Classical Physics
Experimental design of a millifluidic flow-focusing method for biomimetic nanocellulose and hemicellulose-based biopolymer fibres
Moisy Amélie, Voisin Hugo, Davy Joëlle et al. (+2 more)
The mechanical performance of plant fibres is linked to the presence of crystalline elements dispersed within an amorphous cohesive
matrix. The more the crystalline reinforcement is aligned with the fibre axis, the better the mechanical properties of the fibre. With the aim of developing entirely biobased biomimetic fibres as alternatives to synthetic or resource-consuming fibres, we have studied the fabrication of hydrogel filaments made from mixtures of nanocelluloses, biobased crystalline nanoparticles acting as reinforcement, and xyloglucans, a plant wall hemicellulose with a strong affinity for cellulose surfaces. These will ensure cohesion between the nanocelluloses. To optimize the orientation of the nanocelluloses within the filaments, and thus potentially improve the mechanical properties of the fibres, we present a study on the development of a millifluidic method of flow-focusing. The developed setup uses external sheath flows to focus and align a nanocellulose suspension central flow. Different configurations in terms of concentrations,
circuit designs and flow velocities are tested. 3D printed
circuits are explored to produce versatile geometries and optimize the process design. To qualify the orientations during the process, observations with a polarized microscope (POM) are made, as the
alignment of the nanocellulose crystalline structures creates birefringence in suspensions. Significant optical phase shifts related to the nanocellulose particles' orientations are visible by color gradients, varying with the suspensions' concentrations and flow velocities. Results demonstrate successful tuning of nanocellulose orientation into anisotropic hydrogels using different millifluidic
circuit geometries, with the introduction of xyloglucans to produce new types of biosourced fibres.
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All authors Moisy Amélie, Voisin Hugo, Davy Joëlle, Cathala Bernard, Guessasma Sofiane
Fluid Dynamics · Cross-listed from cond-mat.stat-mech
Scaling laws for stationary Navier-Stokes-Fourier flows and the unreasonable effectiveness of hydrodynamics at the molecular level
P.I. Hurtado, J.J. del Pozo and P.L. Garrido
Hydrodynamics provides a universal description of the emergent collective dynamics of vastly different many-body systems, based solely on their
symmetries and conservation laws. Here we harness this universality, encoded in the Navier-Stokes-
Fourier (NSF) equations, to find general scaling laws for the stationary uniaxial solutions of the compressible NSF problem far from
equilibrium. We show for general transport coefficients that the steady density and temperature fields are functions of the pressure and a kinetic field that quantifies the quadratic excess velocity relative to the ratio of heat flux and shear stress. This kinetic field obeys in turn a spatial scaling law controlled by pressure and stress, which is inherited by the stationary density and temperature fields. We develop a scaling approach to measure the associated master curves, and confirm our predictions through compelling data collapses in large-scale molecular dynamics simulations of paradigmatic model fluids. Interestingly, the robustness of the scaling laws in the face of significant finite-size effects reveals the surprising accuracy of NSF equations in describing molecular-scale stationary flows. Overall, these scaling laws provide a novel characterization of stationary states in driven fluids.
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Geophysics
Parameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator
Ayrat Abdullin, Umair Bin Waheed, Leo Eisner et al. (+1 more)
Seismic phase picking is fundamental for microseismic monitoring and subsurface imaging. Manual processing is impractical for real-time applications and large sensor arrays, motivating the use of deep learning-based pickers trained on extensive earthquake catalogs. On a broader scale, these models are generally tuned to perform optimally in high signal-to-noise and long-duration networks and often fail to perform satisfactorily when applied to campaign-based microseismic
datasets, which are characterized by low signal-to-noise ratios, sparse geometries, and limited labeled data. In this study, we present a microseismic adaptation of a network-wide earthquake phase picker, Phase Neural Operator (PhaseNO), using transfer learning and parameter-efficient fine-tuning. Starting from a model pre-trained on more than 57,000 three-component earthquake and noise records, we fine-tune it using only 200 labeled and noisy microseismic recordings from hydraulic fracturing settings. We present a parameter-efficient adaptation of PhaseNO that fine-tunes a small fraction of its parameters (only 3.6%) while retaining its global spatiotemporal representations learned from a large
dataset of earthquake recordings. We then evaluate our adapted model on three independent microseismic
datasets and compare its performance against the original pre-trained PhaseNO, a STA/LTA-based workflow, and two state-of-the-art deep learning models, PhaseNet and EQTransformer. We demonstrate that our adapted model significantly outperforms the original PhaseNO in F1 and accuracy metrics, achieving up to 30% absolute improvements in all test sets and consistently performing better than STA/LTA and state-of-the-art models. With our adaptation being based on a small calibration set, our proposed workflow is a practical and efficient tool to deploy network-wide models in data-limited microseismic applications.
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All authors Ayrat Abdullin, Umair Bin Waheed, Leo Eisner, Naveed Iqbal
Geophysics
Assessment Of Selected Trace Elements Concentration in Eleyele Lake-Water, Ibadan South Western Nigeria
Ifeoluwa Oluwatosin Kunle-John, Segun P. Michaels and Edith N. Okay
Eleyele Lake has enormous economic importance as it is completely surrounded by various communities which discharge their domestic waste directly into the lake. This alters the physical, chemical and biological characteristics of the lake. It is essential to assess the water for its various usage. Twelve (12) samples were collected from various locations of the lake and analysed. Some physical parameters (electrical conductivity, Total Dissolved Solids, Ph and temperature were determined in-situ. The rest of the sample was taken to the laboratory for various chemical analysis and the results were compared to the WHO standards. The chemical extent of the contamination was determined by the contamination factor, degree of contamination and Geo-Accumulation Index. The physical parameters show that the TDS has an average of 122.2ppm and EC was uniform throughout the various points of reading suggesting that the lake is fresh water. The pH averaged at 72, temperature at 27.2 degrees. The selected trace element falls within the WHO acceptable limits. Their contamination indices showed that Ba, Co, Cs, Cu, Mo, Rb, Sr and Zn are generally less than one depicting their geogenic origin. The high degree of contamination is influenced by high levels of Al and Fe due to human activities and industrial waste disposal and can lead to anemia, osteomalacia (brittle or soft bones), cardiac arrest, stomach problems, nausea, and hemochromatosis. Thus, Eleyele lake is not advisable for consumption.
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Optics
From Diffraction to Refraction: a coherence-based conceptual framework
Riccardo Castagna, Gautam Singh, Cristiano Riminesi et al. (+2 more)
Refraction, traditionally viewed as a
geometric event occurring at material interfaces, is now being re-examined through the lens of coherence. Recent studies in optics and photonics, including coherence
tomography, Moire interference, and coherence-engineered diffraction, indicate that phase organization alone can bend light even without index discontinuities. Fraunhofer-based analyses further show that angular deflection can arise from intrinsic phase
curvature within homogeneous media. Here we introduce a coherence-based constitutive framework that systematizes these observations: refraction can occur inside a bulk medium when coherence itself provides the effective boundary. Two near-frequency structured beams write and probe a shared phase field, revealing reproducible angular rotation and coherence-
lensing whose direction and magnitude follow the spectral detuning. The key detuning coefficient is: Krc = 2 * pi * (1/lambda_r - 1/lambda_w). The compact coherence-refraction relation n2 * sin(theta_t) - n1 * sin(theta_i) = Krc_parallel / k0, with k0 = 2 * pi / lambda_r, retains the form of Snell's law while extending it to coherence-driven regimes. This is not a new law but a quantitative rule linking tangential phase matching to observable deflection within homogeneous media.
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All authors Riccardo Castagna, Gautam Singh, Cristiano Riminesi, Andrea Di Donato, Rossen Todorov
Plasma Physics
Charged particle energization by low-amplitude electrostatic waves at cyclotron harmonics
F. Sattin. L. Martinelli
The system made by a charged particle interacting with a single electrostatic wave which propagates perpendicularly to the
magnetic field, at a frequency larger than the cyclotron one, has been extensively studied in literature due to its implications with ion heating in magnetized
plasmas. It is known that a threshold in the electrostatic potential must be exceeded in order for
stochastic particle motion and heating to occur. Regardless its amplitude, however, the electrostatic wave induces a periodic oscillation in the particle motion. We show, by analytical and numerical arguments, that this dynamics is non-adiabatic, meaning that the particle does not land back to its initial state when the wave is slowly turned off. This way, particle energization (although, not rigorously heating) occurs even under sub-threshold conditions.
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Instrumentation and Detectors
A nanopore-gated sub-attoliter silicon nanocavity for single-molecule trapping and analysis
Funing Liu, Qitao Hu, Anton Sabantsev et al. (+5 more)
Biomolecules exhibit dynamic conformations critical to their functions, yet observing these processes at the single-molecule level under native conditions remains a formidable challenge. While surface immobilization has been widely used to extend observation times, it could disrupt molecular dynamics and impede biological function. Moreover, the study of weak molecular interactions requires high local concentrations, often leading to problems with signal saturation in fluorescence-based approaches. Recent advancements in single-molecule trapping techniques have addressed some limitations, but achieving precise, controllable, long-term trapping in a molecularly crowded environment without external forces remains difficult. Here, we introduce a nanopore-gated sub-attoliter silicon nanocavity that enables precise, non-perturbative trapping of individual biomolecules for extended observation times, eliminating the need for external forces. Using nucleosomes as model systems, we demonstrate single-molecule Förster resonance energy transfer (smFRET) to monitor relative distances. Our data also show that an applied electric field can modulate the conformational properties of macromolecules, emphasizing a key advantage of our device: it does not require an electric field to retain trapped molecules. We envision this nanocavity platform as a powerful tool for interrogating molecular dynamics in physiologically relevant environments, offering unperturbed access to weak and transient interactions that are central to biological regulation.
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All authors Funing Liu, Qitao Hu, Anton Sabantsev, Giovanni Di Muccio, Shuangshuang Zeng, Mauro Chinappi, Sebastian Deindl, Zhen Zhang
Geophysics
Seismic amplitude-variation-with-offset inversion using an algebraically equivalent formulation of the exact Zoeppritz equations and a matrix-free adjoint-state framework
Wiktor Waldemar Weibull and Nisar Ahmed
The amplitude-variation-with-offset inversion techniques are formulated to estimate elastic properties by fitting modeled seismic responses to observed data. Solving inverse seismic problems requires minimizing a target objective function for which gradient-based methods are frequently adopted. However, the efficiency and accuracy of these methods depend significantly on the approach used to compute the gradient of the target function. This work first derives an algebraically equivalent reformulation of the exact Knott-Zoeppritz PP equations, yielding a differentiable forward model suitable for gradient-based optimization. The resulting formulation is then used to derive an explicit adjoint-state gradient of a convolution-based objective function for discretized multilayer media, providing an efficient framework for exact nonlinear inversion of P- and S-wave velocities and density, including the consistent treatment of post-critical-angle reflections through the complex-valued Zoeppritz response. The adjoint state-based solution improves computational efficiency by avoiding numerical approximations while maintaining high accuracy in calculating the gradient for seismic inversion. Additionally, using the exact Zoeppritz equation helps overcome the limitations associated with weak elastic property contrasts across subsurface layers. The inversion methodology is validated using 1D well-log-based and 2D synthetic seismic data at varying signal-to-noise ratios, including a 500-member ensemble test of sensitivity to the starting model and a Marmousi comparison of Zoeppritz versus linearized Aki-Richards reflectivity at high contrast and wide angles. Then it is applied to a 2D field data set from the Troll oil and gas field in the Norwegian North Sea. The results demonstrate that the proposed inversion framework provides stable and reliable estimates of elastic property models.
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Instrumentation and Detectors
Performance of USTC first batch resistive AC-LGAD sensor
Han Li, Xiao Yang, Kuo Ma et al. (+9 more)
In this paper, the design and characterization of AC-LGAD sensors at the University of Science and Technology of China is introduced. The sensors are characterized with an infrared
laser Transient Current Technique (TCT) system for evaluating signal response characteristics and spatial resolution. The temporal resolution was quantified with electrons emitted by a Sr-90 radioactive source. The spatial resolution can reach 4 {\mu}m and a temporal resolution of 48 ps is achieved
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All authors Han Li, Xiao Yang, Kuo Ma, Hang Yang, Aonan Wang, De Zhang, Tianao Wang, Xiangxuan Zheng, Jiajin Ge, Yusheng Wu, Hao Liang, Yanwen Liu
Optics
Third Harmonic Generation in Transparent Longitudinal Epsilon-Near-Zero Multilayers
Wallace Jaffray, Sven Stengel, Domenico de Ceglia et al. (+5 more)
Epsilon near zero (ENZ) materials can dramatically enhance local optical fields, enabling nonlinear interactions at relatively low intensities. Yet, near their
plasma frequency, conventional isotropic ENZ media remain highly absorptive, limiting nonlinear operations that require good transparency. Longitudinal epsilon near zero metamaterials (LENZ), characterized by a vanishing permittivity along the optical axis provide an exceptional platform for field enhancement while mitigating absorption losses and impedance mismatch. We experimentally show that a Si and ITO multilayer engineered for a LENZ resonance in the near-infrared enables broadband, high pump transmission while still harnessing ENZ enhanced nonlinearity to generate a strong third harmonic signal. This demonstrates that efficient nonlinear processes can be driven without the high-loss conditions typical of isotropic ENZ media and regardless of intrinsic absorption at the harmonic frequency. The resulting third harmonic efficiency is comparable to isotropic ENZ films but without the absorption-induced heating constraints of ENZ operation. The high pump transmission enables transparent LENZ (TLENZ) stacks to be integrated into optical cavities, where resonant field buildup could amplify the nonlinear response without compromising thermal management. These results establish TLENZ multilayers as a robust, versatile platform for transparent, field enhanced nonlinear nanophotonics, combining strong light matter interaction with low-loss operation.
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All authors Wallace Jaffray, Sven Stengel, Domenico de Ceglia, Neset Akozbek, Filippo Capolino, Marcello Ferrera, Michael Scalora, Maria Antonietta Vincenti
Optics · Cross-listed from quant-ph
Intense-Laser Nondipole-Induced Symmetry Breaking in Solids
Asger Weeth and Lars Bojer Madsen
High-harmonic spectroscopy in solids gives insight into the inner workings of solids, such as reconstructing band structures or probing the topological phase of materials. High-harmonic generation (HHG) is a highly non-linear phenomena and simulations guide interpretation of experimental results. These simulations often rely on the electric dipole approximation, even though the driving fields enter regimes that challenge its accuracy. Here, we investigate effects of including nondipole terms in the light-matter coupling in simulations of HHG in materials with both topologically trivial and non-trivial phases. We show how the inclusion of nondipole terms breaks dipole selection rules, allowing for new polarizations of the generated light. Specifically we find that helicity, completely absent in the dipole approximation, is induced by the nondipole extension, and that this helicity is dependent on the topological phase of the material.
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Computational Physics · Cross-listed from quant-ph
A Joint Quantum Computing, Neural Network and Embedding Theory Approach for the Derivation of the Universal Functional
Martin J. Uttendorfer, Daniel Barragan-Yani, Matthias Sperl et al. (+1 more)
We introduce a novel approach that exploits the intersection of
quantum computing, machine learning and reduced density
matrix functional theory to leverage the potential of
quantum computing to improve simulations of interacting
quantum particles. Our method focuses on obtaining the universal functional using a deep neural network trained with
quantum algorithms. In addition, we use density
matrix embedding theory to strengthen our approach by substantially expanding the space of
Hamiltonians for which the obtained functional can be applied without the need for additional
quantum resources. Since the obtained universal functional can be reused for any system where the interactions within the embedded fragment are identical, our work demonstrates a way to potentially achieve a cumulative
quantum advantage within
quantum computing applications for
quantum chemistry and condensed matter physics.
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All authors Martin J. Uttendorfer, Daniel Barragan-Yani, Matthias Sperl, Marc Landmann
Optics
Metasurface lasers programmed by optical pump patterns
Nelson de Gaay Fortman, Radoslaw Kolkowski, Nick Feldman et al. (+2 more)
Metasurface
lasers offer unprecedented control over light emission, yet their spatial and modal characteristics are typically fixed post-fabrication. Here, we introduce a reconfigurable plasmonic metasurface
laser platform in which the lasing area geometry, and thus the emission properties, are dynamically programmed via spatially structured optical pumping. Using hexagonal arrays of silver nanoparticles embedded in dye-doped waveguides, we demonstrate lasing at high-
symmetry points of the Brillouin zone, including the K and M points. K-point lasing exhibits spontaneous
symmetry breaking (SSB) in relative intensity between degenerate K and K' modes, with no bias induced by pump geometry, even for geometries that explicitly break
symmetry. In contrast, M-point lasing allows deterministic control over emission channels via asymmetric pumping. We further show that spatially separated K-point
lasers synchronize in phase and amplitude, undergoing SSB in lockstep. A theoretical density
matrix approach cast into
stochastic differential equations reproduces the observed real- and
Fourier-space intensity distributions and SSB behavior. Our findings establish spatially programmable metasurface
lasers as a versatile platform for exploring dynamic phenomena in photonic
lattices, with potential applications in
vortex beam shaping, optical logic, and true random number generation.
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All authors Nelson de Gaay Fortman, Radoslaw Kolkowski, Nick Feldman, Peter Schall, A. Femius Koenderink
Optics
Microparticle laser fragmentation in liquids: mechanisms, energetics, and efficiency quantified with single-pulse, single-particle precision
Maximilian Spellauge, Ramon Auer, Meike Tack et al. (+6 more)
Microparticle
laser fragmentation in
liquids has emerged as a promising approach to generate nanoparticles with high efficiency. Despite its advantages, the underlying fragmentation mechanisms, their connection to the nanoparticle size distribution, and the energy efficiency of the process remain poorly understood. In this study for the first time, microparticle fragmentation is investigated in single-pulse, single-particle experiments on Au microparticles. Determining the absorbed peak fluence enables assessment of the process energetics. Pump-probe microscopy identifies photomechanical fracture of the molten microparticle volume and photothermal phase explosion of its superheated surface as the fragmentation mechanisms. We find that 83% of the absorbed
laser energy is converted into cavitation bubble formation, while only 1% contributes to the surface energy of the generated nanoparticles. Despite this small fraction, MP-LFL outperforms
laser ablation in
liquids. The surface energy generated per absorbed energy is 10 times higher, and the overall energy efficiency is 14 times higher. This gain originates from the confined microparticle geometry, which minimizes energy losses and enhances photomechanical fragmentation via pressure focusing. These results position microparticle fragmentation in
liquids as a fundamentally more energy-efficient approach for scalable,
laser-based nanoparticle production than
laser ablation in
liquids.
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All authors Maximilian Spellauge, Ramon Auer, Meike Tack, Florentine Limani, David Redka, Sven Reichenberger, Anna R. Ziefuss, Stephan Barcikowski, Heinz P. Huber
Accelerator Physics
Laser Wakefield Acceleration in a Capillary Gas Cell Producing GeV-Scale High-Quality Electron Beams
Srimanta Maity, Francesco Massimo, Alex Whitehead et al. (+2 more)
Laser Wakefield Acceleration (LWFA) is a promising approach for producing high-brightness electron beams in the GeV energy range, offering significant potential for compact next-generation accelerator facilities. In this work, we present a computational study of LWFA in a specially designed single-stage capillary gas-
cell target aimed at producing high-quality, GeV-class electron beams. The capillary
cell includes a short ($\sim 2$ mm) injection region at the entrance filled with a helium (He) and nitrogen (N$_2$) gas mixture. This is followed by a longer ($\sim 14$ mm) pure He section, which provides the required acceleration length and limits continuous ionization injection, thereby significantly reducing the energy spread of the accelerated beam. Hydrodynamic simulations are performed to optimize the capillary geometry and generate the required two-section gas-pressure profile. The resulting gas-density distributions for various cases are then directly incorporated in Particle-In-
Cell (PIC) simulations to study LWFA. In particular, our hydrodynamic simulations demonstrate how tailored density profiles with longitudinal density tapering in the acceleration section can be realized in a capillary gas
cell, while the corresponding PIC simulations reveal how these profiles influence the acceleration process and the resulting beam quality. Using a 100~TW-class
laser system with parameters relevant to the L2-DUHA
laser at the ELI Beamlines Facility, the PIC results demonstrate electron acceleration to mean energies exceeding $1.0$~GeV with high-quality beam properties. Self-injected He electrons are also observed, and their impact on the main beam quality is evaluated. The findings of this study provide valuable insights for upcoming LWFA experiments planned within the EuPRAXIA Project at the ELI Beamlines Facility.
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All authors Srimanta Maity, Francesco Massimo, Alex Whitehead, Pavel Sasorov, Alexander Molodozhentsev
Optics
Enhanced Detection of Rotational Doppler Shift from Sunlight
Juedong Yang, Yuan Li, Wuhong Zhang et al. (+1 more)
The rotational Doppler effect, for which the frequency shift is proportional to the light's orbital angular momentum $\ell$ and the object's rotational speed ($\Delta f \propto\ell \Omega$), has proven to be a powerful tool for detecting the speed of rotational objects. However, the current detection technique is mainly based on coherent
laser sources. There is scarce mention of using partially coherent light sources, let alone sunlight. In this work, we collect sunlight and direct it into the laboratory, where it is modulated into a partially coherent probing source and then realize rotational Doppler shift detection. Our study reveals that in low-light conditions, where background noise is stronger than the signal, the superposition of rotational Doppler signals at different wavelengths can significantly enhance the signal strength and improve the signal-to-noise ratio, enabling accurate measurement of the rotational speed of objects. Our research provides experimental validation for the application of sunlight in rotational Doppler shift detection, demonstrating its potential value for passive remote sensing.
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All authors Juedong Yang, Yuan Li, Wuhong Zhang, Lixiang Chen
Chemical Physics · Cross-listed from cond-mat.stat-mech
Theory of the $\beta$-Relaxation Beyond Mode-Coupling Theory: A Microscopic Treatment
Corentin C. L. Laudicina, Liesbeth M. C. Janssen and Grzegorz Szamel
We develop a systematic extension of mode-coupling theory (MCT) that incorporates critical dynamical fluctuations. Starting from a microscopic diagrammatic theory, we identify dominant classes of divergent diagrams near the mode-coupling transition and show that the corresponding asymptotic series dominates the mean-field below an upper critical dimension $d_c=8$. To resum these divergences, we construct a mapping to a
stochastic dynamical process in which the order parameter evolves under random spatiotemporal fields. This reformulation provides a controlled, fully dynamical derivation of an effective theory for the $\beta$-relaxation which remarkably coincides with
stochastic beta-relaxation theory [T. Rizzo, EPL 106, 56003 (2014)]. All coupling constants of the latter theory are expressed microscopically in terms of the
liquid static structure factor and are computed for the paradigmatic hard-sphere system. The analysis demonstrates that fluctuations alone restore ergodicity and replace the putative mean-field transition by a smooth crossover. Our results establish a predictive framework for structural relaxation beyond mean-field.
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Chemical Physics · Cross-listed from cond-mat.soft
Microscopic Theory of a Fluctuation-Induced Dynamical Crossover in Supercooled Liquids
Corentin C. L. Laudicina, Liesbeth M. C. Janssen and Grzegorz Szamel
Mean-field theories of the glass transition predict a phase transition to a dynamically arrested state, yet no such transition is observed in experiments or simulations of finite-dimensional systems. We resolve this long-standing discrepancy by incorporating critical dynamical fluctuations into a microscopic mode-coupling framework. We show that these fluctuations round off the mean-field singularity and restore ergodicity at all finite densities (or temperatures) without invoking activated dynamics or facilitation. The resulting effective theory describes the order parameter as a
stochastic process with self-induced, annealed disorder, determined self-consistently at the mean-field level. In the $\beta$-relaxation regime it reduces to
stochastic beta-relaxation theory, thereby unifying mode-coupling and replica-based approaches beyond mean-field. All parameters of the
stochastic $\beta$-relaxation theory are fixed by the static structure, enabling parameter-free predictions that extend mean-field theory into finite dimensions.
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Chemical Physics
Unraveling real-time chemical shifts in the ultrafast regime
Daniel E. Rivas, Lorenzo Paoloni, Rebecca Boll et al. (+21 more)
Traditional x-ray photoelectron spectroscopy (XPS) relies upon a direct mapping between the photoelectron binding energies and the local chemical environment, which is well-characterized by an electrostatic partial charges model for systems in
equilibrium. However, the extension of this technique to out-of-
equilibrium systems has been hampered by the lack of x-ray sources capable of accessing multiple atomic sites with high spectral and temporal resolution, as well as the lack of simple theoretical procedures to interpret the observed signals. In this work we employ multi-site XPS with a narrowband femtosecond x-ray probe to unravel different ultrafast dissociation processes of a polyatomic molecule, fluoromethane (CH$_{3}$F). We show that XPS can follow the cleavage of both the C-F and C-H bonds in real time, despite these channels lying close in binding energy. Additionally, we apply the partial charges model to describe these dynamics, and verify this extension with both advanced ab-initio calculations and experimental data. These results enable the application of this technique to out-of-
equilibrium systems of higher complexity, by correlating real-time information from multiple atomic sites and interpreting the measurements through a viable theoretical modelling.
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All authors Daniel E. Rivas, Lorenzo Paoloni, Rebecca Boll, Alberto De Fanis, Ana Martínez Gutiérrez, Tommaso Mazza, Solène Oberli, Oliver Alexander, André Al-Haddad, Thomas M. Baumann, Christoph Bostedt, Simon Dold, Gianluca Geloni, Markus Ilchen, Dooshaye Moonshiram, Daniel Rolles, Artem Rudenko, Philipp Schmidt, Svitozar Serkez, Sergey Usenko, Ángel Martín Pendás, Michael Meyer, Jesús González-Vázquez, Antonio Picón
Computational Physics · Cross-listed from cond-mat.mtrl-sci
3D lattice Monte Carlo modeling of morphology formation of Si/SiOx nanocomposites during phase separation of nonstoichiometric Si oxide films
Ivan Oliinyk and Andrey Sarikov
In this paper, a three-dimensional
lattice model based on the Monte Carlo approach is presented. This model is developed to investigate the kinetics of morphology change during phase separation in nonstoichiometric Si
oxide (SiOx, x < 2) films. The model takes into account the SiOx local atomic structure and probabilistic migration of oxygen atoms driven by the tendency of free energy minimization. The influence of the initial SiOx stoichiometry index x and film thickness on the morphology of the precipitated Si phase in the Si
oxide matrix is analyzed. The morphology of the Si phase is shown to critically depend on the initial SiOx stoichiometry. Namely, isolated Si nanoparticles form at low excess Si content (x >= 1.4), while interconnected Si networks always appear at x <= 0.8. A dimensional effect on the morphology of the Si phase is revealed. Namely, reducing the film thickness imposes
geometric constraints on the Si network formation. The percolation threshold is found to shift from xp ~= 1.35 for the bulk-like SiOx layers to xp ~= 0.85 for the quasi-two-dimensional films. The transition to the bulk material behavior is observed at a SiOx thickness of approximately 4.2 nm.
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Fluid Dynamics · Cross-listed from cs.LG
Multi-fidelity aerodynamic data fusion by autoencoder transfer learning
Javier Nieto-Centenero, Esther Andrés and Rodrigo Castellanos
Accurate aerodynamic prediction often relies on high-fidelity simulations; however, their prohibitive computational costs severely limit their applicability in data-driven modeling. This limitation motivates the development of multi-fidelity strategies that leverage inexpensive low-fidelity information without compromising accuracy. Addressing this challenge, this work presents a multi-fidelity deep learning framework that combines autoencoder-based transfer learning with a newly developed Multi-Split Conformal Prediction (MSCP) strategy to achieve uncertainty-aware aerodynamic data fusion under extreme data scarcity. The methodology leverages abundant Low-Fidelity (LF) data to learn a compact latent physics representation, which acts as a frozen knowledge base for a decoder that is subsequently fine-tuned using scarce HF samples. Tested on surface-pressure distributions for NACA airfoils (2D) and a transonic wing (3D) databases, the model successfully corrects LF deviations and achieves high-accuracy pressure predictions using minimal HF training data. Furthermore, the MSCP framework produces robust, actionable uncertainty bands with pointwise coverage exceeding 95%. By combining extreme data efficiency with uncertainty quantification, this work offers a scalable and reliable solution for aerodynamic regression in data-scarce environments.
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Geophysics
The EEPAS Model Revisited: Statistical Formalism and a High-Performance, Reproducible Open-Source Framework
Szu-Chi Chung, Chien-Hong Cho and Strong Wen
While short-term models such as the Short-Term Earthquake Probability (STEP) and Epidemic-Type Aftershock Sequence (ETAS) are well established and supported by open-source software, medium- to long-term models, notably the Every Earthquake a Precursor According to Scale (EEPAS) and Proximity to Past Earthquakes (PPE), remain under-documented and largely inaccessible. Despite outperforming time-invariant models in regional studies, their mathematical foundations are often insufficiently formalized. This study addresses these gaps by formally deriving the EEPAS and PPE models within the framework of inhomogeneous Poisson point processes and clarifying the connection between empirical $\Psi$-scaling regressions and likelihood-based inference. We introduce a fully automated, open-source Python implementation of EEPAS that combines analytical modeling with Numba JIT acceleration, NumPy vectorization, and joblib parallelization, all configured via modular JSON files for usability and reproducibility. Integration with pyCSEP enables standardized evaluation and comparison. When applied to the Italy HORUS
dataset, our system reproduces published results within one hour using identical initialization settings. It also provides a comprehensive pipeline from raw catalog to parameter estimation, achieving improved log-likelihoods and passing strict consistency tests without manual $\Psi$ identification. We position our framework as part of a growing open-source ecosystem for seismological research that spans the full workflow from data acquisition to forecast evaluation. Our framework fills a key gap in this ecosystem by providing robust tools for medium- to long-term statistical modeling of earthquake catalogs, which is an essential but underserved component in probabilistic seismic
forecasting.
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Computational Physics · Cross-listed from q-bio.NC
Macular: a multi-scale simulation platform for the retina and the primary visual system
Bruno Cessac, Erwan Demairy, Jérôme Emonet et al. (+7 more)
We developed Macular, a simulation platform with a graphical interface, designed to produce in silico experiment scenarios for the retina and the primary visual system. A scenario consists of generating a three-dimensional structure with interconnected layers, each layer corresponding to a type of '
cell' in the retina or visual cortex. The
cells can correspond to
neurons or more complex structures (such as cortical columns). The inputs are arbitrary
videos. The user can use the
cells and synapses provided with the software, or create their own using a graphical interface where they enter the constituent equations in text format (e.g., LaTeX). They also create the three-dimensional structure via the graphical interface. Macular then automatically generates and compiles the C++ code and generates the simulation interface. This allows the user to view the input
video and the three-dimensional structure in layers. It also allows the user to select
cells and synapses in each layer and view the activity of their state variables. Finally, the user can adjust the phenomenological parameters of the
cells or synapses via the interface. We provide several example scenarios, corresponding to published articles, including an example of a retino-cortical model. Macular was designed for neurobiologists and modelers, specialists in the primary visual system, who want to test hypotheses in silico without the need for programming. By design, this tool allows natural or altered conditions (pharmacology, pathology, development) to be simulated.
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All authors Bruno Cessac, Erwan Demairy, Jérôme Emonet, Evgenia Kartsaki, Thibaud Kloczko, Côme Le Breton, Nicolas Niclausse, Selma Souihel, Jean-Luc Szpyrka, Julien Wintz
Physics Education
The SIREN Program: A Scalable Model for Short-Term Undergraduate Research Experiences at Community Colleges
Emilie Hein, Polin Yadak, Denise Hum et al. (+12 more)
Providing meaningful research experiences for undergraduate students is a well-recognized challenge, particularly at community colleges and teaching-focused institutions where resources are limited and faculty time is dedicated to instruction. To address this, the Summer Introduction to Research and Experimentation in
Nuclear physics (SIREN) was developed as a three-week, intensive summer program that engaged 24 students working in six teams on original research projects centered around
cosmic detection. Each team was supported by an advisor and a peer mentor, combining structured guidance with near-peer learning. After developing a range of technical skills, students built
cosmic detectors and used them to explore a variety of projects. The program also included multiple workshops on research skills and seminars led by guest speakers, giving students access to broader STEM pathways. The evaluation of the program was based on advisor and student surveys, as well as peer mentor observations. The findings suggest that the short-term team-based model promoted engagement, collaboration, and skill development while leading to the completion of advanced projects. The advisors highlighted the effectiveness of the program structure and the excitement generated by the direct application of newly acquired skills to research. Data showed that students were able to significantly develop transferable skills through hands-on activities. Post-program surveys indicated that participants experienced an increased sense of belonging in their majors and greater confidence in pursuing careers in STEM.
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All authors Emilie Hein, Polin Yadak, Denise Hum, Jessica Hurless, Luis Jibaja Prado, Susanne Schubert, Pia Walawalkar, Marco Wehrfritz, Daria Baitazarova, Yuting Duan, Tin Htoo, Codie Lai, Aryanna Mendoza, Eslin Villalta, Valeria Zarco
Fluid Dynamics
Frequency Scaling Laws for Flat Plate Wing Active Separation Control
Stefan Vey, Christian Oliver Paschereit and David Greenblatt
Dimensionless frequency scaling laws for active separation control on flat-plate wings, using dielectric barrier discharge
plasma actuators, were examined on the basis of maximum increases to lift coefficient, and compared with hovering insect wing-flapping frequencies. Data for a range of angles of attack ($24^\circ$ to $32^\circ$), Reynolds numbers (3,000 to 20,000) and semispan wing aspect ratios (0.75 to $\infty$), collapsed best when scaled with the streamwise-directed height of the chord length. The “forcing Strouhal number” that produced the largest lift coefficient increments, equal to $0.26 \pm 0.04$, was linked to the conventional bluff-body Strouhal number by recognizing that drag and lift on flat plate wings are directly proportional at a fixed angle of attack. Flowfield measurements, to determine the time-averaged separation bubble height and local velocity at separation, showed that the universal Strouhal number of approximately $0.16 \pm 0.01$ –developed for bluff-body and separation bubble
vortex shedding–can be further generalized to active separation control, when the natural shedding frequency is substituted by the forcing frequency. Insect wing flapping frequencies in hover were examined on the basis of Strouhal number scaling and corresponded reasonably well to the optimum forcing Strouhal number range, although angle of attack estimates were a source of uncertainty. Furthermore, universal Strouhal number scaling can only be validated with accurate separation bubble height and separation velocity measurements.
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Physics Education
On emerging paradigm of teaching measurement science and technology in times of ubiquitous use of AI tools
Roman Z. Morawski
The ubiquitous use of the tools of artificial intelligence (AI) in techno-science, in higher education and in other existing and potential fields of measurement application generates new challenges for teaching measurement science and technology (MST). The aim of this article is to encourage its readers to modernize their approach to teaching MST in a way as to meet these challenges, in particular - to profit from new technological opportunities, and to respond to the needs of our civilization, identified within a humanistic reflection over its development. First, the state of the art in applications of AI in higher education is briefly characterized. Next, a methodology for using AI tools in MST, referring to the author's meta-model of measurement, is outlined. Finally, conclusions concerning an emerging paradigm of teaching MST are summarized. The most important of them are as follows. The challenges implied by the ubiquitous use of AI tools cannot be effectively faced without enhancing, in the corresponding curricula, of the contents related to mathematical modelling of material entities, on the one hand, and of the contents related to ethics of research and engineering, on the other. Knowledge and skills related to the art of mathematical modelling are indispensable for extensively profiting from the convergence of various technologies around IT tools, including AI tools. The knowledge and skills related to ethics of research and engineering are indispensable for developing safe applications of measurement in such domains as autonomous vehicles, social
robots, biomedical engineering or automated manufacturing.
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Optics
Theory of Remaining Exceptional Points from Nongeneric Splitting in Non-Hermitian Systems
Teng Yin and Hao Zhang
In non-Hermitian physics, high-order exceptional points(HOEPs) with
eigenvalues and eigenvectors coalesce are known for their enhanced sensitivity to perturbations. Typically, they exhibit
eigenvalue splitting that scales as {\epsilon}^(1/n), which is referred to as the generic response. However, under certain conditions, a nongeneric response of HOEPs occurs where the splitting follows a lower order {\epsilon}^(1/m) (m<n). A nongeneric response of HOEPs with a lower order splitting lead to the remaining EPs. While the presence of these remaining EPs is acknowledged, a thorough elucidation of their fundamental properties has yet to be achieved. In this work, we demonstrate those unsplit
eigenvalue points must constitute remaining EPs in a perturbed n-orders HOEPs system. Combining graph theory and topological analysis, the number and splitting order of the remaining EPs is studied. This framework not only resolves a fundamental challenge in HOEPs but also paves the way for exploiting remaining EPs in applications such as anisotropic sensing and the design of Dirac exceptional points.
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Accelerator Physics
Measurement of Position Resolutions of L-band Cavity Beam Position Monitors
Soohyung Lee, Ho Jun Jeong, JongMo Hwang et al. (+6 more)
Beam position monitors (BPMs) are indispensable components of modern particle accelerators, providing real-time diagnostics to ensure precise beam control, stability, and quality. As accelerators such as the International Linear
Collider (ILC) aim for nanometer-scale beam sizes at the interaction point, stringent requirements on position resolution arise. Specifically, the main linac of the ILC demands a BPM resolution better than 5 {\mu}m to support stable beam transport and minimize emittance growth. To address this, we have developed an L-band cavity BPM optimized for the beam conditions of the ILC. In this paper, we introduce a prototype of an L-band cavity BPM and its signal processing system, describe the methodology for position resolution measurements, discuss the problems and solutions encountered in the past experiment, and report the projected position resolutions of about 300 nm at best.
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All authors Soohyung Lee, Ho Jun Jeong, JongMo Hwang, GwangUk Park, Siwon Jang, Konstantin Popov, Alexander Aryshev, Toshiyuki Okugi, Eun San Kim
Biological Physics
Direct Measurement of Inertial Impact and Propulsive Force in a Eukaryotic Swimmer
Katsuya Shimabukuro, Kosaku Horinaga, Kazumo Wakabayashi et al. (+4 more)
The transduction of force into motion for microswimmers at intermediate Reynolds numbers ($Re \sim 1$), where inertia becomes relevant, is a fundamental problem in active matter. Using the multicellular alga Volvox as a model physical system, we perform the first direct measurements that deconvolve a swimmer's inertial impact force from its motor's propulsive force. We discover a $\sim$30 Hz propulsive pulse, the mechanical signature of collective ciliary action. This high-frequency motor output drives a fluctuating velocity in the low-$Re$ V. carteri, but is mechanically filtered by the inertia of the larger V. ferrisii, resulting in a smooth swimming trajectory. Our work demonstrates that for swimmers beyond the Stokes regime, kinematics are not a direct proxy for the underlying motor dynamics, a foundational assumption in the study of microscopic motility.
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All authors Katsuya Shimabukuro, Kosaku Horinaga, Kazumo Wakabayashi, Hikaru Emoto, Noriko Ueki, Ken-ichi Wakabayashi, Noriyo Mitome
Physics and Society
An AI-Based Framework for Assessing Sustainability Conflicts in Medical Device Development
Apala Chakrabarti
Designing sustainable medical devices requires balancing environmental, economic, and social demands, yet trade-offs across these pillars are difficult to identify using manual assessment alone. Current methods depend heavily on expert judgment, lack standardisation, and struggle to integrate diverse lifecycle data, which leads to overlooked conflicts and inconsistent evaluations. This paper introduces an AI-driven framework that automates conflict detection. Machine learning and natural
language processing are used to extract trade-offs from design decisions, while Multi-Criteria Decision Analysis (MCDA) quantifies their magnitude through a composite sustainability score. The approach improves consistency, reduces subjective bias, and supports early design decisions. The results demonstrate how AI-assisted analysis provides scalable, data-driven support for sustainability evaluation in medical device development.
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