positioned about {\nu}0, and the J coupling
matrix must be symmetric about the secondary diagonal. The results were validated by calculating theoretical spectra for 4-, 5-, and 6-
spin systems.
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Optics · Cross-listed from quant-ph
Second-order Stark shifts exceeding 10$\,$GHz in electrically contacted SiV$^-$ centers in diamond
Manuel Rieger, Nori N. Chavira Leal, Rubek Poudel et al. (+7 more)
Negatively charged silicon vacancy centers (SiV$^-$) in diamond exhibit excellent
spin coherence and optical properties, making them promising candidates for
quantum technologies. However, the strain-induced inhomogeneous distribution of optical transition frequencies poses a challenge for scalability. We demonstrate electrical tuning of the SiV$^-$ center zero-
phonon lines using in-plane contacts to apply moderate electric fields up to 45$\,$MV/m. The second-order Stark shift exceeds 10$\,$GHz, which is of the same order of magnitude as the 15$\,$GHz inhomogeneous distribution of SiV$^-$ observed in emitters embedded in optical nanostructures such as photonic
crystal nanocavities. Analysis of individual SiV$^-$ centers shows significant variation in polarizabilities between defects indicating that the polarizability strongly depends on local parameters like strain. The observed polarizabilities are 3-25 times larger than those of tin vacancy centers, which we attribute to valence band resonances that delocalize the $e_u$ wavefunctions. Photoluminescence excitation measurements reveal that optical linewidths increase moderately with applied electric field strength. Our results demonstrate that large electrical Stark shifts can overcome the inhomogeneous distribution of transition frequencies, representing a significant step toward scalable SiV$^-$-based
quantum technologies such as
quantum repeaters.
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All authors Manuel Rieger, Nori N. Chavira Leal, Rubek Poudel, Tobias Waldmann, Lina M. Todenhagen, Stefan Kresta, Viviana Villafane, Martin S. Brandt, Kai Müller, Jonathan J. Finley
Optics
Next-generation interferometry with gauge-invariant linear optical scatterers
Christopher R. Schwarze, Anthony D. Manni, David S. Simon et al. (+2 more)
Measurement technology employing optical interference phenomena such as a fringe pattern or frequency shift has been evolving for more than a century. The systems are being designed better, and their components are being built better. But the major components themselves hardly change. Most modern interferometers rely on the same conventional set of components to separate the electromagnetic field into multiple beams, such as plate optics and beam-splitters. This naturally limits the design scope and thus the potential applicability and performance. However, recent investigations suggest that incorporating novel, higher-dimensional linear-optical splitters in interferometer design can lead to several improvements. In this work, we review the underlying theory of these novel optical scatterers and some demonstrated configurations with enhanced resolution. The basic principles of optical interference and optical phase sensing are discussed in tandem. Emphasis is placed on both familiar and unfamiliar scatterers, such as the maximally-symmetric Grover multiport, whose actions are left unchanged by certain gauge transformations. These higher-dimensional, gauge-invariant multiports embody a new class of building blocks which can tailor optical interference for metrology in unconventional ways.
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All authors Christopher R. Schwarze, Anthony D. Manni, David S. Simon, Abdoulaye Ndao, Alexander V. Sergienko
Computational Physics · Cross-listed from gr-qc
Characteristic Critical Collapse of a Yang-Mills Field With Null Infinity
Rita P. Santos, Krinio Marouda and David Hilditch
Solutions to the Einstein equations near the threshold of black hole formation exhibit remarkable behavior known as critical phenomena gravitational collapse. In this work we perform characteristic
evolution in compactified Bondi coordinates in order to study the critical collapse of a Yang-Mills field, allowing for the extraction of global quantities such as the Bondi mass and news function. Our numerical approach is fourth-order accurate. First, we demonstrate that the collapsing field exhibits local DSS behavior, characterized by an echoing period of~$\Delta \simeq 0.7388$, agreeing with previous works up to the second decimal place. We find that global quantities such as the Bondi mass and news function display the same DSS behavior. We furthermore show that the mass of the black holes formed during near-threshold evolutions scales as a function of the distance to the critical parameter, with a critical exponent of approximately~$\gamma=0.1977\pm0.0009$. Finally, our findings indicate that these results are universal, irrespective of the initial data.
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Plasma Physics
Global Non-Axisymmetric Hall Instabilities in a Rotating Plasma
Alexandre Sainterme and Fatima Ebrahimi
Non-axisymmetric, flow-driven instabilities in the incompressible
Hall-MHD model are studied in a differentially rotating cylindrical
plasma. It is found that in the
Hall-MHD regime, both whistler waves and ion-cyclotron waves can extract energy from the flow shear, resulting in two distinct branches of global instability. The non-axisymmetric whistler modes grow significantly faster than non-axisymmetric, ideal MHD modes. A discussion of the global whistler instability mechanism is presented in the large-ion-skin-depth, `electron-MHD' limit. When the
magnetic field is azimuthal, a subset of the whistler modes having zero axial wave number are uncovered to be destabilized by the `co-rotation amplifier' mechanism. It is observed that the effect of the
Hall term on the non-axisymmetric modes can be appreciable when $d_i$ is on the order of a few % of the width of the cylindrical annulus. Distinct global modes emerge in the strong
Hall-MHD regime at significantly stronger
magnetic fields than those required for unstable global MHD modes, as the
Hall effect weakens the stabilizing `field-line bending' by decoupling ion motion from the
magnetic field. These global non-axisymmetric modes may play an important role in weakly ionized
accretion disks.
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Optics
Design of Carrier-Depletion-based Modulators on Commercially Available SOI Substrates of Varying Device-Layer Thickness
Anjali A R, Syamsundar De and Pranabendu Ganguly
We compare modulator designs on thick-film and thin-film silicon-on-
insulator (SOI) substrates based on the carrier-depletion effect. This effect exhibits a lower junction capacitance as compared to its injection counterparts, leading to a higher electro-optic bandwidth, although the effective refractive index change is low. In this work, commercially available standard SOI substrates with device layer thicknesses of 5 {\mu}m, 3 {\mu}m, and 0.22 {\mu}m are chosen for designing carrier-depletion-based phase shifters, which are then utilized for the design of Mach-Zehnder modulators (MZMs). These MZMs are tested for various on-off-keying (OOK) modulation bit rates employing non-return-to-zero (NRZ) pseudo-random binary signal (PRBS). For the comparison of given designs, we find that the thick-film MZMs, which can provide higher fabrication tolerance and high power handling capabilities, support maximum modulation speeds in the order of a few Gbps, whereas it can go up to 20 Gbps for thin-film MZMs.
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Fluid Dynamics
Enhanced quality factors at resonance in acoustofluidic cavities embedded in matched elastic metamaterials
Valdemar Frederiksen and Henrik Bruus
We show that by
embedding liquid-filled acoustofluidic cavities in a metamaterial, the quality factor of the cavity at selected acoustic resonance modes can be enhanced by 2 to 3 orders of magnitude relative to a comparable conventional cavity by matching the coarse-grained elastic
moduli of the metamaterial to the acoustic properties of the
liquid.
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Biological Physics · Cross-listed from cs.RO
Octopus-like Reaching Motion: A Perspective Inspired by Whipping
Shengyao Zhang, Yiyuan Zhang, Chenrui Zhang et al. (+5 more)
The stereotypical reaching motion of the octopus arm has drawn growing
attention for its efficient control of a highly deformable body. Previous studies suggest that its characteristic bend propagation may share underlying principles with the dynamics of a whip. This work investigates whether whip-like passive dynamics in water can reproduce the kinematic features observed in biological reaching and their similarities and differences. Platform-based whipping tests were performed in water and air while systematically varying material stiffness and driving speed. Image-based quantification revealed that the Ecoflex Gel 2 arm driven at 150 rpm (motor speed) reproduced
curvature propagation similar to that observed in octopus reaching. However, its bend-point velocity decreased monotonically rather than exhibiting the biological bell-shaped profile, confirming that the octopus reaching movement is not merely a passive whipping behavior. The absence of propagation in air further highlights the critical role of the surrounding medium in forming octopus-like reaching motion. This study provides a new perspective for understand biological reaching movement, and offers a potential platform for future hydrodynamic research.
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All authors Shengyao Zhang, Yiyuan Zhang, Chenrui Zhang, Yiming Li, Wenci Xin, Yuliang Liufu, Hong Wei Ng, Cecilia Laschi
Chemical Physics
Electron-wave-stimulated mid-infrared emission from graphene-substrate quantum oscillators
Sunhwa Hong, Moo Jin Kwak, Yunseok Lee et al. (+15 more)
Generating tunable, high-intensity mid-infrared (MIR) to terahertz (THz) radiation on-chip remains a formidable challenge due to the rigid spectral limits of conventional thermal emitters. While
graphene has emerged as a promising platform for light-matter interaction, active control of its radiative properties has been largely confined to surface-limited phenomena mostly associated with plasmons. Here, we introduce a new MIR radiation platform where multi-layer chemical vapor deposition (CVD)
graphene is integrated with modular, vibrationally active dielectric substrates, ranging from organic thin films and inorganic
matrices. A pivotal discovery is that the long-range de Broglie wavelength of drift carriers enables coherent coupling with vibrational transition dipoles deep within the substrate bulk. This transforms the substrate into a three-dimensional volume emission source, where complex spectra of characteristic molecular and
lattice vibration energies are additively combined on demand. The exponential scaling of radiation intensity appears when the electrons' drift velocity in
graphene exceeds the sound velocity of the substrates, consistent with
quantum stimulated amplification associated with Cerenkov electron-
phonon instability. Our work redefines the passive dielectric substrate as an active, programmable component driven by electron waves, paving the way for next-generation system-on-a-chip MIR-THz photonics, environmental and biomedical sensing, and highly efficient mode-specific electrothermal applications.
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All authors Sunhwa Hong, Moo Jin Kwak, Yunseok Lee, Chan-Jin Kim, Sung Jin Hong, Ha Eun Lee, Yejun Lee, Koeun Kim, Juhyen Lee, Minkyung Lee, Youngdeog Koh, Joonhyun Lee, Miyoung Kim, Zee Hwan Kim, Myung Jin Park, Hoon Wee, Byung Hee Hong, Konstantin S. Novoselov