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Physics Physics

physics Watch Physics

Friday 31 October

Classical Physics

Mechanically concealed holes

Kanka Ghosh and Andreas M. Menzel

When a hole is introduced into an elastic material, it will usually act to reduce the overall mechanical stiffness. A general ambition is to investigate whether a stiff shell around the hole can act to maintain the overall mechanical properties. We consider this effect from a macroscopic continuum perspective down to atomistic scales. For this purpose, we focus on the basic continuum example situation of an isotropic, homogeneous, linearly elastic material loaded uniformly under compressive plane strain for low concentrations of holes. As we demonstrate, the thickness of the shell can be adjusted in a way to maintain the overall stiffness of the system. We derive a corresponding mathematical expression for the thickness of the shell that conceals the hole. Thus, one can work with given materials to mask the presence of the holes simply by adjusting the thickness of the surrounding shells, with no need to change the materials. Our predictions from linear elasticity continuum theory are extended to atomistic levels using molecular dynamics simulations of a model Lennard-Jones solid. These extensions attest the robustness of our predictions down to atomistic scales. Thus, they open a straightforward possibility to adjust the strategy of mechanical cloaking via atomistic manipulations. From both perspectives, the underlying concept is important in the context of light-weight construction.
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Physics and Society · Cross-listed from cs.LG

Analysis of Line Break prediction models for detecting defensive breakthrough in football

Shoma Yagi, Jun Ichikawa and Genki Ichinose

In football, attacking teams attempt to break through the opponent's defensive line to create scoring opportunities. This action, known as a Line Break, is a critical indicator of offensive effectiveness and tactical performance, yet previous studies have mainly focused on shots or goal opportunities rather than on how teams break the defensive line. In this study, we develop a machine learning model to predict Line Breaks using event and tracking data from the 2023 J1 League season. The model incorporates 189 features, including player positions, velocities, and spatial configurations, and employs an XGBoost classifier to estimate the probability of Line Breaks. The proposed model achieved high predictive accuracy, with an AUC of 0.982 and a Brier score of 0.015. Furthermore, SHAP analysis revealed that factors such as offensive player speed, gaps in the defensive line, and offensive players' spatial distributions significantly contribute to the occurrence of Line Breaks. Finally, we found a moderate positive correlation between the predicted probability of being Line-Broken and the number of shots and crosses conceded at the team level. These results suggest that Line Breaks are closely linked to the creation of scoring opportunities and provide a quantitative framework for understanding tactical dynamics in football.
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Physics and Society

Social learning moderates the tradeoffs between efficiency, stability, and equity in group foraging

Zexu Li, M. Amin Rahimian and Lei Fang

Collective foragers, from animals to robotic swarms, must balance exploration and exploitation to locate sparse resources efficiently. While social learning is known to facilitate this balance, how the range of information sharing shapes group-level outcomes remains unclear. Here, we develop a minimal collective foraging model in which individuals combine independent exploration, local exploitation, and socially guided movement. We show that foraging efficiency is maximized at an intermediate social learning range, where groups exploit discovered resources without suppressing independent discovery. This optimal regime also minimizes temporal burstiness in resource intake, reducing starvation risk. Increasing social learning range further improves equity among individuals but degrades efficiency through redundant exploitation. Introducing risky (negative) targets shifts the optimal range upward; in contrast, when penalties are ignored, randomly distributed negative cues can further enhance efficiency by constraining unproductive exploration. Together, these results reveal how local information rules regulate a fundamental trade-off between efficiency, stability, and equity, providing design principles for biological foraging systems and engineered collectives.
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Medical Physics

Dark-Field X-Ray Imaging Significantly Improves Deep-Learning based Detection of Synthetic Early-Stage Lung Tumors in Preclinical Models

Joyoni Dey, Hunter C. Meyer and Murtuza S. Taqi

Low-dose computed tomography (LDCT) is the current standard for lung cancer screening, yet its adoption and accessibility remain limited. Many regions lack LDCT infrastructure, and even among those screened, early-stage cancer detection often yield false positives, as shown in the National Lung Screening Trial (NLST) with a sensitivity of 93.8 percent and a false-positive rate of 26.6 percent. We aim to investigate whether X-ray dark-field imaging (DFI) radiograph, a technique sensitive to small-angle scatter from alveolar microstructure and less susceptible to organ shadowing, can significantly improve early-stage lung tumor detection when coupled with deep-learning segmentation. Using paired attenuation (ATTN) and DFI radiograph images of euthanized mouse lungs, we generated realistic synthetic tumors with irregular boundaries and intensity profiles consistent with physical lung contrast. A U-Net segmentation network was trained on small patches using either ATTN, DFI, or a combination of ATTN and DFI channels. Results show that the DFI-only model achieved a true-positive detection rate of 83.7 percent, compared with 51 percent for ATTN-only, while maintaining comparable specificity (90.5 versus 92.9 percent). The combined ATTN and DFI input achieved 79.6 percent sensitivity and 97.6 percent specificity. In conclusion, DFI substantially improves early-tumor detectability in comparison to standard attenuation radiography and shows potential as an accessible, low-cost, low-dose alternative for pre-clinical or limited-resource screening where LDCT is unavailable.
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Physics Education

Teaching competencies in physics for engineering education: A qualitative analysis from teaching practice

Vanessa Cruz Molina, Daniel Sanchez Guzman, Teodoro Rivera Montalvo et al. (+1 more)

Physics teaching in engineering programmes poses discipline-specific demands that intertwine conceptual modelling, experimental inquiry, and computational analysis. This study examines nine teaching competences for physics instruction derived from international and regional frameworks and interpreted within engineering contexts. Nineteen university instructors from the Technological Institute of Toluca completed an open-ended questionnaire; responses were analysed using a grounded theory approach (open and axial coding) complemented by descriptive frequencies. Results indicate stronger development in technical mastery, methodological/digital integration, technology-mediated communication, and innovation (C1, C2, C6, C9), while information literacy for digital content creation/adaptation and digital ethics/safety (C7, C8) remain underdeveloped. A recurrent understanding-application gap was identified, revealing uneven transfer from conceptual awareness to enacted classroom practice. We conclude that advancing physics education for engineers requires institutionally supported, discipline-specific professional development that aligns modelling, laboratory work, and computation with ethical and reproducible digital practices; such alignment can move instructors from adoption/adaptation toward sustained appropriation and innovation in multimodal settings.
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All authors Vanessa Cruz Molina, Daniel Sanchez Guzman, Teodoro Rivera Montalvo, Ricardo Garcia-Salcedo

Chemical Physics

Natural-Orbital-Based Neural Network Configuration Interaction

Louis Thirion, Yorick L. A. Schmerwitz, Max Kroesbergen et al. (+5 more)

Selective configuration interaction methods approximate correlated molecular ground- and excited states by considering only the most relevant Slater determinants in the expansion. While a recently proposed neural-network-assisted approach efficiently identifies such determinants, the procedure typically relies on canonical Hartree-Fock orbitals, which are optimized only at the mean-field level. Here we assess approximate natural orbitals - eigenfunctions of the one-particle density matrix computed from intermediate many-body eigenstates - as an alternative. Across our benchmarks for H$_2$O, NH$_3$, CO, and C$_3$H$_8$ we see a consistent reduction in the required determinants for a given accuracy of the computed correlation energy compared to full configuration interaction calculations. Our results confirm that even approximate natural orbitals constitute a simple yet powerful strategy to enhance the efficiency of neural-network-assisted configuration interaction calculations.
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All authors Louis Thirion, Yorick L. A. Schmerwitz, Max Kroesbergen, Gianluca Levi, Elvar Ö. Jónsson, Pavlo Bilous, Hannes Jónsson, Philipp Hansmann

Optics

Towards a mobile quantitative phase imaging microscope with smartphone phase-detection sensors

Xiangjiang Bao, Zheng-da Hu, Lucas Kreiss et al. (+2 more)

Quantitative phase imaging (QPI) enables visualization and quantitative extraction of the optical phase information of transparent samples. However, conventional QPI techniques typically rely on multi-frame acquisition or complex interferometric optics. In this work, we introduce Quad-Pixel Phase Gradient Imaging ($QP^{2}GI$), a single-shot quantitative phase imaging method based on a commercial quad-pixel phase detection autofocus (PDAF) sensor, where each microlens on the sensor covers a $2\times2$ pixel group. The phase gradients of the sample induce focal spot displacements under each microlens, which lead to intensity imbalances among the four pixels. By deriving the phase gradients of the sample from these imbalances, $QP^{2}GI$ reconstructs quantitative phase maps of the sample from a single exposure. We establish a light-propagation model to describe this process and evaluate its performance in a customized microscopic system. Experiments demonstrate that quantitative phase maps of microbeads and biological specimens can be reconstructed from a single acquisition, while low-coherence illumination improves robustness by suppressing coherence-related noise. These results reveal the potential of quad-pixel PDAF sensors as cost-effective platforms for single-frame QPI.
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All authors Xiangjiang Bao, Zheng-da Hu, Lucas Kreiss, Josh Lerner, Roarke Horstmeyer

Atomic Physics · Cross-listed from quant-ph

Directional quantum scattering transducer in cooperative Rydberg metasurfaces

Jonas von Milczewski, Kelly Werker Smith and Susanne F. Yelin

We present a single-photon transduction scheme using 4-wave-mixing and quantum scattering in planar, cooperative Rydberg arrays that is both efficient and highly directional and may allow for terahertz-to-optical transduction. In the 4-wave-mixing scheme, two lasers drive the system, coherently trapping the system in a dark ground-state and coupling a signal transition, that may be in the terahertz, to an idler transition that may be in the optical. The photon-mediated dipole-dipole interactions between emitters generate collective super-/subradiant dipolar modes, both on the signal and the idler transition. As the array is cooperative with respect to the signal transition, an incident signal photon can efficiently couple into the array and is admixed into dipolar idler modes by the drive. Under specific criticality conditions, this admixture is into a superradiant idler mode which primarily decays into a specific, highly directional optical photon that propagates within the array plane. Outside of the array, this photon may then be coupled into existing quantum devices for further processing. Using a scattering-operator formalism we derive resonance and criticality conditions that govern this two-step process and obtain analytic transduction efficiencies. For infinite lattices, we predict transduction efficiencies into specific spatial directions of up to 50%, while the overall, undirected transduction efficiency can be higher. An analysis for finite arrays of $N^2$ emitters, shows that the output is collimated into lobes that narrow as $1/\sqrt{N}$. Our scheme combines the broadband acceptance of free-space 4-wave mixing with the efficiency, directionality and tunability of cooperative metasurfaces, offering a route towards quantum-coherent THz detection and processing for astronomical spectroscopy, quantum-networked sparse-aperture imaging and other quantum-sensing applications.
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Plasma Physics

Stellarator divertor design by optimizing coils for surfaces with sharp corners

Todd Elder, Matt Landremann, Christoper B. Smiet et al. (+1 more)

In stellarators, achieving effective divertor configurations is challenging due to the three-dimensional nature of the magnetic fields, which often leads to chaotic field lines and fuzzy separatrices. This work presents a novel approach to directly optimize modular stellarator coils for a sharp X-point divertor topology akin to the Large Helical Device's (LHD) helical divertor using a target plasma surface with sharp corners. By minimizing the normal magnetic field component on this surface, we target a clean separatrix with minimal chaos. Notably, this approach demonstrates the first LHD-like helical divertor design using optimized modular coils instead of helical coils. Separatrices are produced with significantly lower chaos than in LHD, demonstrating that a wide chaotic layer is not intrinsic to the helical divertor. Additional optimization methods are implemented to improve engineering feasibility of the coils and reduce chaos, including weighted quadrature and manifold optimization, a method which does not rely on normal field minimization. The results outline several new strategies for divertor design in stellarators, though it remains to achieve these edge divertor features at the same time as internal field qualities like quasisymmetry.
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All authors Todd Elder, Matt Landremann, Christoper B. Smiet, Robert Davies

Fluid Dynamics

Water entry of small disks, cones, or anything

W. Muneo Okiishi, Caleb Becker, Jesse Belden et al. (+2 more)

The water entry of solid and liquid bodies has been studied for over a century, and various researchers have classified the different behaviors that occur when the gas-filled cavity collapses. Although four main cavity collapse regimes have been described and classified for the water entry of small, dense, hydrophobic spheres, only some of these regimes have previously been seen for other impactors, and the scaling used for spheres is not universal across all impactor types. In this paper, we create a unifying scaling to predict cavity collapse regimes, pinch-off time, and pinch-off depth using modified definitions of the Bond, Weber, and Froude numbers for various impactor types. The scaling is based on the downward cavity velocity and an effective diameter, which considers the drag coefficient of the impactor. The impactors we tested include dense solid spheres, disks, and cones, as well as continuous liquid jets and droplet streams. Data for all of these impactor types and behaviors are plotted together with good collapse. Our results indicate that the hydrodynamic characteristics of the impactor, not simply its geometry, govern the global behavior of the cavity.
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All authors W. Muneo Okiishi, Caleb Becker, Jesse Belden, Aren Hellum, Nathan B. Speirs

Optics

Intensity-Correlation Synthetic Wavelength Imaging in Dynamic Scattering Media

Khaled Kassem, Areeba Fatima, Patrick Cornwall et al. (+3 more)

Imaging through dynamic scattering media, such as biological tissue, presents a fundamental challenge due to light scattering and the formation of speckle patterns. These patterns not only degrade image quality but also decorrelate rapidly, limiting the effectiveness of conventional approaches, such as those based on transmission matrix measurements. Here, we introduce an imaging approach based on second-order correlations and synthetic wavelength holography (SWH) to enable robust image reconstruction through thick and dynamic scattering media. By exploiting intensity speckle correlations and using short-exposure intensity images, our method computationally reconstructs images from a hologram without requiring phase stability or static speckles, making it inherently resilient to phase noise. Experimental results demonstrate high-resolution imaging in both static and dynamic scattering scenarios, offering a promising solution for biomedical imaging, remote sensing, and real-time imaging in complex environments.
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All authors Khaled Kassem, Areeba Fatima, Patrick Cornwall, Muralidhar Madabhushi Balaji, Daniele Faccio, Florian Willomitzer

Optics

Self-Oscillatory Light Emission in Plasmonic Molecular Tunnel Junctions

Riccardo Zinelli, Zijia Wu, Christian A. Nijhuis et al. (+1 more)

Self-oscillators are intriguing due to their ability to sustain periodic motion without periodic stimulus. They remain rare as achieving such behavior requires a balance of energy input, dissipation and non-linear feedback mechanism. Here, we report a molecular-scale optoelectronic self-oscillatory system based on electrically excited plasmons. This system generates light via inelastic electron tunnelling, where electrons lose their energy to molecules and excite the surface plasmon polaritons that decay radiatively. Time-series imaging of photon emission in gold-naphthalene-2-thiol-EGaIn junctions, together with correlation mapping of individual emission spots, reveal long-lived (~1000 s), low-frequency oscillations (1-20 mHz) interspersed with transient high-frequency (20-200 mHz) bursts. This behavior can be explained by attributing individual emission spots to single-molecule resistors that follow Kirchhoff's circuit laws. Induced by tunnelling current, these individual spots emit in a correlated way, self-sustaining the overall oscillatory emission from the whole junction. Our observation is of great interest as it resonates with a broader understanding of similar molecular-scale dynamic systems such as picocavities, offering exciting potential for optoelectronic and sensing applications.
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All authors Riccardo Zinelli, Zijia Wu, Christian A. Nijhuis, Qianqi Lin

Computational Physics · Cross-listed from math.NA

A stochastic branching particle method for solving non-conservative reaction-diffusion equations

Liyao Lyu and Huan Lei

We propose a stochastic branching particle-based method for solving nonlinear non-conservative advection-diffusion-reaction equations. The method splits the evolution into an advection-diffusion step, based on a linearized Kolmogorov forward equation and approximated by stochastic particle transport, and a reaction step implemented through a branching birth-death process that provides a consistent temporal discretization of the underlying reaction dynamics. This construction yields a mesh-free, nonnegativity-preserving scheme that naturally accommodates non-conservative systems and remains robust in the presence of singularities or blow-up. We validate the method on two representative two-dimensional systems: the Allen-Cahn equation and the Keller-Segel chemotaxis model. In both cases, the present method accurately captures nonlinear behaviors such as phase separation and aggregation, and achieves reliable performance without the need for adaptive mesh refinement.
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Fluid Dynamics

Optical Micromanipulations based on Model Predictive Control of Thermoviscous Flows

Elena Erben, Ivan Saraev, Weida Liao et al. (+3 more)

High-precision micromanipulation techniques, including optical tweezers and hydrodynamic trapping, have garnered wide-spread interest. Recent advances in optofluidic multiplexed assembly and microrobotics demonstrate significant progress, particularly by iteratively applying laser-induced, localized flow fields to manipulate microparticles in viscous solutions. However, these approaches still face challenges such as undesired hydrodynamic coupling and instabilities when multiple particles are brought into close proximity. By leveraging an analytical model of thermoviscous flows, this work introduces a stochastic optimization approach that selects flow fields for precise particle arrangement without relying on rule-based heuristics. Through minimizing a comprehensive objective function, the method achieves sub-micrometer alignment accuracy even in a crowded setting, avoiding instabilities driven by undesired coupling or particle collisions. An autonomously emerging "action at a distance" strategy - placing the laser scan path farther from the manipulated particles over time - exploits the $1/r^2$ decay of thermoviscous flow to refine positioning. Overall, objective function-based model predictive control enhances the versatility of automated optofluidic manipulations, opening new avenues in assembly, micromanufacturing, robotics, and life sciences.
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All authors Elena Erben, Ivan Saraev, Weida Liao, Fan Nan, Eric Lauga, Moritz Kreysing

Computational Physics

Boron Nitride Nanotubes as Efficient Surface Absorbers for Air Pollutant Gas Molecules: Insights from Density Functional Theory

Chaithanya Purushottam Bhat, Joy Mukherjee, Antara Banerjee et al. (+1 more)

This study investigates into the adsorption sensing capabilities of single-walled (5,5) boron nitride nanotubes (BNNTs) towards environmental pollutant gas molecules, including CH2, SO2, NH3, H2Se, CO2 and CS2. Employing a linear combination of atomic orbital density functional theory (DFT) and spin-polarized generalized gradient approximation (GGA), the investigation reveals the nanotube's robust adsorption behavior without compromising its structural integrity. Thermodynamic and chemical parameters, such as adsorption energy, HOMO-LUMO gap, vertical ionization energy, and vertical electron affinity, highlight the (5,5) BNNTs' potential as efficient absorbents for pollutant molecules. Infrared spectroscopy confirms the formation of distinct BNNT-gas complexes. These findings underscore the promising application of BN nanotubes as absorbents for common gaseous pollutants, essential for developing sensors to enhance indoor air quality.
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All authors Chaithanya Purushottam Bhat, Joy Mukherjee, Antara Banerjee, Debashis Bandyopadhyay

Instrumentation and Detectors

Magnetic wideband VHF localized field probe using magnon polaritons

Gabriel Soares, Nicolò Crescini, Giovanni Carugno et al. (+1 more)

We present here an optimisation and demonstration of a wide band instrument capable of measuring localised and directionally alternated magnetic fields below pT in the very high frequency (VHF) range. We take advantage of the magnon-photon hybridization between a yttrium iron garnet (YIG) sphere and a copper resonant cavity to employ a resonant heterodyne detection scheme. The measurement is near instantaneous due to the strong coupling attained between magnons and photons.In this work measurements are reported showing a significant widening of the measurement bandwidth, obtained by tuning the YIG Larmor frequency with a bias magnetic field and adjusting the magnon-photon coupling strength. Minimum sensitivity in the sub pT regime is demonstrated in the range 150 – 225 MHz at room temperature and expected to go to fT in cryogenic temperatures. Dynamic range is estimated to be above 100 dB. The sensitivity is found to be independent on size, being ready to in-chip miniaturization. Such device can be an important building block to quantum circuits, such as baluns, transducers or signal processing units.
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All authors Gabriel Soares, Nicolò Crescini, Giovanni Carugno, Giuseppe Ruoso

Instrumentation and Detectors

Sensor operating point calibration and monitoring of the ALICE Inner Tracking System during LHC Run 3

D. Agguiaro, G. Aglieri Rinella, L. Aglietta et al. (+288 more)

The new Inner Tracking System (ITS2) of the ALICE experiment began operation in 2021 with the start of LHC Run 3. Compared to its predecessor, ITS2 offers substantial improvements in pointing resolution, tracking efficiency at low transverse momenta, and readout-rate capabilities. The detector employs silicon Monolithic Active Pixel Sensors (MAPS) featuring a pixel size of 26.88$\times$29.24 $\mu$m$^2$ and an intrinsic spatial resolution of approximately 5 $\mu$m. With a remarkably low material budget of 0.36% of radiation length ($X_{0}$) per layer in the three innermost layers and a total sensitive area of about 10 m$^2$, the ITS2 constitutes the largest-scale application of MAPS technology in a high-energy physics experiment and the first of its kind operated at the LHC. For stable data taking, it is crucial to calibrate different parameters of the detector, such as in-pixel charge thresholds and the masking of noisy pixels. The calibration of 24120 monolithic sensors, comprising a total of 12.6$\times$10$^{9}$ pixels, represents a major operational challenge. This paper presents the methods developed for the calibration of the ITS2 and outlines the strategies for monitoring and dynamically adjusting the detector's key performance parameters over time.
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All authors D. Agguiaro, G. Aglieri Rinella, L. Aglietta, M. Agnello, F. Agnese, B. Alessandro, G. Alfarone, J. Alme, E. Anderssen, D. Andreou, M. Angeletti, N. Apadula, P. Atkinson, C. Azzan, R. Baccomi, A. Badalà, A. Balbino, P. Barberis, F. Barile, L. Barioglio, R. Barthel, F. Baruffaldi, N.K. Behera, I. Belikov, A. Benato, M. Benettoni, F. Benotto, S. Beole, N. Bez, A. Bhatti, M. Bhopal, A.P. Bigot, G. Boca, G. Bonomi, M. Bonora, F. Borotto Dalla Vecchia, M. Borri, V. Borshchov, E. Botta, L. Boynton, G. Brower, E. Bruna, O. Brunasso Cattarello, G.E. Bruno, M.D. Buckland, S. Bufalino, P. Camerini, P. Cariola, C. Ceballos Sanchez, M. Chartier, J. Cho, S. Cho, K. Choi, Y. Choi, N.J. Clague, O.A. Clausse, F. Colamaria, D. Colella, S. Coli, A. Collu, M. Concas, G. Contin, Y. Corrales Morales, S. Costanza, J.B. Dainton, E. Danè, W. Degraw, C. De Martin, W. Deng, G. De Robertis, P. Dhankher, A. Di Mauro, F. Dumitrache, D. Elia, M.R. Ersdal, J. Eum, A. Fantoni, G. Feofilov, J. Ferencei, F. Fichera, G. Fiorenza, A.N. Flores, A. Franco, M. Franco, J.P. Fransen, D. Gajanana, A. Galdames Perez, C. Gao, C. Gargiulo, L. Garizzo, P. Giubilato, M. Goffe, A. Grant, E. Grecka, L. Greiner, A. Grelli, A. Grimaldi, O.S. Groettvik, F. Grosa, C. Guo Hu, R.P. Hannigan, H. Helstrup, A. Hill, H. Hillemanns, P. Hindley, G. Huang, M. Iannone, J.P. Iddon, P. Ijzermans, M.A. Imhoff, A. Isakov, J. Jeong, T. Johnson, A. Junique, J. Kaewjai, M. Keil, Z. Khabanova, H. Khan, H. Kim, J. Kim, J. Kim, J. Kim, M. Kim, T. Kim, J. Klein, C. Kobdaj, A. Kotliarov, M.J. Kraan, I. Králik, F. Krizek, T. Kugathasan, C. Kuhn, P.G. Kuijer, S. Kushpil, M.J. Kweon, M. Kwon, Y. Kwon, P. La Rocca, N. Lacalamita, P. Larionov, G. Ledey, S. Lee, T. Lee, R.C. Lemmon, Y. Lesenechal, E.D. Lesser, B.E. Liang-Gilman, F. Librizzi, B. Lim, S. Lim, S. Lindsay, A. Liu, J. Liu, J. Liu, F. Loddo, M. Lupi, M. Mager, A. Maire, G. Mandaglio, V. Manzari, C. Markert, G. Markey, D. Marras, P. Martinengo, S. Martiradonna, M. Masera, A. Mastroserio, G. Mazza, D. Mazzaro, F. Mazzaschi, M. Mazzilli, L. Mcalpine, M. Mongelli, J. Morant, F. Morel, P. Morrall, V. Muccifora, A. Mulliri, L. Musa, A.I. Nambrath, M. Obergger, A. Orlandi, A. Palasciano, R. Panero, E. Paoletti, G.S. Pappalardo, O. Parasole, J. Park, L. Passamonti, C. Pastore, R.N. Patra, F. Pellegrino, A. Pepato, C. Petta, S. Piano, D. Pierluigi, S. Pisano, M. Pĺoskoń, M.T. Poblocki, S. Politano, E. Prakasa, F. Prino, M. Protsenko, M. Puccio, C. Puggioni, A. Rachevski, L. Ramello, M. Rasa, I. Ravasenga, A.U. Rehman, F. Reidt, M. Richter, F. Riggi, M. Rizzi, K. Røed, D. Röhrich, F. Ronchetti, M.J. Rossewij, A. Rossi, A. Russo, B. Di Ruzza, G. Saccà, M. Sacchetti, R. Sadikin, A. Sanchez Gonzalez, U. Savino, J. Schambach, F. Schlepper, R. Schotter, P.J. Secouet, M. Selina, S. Senyukov, J.J. Seo, R. Shahoyan, S. Shaukat, F. Shirokopetlev, K. Sielewicz, G. Simantovic, M. Sitta, R.J.M. Snellings, W. Snoeys, J. Song, J.M. Sonneveld, R. Spijkers, A. Sturniolo, C.P. Stylianidis, M. Šuljić, D. Sun, X. Sun, R.A. Syed, A. Szczepankiewicz, C. Terrevoli, M. Toppi, A. Trifiró, A.S. Triolo, S. Trogolo, V. Trubnikov, M. Turcato, R. Turrisi, T. Tveter, I. Tymchuk, G.L. Usai, V. Valentino, N. Valle, J.B. Van Beelen, J.W. Van Hoorne, T. Vanat, M. Varga-Kofarago, A. Velure, G. Venier, F. Veronese, A. Villani, A. Viticchié, C. Wabnitz, Y. Wang, P. Yang, E.R. Yeats, Z. Yin, I.-K. Yoo, J.H. Yoon, S. Yuan, V. Zaccolo, A. Zampieri, C. Zampolli, E. Zhang, L. Zhang, X. Zhang, Z. Zhang, V. Zherebchevskii, D. Zhou, N. Zurlo

Atomic Physics

Automation in quantum logic experiments with cold molecular ions

Richard Karl, Meissa Diouf, Aleksandr Shlykov et al. (+2 more)

Modern experiments with cold molecular ions have reached a high degree of complexity requiring frequent sample preparation, state initialization and protocol execution while demanding precise control over multiple devices and laser sources. To maintain a high experimental duty cycle and robust measurement conditions, automation becomes essential. We present a fully automated control system for the preparation of trapped state-selected molecular ions and subsequent quantum logic-based experiments. Adaptive feedback routines based on real-time image analysis introduce and identify single molecular ions in atomic-ion Coulomb crystals. By appropriate manipulation of the trapping potentials, excess atomic ions are released from the trap to produce dual-species two-ion strings, here Ca$^+-$N$_2^+$. After mass and state identification of the molecular ion, nanosecond-level synchronization of laser pulses employing the Sinara/ARTIQ framework and real-time data analysis enable quantum-logic-spectroscopic measurements. The present automated control system enables robust, unsupervised operation over extended periods resulting in an increase of the number of experimentation cycles by about a factor of ten compared to manual operation and a factor of about eight in loaded molecules in typical practical situations. The modular, distributed design of the system provides a scalable blueprint for similar molecular-ion experiments.
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All authors Richard Karl, Meissa Diouf, Aleksandr Shlykov, Mikolaj Roguski, Stefan Willitsch

Computational Physics · Cross-listed from cond-mat.mtrl-sci

First-Principles Study of Transition Metal Doped in 2D Polyaramid for Novel Material Modelling

Ravi Trivedi, Chaithanya Purushottam Bhat, Shakti S. Ray et al. (+1 more)

We present a first–principles density functional theory (DFT) study of transition metal (TM = Ti, Cr, Mn, Fe, Co, Ni) functionalized two–dimensional polyaramid (2DPA) to explore their structural, electronic, and magnetic properties. Mechanical parameters, such as bulk modulus, shear modulus, Young's modulus, Poisson's ratio, and Pugh ratio, together with phonon dispersion, confirm the mechanical and dynamic stability of all doped systems. Electronic structure analysis shows strong binding of Co, Cr, Fe, Ni, and Ti with formation energies between –1.15 eV and –2.96 eV, while Mn binds more weakly (–0.67 eV). TM doping introduces new electronic states that reduce the band gap, with Fe-doped 2DPA exhibiting the lowest value of 0.26 eV. The systems display predominantly ferromagnetic ordering, with magnetic moments of 1.14 {\mu}B (Co), 3.57 {\mu}B (Cr), 2.26 {\mu}B (Fe), 4.19 {\mu}B (Mn), and 1.62 {\mu}B (Ti). These results demonstrate that TM–doped 2DPA possesses tunable magnetic and electronic characteristics, highlighting its potential for spintronic applications.
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All authors Ravi Trivedi, Chaithanya Purushottam Bhat, Shakti S. Ray, Debashis Bandyopadhyay

Applied Physics

Voltage-Dependent Photocurrent and Crossover Behavior in Crystalline Silicon Solar Cells

T. Matsuura and S. Saijo

Contrary to the conventional assumption of voltage-independent photocurrent, we show that the photocurrent in monocrystalline silicon p-n junction solar cells depends explicitly on voltage. By applying physically realistic boundary conditions to the classical p-n junction solar-cell model, we derive an analytical expression for the photocurrent that incorporates both the built-in potential and the applied voltage. The model predicts a crossover point between the dark and illuminated $I-V$ characteristics, irrespective of illumination intensity. This prediction is experimentally validated by lock-in measurements on a commercial monocrystalline silicon solar cell. These results establish a more consistent understanding of the quantitative role of the built-in potential in the photovoltaic effect.
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Atmospheric and Oceanic Physics

Spatial Controls of Lower Tropospheric Stability

Senne Van Loon and Maria Rugenstein

Marine low clouds play a crucial role in Earth's radiation budget. These clouds efficiently reflect sunlight and drive the magnitude and sign of the global cloud feedback. Nevertheless, the evolution of shallow cloud decks over the last decades is not well understood. A dominant control of this low cloud cover is the lower tropospheric stability, quantified by the estimated inversion strength (EIS). We quantify how regional EIS depends on local and remote surface temperature, revealing the dynamics controlling the shallow cloud characteristics on annual timescales. We find that global EIS increases with warming in tropical regions of ascent and decreases with warming in regions of descent. In addition to the West Pacific Warm Pool, the Atlantic convection regions and the central Pacific are important predictors. Focusing on subtropical ocean upwelling regions in different ocean basins, where the low cloud decks reside, EIS increases with a fairly complex pattern of remote warming and decreases with local warming. The spatial relationship between surface temperature and EIS is robust across climate models and reanalyses, allowing us to constrain the spread in historical EIS trend estimates. In the Southeast Pacific, historical surface temperature decreased, but we attribute the observed EIS increase since 1980 entirely to remote warming. Our results challenge the canonical dominance of the West Pacific Warm Pool in controlling low cloud feedbacks in the eastern Pacific and give mechanistic insights into the spatial dependence of radiative feedbacks on surface temperature patterns.
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Accelerator Physics

Plasma Processing Of SRF Cavities at Jefferson Lab: Experiment Results and Simulation Insight

I. H. Senevirathne, T. Powers and N. Raut

Plasma processing of superconducting radio frequency (SRF) cavities has been an active research effort at Jefferson Lab (JLab) since 2019, aimed at enhancing cavity performance by removing hydrocarbon contaminants and reducing field emission. In this experiment, processing using argon-oxygen and helium-oxygen gas mixtures to find minimum ignition power at different cavity pressure was investigated. Ongoing simulations are contributing to a better understanding of the plasma surface interactions and the fundamental physics behind the process. These simulations, combined with experimental studies, guide the optimization of key parameters such as gas type, RF power, and pressure to ignite plasma using selected higher-order mode (HOM) frequencies. This paper presents experimental data from argon-oxygen and helium-oxygen gas mixture C75 and C100 cavity plasma ignition studies, as well as simulation results for the C100-type cavity based on the COMSOL model previously applied to the C75 cavity.
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Physics and Society · Cross-listed from cs.SI

Beyond Demographics: Behavioural Segmentation and Spatial Analytics to Enhance Visitor Experience at The British Museum

Naomi Muggleton, Timothy Monteath and Taha Yasseri

This study explores visitor behaviour at The British Museum using data science methods applied to novel sources, including audio guide usage logs and TripAdvisor reviews. Analysing 42,000 visitor journeys and over 50,000 reviews, we identify key drivers of satisfaction, segment visitors by behavioural patterns, examine tour engagement, model spatial navigation, and investigate room popularity. Behavioural clustering uncovered four distinct visitor types: Committed Trekkers, Leisurely Explorers, Targeted Visitors, and Speedy Samplers, each characterised by different levels of engagement and movement. Tour usage analysis revealed high drop-off rates and variation in completion rates across different language groups. Spatial flow modelling revealed that accessibility and proximity, particularly aversion to stairs, shaped visitor paths more than thematic organisation. Room popularity was more strongly predicted by physical accessibility than curatorial content. We propose practical strategies for improving engagement and flow, offering a scalable framework for visitor-centred, data-informed museum planning.
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Fluid Dynamics · Cross-listed from cond-mat.soft

Phase Transitions of Oscillating Droplets on Horizontally Vibrating Substrates

King L. Ng, Luís H. Carnevale, Michał Klamka et al. (+3 more)

Droplet deformations caused by substrate vibrations are ubiquitous in nature and highly relevant for applications such as microreactors and single-cell sorting. The vibrations can induce droplet oscillations, a fundamental process that requires an in-depth understanding. Here, we report on extensive many-body dissipative particle dynamics simulations carried out to study the oscillations of droplets of different liquids on horizontally vibrating substrates, covering a wide range of vibration frequencies and amplitudes as well as substrate wettability. We categorize the phases observed for different parameter sets based on the capillary number and identify the transitions between the observed oscillation phases, which are characterized by means of suitable parameters, such as the angular momentum and vorticity of the droplet. The instability growth rate for oscillation phase II, which leads to highly asymmetric oscillations and eventual droplet breakup, is also determined. Finally, we characterize the state of the droplet for the various scenarios by means of the particle-particle and particle-substrate contacts. We find a steady-state scenario for phase I, metastable breathing modes for phase II, and an out-of-equilibrium state for phase III. Thus, we anticipate that this study provides much needed insights into a fundamental phenomenon in nature with significant relevance for applications.
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All authors King L. Ng, Luís H. Carnevale, Michał Klamka, Piotr Deuar, Tomasz Bobinski, Panagiotis E. Theodorakis

Optics

On chip plasmonic slit cavity platform for room temperature strong coupling with deterministically positioned colloidal quantum dots

Jin Qin, Benedikt Schurr, Patrick Pertsch et al. (+9 more)

Strong coupling between quantum emitters and optical cavities is essential for quantum information processing, high-purity single-photon sources, and nonlinear quantum devices. Achieving this regime at room temperature in a compact, deterministic on-chip platform-critical for integration with nanoelectronic circuitry and scalable device architectures-remains a major challenge, mainly due to the difficulty of fabricating cavities with ultra-small mode volumes and precisely positioning quantum emitters. Here, we demonstrate a robust quantum plasmonic device in which colloidal quantum dots (Qdots) are strongly coupled to plasmonic slit cavities using a dielectrophoresis-based positioning technique with real-time photoluminescence (PL) feedback, providing directly resolvable coupled structures that enable parallel device fabrication and straightforward integration with additional optical elements such as waveguides. Our measurements reveal clear PL resolved Rabi splitting at room temperature with pre characterized cavities, with variations across devices that scale with the average number of coupled Qdots. While electrical tuning via the quantum-confined Stark effect is enabled by integrated electrodes, its impact is largely overshadowed by room-temperature spectral diffusion. Our results pave the way for scalable, electrically tunable quantum plasmonic platforms, offering new opportunities for integrated quantum photonic circuits, active light-matter interactions, and room-temperature quantum technologies.
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All authors Jin Qin, Benedikt Schurr, Patrick Pertsch, Daniel Friedrich, Max Knopf, Saeid Asgarnezhad-Zorgabad, Lars Meschede, Daniel D.A. Clarke, Monika Emmerling, Artur Podhorodecki, Ortwin Hess, Bert Hecht

Plasma Physics

The linear Rayleigh-Taylor instability with foams

Antoine Bret, Audrey DeVault, Skylar Dannhoff et al. (+3 more)

We analyse the behaviour of the linear phase of the Rayleigh-Taylor instability (RTI) in the presence of a foam. Such a problem may be relevant, for example, to some inertial confinement fusion (ICF) scenarios such as foams within the capsule or lining the inner hohlraum wall. The foam displays 3 different phases: by order of increasing stress, it is first elastic, then plastic, and then fractures. Only the elastic and plastic phases can be subject to a linear analysis of the instability. The growth rate is analytically computed in these 2 phases, in terms of the micro-structure of the foam. In the first, elastic, phase, the RTI can be stabilized for some wavelengths. In this elastic phase, a homogenous foam model overestimates the growth because it ignores the elastic nature of the foam. Although this result is derived for a simplified foam model, it is likely valid for most of them. Besides the ICF context considered here, our results could be relevant for many fields of science.
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All authors Antoine Bret, Audrey DeVault, Skylar Dannhoff, Maria Gatu Johnson, Chikang Li, Johan Frenje

Plasma Physics

Fast and accurate calculation of the bootstrap current and radial neoclassical transport in low collisionality stellarator plasmas

Francisco Javier Escoto López

In this PhD thesis, a method for solving fast and accurately the monoenergetic drift-kinetic equation at low collisionality is presented. The algorithm is based on the analytical properties of the drift-kinetic equation when its dependence on the pitch-angle cosine is represented employing Legendre polynomials as basis functions. The Legendre representation of the monoenergetic drift-kinetic equation possesses a tridiagonal structure, which is exploited by the algorithm presented. The monoenergetic drift-kinetic equation can be solved fast and accurately at low collisionality by employing the standard block tridiagonal algorithm for block tridiagonal matrices. The implementation of the aforementioned algorithm leads to the main result of this thesis: the new neoclassical code MONKES (MONoenergetic Kinetic Equation Solver), conceived to satisfy the necessity of fast and accurate calculations of the bootstrap current for stellarators and in particular for stellarator optimization. MONKES is a new neoclassical code for the evaluation of monoenergetic transport coefficients in stellarators. By means of a convergence study and benchmarks with other codes, it is shown that MONKES is accurate and efficient. The combination of spectral discretization in spatial and velocity coordinates with block sparsity allows MONKES to compute monoenergetic coefficients at low collisionality, in a single core, in approximately one minute. MONKES is sufficiently fast to be integrated into stellarator optimization codes for direct optimization of the bootstrap current (and radial neoclassical transport) and to be included in predictive transport suites.
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Chemical Physics

Evaluation of Reference Equations of State for Density Prediction in Regasified LNG Mixtures Using High-Precision Experimental Data

Daniel Lozano-Martín, Dirk Tuma and César R. Chamorro

This study evaluates the performance of three reference equations of state (EoS), AGA8-DC92, GERG-2008, and SGERG-88, in predicting the density of regasified liquefied natural gas (RLNG) mixtures. A synthetic nine-component RLNG mixture was gravimetrically prepared. High-precision density measurements were obtained using a single-sinker magnetic suspension densimeter over a temperature range of (250 to 350) K and pressures up to 20 MPa. The experimental data were compared with EoS predictions to evaluate their accuracy. AGA8-DC92 and GERG-2008 showed excellent agreement with the experimental data, with deviations within their stated uncertainty. In contrast, SGERG-88 exhibited significantly larger deviations for this RLNG mixture, particularly at low temperatures of (250 to 260) K, where discrepancies reached up to 3 %. Even at 300 K, deviations larger than 0.4 % were observed at high pressures, within the model's uncertainty, but notably higher than those of the other two EoSs. The analysis was extended to three conventional 11-component natural gas mixtures (labeled G420 NG, G431 NG, and G432 NG), previously studied by our group using the same methodology. While SGERG-88 showed reduced accuracy for the RLNG mixture, it performed reasonably well for these three mixtures, despite two of them have a very similar composition to the RLNG. This discrepancy is attributed to the lower CO2 and N2 content typical in RLNG mixtures, demonstrating the sensitivity of EoS performance to minor differences in composition. These findings highlight the importance of selecting appropriate EoS models for accurate density prediction in RLNG applications.
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Optics

Unveiling Spin Transition at Single Particle Level in Levitating Spin Crossover Nanoparticles

Elena Pinilla-Cienfuegos, Lucas Mascaró-Burguera, Ramón Torres-Cavanillas et al. (+4 more)

The ability to control and understand the phase transitions of individual nanoscale building blocks is key to advancing the next generation of low-power reconfigurable nanophotonic devices. To address this critical challenge, molecular nanoparticles (NPs) exhibiting a spin crossover (SCO) phenomenon are trapped by coupling a quadrupole Paul trap with a multi-spectral polarization-resolved scattering microscope. This contact-free platform simultaneously confines, optically excites, and monitors the spin transition in Fe(II)-triazole NPs in a pressure-tunable environment, eliminating substrate artifacts. Thus, we show light-driven manipulation of the spin transition in levitating NPs free from substrate-induced effects. Using the robust spin bistability near room temperature of our SCO system, we quantify reversible opto-volumetric changes of up to 6%, revealing precise switching thresholds at the single-particle level. Independent pressure modulation produces a comparable size increase, confirming mechanical control over the same bistable transition. These results constitute full real-time control and readout of spin states in levitating SCO NPs, charting a route toward their integration into ultralow-power optical switches, data-storage elements, and nanoscale sensors.
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All authors Elena Pinilla-Cienfuegos, Lucas Mascaró-Burguera, Ramón Torres-Cavanillas, J. Ignacio Echavarría, Alejandro Regueiro, Eugenio Coronado, Javier Hernandez-Rueda

Physics and Society

Mapping Regional Disparities in Discounted Grocery Products

Antonio Desiderio, Alessia Galdeman, Franziska Bauerlein et al. (+1 more)

Food waste represents a major challenge to global climate resilience, accounting for almost 10% of annual greenhouse gas emissions. The retail sector is a critical player, mediating product flows between producers and consumers, where supply chain inefficiencies can shape which items are put on sale. Yet how these dynamics vary across geographic contexts remains largely unexplored. Here, we analyze data from Denmark's largest retail group on near-expiry products put on sale. We uncover the geospatial variations using a dual-clustering approach. We characterize multi-scale spatial relationships in retail organization by correlating store clustering – measured using shortest-path distances along the street network – with product clustering based on promotion co-occurrence patterns. Using a bipartite network approach, we identify three regional store clusters, and use percolation thresholds to corroborate the scale of their spatial separation. We find that stores in rural communities put meat and dairy products on sale up to 2.2 times more frequently than metropolitan areas. In contrast, metropolitan and capital regions lean toward convenience products, which have more balanced nutritional profiles but less favorable environmental impacts. By linking geographic context to retail inventory, we provide evidence that reducing food waste requires interventions tailored to local retail dynamics, highlighting the importance of region-specific sustainability strategies.
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All authors Antonio Desiderio, Alessia Galdeman, Franziska Bauerlein, Sune Lehmann